GEO series

Heterogeneity in hematopoietic stem cell enables adaptation to the aged microenvironment [celltag_cross_exp4]

GSE326529 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/07/21 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: Mus musculus (mouse)
Samples: 12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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: Transcriptional heterogeneity predicts and enables clonal selection in ageing haematopoiesisResearch square · 2026

Summary (written by the submitters)

Aging reshapes hematopoietic stem cell (HSC) behavior, yet how pre-existing heterogeneity forecasts clonal performance in aged microenvironments remains unclear. Using cellular barcoding and single cell-RNA-seq to longitudinally sample shared sister-clones across hetero- and homochronic transplants in mice, we quantify (i) intra- and inter-cellular transcriptional heterogeneity in young and old HSC, (ii) differential self-renewal and clonal fitness of HSC in reconstituted

Read the full summary ↓

hematopoiesis, and (iii) clone-wise associations between pre-existing programs and fitness in aging contexts via predictive machine learning models. Old HSC exhibit greater heterogeneity and rates of generation of heterogeneity, driven by specific gene sets with known roles in HSC fitness. Old HSC exhibit higher self-renewal in old relative to young hosts, likely reflecting adaptation over life to the aged altered niche. Clones with higher fitness in aged mice are enriched for signatures of quiescence, genome integrity/DNA repair, and inflammaging when compared to lower-fitness clones before transplantation. Pre-existing heterogeneity predicts both higher clone fitness and higher self-renewal in the old hosts after transplantation, providing greater phenotypic variability for selection of adaptive clones. Together, these data argue that intrinsic aging encodes variable clonal ‘starting positions’ that interact with niche age to shape hematopoietic output. Our results contextualize recent clonal tracing studies in native hematopoiesis and aging—where lineage output is clonally restricted and age-expanded clones persist—and extend them by prospectively linking baseline HSC state variation to subsequent fitness under aged conditions. These findings nominate actionable, pre-transplant molecular predictors of clonal performance and suggest strategies to modulate aging programs to rebalance hematopoiesis.

Published in

Transcriptional heterogeneity predicts and enables clonal selection in ageing haematopoiesis

De Dominici M, Qian K, Chen X et al. · Research square 2026 · PMID 42396490 · doi:10.21203/rs.3.rs-10046174/v1

This dataset

Open GSE326529 in finder → Find more mouse 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 GSE326529_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 PRJNA1445837 and SRA study SRP688092. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42396490) ↗

Study design

12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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

GSM9633865 O_vitro_GEX, scRNAseq
GSM9633866 Y_vitro_GEX, scRNAseq
GSM9633867 OO_GEX, scRNAseq
GSM9633868 OY_GEX, scRNAseq
GSM9633869 YO_GEX, scRNAseq
GSM9633870 YY_GEX, scRNAseq
GSM9633871 O_vitro_CSP, hashtag oligo-based multiplexing(HTO)
GSM9633872 Y_vitro_CSP, hashtag oligo-based multiplexing(HTO)
GSM9633873 OO_CSP, hashtag oligo-based multiplexing(HTO)
GSM9633874 OY_CSP, hashtag oligo-based multiplexing(HTO)
GSM9633875 YO_CSP, hashtag oligo-based multiplexing(HTO)
GSM9633876 YY_CSP, hashtag oligo-based multiplexing(HTO)

Datasets on a similar topic

GSE218178 Adaptation to an acid microenvironment promotes pancreatic cancer organoid growth and drug resistance 183 samples shares microenvironment, adaptation
GSE249017 AMPK is necessary for Treg functional adaptation to microenvironmental stress during malignancy and viral pneumonia 41 samples shares microenvironment, adaptation
GSE292146 Tractable mouse TNBC models capture the heterogeneous tumor immune microenvironment and adaptation to PD-L1 blockade 38 samples shares microenvironment, adaptation
GSE269748 A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets 36 samples shares heterogeneity, cross
GSE307143 Single-Cell Atlas of the Tumor Immune Microenvironment Across Syngeneic Murine Models 30 samples shares microenvironment, cross
GSE290082 Enhancer-driven gene regulatory networks reveal transcription factors governing T cell adaptation and differentiation in the tumor microenvironment. 25 samples shares microenvironment, adaptation
GSE267584 Enhancer-driven gene regulatory networks reveal transcription factors governing T cell adaptation and differentiation in the tumor microenvironment. 22 samples shares microenvironment, adaptation
GSE283255 Distinct Vps34-orchestrated signaling processes dictate the transitional heterogeneity and functional adaptation of effector Treg cells [ATAC-seq2] 14 samples shares heterogeneity, adaptation

Search all mouse 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.