rowid,run.accession,experiment.accession,sample.accession,study.accession,bioproject,study.title,study.alias,study.type,study.abstract,study.attributes,study.PMIDs,sample.description,sample.title,sample.alias,sample.centername,sample.attributes,GEOsample.title,GEOsample.dataprocessing,GEOsample.source,GEOsample.treatmentprotocol,GEOsample.extractprotocol,GEOsample.growthprotocol,GEOsample.characteristics,GEOsample.accession,experiment.title,experiment.alias,experiment.library_name,experiment.design_description,experiment.library_construction_protocol,experiment.attributes,experiment.library_strategy,experiment.library_source,experiment.library_selection,experiment.library_layout,experiment.platform,experiment.instrument_model,experiment.spot_descriptor,experiment.study_ref,run.title,run.attributes,run.filename,run.semantic_name,run.total_bases,run.total_spots,run.alias,run.read_lengths,run.base_counts,run.r1_length,run.r2_length,run.r3_length,run.r4_length,run.Acount,run.Ccount,run.Gcount,run.Tcount,run.Ncount,run.experiment,run.pool_member,submission.accession,submission.srasource,submission.bioprojectsource,seqdetective.n_mates,seqdetective.mapping_rate.mate1,seqdetective.mapping_rate.mate2,seqdetective.nofeature_rate.mate1,seqdetective.nofeature_rate.mate2,seqdetective.sparsity.mate1,seqdetective.sparsity.mate2,seqdetective.pos_strand_rate.mate1,seqdetective.pos_strand_rate.mate2,seqdetective.readlen.mate1,seqdetective.readlen.mate2,seqdetective.judgement.mate1,seqdetective.judgement.mate2,seqdetective.judgement.reason,platform_family,instrument_generation,read_bias,selection_class,prep_kit,sc_or_bulk,tech_class,technology,tech_variant,submission.bioprojectsource.country,earliest_date,devstage_curation,devstage_curation_coarse,tissue_curation,tissue_curation_coarse
46259,SRR7662165,SRX4522789,SRS3641107,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 40,GSM3325411,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 40,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325411,GSM3325411: 4mpf 3TD 40; Danio rerio; RNA Seq,GSM3325411,,1,FACS SMART Seq v4,GEO Accession:GSM3325411,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_40.fastq.gz,fastq,46013820.0,605445.0,GSM3325411 r1,0:76 1:0,A:12185751;C:10519473;G:10377588;T:12930202;N:806,76,0,,,12185751,10519473,10377588,12930202,806,SRX4522789,SRS3641107,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.79173,,0.06232,,0.94728,,0.70692,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46260,SRR7662164,SRX4522788,SRS3641104,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 39,GSM3325410,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 39,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325410,GSM3325410: 4mpf 3TD 39; Danio rerio; RNA Seq,GSM3325410,,1,FACS SMART Seq v4,GEO Accession:GSM3325410,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_39.fastq.gz,fastq,43118448.0,567348.0,GSM3325410 r1,0:76 1:0,A:11415668;C:9846910;G:9721395;T:12133691;N:784,76,0,,,11415668,9846910,9721395,12133691,784,SRX4522788,SRS3641104,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81063,,0.07048,,0.94513,,0.71805,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46261,SRR7662163,SRX4522787,SRS3641101,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 38,GSM3325409,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 38,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325409,GSM3325409: 4mpf 3TD 38; Danio rerio; RNA Seq,GSM3325409,,1,FACS SMART Seq v4,GEO Accession:GSM3325409,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_38.fastq.gz,fastq,44153644.0,580969.0,GSM3325409 r1,0:76 1:0,A:11395441;C:10637328;G:10522594;T:11597490;N:791,76,0,,,11395441,10637328,10522594,11597490,791,SRX4522787,SRS3641101,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84207,,0.05155,,0.9567,,0.59382,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46262,SRR7662162,SRX4522786,SRS3641103,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 37,GSM3325408,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 37,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325408,GSM3325408: 4mpf 3TD 37; Danio rerio; RNA Seq,GSM3325408,,1,FACS SMART Seq v4,GEO Accession:GSM3325408,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_37.fastq.gz,fastq,45082288.0,593188.0,GSM3325408 r1,0:76 1:0,A:11656775;C:10621119;G:10456207;T:12347402;N:785,76,0,,,11656775,10621119,10456207,12347402,785,SRX4522786,SRS3641103,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81529,,0.06896,,0.94828,,0.7399,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46263,SRR7662161,SRX4522785,SRS3641100,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 36,GSM3325407,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 36,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325407,GSM3325407: 4mpf 3TD 36; Danio rerio; RNA Seq,GSM3325407,,1,FACS SMART Seq v4,GEO Accession:GSM3325407,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_36.fastq.gz,fastq,43851468.0,576993.0,GSM3325407 r1,0:76 1:0,A:11646235;C:9975569;G:9837875;T:12391004;N:785,76,0,,,11646235,9975569,9837875,12391004,785,SRX4522785,SRS3641100,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81855,,0.10417,,0.94653,,0.705,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46264,SRR7662160,SRX4522784,SRS3641102,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 35,GSM3325406,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 35,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325406,GSM3325406: 4mpf 3TD 35; Danio rerio; RNA Seq,GSM3325406,,1,FACS SMART Seq v4,GEO Accession:GSM3325406,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_35.fastq.gz,fastq,51333820.0,675445.0,GSM3325406 r1,0:76 1:0,A:13522158;C:11828514;G:11668956;T:14313258;N:934,76,0,,,13522158,11828514,11668956,14313258,934,SRX4522784,SRS3641102,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.79211,,0.07922,,0.93361,,0.67836,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46265,SRR7662159,SRX4522783,SRS3641099,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 34,GSM3325405,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 34,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325405,GSM3325405: 4mpf 3TD 34; Danio rerio; RNA Seq,GSM3325405,,1,FACS SMART Seq v4,GEO Accession:GSM3325405,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_34.fastq.gz,fastq,42434448.0,558348.0,GSM3325405 r1,0:76 1:0,A:11034253;C:9951444;G:9757025;T:11690994;N:732,76,0,,,11034253,9951444,9757025,11690994,732,SRX4522783,SRS3641099,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80526,,0.0836,,0.94397,,0.75354,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46266,SRR7662158,SRX4522782,SRS3641097,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 33,GSM3325404,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 33,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325404,GSM3325404: 4mpf 3TD 33; Danio rerio; RNA Seq,GSM3325404,,1,FACS SMART Seq v4,GEO Accession:GSM3325404,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_33.fastq.gz,fastq,41483080.0,545830.0,GSM3325404 r1,0:76 1:0,A:10943748;C:9539565;G:9367638;T:11631347;N:782,76,0,,,10943748,9539565,9367638,11631347,782,SRX4522782,SRS3641097,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84811,,0.0724,,0.94079,,0.70412,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46267,SRR7662157,SRX4522781,SRS3641096,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 32,GSM3325403,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 32,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325403,GSM3325403: 4mpf 3TD 32; Danio rerio; RNA Seq,GSM3325403,,1,FACS SMART Seq v4,GEO Accession:GSM3325403,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_32.fastq.gz,fastq,46634892.0,613617.0,GSM3325403 r1,0:76 1:0,A:12285344;C:10732177;G:10567037;T:13049373;N:961,76,0,,,12285344,10732177,10567037,13049373,961,SRX4522781,SRS3641096,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.7942,,0.0631,,0.94024,,0.69769,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46268,SRR7662156,SRX4522780,SRS3641098,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 31,GSM3325402,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 31,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325402,GSM3325402: 4mpf 3TD 31; Danio rerio; RNA Seq,GSM3325402,,1,FACS SMART Seq v4,GEO Accession:GSM3325402,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_31.fastq.gz,fastq,38137864.0,501814.0,GSM3325402 r1,0:76 1:0,A:11280508;C:7576592;G:7684905;T:11595131;N:728,76,0,,,11280508,7576592,7684905,11595131,728,SRX4522780,SRS3641098,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.75454,,0.31244,,0.96404,,0.83206,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46269,SRR7662155,SRX4522779,SRS3641095,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 30,GSM3325401,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 30,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325401,GSM3325401: 4mpf 3TD 30; Danio rerio; RNA Seq,GSM3325401,,1,FACS SMART Seq v4,GEO Accession:GSM3325401,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_30.fastq.gz,fastq,49097976.0,646026.0,GSM3325401 r1,0:76 1:0,A:12742428;C:11505538;G:11340150;T:13508959;N:901,76,0,,,12742428,11505538,11340150,13508959,901,SRX4522779,SRS3641095,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81308,,0.07957,,0.94584,,0.74123,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46270,SRR7662154,SRX4522778,SRS3641094,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 29,GSM3325400,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 29,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325400,GSM3325400: 4mpf 3TD 29; Danio rerio; RNA Seq,GSM3325400,,1,FACS SMART Seq v4,GEO Accession:GSM3325400,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_29.fastq.gz,fastq,45125152.0,593752.0,GSM3325400 r1,0:76 1:0,A:11801780;C:10486963;G:10345497;T:12489940;N:972,76,0,,,11801780,10486963,10345497,12489940,972,SRX4522778,SRS3641094,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.839,,0.07636,,0.94148,,0.7489,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46271,SRR7662153,SRX4522777,SRS3641090,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 28,GSM3325399,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 28,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325399,GSM3325399: 4mpf 3TD 28; Danio rerio; RNA Seq,GSM3325399,,1,FACS SMART Seq v4,GEO Accession:GSM3325399,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_28.fastq.gz,fastq,44665808.0,587708.0,GSM3325399 r1,0:76 1:0,A:11700132;C:10404707;G:10277473;T:12282706;N:790,76,0,,,11700132,10404707,10277473,12282706,790,SRX4522777,SRS3641090,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82673,,0.09831,,0.94673,,0.70054,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46272,SRR7662152,SRX4522776,SRS3641091,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 27,GSM3325398,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 27,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325398,GSM3325398: 4mpf 3TD 27; Danio rerio; RNA Seq,GSM3325398,,1,FACS SMART Seq v4,GEO Accession:GSM3325398,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_27.fastq.gz,fastq,36148944.0,475644.0,GSM3325398 r1,0:76 1:0,A:9254193;C:8695996;G:8545834;T:9652334;N:587,76,0,,,9254193,8695996,8545834,9652334,587,SRX4522776,SRS3641091,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84555,,0.06314,,0.95187,,0.74348,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46273,SRR7662151,SRX4522775,SRS3641092,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 26,GSM3325397,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 26,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325397,GSM3325397: 4mpf 3TD 26; Danio rerio; RNA Seq,GSM3325397,,1,FACS SMART Seq v4,GEO Accession:GSM3325397,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_26.fastq.gz,fastq,39483976.0,519526.0,GSM3325397 r1,0:76 1:0,A:10243731;C:9320393;G:9145297;T:10773771;N:784,76,0,,,10243731,9320393,9145297,10773771,784,SRX4522775,SRS3641092,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81192,,0.0737,,0.94509,,0.73844,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46274,SRR7662150,SRX4522774,SRS3641089,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 25,GSM3325396,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 25,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325396,GSM3325396: 4mpf 3TD 25; Danio rerio; RNA Seq,GSM3325396,,1,FACS SMART Seq v4,GEO Accession:GSM3325396,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_25.fastq.gz,fastq,39361312.0,517912.0,GSM3325396 r1,0:76 1:0,A:10042879;C:9519207;G:9312521;T:10485999;N:706,76,0,,,10042879,9519207,9312521,10485999,706,SRX4522774,SRS3641089,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8252,,0.04127,,0.94901,,0.75046,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46275,SRR7662149,SRX4522773,SRS3641088,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 24,GSM3325395,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 24,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325395,GSM3325395: 4mpf 3TD 24; Danio rerio; RNA Seq,GSM3325395,,1,FACS SMART Seq v4,GEO Accession:GSM3325395,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_24.fastq.gz,fastq,47431676.0,624101.0,GSM3325395 r1,0:76 1:0,A:12495357;C:10870593;G:10708467;T:13356444;N:815,76,0,,,12495357,10870593,10708467,13356444,815,SRX4522773,SRS3641088,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83398,,0.07885,,0.93608,,0.72127,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46276,SRR7662148,SRX4522772,SRS3641087,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 23,GSM3325394,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 23,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325394,GSM3325394: 4mpf 3TD 23; Danio rerio; RNA Seq,GSM3325394,,1,FACS SMART Seq v4,GEO Accession:GSM3325394,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_23.fastq.gz,fastq,27242048.0,358448.0,GSM3325394 r1,0:76 1:0,A:8135321;C:5430241;G:5413461;T:8262532;N:493,76,0,,,8135321,5430241,5413461,8262532,493,SRX4522772,SRS3641087,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.7264,,0.06277,,0.99277,,0.96356,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46277,SRR7662147,SRX4522771,SRS3641086,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 22,GSM3325393,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 22,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325393,GSM3325393: 4mpf 3TD 22; Danio rerio; RNA Seq,GSM3325393,,1,FACS SMART Seq v4,GEO Accession:GSM3325393,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_22.fastq.gz,fastq,47999320.0,631570.0,GSM3325393 r1,0:76 1:0,A:12468506;C:11445141;G:11330644;T:12754052;N:977,76,0,,,12468506,11445141,11330644,12754052,977,SRX4522771,SRS3641086,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85839,,0.0787,,0.91648,,0.72247,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46278,SRR7662146,SRX4522770,SRS3641085,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 21,GSM3325392,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 21,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325392,GSM3325392: 4mpf 3TD 21; Danio rerio; RNA Seq,GSM3325392,,1,FACS SMART Seq v4,GEO Accession:GSM3325392,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_21.fastq.gz,fastq,43447224.0,571674.0,GSM3325392 r1,0:76 1:0,A:11289500;C:10397979;G:10217101;T:11541647;N:997,76,0,,,11289500,10397979,10217101,11541647,997,SRX4522770,SRS3641085,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85251,,0.09879,,0.95434,,0.57235,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46279,SRR7662145,SRX4522769,SRS3641083,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 20,GSM3325391,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 20,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325391,GSM3325391: 4mpf 3TD 20; Danio rerio; RNA Seq,GSM3325391,,1,FACS SMART Seq v4,GEO Accession:GSM3325391,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_20.fastq.gz,fastq,39193352.0,515702.0,GSM3325391 r1,0:76 1:0,A:10220606;C:9138362;G:8983737;T:10849932;N:715,76,0,,,10220606,9138362,8983737,10849932,715,SRX4522769,SRS3641083,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82235,,0.0909,,0.93661,,0.74939,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46280,SRR7662144,SRX4522768,SRS3641084,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 19,GSM3325390,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 19,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325390,GSM3325390: 4mpf 3TD 19; Danio rerio; RNA Seq,GSM3325390,,1,FACS SMART Seq v4,GEO Accession:GSM3325390,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_19.fastq.gz,fastq,31949640.0,420390.0,GSM3325390 r1,0:76 1:0,A:9254071;C:6775193;G:6680133;T:9239719;N:524,76,0,,,9254071,6775193,6680133,9239719,524,SRX4522768,SRS3641084,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81416,,0.09963,,0.99827,,0.99571,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46281,SRR7662143,SRX4522767,SRS3641082,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 18,GSM3325389,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 18,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325389,GSM3325389: 4mpf 3TD 18; Danio rerio; RNA Seq,GSM3325389,,1,FACS SMART Seq v4,GEO Accession:GSM3325389,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_18.fastq.gz,fastq,35589964.0,468289.0,GSM3325389 r1,0:76 1:0,A:9543684;C:8018292;G:7914665;T:10112629;N:694,76,0,,,9543684,8018292,7914665,10112629,694,SRX4522767,SRS3641082,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82291,,0.11384,,0.96907,,0.84606,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46282,SRR7662142,SRX4522766,SRS3641081,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 17,GSM3325388,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 17,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325388,GSM3325388: 4mpf 3TD 17; Danio rerio; RNA Seq,GSM3325388,,1,FACS SMART Seq v4,GEO Accession:GSM3325388,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_17.fastq.gz,fastq,42951780.0,565155.0,GSM3325388 r1,0:76 1:0,A:11191051;C:10092134;G:9906934;T:11760925;N:736,76,0,,,11191051,10092134,9906934,11760925,736,SRX4522766,SRS3641081,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82212,,0.0726,,0.94972,,0.73169,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46283,SRR7662141,SRX4522765,SRS3641080,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 16,GSM3325387,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 16,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325387,GSM3325387: 4mpf 3TD 16; Danio rerio; RNA Seq,GSM3325387,,1,FACS SMART Seq v4,GEO Accession:GSM3325387,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_16.fastq.gz,fastq,45155476.0,594151.0,GSM3325387 r1,0:76 1:0,A:11524282;C:10805976;G:10604356;T:12220018;N:844,76,0,,,11524282,10805976,10604356,12220018,844,SRX4522765,SRS3641080,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85101,,0.05564,,0.9526,,0.7636,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46284,SRR7662140,SRX4522764,SRS3641079,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 15,GSM3325386,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 15,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325386,GSM3325386: 4mpf 3TD 15; Danio rerio; RNA Seq,GSM3325386,,1,FACS SMART Seq v4,GEO Accession:GSM3325386,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_15.fastq.gz,fastq,45910688.0,604088.0,GSM3325386 r1,0:76 1:0,A:11801725;C:10919101;G:10766830;T:12422219;N:813,76,0,,,11801725,10919101,10766830,12422219,813,SRX4522764,SRS3641079,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.77867,,0.08265,,0.93988,,0.72223,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46285,SRR7662139,SRX4522763,SRS3641078,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 14,GSM3325385,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 14,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325385,GSM3325385: 4mpf 3TD 14; Danio rerio; RNA Seq,GSM3325385,,1,FACS SMART Seq v4,GEO Accession:GSM3325385,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_14.fastq.gz,fastq,52482484.0,690559.0,GSM3325385 r1,0:76 1:0,A:13749272;C:12167720;G:12023268;T:14541201;N:1023,76,0,,,13749272,12167720,12023268,14541201,1023,SRX4522763,SRS3641078,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80454,,0.06714,,0.94637,,0.69874,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46286,SRR7662138,SRX4522762,SRS3641077,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 13,GSM3325384,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 13,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325384,GSM3325384: 4mpf 3TD 13; Danio rerio; RNA Seq,GSM3325384,,1,FACS SMART Seq v4,GEO Accession:GSM3325384,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_13.fastq.gz,fastq,43940996.0,578171.0,GSM3325384 r1,0:76 1:0,A:11341969;C:10437251;G:10292036;T:11868839;N:901,76,0,,,11341969,10437251,10292036,11868839,901,SRX4522762,SRS3641077,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82051,,0.06836,,0.94882,,0.71807,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46287,SRR7662137,SRX4522761,SRS3641106,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 12,GSM3325383,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 12,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325383,GSM3325383: 4mpf 3TD 12; Danio rerio; RNA Seq,GSM3325383,,1,FACS SMART Seq v4,GEO Accession:GSM3325383,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_12.fastq.gz,fastq,48769048.0,641698.0,GSM3325383 r1,0:76 1:0,A:12740843;C:11336073;G:11163790;T:13527391;N:951,76,0,,,12740843,11336073,11163790,13527391,951,SRX4522761,SRS3641106,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81863,,0.06952,,0.94448,,0.67453,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46288,SRR7662136,SRX4522760,SRS3641076,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 11,GSM3325382,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 11,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325382,GSM3325382: 4mpf 3TD 11; Danio rerio; RNA Seq,GSM3325382,,1,FACS SMART Seq v4,GEO Accession:GSM3325382,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_11.fastq.gz,fastq,39690620.0,522245.0,GSM3325382 r1,0:76 1:0,A:10250697;C:9420248;G:9331138;T:10687674;N:863,76,0,,,10250697,9420248,9331138,10687674,863,SRX4522760,SRS3641076,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86066,,0.08665,,0.94785,,0.74372,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46289,SRR7662135,SRX4522759,SRS3641074,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 10,GSM3325381,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 10,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325381,GSM3325381: 4mpf 3TD 10; Danio rerio; RNA Seq,GSM3325381,,1,FACS SMART Seq v4,GEO Accession:GSM3325381,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_10.fastq.gz,fastq,43790136.0,576186.0,GSM3325381 r1,0:76 1:0,A:11318586;C:10326843;G:10152922;T:11990991;N:794,76,0,,,11318586,10326843,10152922,11990991,794,SRX4522759,SRS3641074,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81392,,0.06136,,0.95367,,0.73262,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46290,SRR7662134,SRX4522758,SRS3641073,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 9,GSM3325380,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 9,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325380,GSM3325380: 4mpf 3TD 9; Danio rerio; RNA Seq,GSM3325380,,1,FACS SMART Seq v4,GEO Accession:GSM3325380,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_9.fastq.gz,fastq,47345264.0,622964.0,GSM3325380 r1,0:76 1:0,A:12562689;C:10846875;G:10695825;T:13239048;N:827,76,0,,,12562689,10846875,10695825,13239048,827,SRX4522758,SRS3641073,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82472,,0.0953,,0.94892,,0.6904,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46291,SRR7662133,SRX4522757,SRS3641071,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 8,GSM3325379,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 8,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325379,GSM3325379: 4mpf 3TD 8; Danio rerio; RNA Seq,GSM3325379,,1,FACS SMART Seq v4,GEO Accession:GSM3325379,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_8.fastq.gz,fastq,49055340.0,645465.0,GSM3325379 r1,0:76 1:0,A:12490243;C:11675281;G:11448655;T:13440254;N:907,76,0,,,12490243,11675281,11448655,13440254,907,SRX4522757,SRS3641071,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84802,,0.03965,,0.95203,,0.74982,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46292,SRR7662132,SRX4522756,SRS3641075,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 7,GSM3325378,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 7,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325378,GSM3325378: 4mpf 3TD 7; Danio rerio; RNA Seq,GSM3325378,,1,FACS SMART Seq v4,GEO Accession:GSM3325378,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_7.fastq.gz,fastq,51166164.0,673239.0,GSM3325378 r1,0:76 1:0,A:13623337;C:11647185;G:11552049;T:14342656;N:937,76,0,,,13623337,11647185,11552049,14342656,937,SRX4522756,SRS3641075,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80194,,0.09112,,0.9486,,0.68788,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46293,SRR7662131,SRX4522755,SRS3641070,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 6,GSM3325377,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 6,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325377,GSM3325377: 4mpf 3TD 6; Danio rerio; RNA Seq,GSM3325377,,1,FACS SMART Seq v4,GEO Accession:GSM3325377,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_6.fastq.gz,fastq,6901256.0,90806.0,GSM3325377 r1,0:76 1:0,A:1772241;C:1662103;G:1646027;T:1820762;N:123,76,0,,,1772241,1662103,1646027,1820762,123,SRX4522755,SRS3641070,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84631,,0.08023,,0.96282,,0.67498,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46294,SRR7662130,SRX4522754,SRS3641072,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 5,GSM3325376,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 5,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325376,GSM3325376: 4mpf 3TD 5; Danio rerio; RNA Seq,GSM3325376,,1,FACS SMART Seq v4,GEO Accession:GSM3325376,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_5.fastq.gz,fastq,15475272.0,203622.0,GSM3325376 r1,0:76 1:0,A:4169638;C:3469267;G:3476564;T:4359550;N:253,76,0,,,4169638,3469267,3476564,4359550,253,SRX4522754,SRS3641072,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84462,,0.17124,,0.95663,,0.77918,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46295,SRR7662129,SRX4522753,SRS3641069,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 4,GSM3325375,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 4,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325375,GSM3325375: 4mpf 3TD 4; Danio rerio; RNA Seq,GSM3325375,,1,FACS SMART Seq v4,GEO Accession:GSM3325375,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_4.fastq.gz,fastq,46359544.0,609994.0,GSM3325375 r1,0:76 1:0,A:12049640;C:10891937;G:10750024;T:12667027;N:916,76,0,,,12049640,10891937,10750024,12667027,916,SRX4522753,SRS3641069,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80025,,0.07504,,0.94554,,0.70433,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46296,SRR7662128,SRX4522752,SRS3641068,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 3,GSM3325374,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 3,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325374,GSM3325374: 4mpf 3TD 3; Danio rerio; RNA Seq,GSM3325374,,1,FACS SMART Seq v4,GEO Accession:GSM3325374,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_3.fastq.gz,fastq,51188432.0,673532.0,GSM3325374 r1,0:76 1:0,A:13021216;C:12236781;G:12013420;T:13916078;N:937,76,0,,,13021216,12236781,12013420,13916078,937,SRX4522752,SRS3641068,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85289,,0.04263,,0.94789,,0.75425,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46297,SRR7662127,SRX4522751,SRS3641067,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 2,GSM3325373,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 2,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325373,GSM3325373: 4mpf 3TD 2; Danio rerio; RNA Seq,GSM3325373,,1,FACS SMART Seq v4,GEO Accession:GSM3325373,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_2.fastq.gz,fastq,34888940.0,459065.0,GSM3325373 r1,0:76 1:0,A:9604470;C:7612595;G:7523333;T:10147965;N:577,76,0,,,9604470,7612595,7523333,10147965,577,SRX4522751,SRS3641067,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.812,,0.12255,,0.94787,,0.69174,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46298,SRR7662126,SRX4522750,SRS3641066,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf 3TD 1,GSM3325372,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,4mpf 3TD 1,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:3 times a day feeding,GSM3325372,GSM3325372: 4mpf 3TD 1; Danio rerio; RNA Seq,GSM3325372,,1,FACS SMART Seq v4,GEO Accession:GSM3325372,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_3TD_1.fastq.gz,fastq,47564904.0,625854.0,GSM3325372 r1,0:76 1:0,A:12408153;C:11109359;G:10940225;T:13106272;N:895,76,0,,,12408153,11109359,10940225,13106272,895,SRX4522750,SRS3641066,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8196,,0.09022,,0.9442,,0.72901,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46299,SRR7662125,SRX4522749,SRS3641065,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 55,GSM3325371,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 55,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325371,GSM3325371: 4mpf IF 55; Danio rerio; RNA Seq,GSM3325371,,1,FACS SMART Seq v4,GEO Accession:GSM3325371,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_55.fastq.gz,fastq,52998904.0,697354.0,GSM3325371 r1,0:76 1:0,A:14310362;C:11924556;G:11808914;T:14953988;N:1084,76,0,,,14310362,11924556,11808914,14953988,1084,SRX4522749,SRS3641065,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82648,,0.10954,,0.94006,,0.64951,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46300,SRR7662124,SRX4522748,SRS3641064,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 54,GSM3325370,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 54,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325370,GSM3325370: 4mpf IF 54; Danio rerio; RNA Seq,GSM3325370,,1,FACS SMART Seq v4,GEO Accession:GSM3325370,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_54.fastq.gz,fastq,47887828.0,630103.0,GSM3325370 r1,0:76 1:0,A:12761394;C:10885664;G:10765180;T:13474729;N:861,76,0,,,12761394,10885664,10765180,13474729,861,SRX4522748,SRS3641064,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82634,,0.07647,,0.95743,,0.67836,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46301,SRR7662123,SRX4522747,SRS3641063,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 53,GSM3325369,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 53,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325369,GSM3325369: 4mpf IF 53; Danio rerio; RNA Seq,GSM3325369,,1,FACS SMART Seq v4,GEO Accession:GSM3325369,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_53.fastq.gz,fastq,58635444.0,771519.0,GSM3325369 r1,0:76 1:0,A:15180731;C:13835314;G:13650142;T:15968133;N:1124,76,0,,,15180731,13835314,13650142,15968133,1124,SRX4522747,SRS3641063,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85253,,0.06205,,0.95913,,0.66854,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46302,SRR7662122,SRX4522746,SRS3641061,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 52,GSM3325368,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 52,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325368,GSM3325368: 4mpf IF 52; Danio rerio; RNA Seq,GSM3325368,,1,FACS SMART Seq v4,GEO Accession:GSM3325368,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_52.fastq.gz,fastq,48344892.0,636117.0,GSM3325368 r1,0:76 1:0,A:13262881;C:10596839;G:10539660;T:13944668;N:844,76,0,,,13262881,10596839,10539660,13944668,844,SRX4522746,SRS3641061,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81529,,0.14578,,0.95801,,0.67588,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46303,SRR7662121,SRX4522745,SRS3641062,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 51,GSM3325367,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 51,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325367,GSM3325367: 4mpf IF 51; Danio rerio; RNA Seq,GSM3325367,,1,FACS SMART Seq v4,GEO Accession:GSM3325367,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_51.fastq.gz,fastq,46400432.0,610532.0,GSM3325367 r1,0:76 1:0,A:12083601;C:10963871;G:10782137;T:12570002;N:821,76,0,,,12083601,10963871,10782137,12570002,821,SRX4522745,SRS3641062,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84689,,0.06857,,0.95122,,0.67464,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46304,SRR7662120,SRX4522744,SRS3641060,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 50,GSM3325366,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 50,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325366,GSM3325366: 4mpf IF 50; Danio rerio; RNA Seq,GSM3325366,,1,FACS SMART Seq v4,GEO Accession:GSM3325366,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_50.fastq.gz,fastq,55987072.0,736672.0,GSM3325366 r1,0:76 1:0,A:14594860;C:13215545;G:13041607;T:15133976;N:1084,76,0,,,14594860,13215545,13041607,15133976,1084,SRX4522744,SRS3641060,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84418,,0.1053,,0.94489,,0.62441,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46305,SRR7662119,SRX4522743,SRS3641059,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 49,GSM3325365,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 49,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325365,GSM3325365: 4mpf IF 49; Danio rerio; RNA Seq,GSM3325365,,1,FACS SMART Seq v4,GEO Accession:GSM3325365,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_49.fastq.gz,fastq,15299560.0,201310.0,GSM3325365 r1,0:76 1:0,A:4227832;C:3362859;G:3368691;T:4339906;N:272,76,0,,,4227832,3362859,3368691,4339906,272,SRX4522743,SRS3641059,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84899,,0.19228,,0.95572,,0.61479,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46306,SRR7662118,SRX4522742,SRS3641058,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 48,GSM3325364,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 48,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325364,GSM3325364: 4mpf IF 48; Danio rerio; RNA Seq,GSM3325364,,1,FACS SMART Seq v4,GEO Accession:GSM3325364,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_48.fastq.gz,fastq,38444296.0,505846.0,GSM3325364 r1,0:76 1:0,A:10284940;C:8847375;G:8697915;T:10613326;N:740,76,0,,,10284940,8847375,8697915,10613326,740,SRX4522742,SRS3641058,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83587,,0.10622,,0.96195,,0.65351,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46307,SRR7662117,SRX4522741,SRS3641055,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 47,GSM3325363,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 47,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325363,GSM3325363: 4mpf IF 47; Danio rerio; RNA Seq,GSM3325363,,1,FACS SMART Seq v4,GEO Accession:GSM3325363,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_47.fastq.gz,fastq,43789984.0,576184.0,GSM3325363 r1,0:76 1:0,A:11523891;C:10257103;G:10162326;T:11845765;N:899,76,0,,,11523891,10257103,10162326,11845765,899,SRX4522741,SRS3641055,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83802,,0.08033,,0.94649,,0.60409,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46308,SRR7662116,SRX4522740,SRS3641056,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 46,GSM3325362,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 46,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325362,GSM3325362: 4mpf IF 46; Danio rerio; RNA Seq,GSM3325362,,1,FACS SMART Seq v4,GEO Accession:GSM3325362,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_46.fastq.gz,fastq,42123608.0,554258.0,GSM3325362 r1,0:76 1:0,A:11747868;C:9186318;G:9233447;T:11955236;N:739,76,0,,,11747868,9186318,9233447,11955236,739,SRX4522740,SRS3641056,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84712,,0.10853,,0.95402,,0.55085,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46309,SRR7662115,SRX4522739,SRS3641054,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 45,GSM3325361,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 45,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325361,GSM3325361: 4mpf IF 45; Danio rerio; RNA Seq,GSM3325361,,1,FACS SMART Seq v4,GEO Accession:GSM3325361,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_45.fastq.gz,fastq,25202664.0,331614.0,GSM3325361 r1,0:76 1:0,A:7295930;C:5088213;G:5102598;T:7715445;N:478,76,0,,,7295930,5088213,5102598,7715445,478,SRX4522739,SRS3641054,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.56607,,0.2943,,0.97705,,0.66394,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46310,SRR7662114,SRX4522738,SRS3641057,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 44,GSM3325360,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 44,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325360,GSM3325360: 4mpf IF 44; Danio rerio; RNA Seq,GSM3325360,,1,FACS SMART Seq v4,GEO Accession:GSM3325360,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_44.fastq.gz,fastq,43666712.0,574562.0,GSM3325360 r1,0:76 1:0,A:11476243;C:10259299;G:10150379;T:11779913;N:878,76,0,,,11476243,10259299,10150379,11779913,878,SRX4522738,SRS3641057,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83766,,0.10498,,0.94706,,0.59362,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46311,SRR7662113,SRX4522737,SRS3641053,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 43,GSM3325359,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 43,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325359,GSM3325359: 4mpf IF 43; Danio rerio; RNA Seq,GSM3325359,,1,FACS SMART Seq v4,GEO Accession:GSM3325359,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_43.fastq.gz,fastq,39006772.0,513247.0,GSM3325359 r1,0:76 1:0,A:10889773;C:8486226;G:8476299;T:11153870;N:604,76,0,,,10889773,8486226,8476299,11153870,604,SRX4522737,SRS3641053,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83738,,0.08267,,0.95164,,0.59767,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46312,SRR7662112,SRX4522736,SRS3641051,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 42,GSM3325358,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 42,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325358,GSM3325358: 4mpf IF 42; Danio rerio; RNA Seq,GSM3325358,,1,FACS SMART Seq v4,GEO Accession:GSM3325358,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_42.fastq.gz,fastq,45428012.0,597737.0,GSM3325358 r1,0:76 1:0,A:11838175;C:10827784;G:10713557;T:12047638;N:858,76,0,,,11838175,10827784,10713557,12047638,858,SRX4522736,SRS3641051,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82069,,0.08168,,0.95868,,0.57599,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46313,SRR7662111,SRX4522735,SRS3641052,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 41,GSM3325357,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 41,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325357,GSM3325357: 4mpf IF 41; Danio rerio; RNA Seq,GSM3325357,,1,FACS SMART Seq v4,GEO Accession:GSM3325357,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_41.fastq.gz,fastq,32840208.0,432108.0,GSM3325357 r1,0:76 1:0,A:8619815;C:7776515;G:7701106;T:8742194;N:578,76,0,,,8619815,7776515,7701106,8742194,578,SRX4522735,SRS3641052,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84718,,0.0672,,0.94957,,0.57585,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46314,SRR7662110,SRX4522734,SRS3641050,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 40,GSM3325356,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 40,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325356,GSM3325356: 4mpf IF 40; Danio rerio; RNA Seq,GSM3325356,,1,FACS SMART Seq v4,GEO Accession:GSM3325356,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_40.fastq.gz,fastq,2701876.0,35551.0,GSM3325356 r1,0:76 1:0,A:721393;C:629710;G:647722;T:703014;N:37,76,0,,,721393,629710,647722,703014,37,SRX4522734,SRS3641050,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86299,,0.08071,,0.94935,,0.62592,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46315,SRR7662109,SRX4522733,SRS3641049,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 39,GSM3325355,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 39,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325355,GSM3325355: 4mpf IF 39; Danio rerio; RNA Seq,GSM3325355,,1,FACS SMART Seq v4,GEO Accession:GSM3325355,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_39.fastq.gz,fastq,43743852.0,575577.0,GSM3325355 r1,0:76 1:0,A:11419966;C:10441518;G:10232065;T:11649588;N:715,76,0,,,11419966,10441518,10232065,11649588,715,SRX4522733,SRS3641049,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84654,,0.08856,,0.94815,,0.58853,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46316,SRR7662108,SRX4522732,SRS3641048,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 38,GSM3325354,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 38,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325354,GSM3325354: 4mpf IF 38; Danio rerio; RNA Seq,GSM3325354,,1,FACS SMART Seq v4,GEO Accession:GSM3325354,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_38.fastq.gz,fastq,49032768.0,645168.0,GSM3325354 r1,0:76 1:0,A:12567749;C:11898380;G:11813374;T:12752336;N:929,76,0,,,12567749,11898380,11813374,12752336,929,SRX4522732,SRS3641048,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84386,,0.07513,,0.96376,,0.56093,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46317,SRR7662107,SRX4522731,SRS3641046,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 37,GSM3325353,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 37,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325353,GSM3325353: 4mpf IF 37; Danio rerio; RNA Seq,GSM3325353,,1,FACS SMART Seq v4,GEO Accession:GSM3325353,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_37.fastq.gz,fastq,59641228.0,784753.0,GSM3325353 r1,0:76 1:0,A:15382293;C:14416910;G:14292334;T:15548644;N:1047,76,0,,,15382293,14416910,14292334,15548644,1047,SRX4522731,SRS3641046,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81572,,0.05376,,0.95302,,0.57351,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46318,SRR7662106,SRX4522730,SRS3641045,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 36,GSM3325352,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 36,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325352,GSM3325352: 4mpf IF 36; Danio rerio; RNA Seq,GSM3325352,,1,FACS SMART Seq v4,GEO Accession:GSM3325352,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_36.fastq.gz,fastq,58551844.0,770419.0,GSM3325352 r1,0:76 1:0,A:15018078;C:14178454;G:14037854;T:15316356;N:1102,76,0,,,15018078,14178454,14037854,15316356,1102,SRX4522730,SRS3641045,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84525,,0.04828,,0.95343,,0.63414,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46319,SRR7662105,SRX4522729,SRS3641047,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 35,GSM3325351,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 35,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325351,GSM3325351: 4mpf IF 35; Danio rerio; RNA Seq,GSM3325351,,1,FACS SMART Seq v4,GEO Accession:GSM3325351,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_35.fastq.gz,fastq,47139912.0,620262.0,GSM3325351 r1,0:76 1:0,A:12380168;C:11091607;G:10934854;T:12732309;N:974,76,0,,,12380168,11091607,10934854,12732309,974,SRX4522729,SRS3641047,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84984,,0.07462,,0.96027,,0.59064,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46320,SRR7662104,SRX4522728,SRS3641044,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 34,GSM3325350,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 34,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325350,GSM3325350: 4mpf IF 34; Danio rerio; RNA Seq,GSM3325350,,1,FACS SMART Seq v4,GEO Accession:GSM3325350,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_34.fastq.gz,fastq,54652436.0,719111.0,GSM3325350 r1,0:76 1:0,A:14304618;C:12974551;G:12864931;T:14507245;N:1091,76,0,,,14304618,12974551,12864931,14507245,1091,SRX4522728,SRS3641044,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82867,,0.07429,,0.95507,,0.61233,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46321,SRR7662103,SRX4522727,SRS3641043,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 33,GSM3325349,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 33,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325349,GSM3325349: 4mpf IF 33; Danio rerio; RNA Seq,GSM3325349,,1,FACS SMART Seq v4,GEO Accession:GSM3325349,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_33.fastq.gz,fastq,47925296.0,630596.0,GSM3325349 r1,0:76 1:0,A:12759955;C:10824427;G:10743640;T:13596268;N:1006,76,0,,,12759955,10824427,10743640,13596268,1006,SRX4522727,SRS3641043,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.78516,,0.08708,,0.95246,,0.68657,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46322,SRR7662102,SRX4522726,SRS3641042,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 32,GSM3325348,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 32,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325348,GSM3325348: 4mpf IF 32; Danio rerio; RNA Seq,GSM3325348,,1,FACS SMART Seq v4,GEO Accession:GSM3325348,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_32.fastq.gz,fastq,51398192.0,676292.0,GSM3325348 r1,0:76 1:0,A:13500811;C:12158828;G:12052658;T:13684896;N:999,76,0,,,13500811,12158828,12052658,13684896,999,SRX4522726,SRS3641042,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83442,,0.09255,,0.95509,,0.56413,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46323,SRR7662101,SRX4522725,SRS3641040,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 31,GSM3325347,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 31,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325347,GSM3325347: 4mpf IF 31; Danio rerio; RNA Seq,GSM3325347,,1,FACS SMART Seq v4,GEO Accession:GSM3325347,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_31.fastq.gz,fastq,36844040.0,484790.0,GSM3325347 r1,0:76 1:0,A:9560563;C:8814167;G:8665010;T:9803711;N:589,76,0,,,9560563,8814167,8665010,9803711,589,SRX4522725,SRS3641040,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83689,,0.09471,,0.95101,,0.62526,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46324,SRR7662100,SRX4522724,SRS3641041,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 30,GSM3325346,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 30,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325346,GSM3325346: 4mpf IF 30; Danio rerio; RNA Seq,GSM3325346,,1,FACS SMART Seq v4,GEO Accession:GSM3325346,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_30.fastq.gz,fastq,15602496.0,205296.0,GSM3325346 r1,0:76 1:0,A:4650162;C:3102203;G:3101870;T:4747969;N:292,76,0,,,4650162,3102203,3101870,4747969,292,SRX4522724,SRS3641041,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.70449,,0.13623,,0.98362,,0.94464,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46325,SRR7662099,SRX4522723,SRS3641039,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 29,GSM3325345,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 29,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325345,GSM3325345: 4mpf IF 29; Danio rerio; RNA Seq,GSM3325345,,1,FACS SMART Seq v4,GEO Accession:GSM3325345,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_29.fastq.gz,fastq,34915844.0,459419.0,GSM3325345 r1,0:76 1:0,A:8984073;C:8446613;G:8294708;T:9189835;N:615,76,0,,,8984073,8446613,8294708,9189835,615,SRX4522723,SRS3641039,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84188,,0.05728,,0.95294,,0.62182,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46326,SRR7662098,SRX4522722,SRS3641038,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 28,GSM3325344,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 28,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325344,GSM3325344: 4mpf IF 28; Danio rerio; RNA Seq,GSM3325344,,1,FACS SMART Seq v4,GEO Accession:GSM3325344,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_28.fastq.gz,fastq,38676780.0,508905.0,GSM3325344 r1,0:76 1:0,A:10035445;C:9254767;G:9086353;T:10299584;N:631,76,0,,,10035445,9254767,9086353,10299584,631,SRX4522722,SRS3641038,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8438,,0.08959,,0.95377,,0.64434,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46327,SRR7662097,SRX4522721,SRS3641037,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 27,GSM3325343,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 27,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325343,GSM3325343: 4mpf IF 27; Danio rerio; RNA Seq,GSM3325343,,1,FACS SMART Seq v4,GEO Accession:GSM3325343,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_27.fastq.gz,fastq,19453264.0,255964.0,GSM3325343 r1,0:76 1:0,A:5040810;C:4608368;G:4560312;T:5243498;N:276,76,0,,,5040810,4608368,4560312,5243498,276,SRX4522721,SRS3641037,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85606,,0.15379,,0.95225,,0.6452,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46328,SRR7662096,SRX4522720,SRS3641035,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 26,GSM3325342,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 26,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325342,GSM3325342: 4mpf IF 26; Danio rerio; RNA Seq,GSM3325342,,1,FACS SMART Seq v4,GEO Accession:GSM3325342,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_26.fastq.gz,fastq,40861704.0,537654.0,GSM3325342 r1,0:76 1:0,A:10620649;C:9739457;G:9582234;T:10918599;N:765,76,0,,,10620649,9739457,9582234,10918599,765,SRX4522720,SRS3641035,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85747,,0.08566,,0.95491,,0.65482,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46329,SRR7662095,SRX4522719,SRS3641032,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 25,GSM3325341,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 25,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325341,GSM3325341: 4mpf IF 25; Danio rerio; RNA Seq,GSM3325341,,1,FACS SMART Seq v4,GEO Accession:GSM3325341,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_25.fastq.gz,fastq,19358264.0,254714.0,GSM3325341 r1,0:76 1:0,A:5026278;C:4636184;G:4558560;T:5136917;N:325,76,0,,,5026278,4636184,4558560,5136917,325,SRX4522719,SRS3641032,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83861,,0.07049,,0.94901,,0.58876,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46330,SRR7662094,SRX4522718,SRS3641034,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 24,GSM3325340,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 24,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325340,GSM3325340: 4mpf IF 24; Danio rerio; RNA Seq,GSM3325340,,1,FACS SMART Seq v4,GEO Accession:GSM3325340,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_24.fastq.gz,fastq,3000252.0,39477.0,GSM3325340 r1,0:76 1:0,A:777886;C:718556;G:713936;T:789820;N:54,76,0,,,777886,718556,713936,789820,54,SRX4522718,SRS3641034,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86238,,0.05348,,0.95639,,0.61537,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46331,SRR7662093,SRX4522717,SRS3641033,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 23,GSM3325339,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 23,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325339,GSM3325339: 4mpf IF 23; Danio rerio; RNA Seq,GSM3325339,,1,FACS SMART Seq v4,GEO Accession:GSM3325339,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_23.fastq.gz,fastq,45007580.0,592205.0,GSM3325339 r1,0:76 1:0,A:11913517;C:10509235;G:10356142;T:12227824;N:862,76,0,,,11913517,10509235,10356142,12227824,862,SRX4522717,SRS3641033,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83402,,0.08299,,0.94856,,0.60108,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46332,SRR7662092,SRX4522716,SRS3641031,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 22,GSM3325338,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 22,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325338,GSM3325338: 4mpf IF 22; Danio rerio; RNA Seq,GSM3325338,,1,FACS SMART Seq v4,GEO Accession:GSM3325338,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_22.fastq.gz,fastq,39288124.0,516949.0,GSM3325338 r1,0:76 1:0,A:10232777;C:9385077;G:9229589;T:10439982;N:699,76,0,,,10232777,9385077,9229589,10439982,699,SRX4522716,SRS3641031,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81994,,0.09467,,0.95282,,0.60257,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46333,SRR7662091,SRX4522715,SRS3641029,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 21,GSM3325337,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 21,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325337,GSM3325337: 4mpf IF 21; Danio rerio; RNA Seq,GSM3325337,,1,FACS SMART Seq v4,GEO Accession:GSM3325337,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_21.fastq.gz,fastq,46901576.0,617126.0,GSM3325337 r1,0:76 1:0,A:12372492;C:10979490;G:10855232;T:12693403;N:959,76,0,,,12372492,10979490,10855232,12693403,959,SRX4522715,SRS3641029,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",,,,,,,,,,,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46334,SRR7662090,SRX4522714,SRS3641030,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 20,GSM3325336,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 20,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325336,GSM3325336: 4mpf IF 20; Danio rerio; RNA Seq,GSM3325336,,1,FACS SMART Seq v4,GEO Accession:GSM3325336,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_20.fastq.gz,fastq,48177692.0,633917.0,GSM3325336 r1,0:76 1:0,A:12330925;C:11657051;G:11465382;T:12723407;N:927,76,0,,,12330925,11657051,11465382,12723407,927,SRX4522714,SRS3641030,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84548,,0.06664,,0.95806,,0.65631,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46335,SRR7662089,SRX4522713,SRS3641028,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 19,GSM3325335,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 19,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325335,GSM3325335: 4mpf IF 19; Danio rerio; RNA Seq,GSM3325335,,1,FACS SMART Seq v4,GEO Accession:GSM3325335,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_19.fastq.gz,fastq,45549080.0,599330.0,GSM3325335 r1,0:76 1:0,A:11885398;C:10831249;G:10698937;T:12132696;N:800,76,0,,,11885398,10831249,10698937,12132696,800,SRX4522713,SRS3641028,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84786,,0.06494,,0.95737,,0.55382,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46336,SRR7662088,SRX4522712,SRS3641027,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 18,GSM3325334,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 18,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325334,GSM3325334: 4mpf IF 18; Danio rerio; RNA Seq,GSM3325334,,1,FACS SMART Seq v4,GEO Accession:GSM3325334,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_18.fastq.gz,fastq,44661780.0,587655.0,GSM3325334 r1,0:76 1:0,A:11541522;C:10597248;G:10443606;T:12078437;N:967,76,0,,,11541522,10597248,10443606,12078437,967,SRX4522712,SRS3641027,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85076,,0.0797,,0.94395,,0.6945,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46337,SRR7662087,SRX4522711,SRS3641025,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 17,GSM3325333,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 17,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325333,GSM3325333: 4mpf IF 17; Danio rerio; RNA Seq,GSM3325333,,1,FACS SMART Seq v4,GEO Accession:GSM3325333,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_17.fastq.gz,fastq,15504.0,204.0,GSM3325333 r1,0:76 1:0,A:4580;C:3910;G:3890;T:3124;N:0,76,0,,,4580,3910,3890,3124,0,SRX4522711,SRS3641025,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.07043,,0.00704,,0.99985,,0.44444,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46338,SRR7662086,SRX4522710,SRS3641026,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 16,GSM3325332,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 16,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,GSM3325332,GSM3325332: 4mpf IF 16; Danio rerio; RNA Seq,GSM3325332,,1,FACS SMART Seq v4,GEO Accession:GSM3325332,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_16.fastq.gz,fastq,49501688.0,651338.0,GSM3325332 r1,0:76 1:0,A:12472487;C:12100962;G:11901446;T:13025835;N:958,76,0,,,12472487,12100962,11901446,13025835,958,SRX4522710,SRS3641026,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82421,,0.03404,,0.95045,,0.73497,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Adult,Adult,Pancreas,Endocrine System
46339,SRR7662085,SRX4522709,SRS3641024,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 15,GSM3325331,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325331,GSM3325331: 4mpf IF 15; Danio rerio; RNA Seq,GSM3325331,,1,FACS SMART Seq v4,GEO Accession:GSM3325331,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_15.fastq.gz,fastq,38692740.0,509115.0,GSM3325331 r1,0:76 1:0,A:10062933;C:9072051;G:8963611;T:10593400;N:745,76,0,,,10062933,9072051,8963611,10593400,745,SRX4522709,SRS3641024,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.76397,,0.04664,,0.94844,,0.72888,,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
46340,SRR7662084,SRX4522708,SRS3641023,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 14,GSM3325330,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325330,GSM3325330: 4mpf IF 14; Danio rerio; RNA Seq,GSM3325330,,1,FACS SMART Seq v4,GEO Accession:GSM3325330,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_14.fastq.gz,fastq,25103408.0,330308.0,GSM3325330 r1,0:76 1:0,A:7634838;C:4536036;G:4661434;T:8270600;N:500,76,0,,,7634838,4536036,4661434,8270600,500,SRX4522708,SRS3641023,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.17084,,0.01162,,0.99697,,0.90197,,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
46341,SRR7662083,SRX4522707,SRS3641022,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 13,GSM3325329,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325329,GSM3325329: 4mpf IF 13; Danio rerio; RNA Seq,GSM3325329,,1,FACS SMART Seq v4,GEO Accession:GSM3325329,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_13.fastq.gz,fastq,57887224.0,761674.0,GSM3325329 r1,0:76 1:0,A:14870458;C:13746213;G:13692567;T:15576926;N:1060,76,0,,,14870458,13746213,13692567,15576926,1060,SRX4522707,SRS3641022,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.79829,,0.04064,,0.94905,,0.71895,,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
46342,SRR7662082,SRX4522706,SRS3641021,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 12,GSM3325328,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325328,GSM3325328: 4mpf IF 12; Danio rerio; RNA Seq,GSM3325328,,1,FACS SMART Seq v4,GEO Accession:GSM3325328,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_12.fastq.gz,fastq,52729560.0,693810.0,GSM3325328 r1,0:76 1:0,A:13508966;C:12581307;G:12572412;T:14065991;N:884,76,0,,,13508966,12581307,12572412,14065991,884,SRX4522706,SRS3641021,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81727,,0.04184,,0.95274,,0.71561,,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
46343,SRR7662081,SRX4522705,SRS3641019,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 11,GSM3325327,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325327,GSM3325327: 4mpf IF 11; Danio rerio; RNA Seq,GSM3325327,,1,FACS SMART Seq v4,GEO Accession:GSM3325327,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_11.fastq.gz,fastq,27541032.0,362382.0,GSM3325327 r1,0:76 1:0,A:8485247;C:4924113;G:5042990;T:9088207;N:475,76,0,,,8485247,4924113,5042990,9088207,475,SRX4522705,SRS3641019,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.25211,,0.01209,,0.99819,,0.99354,,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
46344,SRR7662080,SRX4522704,SRS3641018,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 10,GSM3325326,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325326,GSM3325326: 4mpf IF 10; Danio rerio; RNA Seq,GSM3325326,,1,FACS SMART Seq v4,GEO Accession:GSM3325326,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_10.fastq.gz,fastq,49165312.0,646912.0,GSM3325326 r1,0:76 1:0,A:12863633;C:11453796;G:11440327;T:13406655;N:901,76,0,,,12863633,11453796,11440327,13406655,901,SRX4522704,SRS3641018,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80181,,0.06691,,0.95014,,0.75495,,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
46345,SRR7662079,SRX4522703,SRS3641017,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 9,GSM3325325,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325325,GSM3325325: 4mpf IF 9; Danio rerio; RNA Seq,GSM3325325,,1,FACS SMART Seq v4,GEO Accession:GSM3325325,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_9.fastq.gz,fastq,33757300.0,444175.0,GSM3325325 r1,0:76 1:0,A:9913351;C:6590801;G:6737836;T:10514725;N:587,76,0,,,9913351,6590801,6737836,10514725,587,SRX4522703,SRS3641017,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.52091,,0.10313,,0.97624,,0.52694,,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
46346,SRR7662078,SRX4522702,SRS3641016,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 8,GSM3325324,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325324,GSM3325324: 4mpf IF 8; Danio rerio; RNA Seq,GSM3325324,,1,FACS SMART Seq v4,GEO Accession:GSM3325324,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_8.fastq.gz,fastq,35057204.0,461279.0,GSM3325324 r1,0:76 1:0,A:9716002;C:7488510;G:7483526;T:10368547;N:619,76,0,,,9716002,7488510,7483526,10368547,619,SRX4522702,SRS3641016,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.67481,,0.11732,,0.95473,,0.80642,,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
46347,SRR7662077,SRX4522701,SRS3641014,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 7,GSM3325323,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325323,GSM3325323: 4mpf IF 7; Danio rerio; RNA Seq,GSM3325323,,1,FACS SMART Seq v4,GEO Accession:GSM3325323,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_7.fastq.gz,fastq,35995424.0,473624.0,GSM3325323 r1,0:76 1:0,A:9532068;C:8236012;G:8159946;T:10066680;N:718,76,0,,,9532068,8236012,8159946,10066680,718,SRX4522701,SRS3641014,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.75534,,0.05221,,0.95006,,0.70192,,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
46348,SRR7662076,SRX4522700,SRS3641015,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 6,GSM3325322,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325322,GSM3325322: 4mpf IF 6; Danio rerio; RNA Seq,GSM3325322,,1,FACS SMART Seq v4,GEO Accession:GSM3325322,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_6.fastq.gz,fastq,29264180.0,385055.0,GSM3325322 r1,0:76 1:0,A:8684809;C:5557217;G:5683444;T:9338179;N:531,76,0,,,8684809,5557217,5683444,9338179,531,SRX4522700,SRS3641015,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.41724,,0.09244,,0.98354,,0.86626,,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
46349,SRR7662075,SRX4522699,SRS3641013,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 5,GSM3325321,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325321,GSM3325321: 4mpf IF 5; Danio rerio; RNA Seq,GSM3325321,,1,FACS SMART Seq v4,GEO Accession:GSM3325321,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_5.fastq.gz,fastq,22623300.0,297675.0,GSM3325321 r1,0:76 1:0,A:6855661;C:4125424;G:4228863;T:7413019;N:333,76,0,,,6855661,4125424,4228863,7413019,333,SRX4522699,SRS3641013,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.25082,,0.01731,,0.99782,,0.97982,,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
46350,SRR7662074,SRX4522698,SRS3641012,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 4,GSM3325320,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325320,GSM3325320: 4mpf IF 4; Danio rerio; RNA Seq,GSM3325320,,1,FACS SMART Seq v4,GEO Accession:GSM3325320,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_4.fastq.gz,fastq,37995516.0,499941.0,GSM3325320 r1,0:76 1:0,A:10110154;C:8662686;G:8581457;T:10640379;N:840,76,0,,,10110154,8662686,8581457,10640379,840,SRX4522698,SRS3641012,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.72271,,0.0625,,0.95576,,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
46351,SRR7662073,SRX4522697,SRS3641011,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 3,GSM3325319,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325319,GSM3325319: 4mpf IF 3; Danio rerio; RNA Seq,GSM3325319,,1,FACS SMART Seq v4,GEO Accession:GSM3325319,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_3.fastq.gz,fastq,64348592.0,846692.0,GSM3325319 r1,0:76 1:0,A:16997715;C:14731613;G:14721823;T:17896268;N:1173,76,0,,,16997715,14731613,14721823,17896268,1173,SRX4522697,SRS3641011,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.75453,,0.0702,,0.95629,,0.73536,,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
46352,SRR7662072,SRX4522696,SRS3641009,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 2,GSM3325318,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325318,GSM3325318: 4mpf IF 2; Danio rerio; RNA Seq,GSM3325318,,1,FACS SMART Seq v4,GEO Accession:GSM3325318,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_2.fastq.gz,fastq,38781660.0,510285.0,GSM3325318 r1,0:76 1:0,A:10927223;C:8116764;G:8209438;T:11527636;N:599,76,0,,,10927223,8116764,8209438,11527636,599,SRX4522696,SRS3641009,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.65659,,0.08946,,0.9628,,0.72523,,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
46353,SRR7662071,SRX4522695,SRS3641010,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,4mpf IF 1,GSM3325317,,tissue:beta cells|age:4mpf|strain:Tgins:BB1.0L|feeding:Intermittent feeding,4mpf IF 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:Intermittent feeding,GSM3325317,GSM3325317: 4mpf IF 1; Danio rerio; RNA Seq,GSM3325317,,1,FACS SMART Seq v4,GEO Accession:GSM3325317,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,4mpf_IF_1.fastq.gz,fastq,36347684.0,478259.0,GSM3325317 r1,0:76 1:0,A:9886083;C:8058295;G:7998492;T:10404173;N:641,76,0,,,9886083,8058295,7998492,10404173,641,SRX4522695,SRS3641010,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.72889,,0.05497,,0.95651,,0.70201,,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
46354,SRR7662070,SRX4522694,SRS3641006,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,1mpf H12,GSM3325316,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H12,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:1mpf|strain:Tgins:BB1.0L|feeding:Normal,GSM3325316,GSM3325316: 1mpf H12; Danio rerio; RNA Seq,GSM3325316,,1,FACS SMART Seq v4,GEO Accession:GSM3325316,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31465_s1mpf_01_H12_R1.fastq.gz,fastq,87046828.0,1145353.0,GSM3325316 r1,0:76,A:22460488;C:20848962;G:20349576;T:23386128;N:1674,76,,,,22460488,20848962,20349576,23386128,1674,SRX4522694,SRS3641006,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.77299,,0.08312,,0.91691,,0.48876,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Juvenile,Juvenile,Pancreas,Endocrine System
46355,SRR7662069,SRX4522693,SRS3641008,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,1mpf G12,GSM3325315,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G12,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:1mpf|strain:Tgins:BB1.0L|feeding:Normal,GSM3325315,GSM3325315: 1mpf G12; Danio rerio; RNA Seq,GSM3325315,,1,FACS SMART Seq v4,GEO Accession:GSM3325315,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31464_s1mpf_01_G12_R1.fastq.gz,fastq,23797272.0,313122.0,GSM3325315 r1,0:76,A:5936406;C:3935636;G:4436044;T:9488871;N:315,76,,,,5936406,3935636,4436044,9488871,315,SRX4522693,SRS3641008,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.09491,,0.07107,,0.98652,,0.59913,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Juvenile,Juvenile,Pancreas,Endocrine System
46356,SRR7662068,SRX4522692,SRS3641007,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,1mpf F12,GSM3325314,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F12,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:1mpf|strain:Tgins:BB1.0L|feeding:Normal,GSM3325314,GSM3325314: 1mpf F12; Danio rerio; RNA Seq,GSM3325314,,1,FACS SMART Seq v4,GEO Accession:GSM3325314,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31463_s1mpf_01_F12_R1.fastq.gz,fastq,75272072.0,990422.0,GSM3325314 r1,0:76,A:19301333;C:17742731;G:17356276;T:20870342;N:1390,76,,,,19301333,17742731,17356276,20870342,1390,SRX4522692,SRS3641007,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80634,,0.05873,,0.93028,,0.37925,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Juvenile,Juvenile,Pancreas,Endocrine System
46357,SRR7662067,SRX4522691,SRS3641005,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,1mpf E12,GSM3325313,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf E12,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:1mpf|strain:Tgins:BB1.0L|feeding:Normal,GSM3325313,GSM3325313: 1mpf E12; Danio rerio; RNA Seq,GSM3325313,,1,FACS SMART Seq v4,GEO Accession:GSM3325313,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31462_s1mpf_01_E12_R1.fastq.gz,fastq,83267880.0,1095630.0,GSM3325313 r1,0:76,A:21633526;C:19285141;G:19003686;T:23343902;N:1625,76,,,,21633526,19285141,19003686,23343902,1625,SRX4522691,SRS3641005,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83012,,0.07536,,0.89688,,0.63844,,76,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-08-08,Juvenile,Juvenile,Pancreas,Endocrine System
46358,SRR7662066,SRX4522690,SRS3641093,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from various stages. Cells were sorted into a 96 well plates and single cell library prepared using SMART Seq v4 Ultra Low Input RNA Kit . Sequencing was performed on llumina Nextseq500 aiming at an average sequencing depth of 0.5 million reads per cell. Reads were splice aligned to the zebrafish genome  GRCz10  using HISAT2. htseq count was used to assign reads to exons thus eventually getting counts per gene.,,pubmed:30464314,,13mpf D12,GSM3325312,,tissue:beta cells|age:13mpf|strain:Tgins:BB1.0L|feeding:Normal,13mpf D12,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:13mpf|strain:Tgins:BB1.0L|feeding:Normal,GSM3325312,GSM3325312: 13mpf D12; Danio rerio; RNA Seq,GSM3325312,,1,FACS SMART Seq v4,GEO Accession:GSM3325312,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31461_s13mpf_01_D12_R1.fastq.gz,fastq,141520208.0,1862108.0,GSM3325312 r1,0:76 1:0,A:36221896;C:33961983;G:33555097;T:37779735;N:1497,76,0,,,36221896,33961983,33555097,37779735,1497,SRX4522690,SRS3641093,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.69932,,0.06309,,0.93395,,0.63507,,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