RNAquarium Metadata Server
Institute Website Terms of Use Privacy Policy

run_metadata

417 rows where devstage_curation_coarse = "Juvenile" and tissue_curation = "Pancreas"

✎ View and edit SQL

This data as json, CSV (advanced)

technology 2

experiment.library_layout 2

tissue_curation_coarse 1

tissue_curation 1

  • Pancreas · 417 ✖

experiment.platform 1

experiment.library_strategy 1

experiment.library_source 1

experiment.library_selection 1

devstage_curation_coarse 1

  • Juvenile · 417 ✖

devstage_curation 1

Link rowid ▼ run.accession experiment.accession sample.accession study.accession bioproject study.title study.alias study.type study.abstract study.attributes study.PMIDs sample.description sample.title sample.alias sample.centername sample.attributes GEOsample.title GEOsample.dataprocessing GEOsample.source GEOsample.treatmentprotocol GEOsample.extractprotocol GEOsample.growthprotocol GEOsample.characteristics GEOsample.accession experiment.title experiment.alias experiment.library_name experiment.design_description experiment.library_construction_protocol experiment.attributes experiment.library_strategy experiment.library_source experiment.library_selection experiment.library_layout experiment.platform experiment.instrument_model experiment.spot_descriptor experiment.study_ref run.title run.attributes run.filename run.semantic_name run.total_bases run.total_spots run.alias run.read_lengths run.base_counts run.r1_length run.r2_length run.r3_length run.r4_length run.Acount run.Ccount run.Gcount run.Tcount run.Ncount run.experiment run.pool_member submission.accession submission.srasource submission.bioprojectsource seqdetective.n_mates seqdetective.mapping_rate.mate1 seqdetective.mapping_rate.mate2 seqdetective.nofeature_rate.mate1 seqdetective.nofeature_rate.mate2 seqdetective.sparsity.mate1 seqdetective.sparsity.mate2 seqdetective.pos_strand_rate.mate1 seqdetective.pos_strand_rate.mate2 seqdetective.readlen.mate1 seqdetective.readlen.mate2 seqdetective.judgement.mate1 seqdetective.judgement.mate2 seqdetective.judgement.reason platform_family instrument_generation read_bias selection_class prep_kit sc_or_bulk tech_class technology tech_variant submission.bioprojectsource.country earliest_date devstage_curation devstage_curation_coarse tissue_curation tissue_curation_coarse
46354 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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 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 …   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

Next page

Advanced export

JSON shape: default, array, newline-delimited

CSV options: 

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