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Premature senescence impairs hematopoietic stem cell function during sickle cell disease in mice and humans (Mouse CITE-Seq)

GSE277681 Mus musculus (mouse) Single-cell RNA-seq 32 samples Submitted 2026/07/22 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 32 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
32 samples: 32 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
Premature senescence impairs hematopoietic stem cell function during sickle cell disease in mice and humansScience translational medicine · 2026
Summary (written by the submitters)
Curative therapies using autologous genetically modified hematopoietic stem and progenitor cells (HSPCs) are now FDA-approved for sickle cell disease (SCD). However, there are concerns that the pathophysiology of SCD may degrade the quality of HSPCs required for these cellular therapies. We interrogated the phenotype and function of HSPCs in mice and individuals with SCD.
Read the full summary ↓
We observed elevated cycling, DNA damage, reactive oxygen species, and hallmarks of senescence in bone marrow HSPCs from SCD mice, which correlated with a loss of long-term repopulating HSPCs. Bone marrow HSPCs from individuals with SCD also display hallmarks of senescence and diminished function. Transcriptomic profiling of mouse and human HSPCs revealed reduced expression of genes regulating cell cycle, DNA replication, and DNA repair, consistent with senescence. Treatment of SCD mice with the senolytic, ABT-263 (Navitoclax), increased HSPC frequency, restored HSPC transplantation activity, and decreased numbers of HSPCs with DNA damage. Thus, we demonstrate that stress-associated loss of function in the bone marrow of mice and individuals with SCD is reversible with senolytic therapy. Our work suggests a strategy for improving the function and fidelity of HSPCs collected for autologous gene therapy, which could further improve the efficacy and safety of these curative therapies.
Published in
Premature senescence impairs hematopoietic stem cell function during sickle cell disease in mice and humans
Barve A, Dabas P, Cornwell A et al. · Science translational medicine 2026 · PMID 42485438 · doi:10.1126/scitranslmed.adv0628
This dataset
Open GSE277681 in finder → Find more mouse 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 GSE277681_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 PRJNA1163349 and SRA study SRP533894. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42485438) ↗
Study design
32 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 32 sample titles: 32 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
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