metadata: run_metadata: 1,758 rows where experiment.library_layout = "SINGLE", experiment.platform = "ILLUMINA" and tissue_curation

1,758 rows where experiment.library_layout = "SINGLE", experiment.platform = "ILLUMINA" and tissue_curation_coarse = "Endocrine System"

descending

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.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.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.nofeature_rate.mate1	seqdetective.sparsity.mate1	seqdetective.pos_strand_rate.mate1	seqdetective.readlen.mate1	seqdetective.judgement.mate1	seqdetective.judgement.reason	platform_family	instrument_generation	read_bias	selection_class	prep_kit	sc_or_bulk	tech_class	technology	submission.bioprojectsource.country	earliest_date	devstage_curation	devstage_curation_coarse	tissue_curation	tissue_curation_coarse
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
44043	44043	SRR6237746	SRX3345984	SRS2646512	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate2 A6 AMCA	GSM2836694		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	Sample Plate2 A6 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	GSM2836694	GSM2836694: Sample Plate2 A6 AMCA; Danio rerio; RNA Seq	GSM2836694		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836694	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate2_A6_AMCA_Neg.fastq.gz	fastq	1229895822.0	20184545.0	GSM2836694 r1	0:60.93 1:0	A:354923207;C:259144918;G:256606445;T:359057403;N:163849	60	0	354923207	259144918	256606445	359057403	163849	SRX3345984	SRS2646512	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.87646	0.26527	0.8257	0.55303	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44044	44044	SRR6237745	SRX3345983	SRS2646515	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate2 A5 AMCA	GSM2836693		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	Sample Plate2 A5 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	GSM2836693	GSM2836693: Sample Plate2 A5 AMCA; Danio rerio; RNA Seq	GSM2836693		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836693	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate2_A5_AMCA_Pos.fastq.gz	fastq	925676587.0	15189171.0	GSM2836693 r1	0:60.94 1:0	A:282028510;C:179853567;G:179383383;T:284281435;N:129692	60	0	282028510	179853567	179383383	284281435	129692	SRX3345983	SRS2646515	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.90747	0.36422	0.74215	0.54419	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44045	44045	SRR6237744	SRX3345982	SRS2646511	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate2 A4 AMCA	GSM2836692		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	Sample Plate2 A4 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	GSM2836692	GSM2836692: Sample Plate2 A4 AMCA; Danio rerio; RNA Seq	GSM2836692		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836692	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate2_A4_AMCA_Neg.fastq.gz	fastq	1160014329.0	19037174.0	GSM2836692 r1	0:60.93 1:0	A:339245851;C:240318483;G:237965388;T:342331066;N:153541	60	0	339245851	240318483	237965388	342331066	153541	SRX3345982	SRS2646511	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.89175	0.24416	0.82384	0.55389	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44046	44046	SRR6237743	SRX3345981	SRS2646513	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate2 A3 AMCA	GSM2836691		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	Sample Plate2 A3 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	GSM2836691	GSM2836691: Sample Plate2 A3 AMCA; Danio rerio; RNA Seq	GSM2836691		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836691	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate2_A3_AMCA_Pos.fastq.gz	fastq	855655864.0	14040472.0	GSM2836691 r1	0:60.94 1:0	A:257233140;C:169854422;G:169238352;T:259211059;N:118891	60	0	257233140	169854422	169238352	259211059	118891	SRX3345981	SRS2646513	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.90605	0.30761	0.74067	0.55271	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44047	44047	SRR6237742	SRX3345980	SRS2646514	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate2 A2 AMCA	GSM2836690		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	Sample Plate2 A2 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	GSM2836690	GSM2836690: Sample Plate2 A2 AMCA; Danio rerio; RNA Seq	GSM2836690		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836690	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate2_A2_AMCA_Neg.fastq.gz	fastq	1168909471.0	19185392.0	GSM2836690 r1	0:60.93 1:0	A:326281148;C:256738933;G:252744782;T:332989234;N:155374	60	0	326281148	256738933	252744782	332989234	155374	SRX3345980	SRS2646514	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.88849	0.1697	0.86602	0.52913	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44048	44048	SRR6237741	SRX3345979	SRS2646510	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate2 A1 AMCA	GSM2836689		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	Sample Plate2 A1 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	GSM2836689	GSM2836689: Sample Plate2 A1 AMCA; Danio rerio; RNA Seq	GSM2836689		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836689	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate2_A1_AMCA_Pos.fastq.gz	fastq	915106129.0	15016536.0	GSM2836689 r1	0:60.94 1:0	A:271804923;C:184830364;G:184695096;T:273646193;N:129553	60	0	271804923	184830364	184695096	273646193	129553	SRX3345979	SRS2646510	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.909	0.28209	0.74582	0.54537	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44049	44049	SRR6237740	SRX3345978	SRS2646509	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate1 A8 AMCA	GSM2836688		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	Sample Plate1 A8 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	GSM2836688	GSM2836688: Sample Plate1 A8 AMCA; Danio rerio; RNA Seq	GSM2836688		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836688	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate1_A8_AMCA_Neg.fastq.gz	fastq	1174636209.0	19276749.0	GSM2836688 r1	0:60.94 1:0	A:346218731;C:240547553;G:237863913;T:349847274;N:158738	60	0	346218731	240547553	237863913	349847274	158738	SRX3345978	SRS2646509	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.89653	0.29731	0.82388	0.57688	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44050	44050	SRR6237739	SRX3345977	SRS2646507	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate1 A7 AMCA	GSM2836687		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	Sample Plate1 A7 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	GSM2836687	GSM2836687: Sample Plate1 A7 AMCA; Danio rerio; RNA Seq	GSM2836687		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836687	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate1_A7_AMCA_Pos.fastq.gz	fastq	919004945.0	15079582.0	GSM2836687 r1	0:60.94 1:0	A:281188323;C:177657384;G:177254638;T:282775426;N:129174	60	0	281188323	177657384	177254638	282775426	129174	SRX3345977	SRS2646507	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.8999	0.31855	0.7643	0.56856	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44051	44051	SRR6237738	SRX3345976	SRS2646506	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate1 A6 AMCA	GSM2836686		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	Sample Plate1 A6 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Neg\|cell type:non pituicytes	GSM2836686	GSM2836686: Sample Plate1 A6 AMCA; Danio rerio; RNA Seq	GSM2836686		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836686	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate1_A6_AMCA_Neg.fastq.gz	fastq	1217567270.0	19981819.0	GSM2836686 r1	0:60.93 1:0	A:366468588;C:242252329;G:239380324;T:369298417;N:167612	60	0	366468588	242252329	239380324	369298417	167612	SRX3345976	SRS2646506	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.87327	0.29592	0.86026	0.61414	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
44052	44052	SRR6237737	SRX3345975	SRS2646508	SRP123109	PRJNA416462	Transcriptome of zebrafish neurohypophyseal astroglia pituicytes	GSE106371	Transcriptome Analysis	The hypothalamo neurohypophyseal system HNS is an interface through which the brain regulates body homeostasis by means of releasing the hypothalamic neurohormones oxytocin and arginine vasopressin to the general circulation. The basic components of the HNS are the hypothalamic axonal projections endothelial blood vessels and astroglial like cells termed pituicytes. These three tissue types converge and interact at the ventral forebrain to establish an efficient neuro vascular interface which allows the release of neurohormones from the brain to the periphery. However the molecular blueprint of pituicytes specific genes is still unknown. We have labelled and isolated zebrafish pituicytes cells and have identified their molecular signature. Overall design: mRNA profiles of hypophyseal b Ala Lys Ne positive pituicytes and b Ala Lys Ne negative non pituicytes were generated by next generation sequencing in 5 biological replicates using Illumina HiSeq 2500 v4 instrument		pubmed:30449506		Sample Plate1 A5 AMCA	GSM2836685		source name:hypophysis\|strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	Sample Plate1 A5 AMCA	cutadapt used to trim poly A and poly T low quality and adapter fastq raw files submitted are post trimming Reads were mapped with TopHat v2.0.13 iGenomes Danio rerio UCSC danRer10 gene counts were calculated with HTSeq count parameters: s no t exon m intersection strict i gene id DESeq2 was used to normalize and detect differentially expressed genes AMCA+ versus AMCA Genome build: danRer10 Supplementary files format and content: Output of HTSeq and DESeq2	hypophysis	Adult TL male zebrafish were injected with 10ul of PBS with 4.6 mM β Ala Lys Ne AMCA Biotrend # BP0352. 3 hours post injection pituitaries were dissected as described in Toro et al. Gene Expr Patterns 2009.	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	Adult zebrafish were raised and bred according to standard protocols.	strain:TL\|tissue:hypophysis\|age:12 month\|Sex:male\|b ala lys ne amca labelling:Pos\|cell type:pituicytes	GSM2836685	GSM2836685: Sample Plate1 A5 AMCA; Danio rerio; RNA Seq	GSM2836685		1	Dissected pituitaries 5 groups; n= 7 per group were immediately transferred into ice cold HBS buffer in a 1.5 ml tube until dissociation. The buffer were replaced with 250µL ice cold PBS +/+ and pituitary were then dissociated using prewarmed Liberase TM Roche for 12minutes and trypsination along with DNASe for 5 minutes at 30 degree C with occasional pipetting. Dissociation was stopped by adding 50µl of FBS and dissociated cells were pelleted by centrifuging at 500g at 4°C for 5 minutes. Cells were resuspended in 2mL of resuspension buffer Leibovitz L 15 with 0.3mM Glutamine GIBCO 0.8mM CaCl2 Pen 50 U/mL + Strep 0.05/mL FBS 1% and filtered with a 40 μm cell strainer BD Transduction Laboratories San Jose CA. Propidium iodide was added to label dead cells. High speed FACS was performed using an SORP FACSAria machine BD Bioscience San Jose CA with 70µm nozzle. A two gate FACS technique was used to select only AMCA+ cells from non fluorescent and auto fluorescent cells. As a control non AMCA AMCA cells were sorted. The cells were collected into a 384 well sterile plate filled with the 1X lysis buffer and RNAsin SMARTer Ultra Low Input RNA Kit for Sequencing v3 Takara Bio USA Inc. CA. The samples were incubated at room temperature for 5 minutes and snap frozen in liquid nitrogen before storage at 80°C. Two independent FACS experiments were performed yielding six samples 3 AMCA+ and 3 AMCA from 3 groups and four samples 2 AMCA+ and 2 AMCA from 2 groups. Samples were thawed reverse transcribed and amplified to create full length transcriptome using the SMARTer® Ultra™ Low Input RNA for Sequencing v3 kit. Amplification was performed with 15 cycles. Following the amplification clean up was done using Ampure XP beads Beckman Coulter. The amplified cDNA products were sheared by ultrasonicator Covaris E220X. About 3ng of sheared amplified cDNA from each sample were processed as previously described Blecher Gonen R. et al. Nat. Prot. 2013. Different barcode was ligated to each sample to allow multip…	GEO Accession:GSM2836685	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	Illumina HiSeq 2500	SRP123109			Sample_Plate1_A5_AMCA_Pos.fastq.gz	fastq	995781320.0	16339491.0	GSM2836685 r1	0:60.94 1:0	A:304872127;C:192446811;G:191928153;T:306392280;N:141949	60	0	304872127	192446811	191928153	306392280	141949	SRX3345975	SRS2646508	SRA626615	GEO	Bioinformatics Unit, Biological Services, Weizmann Institute of Science	1	0.90464	0.36217	0.74836	0.56177	61	B	usable mapping rate	illumina	hiseq_era	full_length	poly_a	smarter	bulk	unknown	unknown	Israel	2017-10-31	Adult	Adult	Pituitary Gland	Endocrine System
46259	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
46338	46338	SRR7662086	SRX4522710	SRS3641026	SRP131808	PRJNA432257	Single cell RNA sequencing of zebrafish beta cells from various stages	GSE109881	Transcriptome Analysis	Age associated deterioration of cellular physiology leads to pathological conditions and detection of premature aging could provide a window for preventive therapies against age related diseases. For this methods that accurately evaluate cellular age are required. However such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age and its modifiers requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here we implement GERAS GEnetic Reference for Age of Single cell a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise as evaluated by its performance on independent samplings of single cells. Additionally GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells respectively. We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors junb which declines in expression with aging is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit …		pubmed:30464314		4mpf IF 16	GSM3325332		tissue:beta cells\|age:4mpf\|strain:Tgins:BB1.0L\|feeding:Intermittent feeding	4mpf IF 16	Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10	beta cells		FACS SMART Seq v4		age:4mpf\|strain:Tgins:BB1.0L\|feeding:Intermittent feeding	GSM3325332	GSM3325332: 4mpf IF 16; Danio rerio; RNA Seq	GSM3325332		1	FACS SMART Seq v4	GEO Accession:GSM3325332	RNA-Seq	TRANSCRIPTOMIC	cDNA	SINGLE	ILLUMINA	NextSeq 500	SRP131808			4mpf_IF_16.fastq.gz	fastq	49501688.0	651338.0	GSM3325332 r1	0:76 1:0	A:12472487;C:12100962;G:11901446;T:13025835;N:958	76	0	12472487	12100962	11901446	13025835	958	SRX4522710	SRS3641026	SRA654064	GEO	Ninov Lab, Center for Regenerative Therapies Dresden	1	0.82421	0.03404	0.95045	0.73497	76	B	usable mapping rate	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_plate	smartseq	Germany	2018-08-08	Adult	Adult	Pancreas	Endocrine System

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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");;