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Transcriptomic Analysis of Potency Associated Genes in Hematopoietic Stem and Progenitor Cells

GSE322893 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2026/07/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples: 27 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
Local oxygen tension dictates hematopoietic cell growth and potencyLeukemia · 2026
Summary (written by the submitters)
Allogeneic hematopoietic cell transplantation is a potentially curative treatment for malignant and non-malignant hematologic disorders, wherein healthy hematopoietic stem (HSC) and progenitor cells (HSCs) reconstitute a functional blood and immune system following ablation of diseased cells. Functionally potent HSCs/HPCs from bone marrow, mobilized peripheral blood, or umbilical cord blood drive recovery of the hematopoietic system following transplantation.
Read the full summary ↓
Improving initial characterization of the engraftment potential of donor grafts and recovery time will lead to improved patient outcomes, particularly in the case of umbilical cord blood transplantation, which is limited by low total numbers of cells. However, we still lack a complete understanding of the molecular programs that regulate potency, which is the ability of cells to home to and engraft in a hematopoietic niche, then self-renew and differentiate driving multi-lineage repopulation. Here, we use transcriptomic approaches to elucidate gene programs associated with hematopoietic cell potency in CD34+ cells derived from umbilical cord blood, which are enriched for HSCs/HPCs. We compare the transcriptomes of immunophenotypically defined HSCs/HPCs and correlate HSC transcriptomes with engraftment outcomes.
Published in
Local oxygen tension dictates hematopoietic cell growth and potency
Ropa J, Gutch S, Wathen L et al. · Leukemia 2026 · PMID 42135527 · doi:10.1038/s41375-026-02971-w
This dataset
Open GSE322893 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 GSE322893_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 PRJNA1431972 and SRA study SRP680921. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42135527) ↗
Study design
27 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 27 sample titles: 27 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
Datasets on a similar topic

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