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Clonal memory of cell division diverges between human healthy haematopoiesis and acute myeloid leukaemia

GSE300913 Homo sapiens (human) RNA-seq (gene expression) 209 samples Submitted 2026/05/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 209 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
209 samples: 126 × D1408T vs 81 × D920TGroups 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)
Clonal memory, a cellular property inherited across at least two divisions, has emerged as a key driver of cell heterogeneity. To uncover its roles in human haematopoiesis, we developed high-resolution ex vivo tools to track both division and fate commitment of individual primary human haematopoietic stem and progenitor cells (HSPCs).
Read the full summary ↓
We show that human HSPCs display a clonal memory of division, as cells descending from the same ancestor cell divide synchronously over multiple generations. In parallel, HSPCs inherit a clonal memory of fate commitment, independently of lineage identity. Both forms of clonal memory persist over at least two divisions, across different HSPC commitment stages and cell culture conditions. In contrast, malignant haematopoiesis exhibits lower synchronicity, revealing a disruption of clonal memory in leukemic cells. Epigenetic remodelling using a bromodomain inhibitor partially restore the clonal memory in division in leukemic HSPCs, highlighting the plasticity of this trait and its potential for therapeutic modulation. Our findings position clonal memory as a key regulator of human haematopoietic stem cell behaviour. Demonstrating that clonal memory can be modulated opens new avenues for tuning cell heterogeneity in healthy and pathological tissues.
This dataset
Open GSE300913 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 GSE300913_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 PRJNA1282666 and SRA study SRP594907. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
126 × D1408T vs 81 × D920T

Supports a between-group comparison across 207 samples.

How we read this: 2 replicated groups read from 209 sample titles; they account for 207 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

+ 169 more — browse all 209 samples with per-sample file links →

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.