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
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
46362,SRR7662062,SRX4522686,SRS3641001,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H11,GSM3325308,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H11,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325308,GSM3325308: 1mpf H11; Danio rerio; RNA Seq,GSM3325308,,1,FACS SMART Seq v4,GEO Accession:GSM3325308,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31457_s1mpf_01_H11_R1.fastq.gz,fastq,59768756.0,786431.0,GSM3325308 r1,0:76,A:15036753;C:14340995;G:14148814;T:16241289;N:905,76,,,,15036753,14340995,14148814,16241289,905,SRX4522686,SRS3641001,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84779,,0.0456,,0.92046,,0.73503,,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
46363,SRR7662061,SRX4522685,SRS3641002,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G11,GSM3325307,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G11,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325307,GSM3325307: 1mpf G11; Danio rerio; RNA Seq,GSM3325307,,1,FACS SMART Seq v4,GEO Accession:GSM3325307,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31456_s1mpf_01_G11_R1.fastq.gz,fastq,72534780.0,954405.0,GSM3325307 r1,0:76,A:18728542;C:17102905;G:16952137;T:19750041;N:1155,76,,,,18728542,17102905,16952137,19750041,1155,SRX4522685,SRS3641002,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82925,,0.07454,,0.91887,,0.6224,,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
46364,SRR7662060,SRX4522684,SRS3640999,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F11,GSM3325306,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F11,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325306,GSM3325306: 1mpf F11; Danio rerio; RNA Seq,GSM3325306,,1,FACS SMART Seq v4,GEO Accession:GSM3325306,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31455_s1mpf_01_F11_R1.fastq.gz,fastq,74248808.0,976958.0,GSM3325306 r1,0:76,A:19224966;C:17049701;G:16984467;T:20988434;N:1240,76,,,,19224966,17049701,16984467,20988434,1240,SRX4522684,SRS3640999,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80748,,0.06908,,0.89152,,0.64784,,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
46365,SRR7662059,SRX4522683,SRS3640998,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 E11,GSM3325305,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf E11,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325305,GSM3325305: 1mpf E11; Danio rerio; RNA Seq,GSM3325305,,1,FACS SMART Seq v4,GEO Accession:GSM3325305,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31454_s1mpf_01_E11_R1.fastq.gz,fastq,44244312.0,582162.0,GSM3325305 r1,0:76,A:11435166;C:10059677;G:10588318;T:12160566;N:585,76,,,,11435166,10059677,10588318,12160566,585,SRX4522683,SRS3640998,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85486,,0.05347,,0.90301,,0.65208,,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
46369,SRR7662055,SRX4522679,SRS3640994,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H10,GSM3325301,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H10,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325301,GSM3325301: 1mpf H10; Danio rerio; RNA Seq,GSM3325301,,1,FACS SMART Seq v4,GEO Accession:GSM3325301,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31449_s1mpf_01_H10_R1.fastq.gz,fastq,62725916.0,825341.0,GSM3325301 r1,0:76,A:16555253;C:14116818;G:14371400;T:17681508;N:937,76,,,,16555253,14116818,14371400,17681508,937,SRX4522679,SRS3640994,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81646,,0.08993,,0.86387,,0.58838,,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
46370,SRR7662054,SRX4522678,SRS3640993,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G10,GSM3325300,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G10,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325300,GSM3325300: 1mpf G10; Danio rerio; RNA Seq,GSM3325300,,1,FACS SMART Seq v4,GEO Accession:GSM3325300,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31448_s1mpf_01_G10_R1.fastq.gz,fastq,96364732.0,1267957.0,GSM3325300 r1,0:76,A:25230478;C:21979684;G:21724635;T:27428017;N:1918,76,,,,25230478,21979684,21724635,27428017,1918,SRX4522678,SRS3640993,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83839,,0.07228,,0.90636,,0.63507,,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
46371,SRR7662053,SRX4522677,SRS3640992,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F10,GSM3325299,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F10,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325299,GSM3325299: 1mpf F10; Danio rerio; RNA Seq,GSM3325299,,1,FACS SMART Seq v4,GEO Accession:GSM3325299,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31447_s1mpf_01_F10_R1.fastq.gz,fastq,75853852.0,998077.0,GSM3325299 r1,0:76,A:19708464;C:17925990;G:17594384;T:20623656;N:1358,76,,,,19708464,17925990,17594384,20623656,1358,SRX4522677,SRS3640992,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85537,,0.08905,,0.9021,,0.59794,,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
46376,SRR7662052,SRX4522672,SRS3640988,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 E10,GSM3325298,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf E10,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325298,GSM3325298: 1mpf E10; Danio rerio; RNA Seq,GSM3325298,,1,FACS SMART Seq v4,GEO Accession:GSM3325298,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31446_s1mpf_01_E10_R1.fastq.gz,fastq,36976204.0,486529.0,GSM3325298 r1,0:76,A:9968750;C:7848573;G:8365562;T:10792825;N:494,76,,,,9968750,7848573,8365562,10792825,494,SRX4522672,SRS3640988,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82574,,0.08038,,0.88947,,0.6103,,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
46377,SRR7662047,SRX4522671,SRS3640986,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H09,GSM3325293,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H09,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325293,GSM3325293: 1mpf H09; Danio rerio; RNA Seq,GSM3325293,,1,FACS SMART Seq v4,GEO Accession:GSM3325293,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31441_s1mpf_01_H09_R1.fastq.gz,fastq,148637532.0,1955757.0,GSM3325293 r1,0:76,A:38943952;C:34311892;G:33297768;T:42080961;N:2959,76,,,,38943952,34311892,33297768,42080961,2959,SRX4522671,SRS3640986,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82191,,0.06606,,0.91413,,0.66784,,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
46378,SRR7662046,SRX4522670,SRS3640985,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G09,GSM3325292,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G09,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325292,GSM3325292: 1mpf G09; Danio rerio; RNA Seq,GSM3325292,,1,FACS SMART Seq v4,GEO Accession:GSM3325292,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31440_s1mpf_01_G09_R1.fastq.gz,fastq,58015436.0,763361.0,GSM3325292 r1,0:76,A:15463578;C:13008176;G:13279332;T:16263495;N:855,76,,,,15463578,13008176,13279332,16263495,855,SRX4522670,SRS3640985,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83826,,0.09449,,0.88489,,0.56666,,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
46379,SRR7662045,SRX4522669,SRS3640984,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F09,GSM3325291,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F09,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325291,GSM3325291: 1mpf F09; Danio rerio; RNA Seq,GSM3325291,,1,FACS SMART Seq v4,GEO Accession:GSM3325291,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31439_s1mpf_01_F09_R1.fastq.gz,fastq,88872728.0,1169378.0,GSM3325291 r1,0:76,A:23302722;C:20218966;G:20088110;T:25261298;N:1632,76,,,,23302722,20218966,20088110,25261298,1632,SRX4522669,SRS3640984,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83054,,0.08063,,0.87746,,0.64378,,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
46380,SRR7662044,SRX4522668,SRS3640983,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 E09,GSM3325290,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf E09,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325290,GSM3325290: 1mpf E09; Danio rerio; RNA Seq,GSM3325290,,1,FACS SMART Seq v4,GEO Accession:GSM3325290,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31438_s1mpf_01_E09_R1.fastq.gz,fastq,65975980.0,868105.0,GSM3325290 r1,0:76,A:17056308;C:15356312;G:15321531;T:18240843;N:986,76,,,,17056308,15356312,15321531,18240843,986,SRX4522668,SRS3640983,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86465,,0.07299,,0.90761,,0.63639,,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
46385,SRR7662039,SRX4522663,SRS3640978,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H08,GSM3325285,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H08,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325285,GSM3325285: 1mpf H08; Danio rerio; RNA Seq,GSM3325285,,1,FACS SMART Seq v4,GEO Accession:GSM3325285,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31433_s1mpf_01_H08_R1.fastq.gz,fastq,97145252.0,1278227.0,GSM3325285 r1,0:76,A:26683858;C:20984603;G:20928330;T:28546467;N:1994,76,,,,26683858,20984603,20928330,28546467,1994,SRX4522663,SRS3640978,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82967,,0.14365,,0.88536,,0.57508,,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
46386,SRR7662038,SRX4522662,SRS3641036,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G08,GSM3325284,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G08,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325284,GSM3325284: 1mpf G08; Danio rerio; RNA Seq,GSM3325284,,1,FACS SMART Seq v4,GEO Accession:GSM3325284,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31432_s1mpf_01_G08_R1.fastq.gz,fastq,96596228.0,1271003.0,GSM3325284 r1,0:76,A:25246114;C:22546760;G:22239309;T:26562495;N:1550,76,,,,25246114,22546760,22239309,26562495,1550,SRX4522662,SRS3641036,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8536,,0.09571,,0.90713,,0.5795,,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
46387,SRR7662037,SRX4522661,SRS3640976,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F08,GSM3325283,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F08,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325283,GSM3325283: 1mpf F08; Danio rerio; RNA Seq,GSM3325283,,1,FACS SMART Seq v4,GEO Accession:GSM3325283,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31431_s1mpf_01_F08_R1.fastq.gz,fastq,92249408.0,1213808.0,GSM3325283 r1,0:76,A:24812430;C:20949965;G:20457246;T:26028014;N:1753,76,,,,24812430,20949965,20457246,26028014,1753,SRX4522661,SRS3640976,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83608,,0.15915,,0.91171,,0.57919,,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
46388,SRR7662036,SRX4522660,SRS3640977,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 E08,GSM3325282,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf E08,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325282,GSM3325282: 1mpf E08; Danio rerio; RNA Seq,GSM3325282,,1,FACS SMART Seq v4,GEO Accession:GSM3325282,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31430_s1mpf_01_E08_R1.fastq.gz,fastq,46283468.0,608993.0,GSM3325282 r1,0:76,A:11903595;C:10814729;G:11104738;T:12459828;N:578,76,,,,11903595,10814729,11104738,12459828,578,SRX4522660,SRS3640977,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.75916,,0.05552,,0.91601,,0.62085,,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
46393,SRR7662031,SRX4522655,SRS3640969,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H07,GSM3325277,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H07,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325277,GSM3325277: 1mpf H07; Danio rerio; RNA Seq,GSM3325277,,1,FACS SMART Seq v4,GEO Accession:GSM3325277,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31425_s1mpf_01_H07_R1.fastq.gz,fastq,24266952.0,319302.0,GSM3325277 r1,0:76,A:6499258;C:5109630;G:5566598;T:7091102;N:364,76,,,,6499258,5109630,5566598,7091102,364,SRX4522655,SRS3640969,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84026,,0.07321,,0.88347,,0.57131,,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
46394,SRR7662030,SRX4522654,SRS3640970,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G07,GSM3325276,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G07,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325276,GSM3325276: 1mpf G07; Danio rerio; RNA Seq,GSM3325276,,1,FACS SMART Seq v4,GEO Accession:GSM3325276,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31424_s1mpf_01_G07_R1.fastq.gz,fastq,70147316.0,922991.0,GSM3325276 r1,0:76,A:17969858;C:16668436;G:16361563;T:19146146;N:1313,76,,,,17969858,16668436,16361563,19146146,1313,SRX4522654,SRS3640970,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82366,,0.0732,,0.91311,,0.63037,,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
46395,SRR7662029,SRX4522653,SRS3640971,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F07,GSM3325275,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F07,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325275,GSM3325275: 1mpf F07; Danio rerio; RNA Seq,GSM3325275,,1,FACS SMART Seq v4,GEO Accession:GSM3325275,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31423_s1mpf_01_F07_R1.fastq.gz,fastq,82361504.0,1083704.0,GSM3325275 r1,0:76,A:21887192;C:18440994;G:18308324;T:23723464;N:1530,76,,,,21887192,18440994,18308324,23723464,1530,SRX4522653,SRS3640971,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83716,,0.12613,,0.89209,,0.64939,,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
46400,SRR7662024,SRX4522648,SRS3640965,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H06,GSM3325270,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H06,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325270,GSM3325270: 1mpf H06; Danio rerio; RNA Seq,GSM3325270,,1,FACS SMART Seq v4,GEO Accession:GSM3325270,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31417_s1mpf_01_H06_R1.fastq.gz,fastq,94553272.0,1244122.0,GSM3325270 r1,0:76,A:24549228;C:22133471;G:21736101;T:26132625;N:1847,76,,,,24549228,22133471,21736101,26132625,1847,SRX4522648,SRS3640965,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82623,,0.07666,,0.90995,,0.6333,,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
46401,SRR7662023,SRX4522647,SRS3640964,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G06,GSM3325269,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G06,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325269,GSM3325269: 1mpf G06; Danio rerio; RNA Seq,GSM3325269,,1,FACS SMART Seq v4,GEO Accession:GSM3325269,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31416_s1mpf_01_G06_R1.fastq.gz,fastq,85768052.0,1128527.0,GSM3325269 r1,0:76,A:23091113;C:18994082;G:18920826;T:24760325;N:1706,76,,,,23091113,18994082,18920826,24760325,1706,SRX4522647,SRS3640964,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82801,,0.10178,,0.88994,,0.6304,,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
46402,SRR7662022,SRX4522646,SRS3640962,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F06,GSM3325268,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F06,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325268,GSM3325268: 1mpf F06; Danio rerio; RNA Seq,GSM3325268,,1,FACS SMART Seq v4,GEO Accession:GSM3325268,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31415_s1mpf_01_F06_R1.fastq.gz,fastq,73673868.0,969393.0,GSM3325268 r1,0:76,A:18811605;C:16972147;G:16616392;T:21272519;N:1205,76,,,,18811605,16972147,16616392,21272519,1205,SRX4522646,SRS3640962,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81607,,0.05435,,0.91851,,0.73316,,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
46407,SRR7662017,SRX4522641,SRS3640957,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H05,GSM3325263,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H05,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325263,GSM3325263: 1mpf H05; Danio rerio; RNA Seq,GSM3325263,,1,FACS SMART Seq v4,GEO Accession:GSM3325263,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31409_s1mpf_01_H05_R1.fastq.gz,fastq,18638012.0,245237.0,GSM3325263 r1,0:76,A:5080935;C:3909423;G:4326876;T:5320540;N:238,76,,,,5080935,3909423,4326876,5320540,238,SRX4522641,SRS3640957,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85899,,0.09282,,0.90268,,0.5689,,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
46408,SRR7662016,SRX4522640,SRS3640956,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G05,GSM3325262,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G05,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325262,GSM3325262: 1mpf G05; Danio rerio; RNA Seq,GSM3325262,,1,FACS SMART Seq v4,GEO Accession:GSM3325262,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31408_s1mpf_01_G05_R1.fastq.gz,fastq,83631540.0,1100415.0,GSM3325262 r1,0:76,A:21741297;C:19322459;G:19049668;T:23516759;N:1357,76,,,,21741297,19322459,19049668,23516759,1357,SRX4522640,SRS3640956,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8169,,0.07343,,0.90502,,0.60148,,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
46409,SRR7662015,SRX4522639,SRS3640955,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F05,GSM3325261,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F05,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325261,GSM3325261: 1mpf F05; Danio rerio; RNA Seq,GSM3325261,,1,FACS SMART Seq v4,GEO Accession:GSM3325261,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31407_s1mpf_01_F05_R1.fastq.gz,fastq,79606352.0,1047452.0,GSM3325261 r1,0:76,A:20435769;C:18373239;G:18109995;T:22685963;N:1386,76,,,,20435769,18373239,18109995,22685963,1386,SRX4522639,SRS3640955,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8362,,0.05971,,0.91248,,0.70037,,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
46414,SRR7662010,SRX4522634,SRS3640949,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H04,GSM3325256,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H04,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325256,GSM3325256: 1mpf H04; Danio rerio; RNA Seq,GSM3325256,,1,FACS SMART Seq v4,GEO Accession:GSM3325256,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31401_s1mpf_01_H04_R1.fastq.gz,fastq,89558324.0,1178399.0,GSM3325256 r1,0:76,A:23324005;C:20485512;G:20247033;T:25500068;N:1706,76,,,,23324005,20485512,20247033,25500068,1706,SRX4522634,SRS3640949,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.80376,,0.07476,,0.90717,,0.63863,,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
46415,SRR7662009,SRX4522633,SRS3640948,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G04,GSM3325255,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G04,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325255,GSM3325255: 1mpf G04; Danio rerio; RNA Seq,GSM3325255,,1,FACS SMART Seq v4,GEO Accession:GSM3325255,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31400_s1mpf_01_G04_R1.fastq.gz,fastq,88561888.0,1165288.0,GSM3325255 r1,0:76,A:23762013;C:19817479;G:19806853;T:25174051;N:1492,76,,,,23762013,19817479,19806853,25174051,1492,SRX4522633,SRS3640948,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8209,,0.09837,,0.87125,,0.57571,,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
46416,SRR7662008,SRX4522632,SRS3640947,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F04,GSM3325254,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F04,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325254,GSM3325254: 1mpf F04; Danio rerio; RNA Seq,GSM3325254,,1,FACS SMART Seq v4,GEO Accession:GSM3325254,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31399_s1mpf_01_F04_R1.fastq.gz,fastq,35419496.0,466046.0,GSM3325254 r1,0:76,A:9525858;C:7473328;G:7953855;T:10465959;N:496,76,,,,9525858,7473328,7953855,10465959,496,SRX4522632,SRS3640947,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.78893,,0.0988,,0.87426,,0.60531,,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
46421,SRR7662003,SRX4522627,SRS3640942,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H03,GSM3325249,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H03,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325249,GSM3325249: 1mpf H03; Danio rerio; RNA Seq,GSM3325249,,1,FACS SMART Seq v4,GEO Accession:GSM3325249,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31393_s1mpf_01_H03_R1.fastq.gz,fastq,56309616.0,740916.0,GSM3325249 r1,0:76,A:15247903;C:12228485;G:12506349;T:16325753;N:1126,76,,,,15247903,12228485,12506349,16325753,1126,SRX4522627,SRS3640942,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83004,,0.09258,,0.86987,,0.52427,,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
46422,SRR7662002,SRX4522626,SRS3640958,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G03,GSM3325248,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G03,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325248,GSM3325248: 1mpf G03; Danio rerio; RNA Seq,GSM3325248,,1,FACS SMART Seq v4,GEO Accession:GSM3325248,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31392_s1mpf_01_G03_R1.fastq.gz,fastq,114670776.0,1508826.0,GSM3325248 r1,0:76,A:30635159;C:25745255;G:25791380;T:32496722;N:2260,76,,,,30635159,25745255,25791380,32496722,2260,SRX4522626,SRS3640958,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86104,,0.07869,,0.88889,,0.52596,,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
46423,SRR7662001,SRX4522625,SRS3640941,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F03,GSM3325247,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F03,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325247,GSM3325247: 1mpf F03; Danio rerio; RNA Seq,GSM3325247,,1,FACS SMART Seq v4,GEO Accession:GSM3325247,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31391_s1mpf_01_F03_R1.fastq.gz,fastq,89941060.0,1183435.0,GSM3325247 r1,0:76,A:23204460;C:20956173;G:20285952;T:25492781;N:1694,76,,,,23204460,20956173,20285952,25492781,1694,SRX4522625,SRS3640941,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.84866,,0.06822,,0.91541,,0.73218,,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
46429,SRR7661995,SRX4522619,SRS3640936,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H02,GSM3325241,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H02,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325241,GSM3325241: 1mpf H02; Danio rerio; RNA Seq,GSM3325241,,1,FACS SMART Seq v4,GEO Accession:GSM3325241,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31385_s1mpf_01_H02_R1.fastq.gz,fastq,87315336.0,1148886.0,GSM3325241 r1,0:76,A:22477009;C:20126704;G:19713684;T:24996296;N:1643,76,,,,22477009,20126704,19713684,24996296,1643,SRX4522619,SRS3640936,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.83885,,0.07392,,0.89952,,0.711,,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
46430,SRR7661994,SRX4522618,SRS3640935,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G02,GSM3325240,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G02,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325240,GSM3325240: 1mpf G02; Danio rerio; RNA Seq,GSM3325240,,1,FACS SMART Seq v4,GEO Accession:GSM3325240,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31384_s1mpf_01_G02_R1.fastq.gz,fastq,102247132.0,1345357.0,GSM3325240 r1,0:76,A:26447649;C:23536986;G:23239454;T:29020995;N:2048,76,,,,26447649,23536986,23239454,29020995,2048,SRX4522618,SRS3640935,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.79815,,0.06878,,0.90565,,0.63691,,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
46431,SRR7661993,SRX4522617,SRS3640933,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F02,GSM3325239,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F02,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325239,GSM3325239: 1mpf F02; Danio rerio; RNA Seq,GSM3325239,,1,FACS SMART Seq v4,GEO Accession:GSM3325239,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31383_s1mpf_01_F02_R1.fastq.gz,fastq,3647468.0,47993.0,GSM3325239 r1,0:76,A:1044479;C:771091;G:574467;T:1257383;N:48,76,,,,1044479,771091,574467,1257383,48,SRX4522617,SRS3640933,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.09537,,0.01459,,0.98853,,0.62911,,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
46437,SRR7661987,SRX4522611,SRS3640929,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 H01,GSM3325233,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf H01,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325233,GSM3325233: 1mpf H01; Danio rerio; RNA Seq,GSM3325233,,1,FACS SMART Seq v4,GEO Accession:GSM3325233,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31377_s1mpf_01_H01_R1.fastq.gz,fastq,107677104.0,1416804.0,GSM3325233 r1,0:76,A:29036019;C:23529712;G:23686157;T:31423180;N:2036,76,,,,29036019,23529712,23686157,31423180,2036,SRX4522611,SRS3640929,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8787,,0.05685,,0.90471,,0.54933,,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
46438,SRR7661986,SRX4522610,SRS3640927,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 G01,GSM3325232,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf G01,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325232,GSM3325232: 1mpf G01; Danio rerio; RNA Seq,GSM3325232,,1,FACS SMART Seq v4,GEO Accession:GSM3325232,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31376_s1mpf_01_G01_R1.fastq.gz,fastq,42071168.0,553568.0,GSM3325232 r1,0:76,A:11108262;C:9300192;G:9510035;T:12152122;N:557,76,,,,11108262,9300192,9510035,12152122,557,SRX4522610,SRS3640927,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.76599,,0.08136,,0.89406,,0.62677,,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
46439,SRR7661985,SRX4522609,SRS3640926,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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 F01,GSM3325231,,tissue:beta cells|age:1mpf|strain:Tgins:BB1.0L|feeding:Normal,1mpf F01,Trimming using trim galore using default parameters Mapping using HISAT2 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,GSM3325231,GSM3325231: 1mpf F01; Danio rerio; RNA Seq,GSM3325231,,1,FACS SMART Seq v4,GEO Accession:GSM3325231,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,L31375_s1mpf_01_F01_R1.fastq.gz,fastq,28655572.0,377047.0,GSM3325231 r1,0:76,A:7716521;C:6072009;G:6603950;T:8262690;N:402,76,,,,7716521,6072009,6603950,8262690,402,SRX4522609,SRS3640926,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.7809,,0.07616,,0.89396,,0.54989,,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
46537,SRR6655280,SRX3632664,SRS2899393,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 91,GSM2973026,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 91,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973026,GSM2973026: 45dpf 91; Danio rerio; RNA Seq,GSM2973026,,1,FACS SMART Seq v4,GEO Accession:GSM2973026,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_91.fastq.gz,fastq,67450650.0,899342.0,GSM2973026 r1,0:75 1:0,A:17520321;C:15956115;G:15843491;T:18129541;N:1182,75,0,,,17520321,15956115,15843491,18129541,1182,SRX3632664,SRS2899393,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.88168,,0.06345,,0.88724,,0.66773,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46538,SRR6655279,SRX3632663,SRS2899395,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 90,GSM2973025,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 90,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973025,GSM2973025: 45dpf 90; Danio rerio; RNA Seq,GSM2973025,,1,FACS SMART Seq v4,GEO Accession:GSM2973025,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_90.fastq.gz,fastq,56150925.0,748679.0,GSM2973025 r1,0:75 1:0,A:14503401;C:13435332;G:13331368;T:14879809;N:1015,75,0,,,14503401,13435332,13331368,14879809,1015,SRX3632663,SRS2899395,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86514,,0.04901,,0.92413,,0.70573,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46539,SRR6655278,SRX3632662,SRS2899557,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 89,GSM2973024,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 89,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973024,GSM2973024: 45dpf 89; Danio rerio; RNA Seq,GSM2973024,,1,FACS SMART Seq v4,GEO Accession:GSM2973024,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_89.fastq.gz,fastq,71663550.0,955514.0,GSM2973024 r1,0:75 1:0,A:18360198;C:17180479;G:17082722;T:19038832;N:1319,75,0,,,18360198,17180479,17082722,19038832,1319,SRX3632662,SRS2899557,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.88365,,0.05011,,0.90989,,0.64302,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46540,SRR6655277,SRX3632661,SRS2899391,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 88,GSM2973023,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 88,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973023,GSM2973023: 45dpf 88; Danio rerio; RNA Seq,GSM2973023,,1,FACS SMART Seq v4,GEO Accession:GSM2973023,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_88.fastq.gz,fastq,22927125.0,305695.0,GSM2973023 r1,0:75 1:0,A:6023527;C:5416399;G:5405256;T:6081501;N:442,75,0,,,6023527,5416399,5405256,6081501,442,SRX3632661,SRS2899391,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.89917,,0.02979,,0.88353,,0.58306,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46541,SRR6655276,SRX3632660,SRS2899392,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 87,GSM2973022,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 87,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973022,GSM2973022: 45dpf 87; Danio rerio; RNA Seq,GSM2973022,,1,FACS SMART Seq v4,GEO Accession:GSM2973022,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_87.fastq.gz,fastq,3485550.0,46474.0,GSM2973022 r1,0:75 1:0,A:1070709;C:735076;G:796512;T:883205;N:48,75,0,,,1070709,735076,796512,883205,48,SRX3632660,SRS2899392,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8678,,0.10546,,0.93547,,0.64422,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46542,SRR6655275,SRX3632659,SRS2899389,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 86,GSM2973021,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 86,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973021,GSM2973021: 45dpf 86; Danio rerio; RNA Seq,GSM2973021,,1,FACS SMART Seq v4,GEO Accession:GSM2973021,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_86.fastq.gz,fastq,75705600.0,1009408.0,GSM2973021 r1,0:75 1:0,A:19234938;C:18361576;G:18297880;T:19809902;N:1304,75,0,,,19234938,18361576,18297880,19809902,1304,SRX3632659,SRS2899389,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86844,,0.03525,,0.92888,,0.70124,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46543,SRR6655274,SRX3632658,SRS2899390,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 85,GSM2973020,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 85,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973020,GSM2973020: 45dpf 85; Danio rerio; RNA Seq,GSM2973020,,1,FACS SMART Seq v4,GEO Accession:GSM2973020,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_85.fastq.gz,fastq,63094425.0,841259.0,GSM2973020 r1,0:75 1:0,A:16031659;C:15067964;G:14956297;T:17037332;N:1173,75,0,,,16031659,15067964,14956297,17037332,1173,SRX3632658,SRS2899390,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86498,,0.03903,,0.92421,,0.70164,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46544,SRR6655273,SRX3632657,SRS2899386,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 84,GSM2973019,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 84,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973019,GSM2973019: 45dpf 84; Danio rerio; RNA Seq,GSM2973019,,1,FACS SMART Seq v4,GEO Accession:GSM2973019,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_84.fastq.gz,fastq,30219975.0,402933.0,GSM2973019 r1,0:75 1:0,A:8090604;C:6578487;G:6554566;T:8995741;N:577,75,0,,,8090604,6578487,6554566,8995741,577,SRX3632657,SRS2899386,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.66069,,0.01319,,0.96079,,0.85386,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46545,SRR6655272,SRX3632656,SRS2899388,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 83,GSM2973018,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 83,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973018,GSM2973018: 45dpf 83; Danio rerio; RNA Seq,GSM2973018,,1,FACS SMART Seq v4,GEO Accession:GSM2973018,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_83.fastq.gz,fastq,63507375.0,846765.0,GSM2973018 r1,0:75 1:0,A:16264222;C:15194801;G:15141963;T:16905119;N:1270,75,0,,,16264222,15194801,15141963,16905119,1270,SRX3632656,SRS2899388,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86771,,0.04317,,0.92222,,0.67569,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46546,SRR6655271,SRX3632655,SRS2899384,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 82,GSM2973017,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 82,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973017,GSM2973017: 45dpf 82; Danio rerio; RNA Seq,GSM2973017,,1,FACS SMART Seq v4,GEO Accession:GSM2973017,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_82.fastq.gz,fastq,56943900.0,759252.0,GSM2973017 r1,0:75 1:0,A:14666592;C:13599830;G:13608622;T:15067723;N:1133,75,0,,,14666592,13599830,13608622,15067723,1133,SRX3632655,SRS2899384,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87506,,0.05245,,0.92904,,0.42922,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46547,SRR6655270,SRX3632654,SRS2899387,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 81,GSM2973016,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 81,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973016,GSM2973016: 45dpf 81; Danio rerio; RNA Seq,GSM2973016,,1,FACS SMART Seq v4,GEO Accession:GSM2973016,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_81.fastq.gz,fastq,69483975.0,926453.0,GSM2973016 r1,0:75 1:0,A:17612711;C:16788294;G:16727282;T:18354448;N:1240,75,0,,,17612711,16788294,16727282,18354448,1240,SRX3632654,SRS2899387,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8642,,0.04496,,0.92452,,0.69263,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46548,SRR6655269,SRX3632653,SRS2899382,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 80,GSM2973015,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 80,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973015,GSM2973015: 45dpf 80; Danio rerio; RNA Seq,GSM2973015,,1,FACS SMART Seq v4,GEO Accession:GSM2973015,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_80.fastq.gz,fastq,21738300.0,289844.0,GSM2973015 r1,0:75 1:0,A:5491110;C:5342969;G:5281259;T:5622502;N:460,75,0,,,5491110,5342969,5281259,5622502,460,SRX3632653,SRS2899382,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87475,,0.03497,,0.92411,,0.68786,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46549,SRR6655268,SRX3632652,SRS2899383,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 79,GSM2973014,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 79,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973014,GSM2973014: 45dpf 79; Danio rerio; RNA Seq,GSM2973014,,1,FACS SMART Seq v4,GEO Accession:GSM2973014,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_79.fastq.gz,fastq,2387625.0,31835.0,GSM2973014 r1,0:75 1:0,A:1154481;C:356544;G:384605;T:491941;N:54,75,0,,,1154481,356544,384605,491941,54,SRX3632652,SRS2899383,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8592,,0.43789,,0.98472,,0.81393,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46550,SRR6655267,SRX3632651,SRS2899381,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 78,GSM2973013,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 78,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973013,GSM2973013: 45dpf 78; Danio rerio; RNA Seq,GSM2973013,,1,FACS SMART Seq v4,GEO Accession:GSM2973013,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_78.fastq.gz,fastq,69554175.0,927389.0,GSM2973013 r1,0:75 1:0,A:17627981;C:16951705;G:16856354;T:18116906;N:1229,75,0,,,17627981,16951705,16856354,18116906,1229,SRX3632651,SRS2899381,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87114,,0.05302,,0.91457,,0.71222,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46551,SRR6655266,SRX3632650,SRS2899385,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 77,GSM2973012,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 77,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973012,GSM2973012: 45dpf 77; Danio rerio; RNA Seq,GSM2973012,,1,FACS SMART Seq v4,GEO Accession:GSM2973012,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_77.fastq.gz,fastq,65073450.0,867646.0,GSM2973012 r1,0:75 1:0,A:16464558;C:15669864;G:15636096;T:17301647;N:1285,75,0,,,16464558,15669864,15636096,17301647,1285,SRX3632650,SRS2899385,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86248,,0.03856,,0.92701,,0.73679,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46552,SRR6655265,SRX3632649,SRS2899379,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 76,GSM2973011,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 76,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973011,GSM2973011: 45dpf 76; Danio rerio; RNA Seq,GSM2973011,,1,FACS SMART Seq v4,GEO Accession:GSM2973011,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_76.fastq.gz,fastq,68859750.0,918130.0,GSM2973011 r1,0:75 1:0,A:17599817;C:16380483;G:16417010;T:18461164;N:1276,75,0,,,17599817,16380483,16417010,18461164,1276,SRX3632649,SRS2899379,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86041,,0.03789,,0.93221,,0.63731,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46553,SRR6655264,SRX3632648,SRS2899380,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 75,GSM2973010,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 75,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973010,GSM2973010: 45dpf 75; Danio rerio; RNA Seq,GSM2973010,,1,FACS SMART Seq v4,GEO Accession:GSM2973010,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_75.fastq.gz,fastq,52670175.0,702269.0,GSM2973010 r1,0:75 1:0,A:13243746;C:12879387;G:12805531;T:13740619;N:892,75,0,,,13243746,12879387,12805531,13740619,892,SRX3632648,SRS2899380,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87402,,0.04207,,0.91173,,0.70624,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46554,SRR6655263,SRX3632647,SRS2899376,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 74,GSM2973009,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 74,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973009,GSM2973009: 45dpf 74; Danio rerio; RNA Seq,GSM2973009,,1,FACS SMART Seq v4,GEO Accession:GSM2973009,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_74.fastq.gz,fastq,54503475.0,726713.0,GSM2973009 r1,0:75 1:0,A:13798347;C:13322574;G:13200684;T:14180828;N:1042,75,0,,,13798347,13322574,13200684,14180828,1042,SRX3632647,SRS2899376,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87615,,0.03666,,0.9218,,0.69385,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46555,SRR6655262,SRX3632646,SRS2899374,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 73,GSM2973008,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 73,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973008,GSM2973008: 45dpf 73; Danio rerio; RNA Seq,GSM2973008,,1,FACS SMART Seq v4,GEO Accession:GSM2973008,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_73.fastq.gz,fastq,75270300.0,1003604.0,GSM2973008 r1,0:75 1:0,A:19098470;C:17829982;G:17829521;T:20510717;N:1610,75,0,,,19098470,17829982,17829521,20510717,1610,SRX3632646,SRS2899374,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86976,,0.04054,,0.94073,,0.75328,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46556,SRR6655261,SRX3632645,SRS2899378,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 72,GSM2973007,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 72,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973007,GSM2973007: 45dpf 72; Danio rerio; RNA Seq,GSM2973007,,1,FACS SMART Seq v4,GEO Accession:GSM2973007,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_72.fastq.gz,fastq,26405700.0,352076.0,GSM2973007 r1,0:75 1:0,A:6690605;C:6481338;G:6424180;T:6809137;N:440,75,0,,,6690605,6481338,6424180,6809137,440,SRX3632645,SRS2899378,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.88861,,0.0301,,0.90485,,0.6871,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46557,SRR6655260,SRX3632644,SRS2899377,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 71,GSM2973006,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 71,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973006,GSM2973006: 45dpf 71; Danio rerio; RNA Seq,GSM2973006,,1,FACS SMART Seq v4,GEO Accession:GSM2973006,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_71.fastq.gz,fastq,3620775.0,48277.0,GSM2973006 r1,0:75 1:0,A:1019636;C:808853;G:867676;T:924588;N:22,75,0,,,1019636,808853,867676,924588,22,SRX3632644,SRS2899377,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8706,,0.0877,,0.94257,,0.62812,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46558,SRR6655259,SRX3632643,SRS2899375,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 70,GSM2973005,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 70,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973005,GSM2973005: 45dpf 70; Danio rerio; RNA Seq,GSM2973005,,1,FACS SMART Seq v4,GEO Accession:GSM2973005,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_70.fastq.gz,fastq,68031300.0,907084.0,GSM2973005 r1,0:75 1:0,A:17377303;C:16391829;G:16346820;T:17914023;N:1325,75,0,,,17377303,16391829,16346820,17914023,1325,SRX3632643,SRS2899375,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8061,,0.04081,,0.93223,,0.39068,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46559,SRR6655258,SRX3632642,SRS2899372,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 69,GSM2973004,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 69,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973004,GSM2973004: 45dpf 69; Danio rerio; RNA Seq,GSM2973004,,1,FACS SMART Seq v4,GEO Accession:GSM2973004,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_69.fastq.gz,fastq,43971975.0,586293.0,GSM2973004 r1,0:75 1:0,A:11741143;C:9772157;G:9904084;T:12553673;N:918,75,0,,,11741143,9772157,9904084,12553673,918,SRX3632642,SRS2899372,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.82155,,0.12802,,0.95355,,0.8053,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46560,SRR6655257,SRX3632641,SRS2899373,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 68,GSM2973003,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 68,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973003,GSM2973003: 45dpf 68; Danio rerio; RNA Seq,GSM2973003,,1,FACS SMART Seq v4,GEO Accession:GSM2973003,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_68.fastq.gz,fastq,70652175.0,942029.0,GSM2973003 r1,0:75 1:0,A:18249984;C:16900435;G:16844344;T:18656041;N:1371,75,0,,,18249984,16900435,16844344,18656041,1371,SRX3632641,SRS2899373,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.90345,,0.04156,,0.88921,,0.65773,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46561,SRR6655256,SRX3632640,SRS2899371,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 67,GSM2973002,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 67,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973002,GSM2973002: 45dpf 67; Danio rerio; RNA Seq,GSM2973002,,1,FACS SMART Seq v4,GEO Accession:GSM2973002,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_67.fastq.gz,fastq,46920525.0,625607.0,GSM2973002 r1,0:75 1:0,A:13448444;C:9515151;G:9723299;T:14232786;N:845,75,0,,,13448444,9515151,9723299,14232786,845,SRX3632640,SRS2899371,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.77971,,0.28016,,0.97059,,0.49123,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46562,SRR6655255,SRX3632639,SRS2899370,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 66,GSM2973001,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 66,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973001,GSM2973001: 45dpf 66; Danio rerio; RNA Seq,GSM2973001,,1,FACS SMART Seq v4,GEO Accession:GSM2973001,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_66.fastq.gz,fastq,42716700.0,569556.0,GSM2973001 r1,0:75 1:0,A:10804832;C:10481955;G:10402456;T:11026635;N:822,75,0,,,10804832,10481955,10402456,11026635,822,SRX3632639,SRS2899370,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86467,,0.04952,,0.93048,,0.71528,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46563,SRR6655254,SRX3632638,SRS2899365,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 65,GSM2973000,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 65,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2973000,GSM2973000: 45dpf 65; Danio rerio; RNA Seq,GSM2973000,,1,FACS SMART Seq v4,GEO Accession:GSM2973000,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_65.fastq.gz,fastq,62484975.0,833133.0,GSM2973000 r1,0:75 1:0,A:15646705;C:15293376;G:15187798;T:16355875;N:1221,75,0,,,15646705,15293376,15187798,16355875,1221,SRX3632638,SRS2899365,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87052,,0.03269,,0.92957,,0.72516,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46564,SRR6655253,SRX3632637,SRS2899368,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 64,GSM2972999,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 64,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972999,GSM2972999: 45dpf 64; Danio rerio; RNA Seq,GSM2972999,,1,FACS SMART Seq v4,GEO Accession:GSM2972999,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_64.fastq.gz,fastq,21047325.0,280631.0,GSM2972999 r1,0:75 1:0,A:5468128;C:5051093;G:5049880;T:5477847;N:377,75,0,,,5468128,5051093,5049880,5477847,377,SRX3632637,SRS2899368,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.90419,,0.03996,,0.87036,,0.58447,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46565,SRR6655252,SRX3632636,SRS2899366,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 63,GSM2972998,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 63,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972998,GSM2972998: 45dpf 63; Danio rerio; RNA Seq,GSM2972998,,1,FACS SMART Seq v4,GEO Accession:GSM2972998,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_63.fastq.gz,fastq,624600.0,8328.0,GSM2972998 r1,0:75 1:0,A:256340;C:82871;G:190498;T:94886;N:5,75,0,,,256340,82871,190498,94886,5,SRX3632636,SRS2899366,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.73119,,0.30037,,0.98484,,0.74326,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46566,SRR6655251,SRX3632635,SRS2899369,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 62,GSM2972997,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 62,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972997,GSM2972997: 45dpf 62; Danio rerio; RNA Seq,GSM2972997,,1,FACS SMART Seq v4,GEO Accession:GSM2972997,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_62.fastq.gz,fastq,66850725.0,891343.0,GSM2972997 r1,0:75 1:0,A:16906818;C:16345626;G:16237781;T:17359242;N:1258,75,0,,,16906818,16345626,16237781,17359242,1258,SRX3632635,SRS2899369,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87799,,0.03782,,0.93342,,0.70023,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46567,SRR6655250,SRX3632634,SRS2899367,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 61,GSM2972996,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 61,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972996,GSM2972996: 45dpf 61; Danio rerio; RNA Seq,GSM2972996,,1,FACS SMART Seq v4,GEO Accession:GSM2972996,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_61.fastq.gz,fastq,54570525.0,727607.0,GSM2972996 r1,0:75 1:0,A:13729732;C:13274485;G:13157808;T:14407245;N:1255,75,0,,,13729732,13274485,13157808,14407245,1255,SRX3632634,SRS2899367,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86194,,0.03683,,0.93125,,0.69329,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46568,SRR6655249,SRX3632633,SRS2899364,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 60,GSM2972995,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 60,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972995,GSM2972995: 45dpf 60; Danio rerio; RNA Seq,GSM2972995,,1,FACS SMART Seq v4,GEO Accession:GSM2972995,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_60.fastq.gz,fastq,113443275.0,1512577.0,GSM2972995 r1,0:75 1:0,A:28989790;C:27538895;G:27388500;T:29523695;N:2395,75,0,,,28989790,27538895,27388500,29523695,2395,SRX3632633,SRS2899364,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.89851,,0.03148,,0.89627,,0.69061,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46569,SRR6655248,SRX3632632,SRS2899363,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 59,GSM2972994,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 59,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972994,GSM2972994: 45dpf 59; Danio rerio; RNA Seq,GSM2972994,,1,FACS SMART Seq v4,GEO Accession:GSM2972994,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_59.fastq.gz,fastq,55433625.0,739115.0,GSM2972994 r1,0:75 1:0,A:13949362;C:13597060;G:13452727;T:14433275;N:1201,75,0,,,13949362,13597060,13452727,14433275,1201,SRX3632632,SRS2899363,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87884,,0.03848,,0.93482,,0.69116,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46570,SRR6655247,SRX3632631,SRS2899360,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 58,GSM2972993,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 58,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972993,GSM2972993: 45dpf 58; Danio rerio; RNA Seq,GSM2972993,,1,FACS SMART Seq v4,GEO Accession:GSM2972993,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_58.fastq.gz,fastq,49049250.0,653990.0,GSM2972993 r1,0:75 1:0,A:12845190;C:11601790;G:11602628;T:12998553;N:1089,75,0,,,12845190,11601790,11602628,12998553,1089,SRX3632631,SRS2899360,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.90583,,0.05691,,0.89919,,0.52272,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46571,SRR6655246,SRX3632630,SRS2899357,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 57,GSM2972992,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 57,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972992,GSM2972992: 45dpf 57; Danio rerio; RNA Seq,GSM2972992,,1,FACS SMART Seq v4,GEO Accession:GSM2972992,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_57.fastq.gz,fastq,58599225.0,781323.0,GSM2972992 r1,0:75 1:0,A:14879193;C:14360581;G:14236810;T:15121474;N:1167,75,0,,,14879193,14360581,14236810,15121474,1167,SRX3632630,SRS2899357,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.89762,,0.03969,,0.9024,,0.66284,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46572,SRR6655245,SRX3632629,SRS2899361,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 56,GSM2972991,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 56,Trimming using trim galore using default parameters Mapping using HISAT2 with default parameters Counts per gene generated using htseq count with default parameters Genome build: Zebrafish GRCz10,beta cells,,FACS SMART Seq v4,,age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972991,GSM2972991: 45dpf 56; Danio rerio; RNA Seq,GSM2972991,,1,FACS SMART Seq v4,GEO Accession:GSM2972991,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_56.fastq.gz,fastq,7705800.0,102744.0,GSM2972991 r1,0:75 1:0,A:2089585;C:1745487;G:1765715;T:2104801;N:212,75,0,,,2089585,1745487,1765715,2104801,212,SRX3632629,SRS2899361,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.81323,,0.19027,,0.96528,,0.78044,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46573,SRR6655244,SRX3632628,SRS2899358,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 55,GSM2972990,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972990,GSM2972990: 45dpf 55; Danio rerio; RNA Seq,GSM2972990,,1,FACS SMART Seq v4,GEO Accession:GSM2972990,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_55.fastq.gz,fastq,2019900.0,26932.0,GSM2972990 r1,0:75 1:0,A:553633;C:454569;G:514761;T:496927;N:10,75,0,,,553633,454569,514761,496927,10,SRX3632628,SRS2899358,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.857,,0.06779,,0.94813,,0.68468,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46574,SRR6655243,SRX3632627,SRS2899362,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 54,GSM2972989,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972989,GSM2972989: 45dpf 54; Danio rerio; RNA Seq,GSM2972989,,1,FACS SMART Seq v4,GEO Accession:GSM2972989,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_54.fastq.gz,fastq,68366475.0,911553.0,GSM2972989 r1,0:75 1:0,A:20156592;C:14763172;G:14528010;T:18917534;N:1167,75,0,,,20156592,14763172,14528010,18917534,1167,SRX3632627,SRS2899362,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.883,,0.07083,,0.99226,,0.94528,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46575,SRR6655242,SRX3632626,SRS2899359,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 53,GSM2972988,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972988,GSM2972988: 45dpf 53; Danio rerio; RNA Seq,GSM2972988,,1,FACS SMART Seq v4,GEO Accession:GSM2972988,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_53.fastq.gz,fastq,56125275.0,748337.0,GSM2972988 r1,0:75 1:0,A:14335808;C:13570407;G:13503455;T:14714561;N:1044,75,0,,,14335808,13570407,13503455,14714561,1044,SRX3632626,SRS2899359,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.89583,,0.03231,,0.89907,,0.6117,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46576,SRR6655241,SRX3632625,SRS2899355,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 52,GSM2972987,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972987,GSM2972987: 45dpf 52; Danio rerio; RNA Seq,GSM2972987,,1,FACS SMART Seq v4,GEO Accession:GSM2972987,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_52.fastq.gz,fastq,12900.0,172.0,GSM2972987 r1,0:75 1:0,A:5056;C:2451;G:2318;T:3075;N:0,75,0,,,5056,2451,2318,3075,0,SRX3632625,SRS2899355,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.79131,,0.10434,,0.99896,,0.6962,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46577,SRR6655240,SRX3632624,SRS2899356,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 51,GSM2972986,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972986,GSM2972986: 45dpf 51; Danio rerio; RNA Seq,GSM2972986,,1,FACS SMART Seq v4,GEO Accession:GSM2972986,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_51.fastq.gz,fastq,50260350.0,670138.0,GSM2972986 r1,0:75 1:0,A:12687570;C:12307548;G:12234459;T:13029883;N:890,75,0,,,12687570,12307548,12234459,13029883,890,SRX3632624,SRS2899356,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87392,,0.04136,,0.92784,,0.71558,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46578,SRR6655239,SRX3632623,SRS2899351,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 50,GSM2972985,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972985,GSM2972985: 45dpf 50; Danio rerio; RNA Seq,GSM2972985,,1,FACS SMART Seq v4,GEO Accession:GSM2972985,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_50.fastq.gz,fastq,43942950.0,585906.0,GSM2972985 r1,0:75 1:0,A:11210284;C:10694585;G:10668658;T:11368429;N:994,75,0,,,11210284,10694585,10668658,11368429,994,SRX3632623,SRS2899351,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.88539,,0.03965,,0.91423,,0.62345,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46579,SRR6655238,SRX3632622,SRS2899352,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 49,GSM2972984,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972984,GSM2972984: 45dpf 49; Danio rerio; RNA Seq,GSM2972984,,1,FACS SMART Seq v4,GEO Accession:GSM2972984,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_49.fastq.gz,fastq,70265325.0,936871.0,GSM2972984 r1,0:75 1:0,A:17854534;C:16875975;G:16933839;T:18599691;N:1286,75,0,,,17854534,16875975,16933839,18599691,1286,SRX3632622,SRS2899352,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87169,,0.04123,,0.92736,,0.71761,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46580,SRR6655237,SRX3632621,SRS2899348,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 48,GSM2972983,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972983,GSM2972983: 45dpf 48; Danio rerio; RNA Seq,GSM2972983,,1,FACS SMART Seq v4,GEO Accession:GSM2972983,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_48.fastq.gz,fastq,19875.0,265.0,GSM2972983 r1,0:75 1:0,A:6841;C:4303;G:4116;T:4615;N:0,75,0,,,6841,4303,4116,4615,0,SRX3632621,SRS2899348,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.32045,,0.02762,,0.99945,,0.46153,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46581,SRR6655236,SRX3632620,SRS2899354,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 47,GSM2972982,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972982,GSM2972982: 45dpf 47; Danio rerio; RNA Seq,GSM2972982,,1,FACS SMART Seq v4,GEO Accession:GSM2972982,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_47.fastq.gz,fastq,59885325.0,798471.0,GSM2972982 r1,0:75 1:0,A:15158794;C:14560832;G:14502714;T:15661915;N:1070,75,0,,,15158794,14560832,14502714,15661915,1070,SRX3632620,SRS2899354,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87481,,0.03682,,0.89217,,0.69454,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46582,SRR6655235,SRX3632619,SRS2899347,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 46,GSM2972981,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972981,GSM2972981: 45dpf 46; Danio rerio; RNA Seq,GSM2972981,,1,FACS SMART Seq v4,GEO Accession:GSM2972981,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_46.fastq.gz,fastq,54708075.0,729441.0,GSM2972981 r1,0:75 1:0,A:13982887;C:13213135;G:13161091;T:14349824;N:1138,75,0,,,13982887,13213135,13161091,14349824,1138,SRX3632619,SRS2899347,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8784,,0.05447,,0.90563,,0.67985,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46583,SRR6655234,SRX3632618,SRS2899353,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 45,GSM2972980,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972980,GSM2972980: 45dpf 45; Danio rerio; RNA Seq,GSM2972980,,1,FACS SMART Seq v4,GEO Accession:GSM2972980,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_45.fastq.gz,fastq,50844600.0,677928.0,GSM2972980 r1,0:75 1:0,A:12813843;C:12386722;G:12334513;T:13308605;N:917,75,0,,,12813843,12386722,12334513,13308605,917,SRX3632618,SRS2899353,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86779,,0.04491,,0.93632,,0.74377,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46584,SRR6655233,SRX3632617,SRS2899349,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 44,GSM2972979,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972979,GSM2972979: 45dpf 44; Danio rerio; RNA Seq,GSM2972979,,1,FACS SMART Seq v4,GEO Accession:GSM2972979,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_44.fastq.gz,fastq,19984350.0,266458.0,GSM2972979 r1,0:75 1:0,A:5560763;C:4465297;G:4434083;T:5523773;N:434,75,0,,,5560763,4465297,4434083,5523773,434,SRX3632617,SRS2899349,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.85762,,0.23374,,0.96986,,0.65196,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46585,SRR6655232,SRX3632616,SRS2899346,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 43,GSM2972978,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972978,GSM2972978: 45dpf 43; Danio rerio; RNA Seq,GSM2972978,,1,FACS SMART Seq v4,GEO Accession:GSM2972978,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_43.fastq.gz,fastq,55020975.0,733613.0,GSM2972978 r1,0:75 1:0,A:13769179;C:13597547;G:13472856;T:14180372;N:1021,75,0,,,13769179,13597547,13472856,14180372,1021,SRX3632616,SRS2899346,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87568,,0.03036,,0.92385,,0.71321,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46586,SRR6655231,SRX3632615,SRS2899350,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 42,GSM2972977,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972977,GSM2972977: 45dpf 42; Danio rerio; RNA Seq,GSM2972977,,1,FACS SMART Seq v4,GEO Accession:GSM2972977,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_42.fastq.gz,fastq,59080125.0,787735.0,GSM2972977 r1,0:75 1:0,A:15032338;C:14383110;G:14261827;T:15401877;N:973,75,0,,,15032338,14383110,14261827,15401877,973,SRX3632615,SRS2899350,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87282,,0.0566,,0.92403,,0.72967,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46587,SRR6655230,SRX3632614,SRS2899345,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 41,GSM2972976,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972976,GSM2972976: 45dpf 41; Danio rerio; RNA Seq,GSM2972976,,1,FACS SMART Seq v4,GEO Accession:GSM2972976,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_41.fastq.gz,fastq,60664950.0,808866.0,GSM2972976 r1,0:75 1:0,A:15302275;C:14722708;G:14647897;T:15990990;N:1080,75,0,,,15302275,14722708,14647897,15990990,1080,SRX3632614,SRS2899345,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87586,,0.03262,,0.92979,,0.71272,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46588,SRR6655229,SRX3632613,SRS2899344,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 40,GSM2972975,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972975,GSM2972975: 45dpf 40; Danio rerio; RNA Seq,GSM2972975,,1,FACS SMART Seq v4,GEO Accession:GSM2972975,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_40.fastq.gz,fastq,59064900.0,787532.0,GSM2972975 r1,0:75 1:0,A:15090650;C:14285718;G:14147998;T:15539467;N:1067,75,0,,,15090650,14285718,14147998,15539467,1067,SRX3632613,SRS2899344,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.88582,,0.05526,,0.9167,,0.67585,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46589,SRR6655228,SRX3632612,SRS2899339,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 39,GSM2972974,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972974,GSM2972974: 45dpf 39; Danio rerio; RNA Seq,GSM2972974,,1,FACS SMART Seq v4,GEO Accession:GSM2972974,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_39.fastq.gz,fastq,59125800.0,788344.0,GSM2972974 r1,0:75 1:0,A:14943229;C:14320453;G:14250417;T:15610652;N:1049,75,0,,,14943229,14320453,14250417,15610652,1049,SRX3632612,SRS2899339,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87851,,0.04387,,0.88698,,0.70219,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46590,SRR6655227,SRX3632611,SRS2899343,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 38,GSM2972973,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972973,GSM2972973: 45dpf 38; Danio rerio; RNA Seq,GSM2972973,,1,FACS SMART Seq v4,GEO Accession:GSM2972973,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_38.fastq.gz,fastq,37749150.0,503322.0,GSM2972973 r1,0:75 1:0,A:9528021;C:9255919;G:9193015;T:9771356;N:839,75,0,,,9528021,9255919,9193015,9771356,839,SRX3632611,SRS2899343,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87833,,0.04452,,0.91784,,0.69548,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46591,SRR6655226,SRX3632610,SRS2899341,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 37,GSM2972972,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972972,GSM2972972: 45dpf 37; Danio rerio; RNA Seq,GSM2972972,,1,FACS SMART Seq v4,GEO Accession:GSM2972972,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_37.fastq.gz,fastq,66885075.0,891801.0,GSM2972972 r1,0:75 1:0,A:16707565;C:16447218;G:16316560;T:17412461;N:1271,75,0,,,16707565,16447218,16316560,17412461,1271,SRX3632610,SRS2899341,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.87151,,0.03137,,0.93042,,0.726,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46592,SRR6655225,SRX3632609,SRS2899337,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 36,GSM2972971,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972971,GSM2972971: 45dpf 36; Danio rerio; RNA Seq,GSM2972971,,1,FACS SMART Seq v4,GEO Accession:GSM2972971,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_36.fastq.gz,fastq,19723800.0,262984.0,GSM2972971 r1,0:75 1:0,A:4962028;C:4855452;G:4817991;T:5087986;N:343,75,0,,,4962028,4855452,4817991,5087986,343,SRX3632609,SRS2899337,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.86572,,0.04284,,0.90873,,0.73619,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46593,SRR6655224,SRX3632608,SRS2899342,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 35,GSM2972970,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972970,GSM2972970: 45dpf 35; Danio rerio; RNA Seq,GSM2972970,,1,FACS SMART Seq v4,GEO Accession:GSM2972970,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_35.fastq.gz,fastq,1013400.0,13512.0,GSM2972970 r1,0:75 1:0,A:267926;C:210700;G:301368;T:233399;N:7,75,0,,,267926,210700,301368,233399,7,SRX3632608,SRS2899342,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.78719,,0.08092,,0.96542,,0.70243,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46594,SRR6655223,SRX3632607,SRS2899338,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 34,GSM2972969,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972969,GSM2972969: 45dpf 34; Danio rerio; RNA Seq,GSM2972969,,1,FACS SMART Seq v4,GEO Accession:GSM2972969,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_34.fastq.gz,fastq,73417875.0,978905.0,GSM2972969 r1,0:75 1:0,A:18661068;C:17847123;G:17703458;T:19204698;N:1528,75,0,,,18661068,17847123,17703458,19204698,1528,SRX3632607,SRS2899338,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.8755,,0.04028,,0.92514,,0.67343,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System
46595,SRR6655222,SRX3632606,SRS2899340,SRP131808,PRJNA432257,Single cell RNA sequencing of zebrafish beta cells from various stages,GSE109881,Transcriptome Analysis,Age associated deterioration of cellular physiology leads to pathological conditions  and detection of premature aging could provide a window for preventive therapies against age related diseases. For this  methods that accurately evaluate cellular age are required. However  such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age  and its modifiers  requires a framework that can robustly handle the noise associated with single cell sampling protocols. Here  we implement GERAS GEnetic Reference for Age of Single cell  a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes.  GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise  as evaluated by its performance on independent samplings of single cells. Additionally  GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells  respectively.  We further harnessed the predictive power of GERAS to identify genome wide molecular factors that correlate with aging. We show that one of these factors  junb  which declines in expression with aging  is necessary to maintain the proliferative state of juvenile beta cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro aging factors  and uncovering candidate genes associated with premature aging. Overall design: We used fluorescence activated cell sorting FACS coupled with next generation RNA Sequencing to profile beta cells from 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,,45dpf 33,GSM2972968,,tissue:beta cells|age:45dpf|strain:Tgins:BB1.0L|feeding:Normal,45dpf 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:45dpf|strain:Tgins:BB1.0L|feeding:Normal,GSM2972968,GSM2972968: 45dpf 33; Danio rerio; RNA Seq,GSM2972968,,1,FACS SMART Seq v4,GEO Accession:GSM2972968,RNA-Seq,TRANSCRIPTOMIC,cDNA,SINGLE,ILLUMINA,NextSeq 500,,SRP131808,,,45dpf_33.fastq.gz,fastq,33080175.0,441069.0,GSM2972968 r1,0:75 1:0,A:8361589;C:8080618;G:8015142;T:8622142;N:684,75,0,,,8361589,8080618,8015142,8622142,684,SRX3632606,SRS2899340,SRA654064,GEO,"Ninov Lab, Center for Regenerative Therapies Dresden",1,0.88378,,0.04128,,0.94192,,0.65842,,75,,B,,usable mapping rate,illumina,nextseq,unknown,cdna_unspecified,unknown,sc,single_cell_plate,smartseq,,Germany,2018-01-30,Juvenile,Juvenile,Pancreas,Endocrine System