<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>Larissa Traxler</submitter><organism>Homo sapiens</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-16089</full_dataset_link><description>This dataset contains longitudinal transcriptomic profiles of human fibroblasts across in-vitro aging and pharmacological modulation with Metformin and Rapamycin. Longitudinal sampling of human cells is inherently challenging. Thus, we model aging in non-proliferative fibroblast cultures maintained at confluency, which recapitulate key transcriptomic signatures of in-vivo aging. Four female donor fibroblast lines were cultured for six months in a non-proliferative state, with sampling every 30 days to capture pseudo-longitudinal transcriptomic changes. Parallel cultures were treated with the anti-aging compounds Metformin or Rapamycin over the same period to assess their impact on age-associated transcriptional trajectories. We compare transcriptomic aging changes of in-vitro aging to a cohort of donors from 20-90 years. In addition, stable fibroblast lines expressing the neuronal transcription factors Ngn2 and Ascl1 were generated, enabling direct conversion of fibroblasts into induced neurons (iNs) at baseline and after six months of in-vitro aging. This design allows integrative analyses of transcriptomic aging signatures across cell states and pharmacological interventions.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Sample Collection - Cells at the Month 0 timepoint were detached upon reaching confluency using TrypLE, then pelleted and snap-frozen on dry ice, and stored at -80°C. For Months 1-6, cells were harvested after every 30 days.</sample_protocol><sample_protocol>Nucleic Acid Extraction - Frozen cell pellets of Months 0-6 were thawed on ice, and DNA and RNA were extracted simultaneously from the same pellet using the Qiagen AllPrep DNA/RNA Mini Kit. DNA and RNA concentrations were assessed using Qubit fluorometric quantification. Quality of RNA was further assessed using the TAPE Station System (Agilent), and DNA was run on an agarose gel.</sample_protocol><sample_protocol>Library Construction - Strand specific mRNA libraries were generated using the Illumina Truseq stranded mRNA kit.</sample_protocol><sample_protocol>Growth Protocol - Primary cells were isolated from skin punch biopsies, which were immediately digested using 1mg/mL Collagenase and 1mg/mL Dispase at 37 °C overnight, before enabling outgrowth of fibroblasts on Gelatin-coated wells. Pure fibroblast cultures were generated from four donors (female, 53, 55, 67 and 68 years old) and maintained in fibroblast media containing DMEM (ThermoFisher) + 15 % fetal bovine serum (ThermoFisher) + 1x non-essential amino acids (Sigma Aldrich) + 1x antibiotic-antimycotic (ThermoFisher). Cells were expanded by detaching using TrypLE (ThermoFisher) and seeded in a 1:2 ratio.</sample_protocol><sample_protocol>Sequencing - In-vitro aging samples (Donors #1, #3, #4) were sequenced paired-end 150bp on the NovaSeq X Plus at 20 million paired reads per samples (NovoGene). Donor #2 was processed independently and was sequenced paired-end 75bp on the Illumina NextSeq 500/550 High Output Kit v2.5 (150 cycles).  The in-vivo aging samples (Samples 78-91) were sequenced paired-end on the NextSeq at 20 million reads per sample.</sample_protocol><sample_protocol>Sample Treatment - Metformin (1mM) and Rapamycin (25nM) treatments were applied the day after harvesting Month 0 in fibroblast media. Media including compounds were freshly prepared for each media change and continuously applied to the cells throughout the 6-month culture period.</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - Raw counts were generated using featureCounts and Fragments Per Kilobase of transcript per Million mapped reads (fpkm) normalization.</data_protocol><data_protocol>Sequence Alignment - Read trimming was performed using TrimGalore and mapped with STAR to the hg38.</data_protocol><omics_type>Metabolomics</omics_type><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Illumina NovaSeq X</instrument_platform><study_type>RNA-seq of coding RNA</study_type><species>Homo sapiens</species><pubmed_authors>Larissa Traxler</pubmed_authors></additional><is_claimable>false</is_claimable><name>RNA-seq of human fibroblasts during pseudo-longitudinal in-vitro aging and long-term Metformin or Rapamycin treatment</name><description>This dataset contains longitudinal transcriptomic profiles of human fibroblasts across in-vitro aging and pharmacological modulation with Metformin and Rapamycin. Longitudinal sampling of human cells is inherently challenging. Thus, we model aging in non-proliferative fibroblast cultures maintained at confluency, which recapitulate key transcriptomic signatures of in-vivo aging. Four female donor fibroblast lines were cultured for six months in a non-proliferative state, with sampling every 30 days to capture pseudo-longitudinal transcriptomic changes. Parallel cultures were treated with the anti-aging compounds Metformin or Rapamycin over the same period to assess their impact on age-associated transcriptional trajectories. We compare transcriptomic aging changes of in-vitro aging to a cohort of donors from 20-90 years. In addition, stable fibroblast lines expressing the neuronal transcription factors Ngn2 and Ascl1 were generated, enabling direct conversion of fibroblasts into induced neurons (iNs) at baseline and after six months of in-vitro aging. This design allows integrative analyses of transcriptomic aging signatures across cell states and pharmacological interventions.</description><dates><release>2026-06-30T00:00:00Z</release><modification>2026-06-30T09:19:45.236Z</modification><creation>2025-11-11T17:21:54.839Z</creation></dates><accession>E-MTAB-16089</accession><cross_references><ENA>ERP189126</ENA><Biostudies>E-MTAB-15859</Biostudies><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0003789</EFO><EFO>EFO_0004917</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO><EFO>EFO_0003969</EFO></cross_references></HashMap>