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1,368 rows where devstage_curation_coarse = "Adult" and tissue_curation = "Pancreas"

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technology 3

experiment.library_selection 3

experiment.library_strategy 2

experiment.library_source 2

experiment.library_layout 2

tissue_curation_coarse 1

tissue_curation 1

  • Pancreas · 1,368 ✖

experiment.platform 1

devstage_curation_coarse 1

  • Adult · 1,368 ✖

devstage_curation 1

Link 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
321 321 ERR977589 ERX1054572 ERS805778 ERP011346 PRJEB10140 RNAseq from the pancreatic acinar alpha beta and delta cells from zebrafish ena-STUDY-GIGA-R, University of Liege-05-08-2015-10:47:24:447-55 Other We took advantage of zebrafish transgenic tools to isolate by FACS the major pancreatic cell types and obtain pure preparations of endocrine a ß and d cells as well as exocrine acinar and ductal cells. ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2017 01 31   Beta cells from adults purified by FACS Delta cells R3 SAMEA3498629 GIGA-R, University of Liege ENA first public:2017 01 31|ENA last update:2015 08 05|External Id:SAMEA3498629|INSDC center alias:GIGA R University of Liege|INSDC center name:GIGA R University of Liege|INSDC first public:2017 01 31T17:01:11Z|INSDC last update:2015 08 05T16:56:59Z|INSDC status:public|Submitter Id:34|cell type:Pancreatic Delta cells|collected by:Estefania Tarifeño Saldivia|common name:zebrafish|dev stage:Adult|isolate:Tgsst2:GFP|lab host:ZDDM|sample name:34                 Illumina HiSeq 2000 paired end sequencing ena EXPERIMENT GIGA R University of Liege 05 08 2015 16:56:42:489 14 Delta R3 1 Truseq DNA Sample prep   RNA-Seq TRANSCRIPTOMIC Oligo-dT PAIRED ILLUMINA Illumina HiSeq 2000   ERP011346 Illumina HiSeq 2000 paired end sequencing ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2018 11 16 NGS14-B176_SSTcells-03122013_CAGATC_L001_R1_001.fastq.gz NGS14-B176_SSTcells-03122013_CAGATC_L001_R2_001.fastq.gz fastq fastq 17691597128.0 87582164.0 ena RUN GIGA R University of Liege 05 08 2015 16:56:42:489 14 0:101 1:101 A:4843643109;C:3683816237;G:3736882877;T:5332210638;N:95044267 101 101     4843643109 3683816237 3736882877 5332210638 95044267 ERX1054572 ERS805778 ERA463595 GIGA-R, University of Liege|European Nucleotide Archive GIGA-R, University of Liege 2 0.92685 0.84146 0.10249 0.11723 0.76532 0.78309 0.39721 0.43714 101 101 B B biological fallback assumption illumina hiseq_era unknown poly_a trueseq bulk unknown unknown   Belgium 2015-08-05 Adult Adult Pancreas Endocrine System
330 330 ERR977580 ERX1054563 ERS805769 ERP011346 PRJEB10140 RNAseq from the pancreatic acinar alpha beta and delta cells from zebrafish ena-STUDY-GIGA-R, University of Liege-05-08-2015-10:47:24:447-55 Other We took advantage of zebrafish transgenic tools to isolate by FACS the major pancreatic cell types and obtain pure preparations of endocrine a ß and d cells as well as exocrine acinar and ductal cells. ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2017 01 31   Beta cells from adults purified by FACS Beta cells R3 SAMEA3498620 GIGA-R, University of Liege ENA first public:2017 01 31|ENA last update:2015 08 05|External Id:SAMEA3498620|INSDC center alias:GIGA R University of Liege|INSDC center name:GIGA R University of Liege|INSDC first public:2017 01 31T17:01:11Z|INSDC last update:2015 08 05T16:56:59Z|INSDC status:public|Submitter Id:25|cell type:Pancreatic Beta cells|collected by:Estefania Tarifeño Saldivia|common name:zebrafish|dev stage:Adult|isolate:Tgins:GFP|lab host:ZDDM|sample name:25                 Illumina HiSeq 2000 paired end sequencing ena EXPERIMENT GIGA R University of Liege 05 08 2015 16:56:42:487 5 Beta R3 1 Truseq DNA Sample prep   RNA-Seq TRANSCRIPTOMIC Oligo-dT PAIRED ILLUMINA Illumina HiSeq 2000   ERP011346 Illumina HiSeq 2000 paired end sequencing ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2018 11 16 NGS14-B174_Betacells-03122013_GCCAAT_L002_R1_001.fastq.gz NGS14-B174_Betacells-03122013_GCCAAT_L002_R2_001.fastq.gz fastq fastq 17625660086.0 87255743.0 ena RUN GIGA R University of Liege 05 08 2015 16:56:42:487 5 0:101 1:101 A:4840276660;C:3724973884;G:3755195525;T:5214977933;N:90236084 101 101     4840276660 3724973884 3755195525 5214977933 90236084 ERX1054563 ERS805769 ERA463595 GIGA-R, University of Liege|European Nucleotide Archive GIGA-R, University of Liege 2 0.90631 0.8718 0.11791 0.11942 0.76203 0.77638 0.53654 0.49457 101 101 B B biological fallback assumption illumina hiseq_era unknown poly_a trueseq bulk unknown unknown   Belgium 2015-08-05 Adult Adult Pancreas Endocrine System
331 331 ERR977579 ERX1054562 ERS805768 ERP011346 PRJEB10140 RNAseq from the pancreatic acinar alpha beta and delta cells from zebrafish ena-STUDY-GIGA-R, University of Liege-05-08-2015-10:47:24:447-55 Other We took advantage of zebrafish transgenic tools to isolate by FACS the major pancreatic cell types and obtain pure preparations of endocrine a ß and d cells as well as exocrine acinar and ductal cells. ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2017 01 31   Beta cells from adults purified by FACS Beta cells R2 2 SAMEA3498619 GIGA-R, University of Liege ENA first public:2017 01 31|ENA last update:2015 08 05|External Id:SAMEA3498619|INSDC center alias:GIGA R University of Liege|INSDC center name:GIGA R University of Liege|INSDC first public:2017 01 31T17:01:11Z|INSDC last update:2015 08 05T16:56:59Z|INSDC status:public|Submitter Id:24|cell type:Pancreatic Beta cells|collected by:Estefania Tarifeño Saldivia|common name:zebrafish|dev stage:Adult|isolate:Tgins:GFP|lab host:ZDDM|sample name:24                 Illumina HiSeq 2000 paired end sequencing ena EXPERIMENT GIGA R University of Liege 05 08 2015 16:56:42:487 4 Beta R2 2 1 Truseq DNA Sample prep   RNA-Seq TRANSCRIPTOMIC Oligo-dT PAIRED ILLUMINA Illumina HiSeq 2000   ERP011346 Illumina HiSeq 2000 paired end sequencing ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2018 11 16 BetaCell2_A026_ATGTCA_L004_R1_001.fastq.gz BetaCell2_A026_ATGTCA_L004_R2_001.fastq.gz fastq fastq 8532015198.0 42237699.0 ena RUN GIGA R University of Liege 05 08 2015 16:56:42:487 4 0:101 1:101 A:2314776471;C:1823939144;G:1839244845;T:2552984556;N:1070182 101 101     2314776471 1823939144 1839244845 2552984556 1070182 ERX1054562 ERS805768 ERA463595 GIGA-R, University of Liege|European Nucleotide Archive GIGA-R, University of Liege 2 0.69844 0.57999 0.09124 0.07501 0.80876 0.82964 0.56023 0.5159 101 101 B B biological fallback assumption illumina hiseq_era unknown poly_a trueseq bulk unknown unknown   Belgium 2015-08-05 Adult Adult Pancreas Endocrine System
332 332 ERR977578 ERX1054561 ERS805767 ERP011346 PRJEB10140 RNAseq from the pancreatic acinar alpha beta and delta cells from zebrafish ena-STUDY-GIGA-R, University of Liege-05-08-2015-10:47:24:447-55 Other We took advantage of zebrafish transgenic tools to isolate by FACS the major pancreatic cell types and obtain pure preparations of endocrine a ß and d cells as well as exocrine acinar and ductal cells. ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2017 01 31   Beta cells from adults purified by FACS Beta cells R2 1 SAMEA3498618 GIGA-R, University of Liege ENA first public:2017 01 31|ENA last update:2015 08 05|External Id:SAMEA3498618|INSDC center alias:GIGA R University of Liege|INSDC center name:GIGA R University of Liege|INSDC first public:2017 01 31T17:01:11Z|INSDC last update:2015 08 05T16:56:59Z|INSDC status:public|Submitter Id:23|cell type:Pancreatic Beta cells|collected by:Estefania Tarifeño Saldivia|common name:zebrafish|dev stage:Adult|isolate:Tgins:GFP|lab host:ZDDM|sample name:23                 Illumina HiSeq 2000 paired end sequencing ena EXPERIMENT GIGA R University of Liege 05 08 2015 16:56:42:487 3 Beta R2 1 1 Truseq DNA Sample prep   RNA-Seq TRANSCRIPTOMIC Oligo-dT PAIRED ILLUMINA Illumina HiSeq 2000   ERP011346 Illumina HiSeq 2000 paired end sequencing ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2018 11 16 BetaCell_ATGTCA_L005_R1_001.fastq.gz BetaCell_ATGTCA_L005_R2_001.fastq.gz fastq fastq 3250611068.0 16092134.0 ena RUN GIGA R University of Liege 05 08 2015 16:56:42:487 3 0:101 1:101 A:854826460;C:691024995;G:695294721;T:954934438;N:54530454 101 101     854826460 691024995 695294721 954934438 54530454 ERX1054561 ERS805767 ERA463595 GIGA-R, University of Liege|European Nucleotide Archive GIGA-R, University of Liege 2 0.67496 0.53873 0.08771 0.06606 0.80969 0.83433 0.56231 0.51717 101 101 B B biological fallback assumption illumina hiseq_era unknown poly_a trueseq bulk unknown unknown   Belgium 2015-08-05 Adult Adult Pancreas Endocrine System
333 333 ERR977577 ERX1054560 ERS805766 ERP011346 PRJEB10140 RNAseq from the pancreatic acinar alpha beta and delta cells from zebrafish ena-STUDY-GIGA-R, University of Liege-05-08-2015-10:47:24:447-55 Other We took advantage of zebrafish transgenic tools to isolate by FACS the major pancreatic cell types and obtain pure preparations of endocrine a ß and d cells as well as exocrine acinar and ductal cells. ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2017 01 31   Beta cells from adults purified by FACS Beta cells R1 2 SAMEA3498617 GIGA-R, University of Liege ENA first public:2017 01 31|ENA last update:2015 08 05|External Id:SAMEA3498617|INSDC center alias:GIGA R University of Liege|INSDC center name:GIGA R University of Liege|INSDC first public:2017 01 31T17:01:11Z|INSDC last update:2015 08 05T16:56:59Z|INSDC status:public|Submitter Id:22|cell type:Pancreatic Beta cells|collected by:Estefania Tarifeño Saldivia|common name:zebrafish|dev stage:Adult|isolate:Tgins:GFP|lab host:ZDDM|sample name:22                 Illumina HiSeq 2000 paired end sequencing ena EXPERIMENT GIGA R University of Liege 05 08 2015 16:56:42:487 2 Beta R1 2 1 Truseq DNA Sample prep   RNA-Seq TRANSCRIPTOMIC Oligo-dT PAIRED ILLUMINA Illumina HiSeq 2000   ERP011346 Illumina HiSeq 2000 paired end sequencing ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2018 11 16 BetaCell1_A003_GTCCGC_L004_R1_001.fastq.gz BetaCell1_A003_GTCCGC_L004_R2_001.fastq.gz fastq fastq 3765102038.0 18639119.0 ena RUN GIGA R University of Liege 05 08 2015 16:56:42:487 2 0:101 1:101 A:1050226787;C:789381360;G:801049952;T:1123973291;N:470648 101 101     1050226787 789381360 801049952 1123973291 470648 ERX1054560 ERS805766 ERA463595 GIGA-R, University of Liege|European Nucleotide Archive GIGA-R, University of Liege 2 0.90456 0.86125 0.12934 0.13194 0.77721 0.79109 0.56708 0.53213 101 101 B B biological fallback assumption illumina hiseq_era unknown poly_a trueseq bulk unknown unknown   Belgium 2015-08-05 Adult Adult Pancreas Endocrine System
334 334 ERR977576 ERX1054559 ERS805765 ERP011346 PRJEB10140 RNAseq from the pancreatic acinar alpha beta and delta cells from zebrafish ena-STUDY-GIGA-R, University of Liege-05-08-2015-10:47:24:447-55 Other We took advantage of zebrafish transgenic tools to isolate by FACS the major pancreatic cell types and obtain pure preparations of endocrine a ß and d cells as well as exocrine acinar and ductal cells. ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2017 01 31   Beta cells from adults purified by FACS Beta cells R1 1 SAMEA3498616 GIGA-R, University of Liege ENA first public:2017 01 31|ENA last update:2015 08 05|External Id:SAMEA3498616|INSDC center alias:GIGA R University of Liege|INSDC center name:GIGA R University of Liege|INSDC first public:2017 01 31T17:01:11Z|INSDC last update:2015 08 05T16:56:59Z|INSDC status:public|Submitter Id:21|cell type:Pancreatic Beta cells|collected by:Estefania Tarifeño Saldivia|common name:zebrafish|dev stage:Adult|isolate:Tgins:GFP|lab host:ZDDM|sample name:21                 Illumina HiSeq 2000 paired end sequencing ena EXPERIMENT GIGA R University of Liege 05 08 2015 16:56:42:486 1 Beta R1 1 1 Truseq DNA Sample prep   RNA-Seq TRANSCRIPTOMIC Oligo-dT PAIRED ILLUMINA Illumina HiSeq 2000   ERP011346 Illumina HiSeq 2000 paired end sequencing ENA FIRST PUBLIC:2017 01 31|ENA LAST UPDATE:2018 11 16 BetaCells_30000_GTCCGC_L008_R1_001.fastq.gz BetaCells_30000_GTCCGC_L008_R2_001.fastq.gz fastq fastq 10597717092.0 52463946.0 ena RUN GIGA R University of Liege 05 08 2015 16:56:42:486 1 0:101 1:101 A:2951812422;C:2213949786;G:2252957090;T:3178649938;N:347856 101 101     2951812422 2213949786 2252957090 3178649938 347856 ERX1054559 ERS805765 ERA463595 GIGA-R, University of Liege|European Nucleotide Archive GIGA-R, University of Liege 2 0.90302 0.84857 0.12843 0.12749 0.77745 0.79157 0.56129 0.52011 101 101 B B biological fallback assumption illumina hiseq_era unknown poly_a trueseq bulk unknown unknown   Belgium 2015-08-05 Adult Adult Pancreas Endocrine System
10002 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 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 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 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 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 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 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 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 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 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
43988 43988 SRR6811827 SRX3768867 SRS3023384 SRP121343 PRJNA415636 Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars GSE106121 Other A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.   pubmed:29644996   Pancreas 3 endo scar GSM3032170   source name:Primary pancreatic islet|strain/background:Zebrabow M|tissue:Primary pancreatic islet|developmental stage:Adult Pancreas 3 endo scar Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim… Primary pancreatic islet   Single cell dissociation. 10X Genomics Chromium   strain/background:Zebrabow M|tissue:Primary pancreatic islet|developmental stage:Adult GSM3032170 GSM3032170: Pancreas 3 endo scar; Danio rerio; OTHER GSM3032170   1 Single cell dissociation. 10X Genomics Chromium GEO Accession:GSM3032170 OTHER TRANSCRIPTOMIC other PAIRED ILLUMINA NextSeq 500   SRP121343     P7endo_scar_R1.fastq.gz P7endo_scar_R2.fastq.gz fastq fastq 2537781396.0 20465979.0 GSM3032170 r1 0:26 1:98 A:741745261;C:801599713;G:561531527;T:431666561;N:1238334 26 98     741745261 801599713 561531527 431666561 1238334 SRX3768867 SRS3023384 SRA623333 GEO Max Delbrück Center 2 0.00014 0.00289 0.00012 0.00028 0.99995 0.99602 1.0 0.68478 26 98 T T mates < 9% mapping rate illumina nextseq unknown other unknown sc single_cell_droplet 10x   Germany 2018-03-06 Adult Adult Pancreas Endocrine System
43994 43994 SRR6811821 SRX3768861 SRS3023378 SRP121343 PRJNA415636 Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars GSE106121 Other A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.   pubmed:29644996   Pancreas 3 endo mRNA GSM3032164   source name:Primary pancreatic islet|strain/background:Zebrabow M|tissue:Primary pancreatic islet|developmental stage:Adult Pancreas 3 endo mRNA Alignment and transcript counting of libraries were done using Cell Ranger 2.0.2. Cell numbers to be extracted were set at a minimum of 6000 but were increased if there were substantially more cells with more than 500 unique transcripts. Exact numbers can be found in Supplementary Table 1 of publication. Genome build: GRCz10 release 90 Supplementary files format and content: * matrix.mtx: Single cell transcript count table; * barcodes.tsv: List of cell barcodes. Primary pancreatic islet   Single cell dissociation. 10X Genomics Chromium   strain/background:Zebrabow M|tissue:Primary pancreatic islet|developmental stage:Adult GSM3032164 GSM3032164: Pancreas 3 endo mRNA; Danio rerio; RNA Seq GSM3032164   1 Single cell dissociation. 10X Genomics Chromium GEO Accession:GSM3032164 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA NextSeq 500   SRP121343     P7endo_wt_R1.fastq.gz P7endo_wt_R2.fastq.gz fastq fastq 41593781900.0 335433725.0 GSM3032164 r1 0:26 1:98 A:11833690021;C:9547054378;G:9724089290;T:10470148302;N:18799909 26 98     11833690021 9547054378 9724089290 10470148302 18799909 SRX3768861 SRS3023378 SRA623333 GEO Max Delbrück Center 2 0.00308 0.92936 0.00192 0.05695 0.9973 0.8686 0.45945 0.58814 26 98 T B sc-like readlen illumina nextseq unknown cdna_unspecified unknown sc single_cell_droplet 10x   Germany 2018-03-06 Adult Adult Pancreas Endocrine System
44907 44907 SRR6293905 SRX3395016 SRS2689202 SRP125042 PRJNA418467 Age related Islet Inflammation Marks the Proliferative Decline of Pancreatic Beta cells in Zebrafish GSE106938 Transcriptome Analysis Individual organisms age at different rates however it remains unclear how aging alters the properties of individual cells. Here we show that zebrafish pancreatic beta cells exhibit heterogeneity in both gene expression and proliferation with age. Individual beta cells show marked variability in transcripts involved in endoplasmic reticulum stress inhibition of growth factor signaling and inflammation including NF kB signaling. Using a reporter line we show that NF kB signaling is indeed activated heterogeneously with age. Notably beta cells with higher NF kB activity proliferate less compared to neighbors with lower activity. Furthermore NF kB signalinghigh beta cells from younger islets upregulate socs2 a gene naturally expressed in beta cells from older islets. In turn socs2 can inhibit proliferation cell autonomously. NF kB activation correlates with the recruitment of tnfa expressing immune cells pointing towards a role for the islet microenvironment in this activity. We propose that aging is heterogeneous across individual beta cells and identify NF kB signaling as a marker of heterogeneity. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 3 mpf and 1 year post fertilization animals. total RNA was extracted from FACS sorted beta cells using Quick RNA MicroPrep kit R1050 Zymo Research. Sequencing was performed on llumina HiSeq2500 in 2x75bp paired end mode. 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:29624168   1ypf rep2 GSM2857832   tissue:beta cells|age:1 year|strain:Tgins:BB1.0L 1ypf rep2 Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters sam files converted to bam using samtools Genome build: Zebrafish GRCz10 Supplementary files format and content: read counts were generated using htseq count beta cells   FACS llumina HiSeq2500 in 2x75bp paired end mode   age:1 year|strain:Tgins:BB1.0L GSM2857832 GSM2857832: 1ypf rep2; Danio rerio; RNA Seq GSM2857832   1 FACS llumina HiSeq2500 in 2x75bp paired end mode GEO Accession:GSM2857832 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA Illumina HiSeq 2500   SRP125042     L10760_Track-31237_R1.fastq.gz L10760_Track-31237_R2.fastq.gz fastq fastq 5627077088.0 37020244.0 GSM2857832 r1 0:76 1:76 A:1481195914;C:1302350910;G:1313408578;T:1524234346;N:5887340 76 76     1481195914 1302350910 1313408578 1524234346 5887340 SRX3395016 SRS2689202 SRA631121 GEO Ninov Lab, CRTD 2 0.84288 0.84015 0.15135 0.15241 0.75089 0.75436 0.60917 0.60027 76 76 B B biological fallback assumption illumina hiseq_era unknown cdna_unspecified unknown bulk unknown unknown   Germany 2017-11-15 Adult Adult Pancreas Endocrine System
44908 44908 SRR6293904 SRX3395015 SRS2689200 SRP125042 PRJNA418467 Age related Islet Inflammation Marks the Proliferative Decline of Pancreatic Beta cells in Zebrafish GSE106938 Transcriptome Analysis Individual organisms age at different rates however it remains unclear how aging alters the properties of individual cells. Here we show that zebrafish pancreatic beta cells exhibit heterogeneity in both gene expression and proliferation with age. Individual beta cells show marked variability in transcripts involved in endoplasmic reticulum stress inhibition of growth factor signaling and inflammation including NF kB signaling. Using a reporter line we show that NF kB signaling is indeed activated heterogeneously with age. Notably beta cells with higher NF kB activity proliferate less compared to neighbors with lower activity. Furthermore NF kB signalinghigh beta cells from younger islets upregulate socs2 a gene naturally expressed in beta cells from older islets. In turn socs2 can inhibit proliferation cell autonomously. NF kB activation correlates with the recruitment of tnfa expressing immune cells pointing towards a role for the islet microenvironment in this activity. We propose that aging is heterogeneous across individual beta cells and identify NF kB signaling as a marker of heterogeneity. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 3 mpf and 1 year post fertilization animals. total RNA was extracted from FACS sorted beta cells using Quick RNA MicroPrep kit R1050 Zymo Research. Sequencing was performed on llumina HiSeq2500 in 2x75bp paired end mode. 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:29624168   1ypf rep1 GSM2857831   tissue:beta cells|age:1 year|strain:Tgins:BB1.0L 1ypf rep1 Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters sam files converted to bam using samtools Genome build: Zebrafish GRCz10 Supplementary files format and content: read counts were generated using htseq count beta cells   FACS llumina HiSeq2500 in 2x75bp paired end mode   age:1 year|strain:Tgins:BB1.0L GSM2857831 GSM2857831: 1ypf rep1; Danio rerio; RNA Seq GSM2857831   1 FACS llumina HiSeq2500 in 2x75bp paired end mode GEO Accession:GSM2857831 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA Illumina HiSeq 2500   SRP125042     L10759_Track-31236_R1.fastq.gz L10759_Track-31236_R2.fastq.gz fastq fastq 5128054856.0 33737203.0 GSM2857831 r1 0:76 1:76 A:1350542699;C:1184925079;G:1197353068;T:1389942509;N:5291501 76 76     1350542699 1184925079 1197353068 1389942509 5291501 SRX3395015 SRS2689200 SRA631121 GEO Ninov Lab, CRTD 2 0.84827 0.85287 0.16376 0.1636 0.75645 0.75797 0.59534 0.59534 76 76 B B biological fallback assumption illumina hiseq_era unknown cdna_unspecified unknown bulk unknown unknown   Germany 2017-11-15 Adult Adult Pancreas Endocrine System
44909 44909 SRR6293903 SRX3395014 SRS2689199 SRP125042 PRJNA418467 Age related Islet Inflammation Marks the Proliferative Decline of Pancreatic Beta cells in Zebrafish GSE106938 Transcriptome Analysis Individual organisms age at different rates however it remains unclear how aging alters the properties of individual cells. Here we show that zebrafish pancreatic beta cells exhibit heterogeneity in both gene expression and proliferation with age. Individual beta cells show marked variability in transcripts involved in endoplasmic reticulum stress inhibition of growth factor signaling and inflammation including NF kB signaling. Using a reporter line we show that NF kB signaling is indeed activated heterogeneously with age. Notably beta cells with higher NF kB activity proliferate less compared to neighbors with lower activity. Furthermore NF kB signalinghigh beta cells from younger islets upregulate socs2 a gene naturally expressed in beta cells from older islets. In turn socs2 can inhibit proliferation cell autonomously. NF kB activation correlates with the recruitment of tnfa expressing immune cells pointing towards a role for the islet microenvironment in this activity. We propose that aging is heterogeneous across individual beta cells and identify NF kB signaling as a marker of heterogeneity. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 3 mpf and 1 year post fertilization animals. total RNA was extracted from FACS sorted beta cells using Quick RNA MicroPrep kit R1050 Zymo Research. Sequencing was performed on llumina HiSeq2500 in 2x75bp paired end mode. 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:29624168   3mpf rep1 GSM2857830   tissue:beta cells|age:3 month|strain:Tgins:BB1.0L 3mpf rep1 Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters sam files converted to bam using samtools Genome build: Zebrafish GRCz10 Supplementary files format and content: read counts were generated using htseq count beta cells   FACS llumina HiSeq2500 in 2x75bp paired end mode   age:3 month|strain:Tgins:BB1.0L GSM2857830 GSM2857830: 3mpf rep1; Danio rerio; RNA Seq GSM2857830   1 FACS llumina HiSeq2500 in 2x75bp paired end mode GEO Accession:GSM2857830 RNA-Seq TRANSCRIPTOMIC cDNA PAIRED ILLUMINA Illumina HiSeq 2500   SRP125042     L10758_Track-31235_R1.fastq.gz L10758_Track-31235_R2.fastq.gz fastq fastq 4259609848.0 28023749.0 GSM2857830 r1 0:76 1:76 A:1095866394;C:1004201489;G:1014981602;T:1140141196;N:4419167 76 76     1095866394 1004201489 1014981602 1140141196 4419167 SRX3395014 SRS2689199 SRA631121 GEO Ninov Lab, CRTD 2 0.87618 0.87022 0.1249 0.12602 0.76368 0.76469 0.65739 0.64564 76 76 B B biological fallback assumption illumina hiseq_era unknown cdna_unspecified unknown bulk unknown unknown   Germany 2017-11-15 Adult Adult Pancreas Endocrine System
46259 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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

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CREATE TABLE run_metadata("run.accession" VARCHAR, "experiment.accession" VARCHAR, "sample.accession" VARCHAR, "study.accession" VARCHAR, bioproject VARCHAR, "study.title" VARCHAR, "study.alias" VARCHAR, "study.type" VARCHAR, "study.abstract" VARCHAR, "study.attributes" VARCHAR, "study.PMIDs" VARCHAR, "sample.description" VARCHAR, "sample.title" VARCHAR, "sample.alias" VARCHAR, "sample.centername" VARCHAR, "sample.attributes" VARCHAR, "GEOsample.title" VARCHAR, "GEOsample.dataprocessing" VARCHAR, "GEOsample.source" VARCHAR, "GEOsample.treatmentprotocol" VARCHAR, "GEOsample.extractprotocol" VARCHAR, "GEOsample.growthprotocol" VARCHAR, "GEOsample.characteristics" VARCHAR, "GEOsample.accession" VARCHAR, "experiment.title" VARCHAR, "experiment.alias" VARCHAR, "experiment.library_name" VARCHAR, "experiment.design_description" VARCHAR, "experiment.library_construction_protocol" VARCHAR, "experiment.attributes" VARCHAR, "experiment.library_strategy" VARCHAR, "experiment.library_source" VARCHAR, "experiment.library_selection" VARCHAR, "experiment.library_layout" VARCHAR, "experiment.platform" VARCHAR, "experiment.instrument_model" VARCHAR, "experiment.spot_descriptor" VARCHAR, "experiment.study_ref" VARCHAR, "run.title" VARCHAR, "run.attributes" VARCHAR, "run.filename" VARCHAR, "run.semantic_name" VARCHAR, "run.total_bases" DOUBLE, "run.total_spots" DOUBLE, "run.alias" VARCHAR, "run.read_lengths" VARCHAR, "run.base_counts" VARCHAR, "run.r1_length" BIGINT, "run.r2_length" BIGINT, "run.r3_length" BIGINT, "run.r4_length" BIGINT, "run.Acount" BIGINT, "run.Ccount" BIGINT, "run.Gcount" BIGINT, "run.Tcount" BIGINT, "run.Ncount" BIGINT, "run.experiment" VARCHAR, "run.pool_member" VARCHAR, "submission.accession" VARCHAR, "submission.srasource" VARCHAR, "submission.bioprojectsource" VARCHAR, "seqdetective.n_mates" BIGINT, "seqdetective.mapping_rate.mate1" DOUBLE, "seqdetective.mapping_rate.mate2" DOUBLE, "seqdetective.nofeature_rate.mate1" DOUBLE, "seqdetective.nofeature_rate.mate2" DOUBLE, "seqdetective.sparsity.mate1" DOUBLE, "seqdetective.sparsity.mate2" DOUBLE, "seqdetective.pos_strand_rate.mate1" DOUBLE, "seqdetective.pos_strand_rate.mate2" DOUBLE, "seqdetective.readlen.mate1" BIGINT, "seqdetective.readlen.mate2" BIGINT, "seqdetective.judgement.mate1" VARCHAR, "seqdetective.judgement.mate2" VARCHAR, "seqdetective.judgement.reason" VARCHAR, platform_family VARCHAR, instrument_generation VARCHAR, read_bias VARCHAR, selection_class VARCHAR, prep_kit VARCHAR, sc_or_bulk VARCHAR, tech_class VARCHAR, technology VARCHAR, tech_variant VARCHAR, "submission.bioprojectsource.country" VARCHAR, earliest_date DATE, devstage_curation VARCHAR, devstage_curation_coarse VARCHAR, tissue_curation VARCHAR, tissue_curation_coarse VARCHAR);;
CREATE INDEX idx_run_bioproject ON run_metadata(bioproject);;
CREATE INDEX idx_run_run_accession ON run_metadata("run.accession");;