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CRISPR-based genetic screens to interrogate therapeutic targets on tumor maintenance and heterogeneity in AML patient cells

GSE221578 Homo sapiens (human) Single-cell RNA-seq 31 samples Submitted 2026/02/26 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 31 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)
Topic
Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples
31 samples: 31 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
CRISPR-based functional genomics for dissecting therapeutic dependency in primary acute myeloid leukemia samplesMolecular cell · 2026
Summary (written by the submitters)
Functional genomics directly in primary tumor cells is warranted to better understand the complexity of the diseases in a context of high clinical relevance. We optimized ex vivo and in vivo CRISPR-based genome/epigenome editing approaches in patient-derived xenograft (PDX) and primary acute myeloid leukemia (AML) cells, including those harboring DNMT3A/FIT3-ITD/NPM1c that predicts a poor clinical outcome and has no model to fully represent.
Read the full summary ↓
We deployed CRISPR and CRISPRi “drop-out” screenings on patient cells in vivo and prioritized AML-biased gene and cis-element dependencies, including an uncharacterized MYB regulatory element. We further developed a Perturb-seq pipeline for patient cells, where we perturbed known AML therapeutic targets and delineated complex regulatory networks and changes of cell cycle stages and cellular hierarchy in response to perturbations by scRNA-seq. We uncovered an unanticipated role for SETDB1, a suppressor of interferon response, whose depletion caused the emergence of Hematopoietic stem cell (HSC) and progenitor-like population marked with high HOXA expression in a PDX model, and myeloid differentiation were re-induced by DOT1L inhibition, preceding a combinatorial therapy to co-suppress SETDB1 and the HOXA cluster. Our study proposed a strategy to annotate personalized vulnerabilities to predict therapeutic response in AML.
Published in
CRISPR-based functional genomics for dissecting therapeutic dependency in primary acute myeloid leukemia samples
Cao Z, Yu S, Peng J et al. · Molecular cell 2026 · PMID 41759529 · doi:10.1016/j.molcel.2026.02.003
This dataset
Open GSE221578 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia landscape →
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 GSE221578_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 PRJNA915027 and SRA study SRP414740. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41759529) ↗
Study design
31 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 31 sample titles: 31 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
More Acute myeloid leukemia datasets

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