← BioTransfer GEO Dataset Finder
GEO series

Human microphysiological systems of aging recreate the in vivo process expediting evaluation of anti-gerontic strategies

GSE280361 Homo sapiens (human) RNA-seq (gene expression) 34 samples Submitted 2025/12/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 34 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
34 samples: 10 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
The search for biological mechanisms of human aging is stalled by a lack of suitable models and it remains unknown whether, and to what degree, rejuvenation reported in rodents translates to people. Here we report a hiPSC-derived microphysiological system modelling the white adipose tissue-liver axis in the presence of heterochronic human serum to study aging and rejuvenation in humans.
Read the full summary ↓
We reveal changes in functional and molecular hallmarks of aging and rejuvenation, and we investigate unknown biomarkers and mechanisms of plasticity in human tissue aging as well as potential rejuvenation strategies. The microphysiological chip recapitulates, in 4 days, aging-associated hallmarks that occur after decades of aging in people, including gerontic shifts in gene expression and oxidative DNA damage. We uncover unknown signaling networks in human aging, knock-on effects of aging in fat on liver, sexual polymorphisms of aging, tissue memory of age, and develop a custom machine learning model for biological age. Combining heterochronic human serum with the microphysiological system allows for rapidly establishing human tissue aging, discovering clinically relevant mechanisms, biomarkers, and testing of anti-gerontic approaches.
This dataset
Open GSE280361 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE280361_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1177979 and SRA study SRP540944. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly in triplicate
Adiopicyte, OY, biol ×4 Adiopicyte, male YCM 1, biol ×3 Adiopicyte, male OCM 1, biol ×3 Adiopicyte, female OCM 2, biol ×3 Adiopicyte, Not treated, biol ×3 Adiopicyte, YY, biol ×3 Adiopicyte, YO, biol ×3 Adiopicyte, OO, biol ×3 +7 more

Supports a case/control comparison: 3 samples read as cases, 4 as controls.

How we read this: 10 replicated groups read from 34 sample titles; they account for 29 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.