rowid,run.accession,experiment.accession,sample.accession,study.accession,bioproject,study.title,study.alias,study.type,study.abstract,study.attributes,study.PMIDs,sample.description,sample.title,sample.alias,sample.centername,sample.attributes,GEOsample.title,GEOsample.dataprocessing,GEOsample.source,GEOsample.treatmentprotocol,GEOsample.extractprotocol,GEOsample.growthprotocol,GEOsample.characteristics,GEOsample.accession,experiment.title,experiment.alias,experiment.library_name,experiment.design_description,experiment.library_construction_protocol,experiment.attributes,experiment.library_strategy,experiment.library_source,experiment.library_selection,experiment.library_layout,experiment.platform,experiment.instrument_model,experiment.spot_descriptor,experiment.study_ref,run.title,run.attributes,run.filename,run.semantic_name,run.total_bases,run.total_spots,run.alias,run.read_lengths,run.base_counts,run.r1_length,run.r2_length,run.r3_length,run.r4_length,run.Acount,run.Ccount,run.Gcount,run.Tcount,run.Ncount,run.experiment,run.pool_member,submission.accession,submission.srasource,submission.bioprojectsource,seqdetective.n_mates,seqdetective.mapping_rate.mate1,seqdetective.mapping_rate.mate2,seqdetective.nofeature_rate.mate1,seqdetective.nofeature_rate.mate2,seqdetective.sparsity.mate1,seqdetective.sparsity.mate2,seqdetective.pos_strand_rate.mate1,seqdetective.pos_strand_rate.mate2,seqdetective.readlen.mate1,seqdetective.readlen.mate2,seqdetective.judgement.mate1,seqdetective.judgement.mate2,seqdetective.judgement.reason,platform_family,instrument_generation,read_bias,selection_class,prep_kit,sc_or_bulk,tech_class,technology,tech_variant,submission.bioprojectsource.country,earliest_date,devstage_curation,devstage_curation_coarse,tissue_curation,tissue_curation_coarse
10002,ERR6806875,ERX6430468,ERS5060069,ERP123913,PRJEB40292,Multidimensional chromatin profiling of zebrafish and human pancreas to uncover and validate disease related enhancers,ena-STUDY-I3S-10-09-2020-08:12:35:299-4,Other,The pancreas is a central organ for human diseases. Most disease associated alleles overlap with non coding cis regulatory elements of DNA  suggesting that alterations in regulatory sequences contribute to pancreatic diseases. However  the interspecies identification of equivalent cis regulatory elements required for in vivo testing face fundamental challenges  including lack of sequence conservation. In this work  we performed a combined analysis of ATAC seq  ChIP seq  4C seq and HiChIP seq data from zebrafish and human pancreatic cells to identify interspecies functionally equivalent cis regulatory elements  regardless of sequence conservation. To link cis regulation with the expression of target genes in the pancreas  we additionally integrated in our analysis own and public RNA seq data from zebrafish pancreatic cell types. Among several disease associated sequences  we identified a zebrafish ptf1a distal enhancer whose deletion generates pancreatic agenesis  demonstrating the causality of this condition in humans. Our results further demonstrate that this phenotype is a consequence of loss of pancreas progenitor cells. Overall  we show that chromatin profiling can uncover interspecies functional equivalency of cis regulatory elements  contributing to the prediction of new disease causative enhancers and their role in human disease.,ChIP seq  ATAC seq  HiChIP seq  4C seq  RNA seq|chromatin accessibility|cis regulatory mutations|pancreas and pancreatic diseases|transcriptional enhancers|ENA FIRST PUBLIC:2020 09 11|ENA LAST UPDATE:2021 09 22,,Adult zebrafish whole pancreas RNA seq replicate2 raw reads,Zebrafish Whole Pancreas RNA seq replicate2,SAMEA7301510,I3S,ENA first public:2021 09 23|ENA last update:2021 09 23|External Id:SAMEA7301510|INSDC center alias:I3S|INSDC center name:I3S|INSDC first public:2021 09 23T20:37:00Z|INSDC last update:2021 09 23T20:37:00Z|INSDC status:public|Submitter Id:Zebrafish Whole Pancreas RNA seq replicate2|common name:zebrafish|sample name:Zebrafish Whole Pancreas RNA seq replicate2|tissue type:whole pancreas,,,,,,,,,Illumina HiSeq 2000 sequencing,ena EXPERIMENT I3S 23 09 2021 16:43:09:330 6,Exocrine2,1,Total RNA extracted with TRIZOL from zebrafish whole pancreas and preprared for sequencing with the TruSeq kit,,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2000,,ERP123913,Illumina HiSeq 2000 sequencing,ENA FIRST PUBLIC:2021 09 23|ENA LAST UPDATE:2021 09 23,Exocrine_Old_1.fastq.gz,fastq,1715678250.0,34313565.0,ena RUN I3S 23 09 2021 16:43:09:330 6,0:50 1:0,A:423211245;C:429948631;G:413857387;T:448546996;N:113991,50,0,,,423211245,429948631,413857387,448546996,113991,ERX6430468,ERS5060069,ERA6385885,I3S|European Nucleotide Archive,I3S,1,0.94581,,0.03181,,0.80026,,0.48102,,50,,B,,usable mapping rate,illumina,hiseq_era,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,Portugal,2020-09-11,Adult,Adult,Pancreas,Endocrine System
10003,ERR6806874,ERX6430467,ERS5060068,ERP123913,PRJEB40292,Multidimensional chromatin profiling of zebrafish and human pancreas to uncover and validate disease related enhancers,ena-STUDY-I3S-10-09-2020-08:12:35:299-4,Other,The pancreas is a central organ for human diseases. Most disease associated alleles overlap with non coding cis regulatory elements of DNA  suggesting that alterations in regulatory sequences contribute to pancreatic diseases. However  the interspecies identification of equivalent cis regulatory elements required for in vivo testing face fundamental challenges  including lack of sequence conservation. In this work  we performed a combined analysis of ATAC seq  ChIP seq  4C seq and HiChIP seq data from zebrafish and human pancreatic cells to identify interspecies functionally equivalent cis regulatory elements  regardless of sequence conservation. To link cis regulation with the expression of target genes in the pancreas  we additionally integrated in our analysis own and public RNA seq data from zebrafish pancreatic cell types. Among several disease associated sequences  we identified a zebrafish ptf1a distal enhancer whose deletion generates pancreatic agenesis  demonstrating the causality of this condition in humans. Our results further demonstrate that this phenotype is a consequence of loss of pancreas progenitor cells. Overall  we show that chromatin profiling can uncover interspecies functional equivalency of cis regulatory elements  contributing to the prediction of new disease causative enhancers and their role in human disease.,ChIP seq  ATAC seq  HiChIP seq  4C seq  RNA seq|chromatin accessibility|cis regulatory mutations|pancreas and pancreatic diseases|transcriptional enhancers|ENA FIRST PUBLIC:2020 09 11|ENA LAST UPDATE:2021 09 22,,Adult zebrafish whole pancreas RNA seq replicate1 raw reads,Zebrafish Whole Pancreas RNA seq replicate1,SAMEA7301509,I3S,ENA first public:2021 09 23|ENA last update:2021 09 23|External Id:SAMEA7301509|INSDC center alias:I3S|INSDC center name:I3S|INSDC first public:2021 09 23T20:37:00Z|INSDC last update:2021 09 23T20:37:00Z|INSDC status:public|Submitter Id:Zebrafish Whole Pancreas RNA seq replicate1|common name:zebrafish|sample name:Zebrafish Whole Pancreas RNA seq replicate1|tissue type:whole pancreas,,,,,,,,,Illumina HiSeq 2000 sequencing,ena EXPERIMENT I3S 23 09 2021 16:43:09:330 5,Exocrine1,1,Total RNA extracted with TRIZOL from zebrafish whole pancreas and preprared for sequencing with the TruSeq kit,,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2000,,ERP123913,Illumina HiSeq 2000 sequencing,ENA FIRST PUBLIC:2021 09 23|ENA LAST UPDATE:2021 09 23,Exocrine_Young_1.fastq.gz,fastq,1751070500.0,35021410.0,ena RUN I3S 23 09 2021 16:43:09:330 5,0:50 1:0,A:412303526;C:454121976;G:438481251;T:446048184;N:115563,50,0,,,412303526,454121976,438481251,446048184,115563,ERX6430467,ERS5060068,ERA6385885,I3S|European Nucleotide Archive,I3S,1,0.93968,,0.02618,,0.8003,,0.58539,,50,,B,,usable mapping rate,illumina,hiseq_era,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,Portugal,2020-09-11,Adult,Adult,Pancreas,Endocrine System
10004,ERR6806873,ERX6430466,ERS5060036,ERP123913,PRJEB40292,Multidimensional chromatin profiling of zebrafish and human pancreas to uncover and validate disease related enhancers,ena-STUDY-I3S-10-09-2020-08:12:35:299-4,Other,The pancreas is a central organ for human diseases. Most disease associated alleles overlap with non coding cis regulatory elements of DNA  suggesting that alterations in regulatory sequences contribute to pancreatic diseases. However  the interspecies identification of equivalent cis regulatory elements required for in vivo testing face fundamental challenges  including lack of sequence conservation. In this work  we performed a combined analysis of ATAC seq  ChIP seq  4C seq and HiChIP seq data from zebrafish and human pancreatic cells to identify interspecies functionally equivalent cis regulatory elements  regardless of sequence conservation. To link cis regulation with the expression of target genes in the pancreas  we additionally integrated in our analysis own and public RNA seq data from zebrafish pancreatic cell types. Among several disease associated sequences  we identified a zebrafish ptf1a distal enhancer whose deletion generates pancreatic agenesis  demonstrating the causality of this condition in humans. Our results further demonstrate that this phenotype is a consequence of loss of pancreas progenitor cells. Overall  we show that chromatin profiling can uncover interspecies functional equivalency of cis regulatory elements  contributing to the prediction of new disease causative enhancers and their role in human disease.,ChIP seq  ATAC seq  HiChIP seq  4C seq  RNA seq|chromatin accessibility|cis regulatory mutations|pancreas and pancreatic diseases|transcriptional enhancers|ENA FIRST PUBLIC:2020 09 11|ENA LAST UPDATE:2021 09 22,,Adult zebrafish endocrine pancreas RNA seq replicate4 raw reads,Zebrafish Endocrine Pancreas RNA seq replicate4,SAMEA7301477,I3S,ENA first public:2021 09 23|ENA last update:2021 09 23|External Id:SAMEA7301477|INSDC center alias:I3S|INSDC center name:I3S|INSDC first public:2021 09 23T20:37:00Z|INSDC last update:2021 09 23T20:37:00Z|INSDC status:public|Submitter Id:Zebrafish Endocrine Pancreas RNA seq replicate4|common name:zebrafish|sample name:Zebrafish Endocrine Pancreas RNA seq replicate4|tissue type:endocrine pancreas principal islet,,,,,,,,,Illumina HiSeq 2000 sequencing,ena EXPERIMENT I3S 23 09 2021 16:43:09:330 4,Endocrine4,1,Total RNA extracted with TRIZOL from zebrafish primary pancreatic islet and preprared for sequencing with the TruSeq kit,,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2000,,ERP123913,Illumina HiSeq 2000 sequencing,ENA FIRST PUBLIC:2021 09 23|ENA LAST UPDATE:2021 09 23,Endocrine_Old_2.fastq.gz,fastq,1710560600.0,34211212.0,ena RUN I3S 23 09 2021 16:43:09:330 4,0:50 1:0,A:441527784;C:412091003;G:396100295;T:460728135;N:113383,50,0,,,441527784,412091003,396100295,460728135,113383,ERX6430466,ERS5060036,ERA6385885,I3S|European Nucleotide Archive,I3S,1,0.95616,,0.04334,,0.84585,,0.17182,,50,,B,,usable mapping rate,illumina,hiseq_era,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,Portugal,2020-09-11,Adult,Adult,Pancreas,Endocrine System
10005,ERR6806872,ERX6430465,ERS5060035,ERP123913,PRJEB40292,Multidimensional chromatin profiling of zebrafish and human pancreas to uncover and validate disease related enhancers,ena-STUDY-I3S-10-09-2020-08:12:35:299-4,Other,The pancreas is a central organ for human diseases. Most disease associated alleles overlap with non coding cis regulatory elements of DNA  suggesting that alterations in regulatory sequences contribute to pancreatic diseases. However  the interspecies identification of equivalent cis regulatory elements required for in vivo testing face fundamental challenges  including lack of sequence conservation. In this work  we performed a combined analysis of ATAC seq  ChIP seq  4C seq and HiChIP seq data from zebrafish and human pancreatic cells to identify interspecies functionally equivalent cis regulatory elements  regardless of sequence conservation. To link cis regulation with the expression of target genes in the pancreas  we additionally integrated in our analysis own and public RNA seq data from zebrafish pancreatic cell types. Among several disease associated sequences  we identified a zebrafish ptf1a distal enhancer whose deletion generates pancreatic agenesis  demonstrating the causality of this condition in humans. Our results further demonstrate that this phenotype is a consequence of loss of pancreas progenitor cells. Overall  we show that chromatin profiling can uncover interspecies functional equivalency of cis regulatory elements  contributing to the prediction of new disease causative enhancers and their role in human disease.,ChIP seq  ATAC seq  HiChIP seq  4C seq  RNA seq|chromatin accessibility|cis regulatory mutations|pancreas and pancreatic diseases|transcriptional enhancers|ENA FIRST PUBLIC:2020 09 11|ENA LAST UPDATE:2021 09 22,,Adult zebrafish endocrine pancreas RNA seq replicate3 raw reads,Zebrafish Endocrine Pancreas RNA seq replicate3,SAMEA7301476,I3S,ENA first public:2021 09 23|ENA last update:2021 09 23|External Id:SAMEA7301476|INSDC center alias:I3S|INSDC center name:I3S|INSDC first public:2021 09 23T20:37:00Z|INSDC last update:2021 09 23T20:37:00Z|INSDC status:public|Submitter Id:Zebrafish Endocrine Pancreas RNA seq replicate3|common name:zebrafish|sample name:Zebrafish Endocrine Pancreas RNA seq replicate3|tissue type:endocrine pancreas principal islet,,,,,,,,,Illumina HiSeq 2000 sequencing,ena EXPERIMENT I3S 23 09 2021 16:43:09:330 3,Endocrine3,1,Total RNA extracted with TRIZOL from zebrafish primary pancreatic islet and preprared for sequencing with the TruSeq kit,,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2000,,ERP123913,Illumina HiSeq 2000 sequencing,ENA FIRST PUBLIC:2021 09 23|ENA LAST UPDATE:2021 09 23,Endocrine_Old_1.fastq.gz,fastq,1979582750.0,39591655.0,ena RUN I3S 23 09 2021 16:43:09:330 3,0:50 1:0,A:483713055;C:499972514;G:479484600;T:516281513;N:131068,50,0,,,483713055,499972514,479484600,516281513,131068,ERX6430465,ERS5060035,ERA6385885,I3S|European Nucleotide Archive,I3S,1,0.93722,,0.04096,,0.7559,,0.52069,,50,,B,,usable mapping rate,illumina,hiseq_era,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,Portugal,2020-09-11,Adult,Adult,Pancreas,Endocrine System
10006,ERR6806871,ERX6430464,ERS5060034,ERP123913,PRJEB40292,Multidimensional chromatin profiling of zebrafish and human pancreas to uncover and validate disease related enhancers,ena-STUDY-I3S-10-09-2020-08:12:35:299-4,Other,The pancreas is a central organ for human diseases. Most disease associated alleles overlap with non coding cis regulatory elements of DNA  suggesting that alterations in regulatory sequences contribute to pancreatic diseases. However  the interspecies identification of equivalent cis regulatory elements required for in vivo testing face fundamental challenges  including lack of sequence conservation. In this work  we performed a combined analysis of ATAC seq  ChIP seq  4C seq and HiChIP seq data from zebrafish and human pancreatic cells to identify interspecies functionally equivalent cis regulatory elements  regardless of sequence conservation. To link cis regulation with the expression of target genes in the pancreas  we additionally integrated in our analysis own and public RNA seq data from zebrafish pancreatic cell types. Among several disease associated sequences  we identified a zebrafish ptf1a distal enhancer whose deletion generates pancreatic agenesis  demonstrating the causality of this condition in humans. Our results further demonstrate that this phenotype is a consequence of loss of pancreas progenitor cells. Overall  we show that chromatin profiling can uncover interspecies functional equivalency of cis regulatory elements  contributing to the prediction of new disease causative enhancers and their role in human disease.,ChIP seq  ATAC seq  HiChIP seq  4C seq  RNA seq|chromatin accessibility|cis regulatory mutations|pancreas and pancreatic diseases|transcriptional enhancers|ENA FIRST PUBLIC:2020 09 11|ENA LAST UPDATE:2021 09 22,,Adult zebrafish endocrine pancreas principal islet RNA seq raw reads replicate2,Zebrafish Endocrine Pancreas RNA seq replicate2,SAMEA7301475,I3S,ENA first public:2021 09 23|ENA last update:2021 09 23|External Id:SAMEA7301475|INSDC center alias:I3S|INSDC center name:I3S|INSDC first public:2021 09 23T20:37:00Z|INSDC last update:2021 09 23T20:37:00Z|INSDC status:public|Submitter Id:Zebrafish Endocrine Pancreas RNA seq replicate2|common name:zebrafish|sample name:Zebrafish Endocrine Pancreas RNA seq replicate2|tissue type:endocrine pancreas principal islet,,,,,,,,,Illumina HiSeq 2000 sequencing,ena EXPERIMENT I3S 23 09 2021 16:43:09:329 2,Endocrine2,1,Total RNA extracted with TRIZOL from zebrafish primary pancreatic islet and preprared for sequencing with the TruSeq kit,,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2000,,ERP123913,Illumina HiSeq 2000 sequencing,ENA FIRST PUBLIC:2021 09 23|ENA LAST UPDATE:2021 09 23,Endocrine_Young_2.fastq.gz,fastq,1846315600.0,36926312.0,ena RUN I3S 23 09 2021 16:43:09:329 2,0:50 1:0,A:466245839;C:453675875;G:433502059;T:492768958;N:122869,50,0,,,466245839,453675875,433502059,492768958,122869,ERX6430464,ERS5060034,ERA6385885,I3S|European Nucleotide Archive,I3S,1,0.93566,,0.06945,,0.74065,,0.47497,,50,,B,,usable mapping rate,illumina,hiseq_era,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,Portugal,2020-09-11,Adult,Adult,Pancreas,Endocrine System
10007,ERR6806870,ERX6430463,ERS5060033,ERP123913,PRJEB40292,Multidimensional chromatin profiling of zebrafish and human pancreas to uncover and validate disease related enhancers,ena-STUDY-I3S-10-09-2020-08:12:35:299-4,Other,The pancreas is a central organ for human diseases. Most disease associated alleles overlap with non coding cis regulatory elements of DNA  suggesting that alterations in regulatory sequences contribute to pancreatic diseases. However  the interspecies identification of equivalent cis regulatory elements required for in vivo testing face fundamental challenges  including lack of sequence conservation. In this work  we performed a combined analysis of ATAC seq  ChIP seq  4C seq and HiChIP seq data from zebrafish and human pancreatic cells to identify interspecies functionally equivalent cis regulatory elements  regardless of sequence conservation. To link cis regulation with the expression of target genes in the pancreas  we additionally integrated in our analysis own and public RNA seq data from zebrafish pancreatic cell types. Among several disease associated sequences  we identified a zebrafish ptf1a distal enhancer whose deletion generates pancreatic agenesis  demonstrating the causality of this condition in humans. Our results further demonstrate that this phenotype is a consequence of loss of pancreas progenitor cells. Overall  we show that chromatin profiling can uncover interspecies functional equivalency of cis regulatory elements  contributing to the prediction of new disease causative enhancers and their role in human disease.,ChIP seq  ATAC seq  HiChIP seq  4C seq  RNA seq|chromatin accessibility|cis regulatory mutations|pancreas and pancreatic diseases|transcriptional enhancers|ENA FIRST PUBLIC:2020 09 11|ENA LAST UPDATE:2021 09 22,,Adult zebrafish endocrine pancreas principal islet RNA seq raw reads replicate1,Zebrafish Endocrine Pancreas RNA seq replicate1,SAMEA7301474,I3S,ENA first public:2021 09 23|ENA last update:2021 09 23|External Id:SAMEA7301474|INSDC center alias:I3S|INSDC center name:I3S|INSDC first public:2021 09 23T20:37:00Z|INSDC last update:2021 09 23T20:37:00Z|INSDC status:public|Submitter Id:Zebrafish Endocrine Pancreas RNA seq replicate1|common name:zebrafish|sample name:Zebrafish Endocrine Pancreas RNA seq replicate1|tissue type:endocrine pancreas principal islet,,,,,,,,,Illumina HiSeq 2000 sequencing,ena EXPERIMENT I3S 23 09 2021 16:43:09:329 1,Endocrine1,1,Total RNA extracted with TRIZOL from zebrafish primary pancreatic islet and preprared for sequencing with the TruSeq kit,,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2000,,ERP123913,Illumina HiSeq 2000 sequencing,ENA FIRST PUBLIC:2021 09 23|ENA LAST UPDATE:2021 09 23,Endocrine_Young_1.fastq.gz,fastq,1331184450.0,26623689.0,ena RUN I3S 23 09 2021 16:43:09:329 1,0:50 1:0,A:336761837;C:326693218;G:311881921;T:355763246;N:84228,50,0,,,336761837,326693218,311881921,355763246,84228,ERX6430463,ERS5060033,ERA6385885,I3S|European Nucleotide Archive,I3S,1,0.93154,,0.07422,,0.73064,,0.53917,,50,,B,,usable mapping rate,illumina,hiseq_era,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,Portugal,2020-09-11,Adult,Adult,Pancreas,Endocrine System
39917,SRR2353210,SRX1225282,SRS1065171,SRP063624,PRJNA295427,Expression profiling of centroacinar cells from adult zebrafish pancreas,GSE72963,Transcriptome Analysis,We sequenced mRNA from two preparations of isolated Notch responsive ductal pancreas cells and compared transcript expression to all other non Notch responsive cells from each sample to charactarize zebrafish centroacinar cells. Overall design: Determination of gene expression levels in centroacinar cells and non centroacinar cells from adult pancreas.,,pubmed:26153247,,non CAC rep 2,GSM1875475,,source name:pancreas non CAC|tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated non centroacinar cells,non CAC rep 2,Reads were processed and mapped to Zv9/danRer7 using RSEM EBseq was used to determine differential expression and significance values Genome build: Zv9/danRer7 Supplementary files format and content: tab delimited text files using corrected and uncorrected foldchange values,pancreas non CAC,,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,,tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated non centroacinar cells,GSM1875475,GSM1875475: non CAC rep 2; Danio rerio; RNA Seq,GSM1875475,,1,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,GEO Accession:GSM1875475,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina Genome Analyzer II,,SRP063624,,,H0J49ADXXs_2_2_merged.fastq.bz2,fastq,8206500750.0,109420010.0,GSM1875475 r1,0:75,A:2316013641;C:1680081024;G:1838151157;T:2362455717;N:9799211,75,,,,2316013641,1680081024,1838151157,2362455717,9799211,SRX1225282,SRS1065171,SRA297337,GEO,"Institute of Genetic Medicine, Johns Hopkins University",1,0.5785,,0.17732,,0.77469,,0.62483,,75,,B,,usable mapping rate,illumina,early_illumina,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,United States,2015-09-11,Adult,Adult,Pancreas,Endocrine System
39918,SRR2353209,SRX1225281,SRS1065172,SRP063624,PRJNA295427,Expression profiling of centroacinar cells from adult zebrafish pancreas,GSE72963,Transcriptome Analysis,We sequenced mRNA from two preparations of isolated Notch responsive ductal pancreas cells and compared transcript expression to all other non Notch responsive cells from each sample to charactarize zebrafish centroacinar cells. Overall design: Determination of gene expression levels in centroacinar cells and non centroacinar cells from adult pancreas.,,pubmed:26153247,,CAC rep 2,GSM1875474,,source name:pancreas CAC|tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated centroacinar cells,CAC rep 2,Reads were processed and mapped to Zv9/danRer7 using RSEM EBseq was used to determine differential expression and significance values Genome build: Zv9/danRer7 Supplementary files format and content: tab delimited text files using corrected and uncorrected foldchange values,pancreas CAC,,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,,tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated centroacinar cells,GSM1875474,GSM1875474: CAC rep 2; Danio rerio; RNA Seq,GSM1875474,,1,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,GEO Accession:GSM1875474,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina Genome Analyzer II,,SRP063624,,,H0J49ADXXs_2_1_merged.fastq.bz2,fastq,8206500750.0,109420010.0,GSM1875474 r1,0:75,A:2399854503;C:1714800726;G:1801618139;T:2289278152;N:949230,75,,,,2399854503,1714800726,1801618139,2289278152,949230,SRX1225281,SRS1065172,SRA297337,GEO,"Institute of Genetic Medicine, Johns Hopkins University",1,0.66738,,0.21282,,0.75694,,0.6409,,75,,B,,usable mapping rate,illumina,early_illumina,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,United States,2015-09-11,Adult,Adult,Pancreas,Endocrine System
39919,SRR2353208,SRX1225280,SRS1065173,SRP063624,PRJNA295427,Expression profiling of centroacinar cells from adult zebrafish pancreas,GSE72963,Transcriptome Analysis,We sequenced mRNA from two preparations of isolated Notch responsive ductal pancreas cells and compared transcript expression to all other non Notch responsive cells from each sample to charactarize zebrafish centroacinar cells. Overall design: Determination of gene expression levels in centroacinar cells and non centroacinar cells from adult pancreas.,,pubmed:26153247,,non CAC rep 1,GSM1875473,,source name:pancreas non CAC|tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated non centroacinar cells,non CAC rep 1,Reads were processed and mapped to Zv9/danRer7 using RSEM EBseq was used to determine differential expression and significance values Genome build: Zv9/danRer7 Supplementary files format and content: tab delimited text files using corrected and uncorrected foldchange values,pancreas non CAC,,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,,tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated non centroacinar cells,GSM1875473,GSM1875473: non CAC rep 1; Danio rerio; RNA Seq,GSM1875473,,1,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,GEO Accession:GSM1875473,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina Genome Analyzer II,,SRP063624,,,H0J49ADXXs_1_2_merged.fastq.bz2,fastq,8167909125.0,108905455.0,GSM1875473 r1,0:75,A:2352532315;C:1630885687;G:1811590860;T:2358426654;N:14473609,75,,,,2352532315,1630885687,1811590860,2358426654,14473609,SRX1225280,SRS1065173,SRA297337,GEO,"Institute of Genetic Medicine, Johns Hopkins University",1,0.56456,,0.17736,,0.74525,,0.57983,,75,,B,,usable mapping rate,illumina,early_illumina,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,United States,2015-09-11,Adult,Adult,Pancreas,Endocrine System
39920,SRR2353207,SRX1225279,SRS1065174,SRP063624,PRJNA295427,Expression profiling of centroacinar cells from adult zebrafish pancreas,GSE72963,Transcriptome Analysis,We sequenced mRNA from two preparations of isolated Notch responsive ductal pancreas cells and compared transcript expression to all other non Notch responsive cells from each sample to charactarize zebrafish centroacinar cells. Overall design: Determination of gene expression levels in centroacinar cells and non centroacinar cells from adult pancreas.,,pubmed:26153247,,CAC rep 1,GSM1875472,,source name:pancreas CAC|tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated centroacinar cells,CAC rep 1,Reads were processed and mapped to Zv9/danRer7 using RSEM EBseq was used to determine differential expression and significance values Genome build: Zv9/danRer7 Supplementary files format and content: tab delimited text files using corrected and uncorrected foldchange values,pancreas CAC,,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,,tissue:pancreas|developmental stage:adult|strain:AB|cell type:isolated centroacinar cells,GSM1875472,GSM1875472: CAC rep 1; Danio rerio; RNA Seq,GSM1875472,,1,Pancreas was dissected on ice and dissociated. CACs and non CACs were sorted by FACS and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit Cat#FC 122 1001 was used for the construction of sequencing libraries. Libraries were constructed using standard Illumina protocols,GEO Accession:GSM1875472,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina Genome Analyzer II,,SRP063624,,,H0J49ADXXs_1_1_merged.fastq.bz2,fastq,8167909125.0,108905455.0,GSM1875472 r1,0:75,A:2418955841;C:1674627530;G:1764871153;T:2308386909;N:1067692,75,,,,2418955841,1674627530,1764871153,2308386909,1067692,SRX1225279,SRS1065174,SRA297337,GEO,"Institute of Genetic Medicine, Johns Hopkins University",1,0.66215,,0.21771,,0.72437,,0.57789,,75,,B,,usable mapping rate,illumina,early_illumina,unknown,cdna_unspecified,trueseq,bulk,unknown,unknown,,United States,2015-09-11,Adult,Adult,Pancreas,Endocrine System
44043,SRR6237746,SRX3345984,SRS2646512,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate2 A6 AMCA,GSM2836694,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,Sample Plate2 A6 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,GSM2836694,GSM2836694: Sample Plate2 A6 AMCA; Danio rerio; RNA Seq,GSM2836694,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836694,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate2_A6_AMCA_Neg.fastq.gz,fastq,1229895822.0,20184545.0,GSM2836694 r1,0:60.93 1:0,A:354923207;C:259144918;G:256606445;T:359057403;N:163849,60,0,,,354923207,259144918,256606445,359057403,163849,SRX3345984,SRS2646512,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.87646,,0.26527,,0.8257,,0.55303,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44044,SRR6237745,SRX3345983,SRS2646515,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate2 A5 AMCA,GSM2836693,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,Sample Plate2 A5 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,GSM2836693,GSM2836693: Sample Plate2 A5 AMCA; Danio rerio; RNA Seq,GSM2836693,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836693,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate2_A5_AMCA_Pos.fastq.gz,fastq,925676587.0,15189171.0,GSM2836693 r1,0:60.94 1:0,A:282028510;C:179853567;G:179383383;T:284281435;N:129692,60,0,,,282028510,179853567,179383383,284281435,129692,SRX3345983,SRS2646515,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.90747,,0.36422,,0.74215,,0.54419,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44045,SRR6237744,SRX3345982,SRS2646511,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate2 A4 AMCA,GSM2836692,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,Sample Plate2 A4 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,GSM2836692,GSM2836692: Sample Plate2 A4 AMCA; Danio rerio; RNA Seq,GSM2836692,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836692,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate2_A4_AMCA_Neg.fastq.gz,fastq,1160014329.0,19037174.0,GSM2836692 r1,0:60.93 1:0,A:339245851;C:240318483;G:237965388;T:342331066;N:153541,60,0,,,339245851,240318483,237965388,342331066,153541,SRX3345982,SRS2646511,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.89175,,0.24416,,0.82384,,0.55389,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44046,SRR6237743,SRX3345981,SRS2646513,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate2 A3 AMCA,GSM2836691,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,Sample Plate2 A3 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,GSM2836691,GSM2836691: Sample Plate2 A3 AMCA; Danio rerio; RNA Seq,GSM2836691,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836691,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate2_A3_AMCA_Pos.fastq.gz,fastq,855655864.0,14040472.0,GSM2836691 r1,0:60.94 1:0,A:257233140;C:169854422;G:169238352;T:259211059;N:118891,60,0,,,257233140,169854422,169238352,259211059,118891,SRX3345981,SRS2646513,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.90605,,0.30761,,0.74067,,0.55271,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44047,SRR6237742,SRX3345980,SRS2646514,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate2 A2 AMCA,GSM2836690,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,Sample Plate2 A2 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,GSM2836690,GSM2836690: Sample Plate2 A2 AMCA; Danio rerio; RNA Seq,GSM2836690,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836690,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate2_A2_AMCA_Neg.fastq.gz,fastq,1168909471.0,19185392.0,GSM2836690 r1,0:60.93 1:0,A:326281148;C:256738933;G:252744782;T:332989234;N:155374,60,0,,,326281148,256738933,252744782,332989234,155374,SRX3345980,SRS2646514,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.88849,,0.1697,,0.86602,,0.52913,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44048,SRR6237741,SRX3345979,SRS2646510,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate2 A1 AMCA,GSM2836689,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,Sample Plate2 A1 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,GSM2836689,GSM2836689: Sample Plate2 A1 AMCA; Danio rerio; RNA Seq,GSM2836689,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836689,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate2_A1_AMCA_Pos.fastq.gz,fastq,915106129.0,15016536.0,GSM2836689 r1,0:60.94 1:0,A:271804923;C:184830364;G:184695096;T:273646193;N:129553,60,0,,,271804923,184830364,184695096,273646193,129553,SRX3345979,SRS2646510,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.909,,0.28209,,0.74582,,0.54537,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44049,SRR6237740,SRX3345978,SRS2646509,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate1 A8 AMCA,GSM2836688,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,Sample Plate1 A8 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,GSM2836688,GSM2836688: Sample Plate1 A8 AMCA; Danio rerio; RNA Seq,GSM2836688,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836688,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate1_A8_AMCA_Neg.fastq.gz,fastq,1174636209.0,19276749.0,GSM2836688 r1,0:60.94 1:0,A:346218731;C:240547553;G:237863913;T:349847274;N:158738,60,0,,,346218731,240547553,237863913,349847274,158738,SRX3345978,SRS2646509,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.89653,,0.29731,,0.82388,,0.57688,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44050,SRR6237739,SRX3345977,SRS2646507,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate1 A7 AMCA,GSM2836687,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,Sample Plate1 A7 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,GSM2836687,GSM2836687: Sample Plate1 A7 AMCA; Danio rerio; RNA Seq,GSM2836687,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836687,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate1_A7_AMCA_Pos.fastq.gz,fastq,919004945.0,15079582.0,GSM2836687 r1,0:60.94 1:0,A:281188323;C:177657384;G:177254638;T:282775426;N:129174,60,0,,,281188323,177657384,177254638,282775426,129174,SRX3345977,SRS2646507,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.8999,,0.31855,,0.7643,,0.56856,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44051,SRR6237738,SRX3345976,SRS2646506,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate1 A6 AMCA,GSM2836686,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,Sample Plate1 A6 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Neg|cell type:non pituicytes,GSM2836686,GSM2836686: Sample Plate1 A6 AMCA; Danio rerio; RNA Seq,GSM2836686,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836686,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate1_A6_AMCA_Neg.fastq.gz,fastq,1217567270.0,19981819.0,GSM2836686 r1,0:60.93 1:0,A:366468588;C:242252329;G:239380324;T:369298417;N:167612,60,0,,,366468588,242252329,239380324,369298417,167612,SRX3345976,SRS2646506,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.87327,,0.29592,,0.86026,,0.61414,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
44052,SRR6237737,SRX3345975,SRS2646508,SRP123109,PRJNA416462,Transcriptome of zebrafish neurohypophyseal astroglia pituicytes,GSE106371,Transcriptome Analysis,The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections  endothelial blood vessels and astroglial like cells  termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface  which allows the release of neurohormones from the brain to the periphery. However  the molecular blueprint of pituicytes specific genes is  still unknown.  We have labelled and isolated  zebrafish pituicytes cells and have  identified their  molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing  in 5 biological replicates  using Illumina HiSeq 2500 v4 instrument,,pubmed:30449506,,Sample Plate1 A5 AMCA,GSM2836685,,source name:hypophysis|strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,Sample Plate1 A5 AMCA,cutadapt used to trim poly A and poly T   low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters:  s no  t exon  m intersection strict  i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA  Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2,hypophysis,Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al.  Gene Expr Patterns 2009.,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,Adult zebrafish were raised and bred according to standard protocols.,strain:TL|tissue:hypophysis|age:12 month|Sex:male|b ala lys ne amca labelling:Pos|cell type:pituicytes,GSM2836685,GSM2836685: Sample Plate1 A5 AMCA; Danio rerio; RNA Seq,GSM2836685,,1,Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting.  Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO  0.8mM CaCl2  Pen 50 U/mL + Strep 0.05/mL  FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories  San Jose  CA. Propidium iodide was added to label dead cells.  High speed FACS was performed using an SORP FACSAria machine BD  Bioscience  San Jose  CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control  non AMCA AMCA  cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing   v3 Takara Bio USA  Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at  80°C. Two independent FACS experiments were performed  yielding six samples 3 AMCA+ and 3 AMCA  from 3 groups and four samples 2 AMCA+ and 2 AMCA   from 2 groups. Samples were thawed  reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing   v3 kit. Amplification was performed with 15 cycles. Following the amplification  clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X.  About 3ng of sheared  amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multiplexing of 10 samples on 1 sequencing lane. Between 18 22 million single end 61bp reads were sequenced per sample on Illumina HiSeq 2500 v4 instrument.,GEO Accession:GSM2836685,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,Illumina HiSeq 2500,,SRP123109,,,Sample_Plate1_A5_AMCA_Pos.fastq.gz,fastq,995781320.0,16339491.0,GSM2836685 r1,0:60.94 1:0,A:304872127;C:192446811;G:191928153;T:306392280;N:141949,60,0,,,304872127,192446811,191928153,306392280,141949,SRX3345975,SRS2646508,SRA626615,GEO,"Bioinformatics Unit, Biological Services, Weizmann Institute of Science",1,0.90464,,0.36217,,0.74836,,0.56177,,61,,B,,usable mapping rate,illumina,hiseq_era,full_length,poly_a,smarter,bulk,unknown,unknown,,Israel,2017-10-31,Adult,Adult,Pituitary Gland,Endocrine System
46259,SRR7662165,SRX4522789,SRS3641107,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 40,GSM3325411,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 40,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325411,GSM3325411: 4mpf 3TD 40; Danio rerio; RNA Seq,GSM3325411,,1,FACS SMART Seq v4,GEO Accession:GSM3325411,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_40.fastq.gz,fastq,46013820.0,605445.0,GSM3325411 r1,0:76 1:0,A:12185751;C:10519473;G:10377588;T:12930202;N:806,76,0,,,12185751,10519473,10377588,12930202,806,SRX4522789,SRS3641107,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.79173,,0.06232,,0.94728,,0.70692,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46260,SRR7662164,SRX4522788,SRS3641104,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 39,GSM3325410,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 39,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325410,GSM3325410: 4mpf 3TD 39; Danio rerio; RNA Seq,GSM3325410,,1,FACS SMART Seq v4,GEO Accession:GSM3325410,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_39.fastq.gz,fastq,43118448.0,567348.0,GSM3325410 r1,0:76 1:0,A:11415668;C:9846910;G:9721395;T:12133691;N:784,76,0,,,11415668,9846910,9721395,12133691,784,SRX4522788,SRS3641104,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81063,,0.07048,,0.94513,,0.71805,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46261,SRR7662163,SRX4522787,SRS3641101,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 38,GSM3325409,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 38,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325409,GSM3325409: 4mpf 3TD 38; Danio rerio; RNA Seq,GSM3325409,,1,FACS SMART Seq v4,GEO Accession:GSM3325409,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_38.fastq.gz,fastq,44153644.0,580969.0,GSM3325409 r1,0:76 1:0,A:11395441;C:10637328;G:10522594;T:11597490;N:791,76,0,,,11395441,10637328,10522594,11597490,791,SRX4522787,SRS3641101,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84207,,0.05155,,0.9567,,0.59382,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46262,SRR7662162,SRX4522786,SRS3641103,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 37,GSM3325408,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 37,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325408,GSM3325408: 4mpf 3TD 37; Danio rerio; RNA Seq,GSM3325408,,1,FACS SMART Seq v4,GEO Accession:GSM3325408,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_37.fastq.gz,fastq,45082288.0,593188.0,GSM3325408 r1,0:76 1:0,A:11656775;C:10621119;G:10456207;T:12347402;N:785,76,0,,,11656775,10621119,10456207,12347402,785,SRX4522786,SRS3641103,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81529,,0.06896,,0.94828,,0.7399,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46263,SRR7662161,SRX4522785,SRS3641100,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 36,GSM3325407,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 36,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325407,GSM3325407: 4mpf 3TD 36; Danio rerio; RNA Seq,GSM3325407,,1,FACS SMART Seq v4,GEO Accession:GSM3325407,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_36.fastq.gz,fastq,43851468.0,576993.0,GSM3325407 r1,0:76 1:0,A:11646235;C:9975569;G:9837875;T:12391004;N:785,76,0,,,11646235,9975569,9837875,12391004,785,SRX4522785,SRS3641100,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81855,,0.10417,,0.94653,,0.705,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46264,SRR7662160,SRX4522784,SRS3641102,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 35,GSM3325406,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 35,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325406,GSM3325406: 4mpf 3TD 35; Danio rerio; RNA Seq,GSM3325406,,1,FACS SMART Seq v4,GEO Accession:GSM3325406,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_35.fastq.gz,fastq,51333820.0,675445.0,GSM3325406 r1,0:76 1:0,A:13522158;C:11828514;G:11668956;T:14313258;N:934,76,0,,,13522158,11828514,11668956,14313258,934,SRX4522784,SRS3641102,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.79211,,0.07922,,0.93361,,0.67836,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46265,SRR7662159,SRX4522783,SRS3641099,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 34,GSM3325405,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 34,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325405,GSM3325405: 4mpf 3TD 34; Danio rerio; RNA Seq,GSM3325405,,1,FACS SMART Seq v4,GEO Accession:GSM3325405,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_34.fastq.gz,fastq,42434448.0,558348.0,GSM3325405 r1,0:76 1:0,A:11034253;C:9951444;G:9757025;T:11690994;N:732,76,0,,,11034253,9951444,9757025,11690994,732,SRX4522783,SRS3641099,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80526,,0.0836,,0.94397,,0.75354,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46266,SRR7662158,SRX4522782,SRS3641097,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 33,GSM3325404,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 33,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325404,GSM3325404: 4mpf 3TD 33; Danio rerio; RNA Seq,GSM3325404,,1,FACS SMART Seq v4,GEO Accession:GSM3325404,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_33.fastq.gz,fastq,41483080.0,545830.0,GSM3325404 r1,0:76 1:0,A:10943748;C:9539565;G:9367638;T:11631347;N:782,76,0,,,10943748,9539565,9367638,11631347,782,SRX4522782,SRS3641097,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84811,,0.0724,,0.94079,,0.70412,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46267,SRR7662157,SRX4522781,SRS3641096,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 32,GSM3325403,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 32,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325403,GSM3325403: 4mpf 3TD 32; Danio rerio; RNA Seq,GSM3325403,,1,FACS SMART Seq v4,GEO Accession:GSM3325403,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_32.fastq.gz,fastq,46634892.0,613617.0,GSM3325403 r1,0:76 1:0,A:12285344;C:10732177;G:10567037;T:13049373;N:961,76,0,,,12285344,10732177,10567037,13049373,961,SRX4522781,SRS3641096,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.7942,,0.0631,,0.94024,,0.69769,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46268,SRR7662156,SRX4522780,SRS3641098,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 31,GSM3325402,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 31,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325402,GSM3325402: 4mpf 3TD 31; Danio rerio; RNA Seq,GSM3325402,,1,FACS SMART Seq v4,GEO Accession:GSM3325402,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_31.fastq.gz,fastq,38137864.0,501814.0,GSM3325402 r1,0:76 1:0,A:11280508;C:7576592;G:7684905;T:11595131;N:728,76,0,,,11280508,7576592,7684905,11595131,728,SRX4522780,SRS3641098,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.75454,,0.31244,,0.96404,,0.83206,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46269,SRR7662155,SRX4522779,SRS3641095,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 30,GSM3325401,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 30,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325401,GSM3325401: 4mpf 3TD 30; Danio rerio; RNA Seq,GSM3325401,,1,FACS SMART Seq v4,GEO Accession:GSM3325401,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_30.fastq.gz,fastq,49097976.0,646026.0,GSM3325401 r1,0:76 1:0,A:12742428;C:11505538;G:11340150;T:13508959;N:901,76,0,,,12742428,11505538,11340150,13508959,901,SRX4522779,SRS3641095,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81308,,0.07957,,0.94584,,0.74123,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46270,SRR7662154,SRX4522778,SRS3641094,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 29,GSM3325400,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 29,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325400,GSM3325400: 4mpf 3TD 29; Danio rerio; RNA Seq,GSM3325400,,1,FACS SMART Seq v4,GEO Accession:GSM3325400,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_29.fastq.gz,fastq,45125152.0,593752.0,GSM3325400 r1,0:76 1:0,A:11801780;C:10486963;G:10345497;T:12489940;N:972,76,0,,,11801780,10486963,10345497,12489940,972,SRX4522778,SRS3641094,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.839,,0.07636,,0.94148,,0.7489,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46271,SRR7662153,SRX4522777,SRS3641090,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 28,GSM3325399,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 28,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325399,GSM3325399: 4mpf 3TD 28; Danio rerio; RNA Seq,GSM3325399,,1,FACS SMART Seq v4,GEO Accession:GSM3325399,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_28.fastq.gz,fastq,44665808.0,587708.0,GSM3325399 r1,0:76 1:0,A:11700132;C:10404707;G:10277473;T:12282706;N:790,76,0,,,11700132,10404707,10277473,12282706,790,SRX4522777,SRS3641090,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82673,,0.09831,,0.94673,,0.70054,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46272,SRR7662152,SRX4522776,SRS3641091,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 27,GSM3325398,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 27,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325398,GSM3325398: 4mpf 3TD 27; Danio rerio; RNA Seq,GSM3325398,,1,FACS SMART Seq v4,GEO Accession:GSM3325398,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_27.fastq.gz,fastq,36148944.0,475644.0,GSM3325398 r1,0:76 1:0,A:9254193;C:8695996;G:8545834;T:9652334;N:587,76,0,,,9254193,8695996,8545834,9652334,587,SRX4522776,SRS3641091,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84555,,0.06314,,0.95187,,0.74348,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46273,SRR7662151,SRX4522775,SRS3641092,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 26,GSM3325397,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 26,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325397,GSM3325397: 4mpf 3TD 26; Danio rerio; RNA Seq,GSM3325397,,1,FACS SMART Seq v4,GEO Accession:GSM3325397,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_26.fastq.gz,fastq,39483976.0,519526.0,GSM3325397 r1,0:76 1:0,A:10243731;C:9320393;G:9145297;T:10773771;N:784,76,0,,,10243731,9320393,9145297,10773771,784,SRX4522775,SRS3641092,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81192,,0.0737,,0.94509,,0.73844,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46274,SRR7662150,SRX4522774,SRS3641089,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 25,GSM3325396,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 25,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325396,GSM3325396: 4mpf 3TD 25; Danio rerio; RNA Seq,GSM3325396,,1,FACS SMART Seq v4,GEO Accession:GSM3325396,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_25.fastq.gz,fastq,39361312.0,517912.0,GSM3325396 r1,0:76 1:0,A:10042879;C:9519207;G:9312521;T:10485999;N:706,76,0,,,10042879,9519207,9312521,10485999,706,SRX4522774,SRS3641089,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8252,,0.04127,,0.94901,,0.75046,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46275,SRR7662149,SRX4522773,SRS3641088,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 24,GSM3325395,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 24,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325395,GSM3325395: 4mpf 3TD 24; Danio rerio; RNA Seq,GSM3325395,,1,FACS SMART Seq v4,GEO Accession:GSM3325395,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_24.fastq.gz,fastq,47431676.0,624101.0,GSM3325395 r1,0:76 1:0,A:12495357;C:10870593;G:10708467;T:13356444;N:815,76,0,,,12495357,10870593,10708467,13356444,815,SRX4522773,SRS3641088,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83398,,0.07885,,0.93608,,0.72127,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46276,SRR7662148,SRX4522772,SRS3641087,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 23,GSM3325394,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 23,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325394,GSM3325394: 4mpf 3TD 23; Danio rerio; RNA Seq,GSM3325394,,1,FACS SMART Seq v4,GEO Accession:GSM3325394,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_23.fastq.gz,fastq,27242048.0,358448.0,GSM3325394 r1,0:76 1:0,A:8135321;C:5430241;G:5413461;T:8262532;N:493,76,0,,,8135321,5430241,5413461,8262532,493,SRX4522772,SRS3641087,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.7264,,0.06277,,0.99277,,0.96356,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46277,SRR7662147,SRX4522771,SRS3641086,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 22,GSM3325393,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 22,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325393,GSM3325393: 4mpf 3TD 22; Danio rerio; RNA Seq,GSM3325393,,1,FACS SMART Seq v4,GEO Accession:GSM3325393,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_22.fastq.gz,fastq,47999320.0,631570.0,GSM3325393 r1,0:76 1:0,A:12468506;C:11445141;G:11330644;T:12754052;N:977,76,0,,,12468506,11445141,11330644,12754052,977,SRX4522771,SRS3641086,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85839,,0.0787,,0.91648,,0.72247,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46278,SRR7662146,SRX4522770,SRS3641085,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 21,GSM3325392,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 21,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325392,GSM3325392: 4mpf 3TD 21; Danio rerio; RNA Seq,GSM3325392,,1,FACS SMART Seq v4,GEO Accession:GSM3325392,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_21.fastq.gz,fastq,43447224.0,571674.0,GSM3325392 r1,0:76 1:0,A:11289500;C:10397979;G:10217101;T:11541647;N:997,76,0,,,11289500,10397979,10217101,11541647,997,SRX4522770,SRS3641085,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85251,,0.09879,,0.95434,,0.57235,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46279,SRR7662145,SRX4522769,SRS3641083,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 20,GSM3325391,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 20,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325391,GSM3325391: 4mpf 3TD 20; Danio rerio; RNA Seq,GSM3325391,,1,FACS SMART Seq v4,GEO Accession:GSM3325391,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_20.fastq.gz,fastq,39193352.0,515702.0,GSM3325391 r1,0:76 1:0,A:10220606;C:9138362;G:8983737;T:10849932;N:715,76,0,,,10220606,9138362,8983737,10849932,715,SRX4522769,SRS3641083,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82235,,0.0909,,0.93661,,0.74939,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46280,SRR7662144,SRX4522768,SRS3641084,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 19,GSM3325390,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 19,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325390,GSM3325390: 4mpf 3TD 19; Danio rerio; RNA Seq,GSM3325390,,1,FACS SMART Seq v4,GEO Accession:GSM3325390,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_19.fastq.gz,fastq,31949640.0,420390.0,GSM3325390 r1,0:76 1:0,A:9254071;C:6775193;G:6680133;T:9239719;N:524,76,0,,,9254071,6775193,6680133,9239719,524,SRX4522768,SRS3641084,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81416,,0.09963,,0.99827,,0.99571,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46281,SRR7662143,SRX4522767,SRS3641082,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 18,GSM3325389,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 18,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325389,GSM3325389: 4mpf 3TD 18; Danio rerio; RNA Seq,GSM3325389,,1,FACS SMART Seq v4,GEO Accession:GSM3325389,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_18.fastq.gz,fastq,35589964.0,468289.0,GSM3325389 r1,0:76 1:0,A:9543684;C:8018292;G:7914665;T:10112629;N:694,76,0,,,9543684,8018292,7914665,10112629,694,SRX4522767,SRS3641082,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82291,,0.11384,,0.96907,,0.84606,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46282,SRR7662142,SRX4522766,SRS3641081,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 17,GSM3325388,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 17,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325388,GSM3325388: 4mpf 3TD 17; Danio rerio; RNA Seq,GSM3325388,,1,FACS SMART Seq v4,GEO Accession:GSM3325388,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_17.fastq.gz,fastq,42951780.0,565155.0,GSM3325388 r1,0:76 1:0,A:11191051;C:10092134;G:9906934;T:11760925;N:736,76,0,,,11191051,10092134,9906934,11760925,736,SRX4522766,SRS3641081,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82212,,0.0726,,0.94972,,0.73169,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46283,SRR7662141,SRX4522765,SRS3641080,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 16,GSM3325387,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 16,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325387,GSM3325387: 4mpf 3TD 16; Danio rerio; RNA Seq,GSM3325387,,1,FACS SMART Seq v4,GEO Accession:GSM3325387,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_16.fastq.gz,fastq,45155476.0,594151.0,GSM3325387 r1,0:76 1:0,A:11524282;C:10805976;G:10604356;T:12220018;N:844,76,0,,,11524282,10805976,10604356,12220018,844,SRX4522765,SRS3641080,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85101,,0.05564,,0.9526,,0.7636,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46284,SRR7662140,SRX4522764,SRS3641079,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 15,GSM3325386,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 15,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325386,GSM3325386: 4mpf 3TD 15; Danio rerio; RNA Seq,GSM3325386,,1,FACS SMART Seq v4,GEO Accession:GSM3325386,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_15.fastq.gz,fastq,45910688.0,604088.0,GSM3325386 r1,0:76 1:0,A:11801725;C:10919101;G:10766830;T:12422219;N:813,76,0,,,11801725,10919101,10766830,12422219,813,SRX4522764,SRS3641079,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.77867,,0.08265,,0.93988,,0.72223,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46285,SRR7662139,SRX4522763,SRS3641078,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 14,GSM3325385,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 14,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325385,GSM3325385: 4mpf 3TD 14; Danio rerio; RNA Seq,GSM3325385,,1,FACS SMART Seq v4,GEO Accession:GSM3325385,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_14.fastq.gz,fastq,52482484.0,690559.0,GSM3325385 r1,0:76 1:0,A:13749272;C:12167720;G:12023268;T:14541201;N:1023,76,0,,,13749272,12167720,12023268,14541201,1023,SRX4522763,SRS3641078,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80454,,0.06714,,0.94637,,0.69874,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46286,SRR7662138,SRX4522762,SRS3641077,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 13,GSM3325384,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 13,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325384,GSM3325384: 4mpf 3TD 13; Danio rerio; RNA Seq,GSM3325384,,1,FACS SMART Seq v4,GEO Accession:GSM3325384,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_13.fastq.gz,fastq,43940996.0,578171.0,GSM3325384 r1,0:76 1:0,A:11341969;C:10437251;G:10292036;T:11868839;N:901,76,0,,,11341969,10437251,10292036,11868839,901,SRX4522762,SRS3641077,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82051,,0.06836,,0.94882,,0.71807,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46287,SRR7662137,SRX4522761,SRS3641106,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 12,GSM3325383,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 12,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325383,GSM3325383: 4mpf 3TD 12; Danio rerio; RNA Seq,GSM3325383,,1,FACS SMART Seq v4,GEO Accession:GSM3325383,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_12.fastq.gz,fastq,48769048.0,641698.0,GSM3325383 r1,0:76 1:0,A:12740843;C:11336073;G:11163790;T:13527391;N:951,76,0,,,12740843,11336073,11163790,13527391,951,SRX4522761,SRS3641106,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81863,,0.06952,,0.94448,,0.67453,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46288,SRR7662136,SRX4522760,SRS3641076,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 11,GSM3325382,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 11,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325382,GSM3325382: 4mpf 3TD 11; Danio rerio; RNA Seq,GSM3325382,,1,FACS SMART Seq v4,GEO Accession:GSM3325382,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_11.fastq.gz,fastq,39690620.0,522245.0,GSM3325382 r1,0:76 1:0,A:10250697;C:9420248;G:9331138;T:10687674;N:863,76,0,,,10250697,9420248,9331138,10687674,863,SRX4522760,SRS3641076,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86066,,0.08665,,0.94785,,0.74372,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46289,SRR7662135,SRX4522759,SRS3641074,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 10,GSM3325381,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 10,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325381,GSM3325381: 4mpf 3TD 10; Danio rerio; RNA Seq,GSM3325381,,1,FACS SMART Seq v4,GEO Accession:GSM3325381,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_10.fastq.gz,fastq,43790136.0,576186.0,GSM3325381 r1,0:76 1:0,A:11318586;C:10326843;G:10152922;T:11990991;N:794,76,0,,,11318586,10326843,10152922,11990991,794,SRX4522759,SRS3641074,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81392,,0.06136,,0.95367,,0.73262,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46290,SRR7662134,SRX4522758,SRS3641073,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 9,GSM3325380,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 9,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325380,GSM3325380: 4mpf 3TD 9; Danio rerio; RNA Seq,GSM3325380,,1,FACS SMART Seq v4,GEO Accession:GSM3325380,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_9.fastq.gz,fastq,47345264.0,622964.0,GSM3325380 r1,0:76 1:0,A:12562689;C:10846875;G:10695825;T:13239048;N:827,76,0,,,12562689,10846875,10695825,13239048,827,SRX4522758,SRS3641073,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82472,,0.0953,,0.94892,,0.6904,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46291,SRR7662133,SRX4522757,SRS3641071,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 8,GSM3325379,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 8,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325379,GSM3325379: 4mpf 3TD 8; Danio rerio; RNA Seq,GSM3325379,,1,FACS SMART Seq v4,GEO Accession:GSM3325379,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_8.fastq.gz,fastq,49055340.0,645465.0,GSM3325379 r1,0:76 1:0,A:12490243;C:11675281;G:11448655;T:13440254;N:907,76,0,,,12490243,11675281,11448655,13440254,907,SRX4522757,SRS3641071,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84802,,0.03965,,0.95203,,0.74982,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46292,SRR7662132,SRX4522756,SRS3641075,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 7,GSM3325378,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 7,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325378,GSM3325378: 4mpf 3TD 7; Danio rerio; RNA Seq,GSM3325378,,1,FACS SMART Seq v4,GEO Accession:GSM3325378,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_7.fastq.gz,fastq,51166164.0,673239.0,GSM3325378 r1,0:76 1:0,A:13623337;C:11647185;G:11552049;T:14342656;N:937,76,0,,,13623337,11647185,11552049,14342656,937,SRX4522756,SRS3641075,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80194,,0.09112,,0.9486,,0.68788,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46293,SRR7662131,SRX4522755,SRS3641070,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 6,GSM3325377,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 6,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325377,GSM3325377: 4mpf 3TD 6; Danio rerio; RNA Seq,GSM3325377,,1,FACS SMART Seq v4,GEO Accession:GSM3325377,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_6.fastq.gz,fastq,6901256.0,90806.0,GSM3325377 r1,0:76 1:0,A:1772241;C:1662103;G:1646027;T:1820762;N:123,76,0,,,1772241,1662103,1646027,1820762,123,SRX4522755,SRS3641070,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84631,,0.08023,,0.96282,,0.67498,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46294,SRR7662130,SRX4522754,SRS3641072,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 5,GSM3325376,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 5,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325376,GSM3325376: 4mpf 3TD 5; Danio rerio; RNA Seq,GSM3325376,,1,FACS SMART Seq v4,GEO Accession:GSM3325376,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_5.fastq.gz,fastq,15475272.0,203622.0,GSM3325376 r1,0:76 1:0,A:4169638;C:3469267;G:3476564;T:4359550;N:253,76,0,,,4169638,3469267,3476564,4359550,253,SRX4522754,SRS3641072,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84462,,0.17124,,0.95663,,0.77918,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46295,SRR7662129,SRX4522753,SRS3641069,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 4,GSM3325375,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 4,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325375,GSM3325375: 4mpf 3TD 4; Danio rerio; RNA Seq,GSM3325375,,1,FACS SMART Seq v4,GEO Accession:GSM3325375,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_4.fastq.gz,fastq,46359544.0,609994.0,GSM3325375 r1,0:76 1:0,A:12049640;C:10891937;G:10750024;T:12667027;N:916,76,0,,,12049640,10891937,10750024,12667027,916,SRX4522753,SRS3641069,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80025,,0.07504,,0.94554,,0.70433,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46296,SRR7662128,SRX4522752,SRS3641068,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 3,GSM3325374,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 3,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325374,GSM3325374: 4mpf 3TD 3; Danio rerio; RNA Seq,GSM3325374,,1,FACS SMART Seq v4,GEO Accession:GSM3325374,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_3.fastq.gz,fastq,51188432.0,673532.0,GSM3325374 r1,0:76 1:0,A:13021216;C:12236781;G:12013420;T:13916078;N:937,76,0,,,13021216,12236781,12013420,13916078,937,SRX4522752,SRS3641068,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85289,,0.04263,,0.94789,,0.75425,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46297,SRR7662127,SRX4522751,SRS3641067,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 2,GSM3325373,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 2,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325373,GSM3325373: 4mpf 3TD 2; Danio rerio; RNA Seq,GSM3325373,,1,FACS SMART Seq v4,GEO Accession:GSM3325373,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_2.fastq.gz,fastq,34888940.0,459065.0,GSM3325373 r1,0:76 1:0,A:9604470;C:7612595;G:7523333;T:10147965;N:577,76,0,,,9604470,7612595,7523333,10147965,577,SRX4522751,SRS3641067,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.812,,0.12255,,0.94787,,0.69174,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46298,SRR7662126,SRX4522750,SRS3641066,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 1,GSM3325372,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 1,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325372,GSM3325372: 4mpf 3TD 1; Danio rerio; RNA Seq,GSM3325372,,1,FACS SMART Seq v4,GEO Accession:GSM3325372,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_1.fastq.gz,fastq,47564904.0,625854.0,GSM3325372 r1,0:76 1:0,A:12408153;C:11109359;G:10940225;T:13106272;N:895,76,0,,,12408153,11109359,10940225,13106272,895,SRX4522750,SRS3641066,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8196,,0.09022,,0.9442,,0.72901,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46299,SRR7662125,SRX4522749,SRS3641065,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 55,GSM3325371,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 55,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325371,GSM3325371: 4mpf IF 55; Danio rerio; RNA Seq,GSM3325371,,1,FACS SMART Seq v4,GEO Accession:GSM3325371,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_55.fastq.gz,fastq,52998904.0,697354.0,GSM3325371 r1,0:76 1:0,A:14310362;C:11924556;G:11808914;T:14953988;N:1084,76,0,,,14310362,11924556,11808914,14953988,1084,SRX4522749,SRS3641065,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82648,,0.10954,,0.94006,,0.64951,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46300,SRR7662124,SRX4522748,SRS3641064,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 54,GSM3325370,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 54,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325370,GSM3325370: 4mpf IF 54; Danio rerio; RNA Seq,GSM3325370,,1,FACS SMART Seq v4,GEO Accession:GSM3325370,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_54.fastq.gz,fastq,47887828.0,630103.0,GSM3325370 r1,0:76 1:0,A:12761394;C:10885664;G:10765180;T:13474729;N:861,76,0,,,12761394,10885664,10765180,13474729,861,SRX4522748,SRS3641064,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82634,,0.07647,,0.95743,,0.67836,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46301,SRR7662123,SRX4522747,SRS3641063,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 53,GSM3325369,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 53,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325369,GSM3325369: 4mpf IF 53; Danio rerio; RNA Seq,GSM3325369,,1,FACS SMART Seq v4,GEO Accession:GSM3325369,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_53.fastq.gz,fastq,58635444.0,771519.0,GSM3325369 r1,0:76 1:0,A:15180731;C:13835314;G:13650142;T:15968133;N:1124,76,0,,,15180731,13835314,13650142,15968133,1124,SRX4522747,SRS3641063,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85253,,0.06205,,0.95913,,0.66854,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46302,SRR7662122,SRX4522746,SRS3641061,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 52,GSM3325368,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 52,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325368,GSM3325368: 4mpf IF 52; Danio rerio; RNA Seq,GSM3325368,,1,FACS SMART Seq v4,GEO Accession:GSM3325368,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_52.fastq.gz,fastq,48344892.0,636117.0,GSM3325368 r1,0:76 1:0,A:13262881;C:10596839;G:10539660;T:13944668;N:844,76,0,,,13262881,10596839,10539660,13944668,844,SRX4522746,SRS3641061,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81529,,0.14578,,0.95801,,0.67588,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46303,SRR7662121,SRX4522745,SRS3641062,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 51,GSM3325367,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 51,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325367,GSM3325367: 4mpf IF 51; Danio rerio; RNA Seq,GSM3325367,,1,FACS SMART Seq v4,GEO Accession:GSM3325367,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_51.fastq.gz,fastq,46400432.0,610532.0,GSM3325367 r1,0:76 1:0,A:12083601;C:10963871;G:10782137;T:12570002;N:821,76,0,,,12083601,10963871,10782137,12570002,821,SRX4522745,SRS3641062,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84689,,0.06857,,0.95122,,0.67464,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46304,SRR7662120,SRX4522744,SRS3641060,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 50,GSM3325366,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 50,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325366,GSM3325366: 4mpf IF 50; Danio rerio; RNA Seq,GSM3325366,,1,FACS SMART Seq v4,GEO Accession:GSM3325366,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_50.fastq.gz,fastq,55987072.0,736672.0,GSM3325366 r1,0:76 1:0,A:14594860;C:13215545;G:13041607;T:15133976;N:1084,76,0,,,14594860,13215545,13041607,15133976,1084,SRX4522744,SRS3641060,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84418,,0.1053,,0.94489,,0.62441,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46305,SRR7662119,SRX4522743,SRS3641059,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 49,GSM3325365,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 49,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325365,GSM3325365: 4mpf IF 49; Danio rerio; RNA Seq,GSM3325365,,1,FACS SMART Seq v4,GEO Accession:GSM3325365,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_49.fastq.gz,fastq,15299560.0,201310.0,GSM3325365 r1,0:76 1:0,A:4227832;C:3362859;G:3368691;T:4339906;N:272,76,0,,,4227832,3362859,3368691,4339906,272,SRX4522743,SRS3641059,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84899,,0.19228,,0.95572,,0.61479,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46306,SRR7662118,SRX4522742,SRS3641058,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 48,GSM3325364,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 48,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325364,GSM3325364: 4mpf IF 48; Danio rerio; RNA Seq,GSM3325364,,1,FACS SMART Seq v4,GEO Accession:GSM3325364,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_48.fastq.gz,fastq,38444296.0,505846.0,GSM3325364 r1,0:76 1:0,A:10284940;C:8847375;G:8697915;T:10613326;N:740,76,0,,,10284940,8847375,8697915,10613326,740,SRX4522742,SRS3641058,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83587,,0.10622,,0.96195,,0.65351,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46307,SRR7662117,SRX4522741,SRS3641055,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 47,GSM3325363,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 47,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325363,GSM3325363: 4mpf IF 47; Danio rerio; RNA Seq,GSM3325363,,1,FACS SMART Seq v4,GEO Accession:GSM3325363,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_47.fastq.gz,fastq,43789984.0,576184.0,GSM3325363 r1,0:76 1:0,A:11523891;C:10257103;G:10162326;T:11845765;N:899,76,0,,,11523891,10257103,10162326,11845765,899,SRX4522741,SRS3641055,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83802,,0.08033,,0.94649,,0.60409,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46308,SRR7662116,SRX4522740,SRS3641056,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 46,GSM3325362,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 46,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325362,GSM3325362: 4mpf IF 46; Danio rerio; RNA Seq,GSM3325362,,1,FACS SMART Seq v4,GEO Accession:GSM3325362,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_46.fastq.gz,fastq,42123608.0,554258.0,GSM3325362 r1,0:76 1:0,A:11747868;C:9186318;G:9233447;T:11955236;N:739,76,0,,,11747868,9186318,9233447,11955236,739,SRX4522740,SRS3641056,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84712,,0.10853,,0.95402,,0.55085,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46309,SRR7662115,SRX4522739,SRS3641054,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 45,GSM3325361,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 45,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325361,GSM3325361: 4mpf IF 45; Danio rerio; RNA Seq,GSM3325361,,1,FACS SMART Seq v4,GEO Accession:GSM3325361,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_45.fastq.gz,fastq,25202664.0,331614.0,GSM3325361 r1,0:76 1:0,A:7295930;C:5088213;G:5102598;T:7715445;N:478,76,0,,,7295930,5088213,5102598,7715445,478,SRX4522739,SRS3641054,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.56607,,0.2943,,0.97705,,0.66394,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46310,SRR7662114,SRX4522738,SRS3641057,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 44,GSM3325360,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 44,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325360,GSM3325360: 4mpf IF 44; Danio rerio; RNA Seq,GSM3325360,,1,FACS SMART Seq v4,GEO Accession:GSM3325360,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_44.fastq.gz,fastq,43666712.0,574562.0,GSM3325360 r1,0:76 1:0,A:11476243;C:10259299;G:10150379;T:11779913;N:878,76,0,,,11476243,10259299,10150379,11779913,878,SRX4522738,SRS3641057,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83766,,0.10498,,0.94706,,0.59362,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46311,SRR7662113,SRX4522737,SRS3641053,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 43,GSM3325359,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 43,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325359,GSM3325359: 4mpf IF 43; Danio rerio; RNA Seq,GSM3325359,,1,FACS SMART Seq v4,GEO Accession:GSM3325359,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_43.fastq.gz,fastq,39006772.0,513247.0,GSM3325359 r1,0:76 1:0,A:10889773;C:8486226;G:8476299;T:11153870;N:604,76,0,,,10889773,8486226,8476299,11153870,604,SRX4522737,SRS3641053,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83738,,0.08267,,0.95164,,0.59767,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46312,SRR7662112,SRX4522736,SRS3641051,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 42,GSM3325358,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 42,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325358,GSM3325358: 4mpf IF 42; Danio rerio; RNA Seq,GSM3325358,,1,FACS SMART Seq v4,GEO Accession:GSM3325358,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_42.fastq.gz,fastq,45428012.0,597737.0,GSM3325358 r1,0:76 1:0,A:11838175;C:10827784;G:10713557;T:12047638;N:858,76,0,,,11838175,10827784,10713557,12047638,858,SRX4522736,SRS3641051,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82069,,0.08168,,0.95868,,0.57599,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46313,SRR7662111,SRX4522735,SRS3641052,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 41,GSM3325357,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 41,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325357,GSM3325357: 4mpf IF 41; Danio rerio; RNA Seq,GSM3325357,,1,FACS SMART Seq v4,GEO Accession:GSM3325357,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_41.fastq.gz,fastq,32840208.0,432108.0,GSM3325357 r1,0:76 1:0,A:8619815;C:7776515;G:7701106;T:8742194;N:578,76,0,,,8619815,7776515,7701106,8742194,578,SRX4522735,SRS3641052,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84718,,0.0672,,0.94957,,0.57585,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46314,SRR7662110,SRX4522734,SRS3641050,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 40,GSM3325356,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 40,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325356,GSM3325356: 4mpf IF 40; Danio rerio; RNA Seq,GSM3325356,,1,FACS SMART Seq v4,GEO Accession:GSM3325356,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_40.fastq.gz,fastq,2701876.0,35551.0,GSM3325356 r1,0:76 1:0,A:721393;C:629710;G:647722;T:703014;N:37,76,0,,,721393,629710,647722,703014,37,SRX4522734,SRS3641050,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86299,,0.08071,,0.94935,,0.62592,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46315,SRR7662109,SRX4522733,SRS3641049,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 39,GSM3325355,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 39,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325355,GSM3325355: 4mpf IF 39; Danio rerio; RNA Seq,GSM3325355,,1,FACS SMART Seq v4,GEO Accession:GSM3325355,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_39.fastq.gz,fastq,43743852.0,575577.0,GSM3325355 r1,0:76 1:0,A:11419966;C:10441518;G:10232065;T:11649588;N:715,76,0,,,11419966,10441518,10232065,11649588,715,SRX4522733,SRS3641049,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84654,,0.08856,,0.94815,,0.58853,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46316,SRR7662108,SRX4522732,SRS3641048,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 38,GSM3325354,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 38,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325354,GSM3325354: 4mpf IF 38; Danio rerio; RNA Seq,GSM3325354,,1,FACS SMART Seq v4,GEO Accession:GSM3325354,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_38.fastq.gz,fastq,49032768.0,645168.0,GSM3325354 r1,0:76 1:0,A:12567749;C:11898380;G:11813374;T:12752336;N:929,76,0,,,12567749,11898380,11813374,12752336,929,SRX4522732,SRS3641048,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84386,,0.07513,,0.96376,,0.56093,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46317,SRR7662107,SRX4522731,SRS3641046,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 37,GSM3325353,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 37,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325353,GSM3325353: 4mpf IF 37; Danio rerio; RNA Seq,GSM3325353,,1,FACS SMART Seq v4,GEO Accession:GSM3325353,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_37.fastq.gz,fastq,59641228.0,784753.0,GSM3325353 r1,0:76 1:0,A:15382293;C:14416910;G:14292334;T:15548644;N:1047,76,0,,,15382293,14416910,14292334,15548644,1047,SRX4522731,SRS3641046,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81572,,0.05376,,0.95302,,0.57351,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46318,SRR7662106,SRX4522730,SRS3641045,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 36,GSM3325352,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 36,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325352,GSM3325352: 4mpf IF 36; Danio rerio; RNA Seq,GSM3325352,,1,FACS SMART Seq v4,GEO Accession:GSM3325352,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_36.fastq.gz,fastq,58551844.0,770419.0,GSM3325352 r1,0:76 1:0,A:15018078;C:14178454;G:14037854;T:15316356;N:1102,76,0,,,15018078,14178454,14037854,15316356,1102,SRX4522730,SRS3641045,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84525,,0.04828,,0.95343,,0.63414,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46319,SRR7662105,SRX4522729,SRS3641047,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 35,GSM3325351,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 35,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325351,GSM3325351: 4mpf IF 35; Danio rerio; RNA Seq,GSM3325351,,1,FACS SMART Seq v4,GEO Accession:GSM3325351,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_35.fastq.gz,fastq,47139912.0,620262.0,GSM3325351 r1,0:76 1:0,A:12380168;C:11091607;G:10934854;T:12732309;N:974,76,0,,,12380168,11091607,10934854,12732309,974,SRX4522729,SRS3641047,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84984,,0.07462,,0.96027,,0.59064,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46320,SRR7662104,SRX4522728,SRS3641044,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 34,GSM3325350,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 34,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325350,GSM3325350: 4mpf IF 34; Danio rerio; RNA Seq,GSM3325350,,1,FACS SMART Seq v4,GEO Accession:GSM3325350,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_34.fastq.gz,fastq,54652436.0,719111.0,GSM3325350 r1,0:76 1:0,A:14304618;C:12974551;G:12864931;T:14507245;N:1091,76,0,,,14304618,12974551,12864931,14507245,1091,SRX4522728,SRS3641044,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82867,,0.07429,,0.95507,,0.61233,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46321,SRR7662103,SRX4522727,SRS3641043,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 33,GSM3325349,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 33,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325349,GSM3325349: 4mpf IF 33; Danio rerio; RNA Seq,GSM3325349,,1,FACS SMART Seq v4,GEO Accession:GSM3325349,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_33.fastq.gz,fastq,47925296.0,630596.0,GSM3325349 r1,0:76 1:0,A:12759955;C:10824427;G:10743640;T:13596268;N:1006,76,0,,,12759955,10824427,10743640,13596268,1006,SRX4522727,SRS3641043,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.78516,,0.08708,,0.95246,,0.68657,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46322,SRR7662102,SRX4522726,SRS3641042,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 32,GSM3325348,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 32,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325348,GSM3325348: 4mpf IF 32; Danio rerio; RNA Seq,GSM3325348,,1,FACS SMART Seq v4,GEO Accession:GSM3325348,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_32.fastq.gz,fastq,51398192.0,676292.0,GSM3325348 r1,0:76 1:0,A:13500811;C:12158828;G:12052658;T:13684896;N:999,76,0,,,13500811,12158828,12052658,13684896,999,SRX4522726,SRS3641042,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83442,,0.09255,,0.95509,,0.56413,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46323,SRR7662101,SRX4522725,SRS3641040,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 31,GSM3325347,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 31,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325347,GSM3325347: 4mpf IF 31; Danio rerio; RNA Seq,GSM3325347,,1,FACS SMART Seq v4,GEO Accession:GSM3325347,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_31.fastq.gz,fastq,36844040.0,484790.0,GSM3325347 r1,0:76 1:0,A:9560563;C:8814167;G:8665010;T:9803711;N:589,76,0,,,9560563,8814167,8665010,9803711,589,SRX4522725,SRS3641040,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83689,,0.09471,,0.95101,,0.62526,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46324,SRR7662100,SRX4522724,SRS3641041,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 30,GSM3325346,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 30,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325346,GSM3325346: 4mpf IF 30; Danio rerio; RNA Seq,GSM3325346,,1,FACS SMART Seq v4,GEO Accession:GSM3325346,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_30.fastq.gz,fastq,15602496.0,205296.0,GSM3325346 r1,0:76 1:0,A:4650162;C:3102203;G:3101870;T:4747969;N:292,76,0,,,4650162,3102203,3101870,4747969,292,SRX4522724,SRS3641041,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.70449,,0.13623,,0.98362,,0.94464,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46325,SRR7662099,SRX4522723,SRS3641039,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 29,GSM3325345,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 29,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325345,GSM3325345: 4mpf IF 29; Danio rerio; RNA Seq,GSM3325345,,1,FACS SMART Seq v4,GEO Accession:GSM3325345,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_29.fastq.gz,fastq,34915844.0,459419.0,GSM3325345 r1,0:76 1:0,A:8984073;C:8446613;G:8294708;T:9189835;N:615,76,0,,,8984073,8446613,8294708,9189835,615,SRX4522723,SRS3641039,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84188,,0.05728,,0.95294,,0.62182,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46326,SRR7662098,SRX4522722,SRS3641038,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 28,GSM3325344,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 28,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325344,GSM3325344: 4mpf IF 28; Danio rerio; RNA Seq,GSM3325344,,1,FACS SMART Seq v4,GEO Accession:GSM3325344,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_28.fastq.gz,fastq,38676780.0,508905.0,GSM3325344 r1,0:76 1:0,A:10035445;C:9254767;G:9086353;T:10299584;N:631,76,0,,,10035445,9254767,9086353,10299584,631,SRX4522722,SRS3641038,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8438,,0.08959,,0.95377,,0.64434,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46327,SRR7662097,SRX4522721,SRS3641037,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 27,GSM3325343,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 27,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325343,GSM3325343: 4mpf IF 27; Danio rerio; RNA Seq,GSM3325343,,1,FACS SMART Seq v4,GEO Accession:GSM3325343,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_27.fastq.gz,fastq,19453264.0,255964.0,GSM3325343 r1,0:76 1:0,A:5040810;C:4608368;G:4560312;T:5243498;N:276,76,0,,,5040810,4608368,4560312,5243498,276,SRX4522721,SRS3641037,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85606,,0.15379,,0.95225,,0.6452,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46328,SRR7662096,SRX4522720,SRS3641035,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 26,GSM3325342,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 26,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325342,GSM3325342: 4mpf IF 26; Danio rerio; RNA Seq,GSM3325342,,1,FACS SMART Seq v4,GEO Accession:GSM3325342,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_26.fastq.gz,fastq,40861704.0,537654.0,GSM3325342 r1,0:76 1:0,A:10620649;C:9739457;G:9582234;T:10918599;N:765,76,0,,,10620649,9739457,9582234,10918599,765,SRX4522720,SRS3641035,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85747,,0.08566,,0.95491,,0.65482,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46329,SRR7662095,SRX4522719,SRS3641032,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 25,GSM3325341,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 25,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325341,GSM3325341: 4mpf IF 25; Danio rerio; RNA Seq,GSM3325341,,1,FACS SMART Seq v4,GEO Accession:GSM3325341,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_25.fastq.gz,fastq,19358264.0,254714.0,GSM3325341 r1,0:76 1:0,A:5026278;C:4636184;G:4558560;T:5136917;N:325,76,0,,,5026278,4636184,4558560,5136917,325,SRX4522719,SRS3641032,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83861,,0.07049,,0.94901,,0.58876,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46330,SRR7662094,SRX4522718,SRS3641034,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 24,GSM3325340,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 24,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325340,GSM3325340: 4mpf IF 24; Danio rerio; RNA Seq,GSM3325340,,1,FACS SMART Seq v4,GEO Accession:GSM3325340,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_24.fastq.gz,fastq,3000252.0,39477.0,GSM3325340 r1,0:76 1:0,A:777886;C:718556;G:713936;T:789820;N:54,76,0,,,777886,718556,713936,789820,54,SRX4522718,SRS3641034,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86238,,0.05348,,0.95639,,0.61537,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46331,SRR7662093,SRX4522717,SRS3641033,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 23,GSM3325339,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 23,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325339,GSM3325339: 4mpf IF 23; Danio rerio; RNA Seq,GSM3325339,,1,FACS SMART Seq v4,GEO Accession:GSM3325339,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_23.fastq.gz,fastq,45007580.0,592205.0,GSM3325339 r1,0:76 1:0,A:11913517;C:10509235;G:10356142;T:12227824;N:862,76,0,,,11913517,10509235,10356142,12227824,862,SRX4522717,SRS3641033,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83402,,0.08299,,0.94856,,0.60108,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46332,SRR7662092,SRX4522716,SRS3641031,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 22,GSM3325338,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 22,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325338,GSM3325338: 4mpf IF 22; Danio rerio; RNA Seq,GSM3325338,,1,FACS SMART Seq v4,GEO Accession:GSM3325338,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_22.fastq.gz,fastq,39288124.0,516949.0,GSM3325338 r1,0:76 1:0,A:10232777;C:9385077;G:9229589;T:10439982;N:699,76,0,,,10232777,9385077,9229589,10439982,699,SRX4522716,SRS3641031,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81994,,0.09467,,0.95282,,0.60257,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46333,SRR7662091,SRX4522715,SRS3641029,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 21,GSM3325337,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 21,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325337,GSM3325337: 4mpf IF 21; Danio rerio; RNA Seq,GSM3325337,,1,FACS SMART Seq v4,GEO Accession:GSM3325337,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_21.fastq.gz,fastq,46901576.0,617126.0,GSM3325337 r1,0:76 1:0,A:12372492;C:10979490;G:10855232;T:12693403;N:959,76,0,,,12372492,10979490,10855232,12693403,959,SRX4522715,SRS3641029,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",,,,,,,,,,,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46334,SRR7662090,SRX4522714,SRS3641030,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 20,GSM3325336,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 20,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325336,GSM3325336: 4mpf IF 20; Danio rerio; RNA Seq,GSM3325336,,1,FACS SMART Seq v4,GEO Accession:GSM3325336,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_20.fastq.gz,fastq,48177692.0,633917.0,GSM3325336 r1,0:76 1:0,A:12330925;C:11657051;G:11465382;T:12723407;N:927,76,0,,,12330925,11657051,11465382,12723407,927,SRX4522714,SRS3641030,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84548,,0.06664,,0.95806,,0.65631,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46335,SRR7662089,SRX4522713,SRS3641028,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 19,GSM3325335,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 19,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325335,GSM3325335: 4mpf IF 19; Danio rerio; RNA Seq,GSM3325335,,1,FACS SMART Seq v4,GEO Accession:GSM3325335,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_19.fastq.gz,fastq,45549080.0,599330.0,GSM3325335 r1,0:76 1:0,A:11885398;C:10831249;G:10698937;T:12132696;N:800,76,0,,,11885398,10831249,10698937,12132696,800,SRX4522713,SRS3641028,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84786,,0.06494,,0.95737,,0.55382,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46336,SRR7662088,SRX4522712,SRS3641027,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 18,GSM3325334,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 18,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325334,GSM3325334: 4mpf IF 18; Danio rerio; RNA Seq,GSM3325334,,1,FACS SMART Seq v4,GEO Accession:GSM3325334,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_18.fastq.gz,fastq,44661780.0,587655.0,GSM3325334 r1,0:76 1:0,A:11541522;C:10597248;G:10443606;T:12078437;N:967,76,0,,,11541522,10597248,10443606,12078437,967,SRX4522712,SRS3641027,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85076,,0.0797,,0.94395,,0.6945,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46337,SRR7662087,SRX4522711,SRS3641025,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 17,GSM3325333,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 17,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325333,GSM3325333: 4mpf IF 17; Danio rerio; RNA Seq,GSM3325333,,1,FACS SMART Seq v4,GEO Accession:GSM3325333,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_17.fastq.gz,fastq,15504.0,204.0,GSM3325333 r1,0:76 1:0,A:4580;C:3910;G:3890;T:3124;N:0,76,0,,,4580,3910,3890,3124,0,SRX4522711,SRS3641025,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.07043,,0.00704,,0.99985,,0.44444,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46338,SRR7662086,SRX4522710,SRS3641026,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 16,GSM3325332,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 16,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325332,GSM3325332: 4mpf IF 16; Danio rerio; RNA Seq,GSM3325332,,1,FACS SMART Seq v4,GEO Accession:GSM3325332,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_16.fastq.gz,fastq,49501688.0,651338.0,GSM3325332 r1,0:76 1:0,A:12472487;C:12100962;G:11901446;T:13025835;N:958,76,0,,,12472487,12100962,11901446,13025835,958,SRX4522710,SRS3641026,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82421,,0.03404,,0.95045,,0.73497,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System