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An inflammatory and quiescent HSC subpopulation expands with age in humans

GSE302126 Homo sapiens (human) Single-cell RNA-seq 17 samples Submitted 2025/12/18 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 17 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
17 samples: 6 conditions, mostly in duplicateGroups 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.
Paper
An Inflammatory and Quiescent HSC Subpopulation Expands with Age in HumansbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Aging of the blood system impacts systemic health and can be traced to hematopoietic stem cells (HSCs). Despite multiple reports on human HSC aging, a unified map detailing their molecular age-related changes is lacking. We developed a consensus map of gene expression in HSCs by integrating seven single-cell datasets. This map reveals previously unappreciated heterogeneity within the HSC population.
Read the full summary ↓
It also links inflammatory pathway activation (TNF/NFκB, AP-1) and quiescence within a single gene expression program. This program dominates an inflammatory HSC subpopulation that increases with age, highlighting a potential target for further experimental studies and anti-aging interventions.
Published in
An Inflammatory and Quiescent HSC Subpopulation Expands with Age in Humans
Safina KR, Kotliar DA, Curtis M et al. · bioRxiv : the preprint server for biology 2025 · PMID 40949975 · doi:10.1101/2025.09.01.673389
This dataset
Open GSE302126 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 GSE302126_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1289035 and SRA study SRP599434. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40949975) ↗
Study design
6 conditions, mostly in duplicate
Technical replicate 1 of sample SBM ×4 Technical replicate 2 of sample SBM ×4 Sample SBM ×3 Technical replicate 1 of sample BM ×2 Technical replicate 2 of sample BM ×2 Technical replicate 3 of sample BM ×2

Supports a between-group comparison across 17 samples.

How we read this: 6 replicated groups read from 17 sample titles; they account for 17 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

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