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Gene expression change after inactivation of ARHGAP45 by CRISPR-Cas9 and/or treatment of MBQ-167

GSE268874 Homo sapiens (human) Single-cell RNA-seq 44 samples Submitted 2025/07/27 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 44 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)
Samples
44 samples: 16 conditions, mostly in triplicateGroups 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
Systematic Evaluation of GAPs and GEFs Identifies a Targetable Dependency for Hematopoietic MalignanciesCancer discovery · 2025
Summary (written by the submitters)
GTPase-activating proteins (GAPs) and guanine nucleotide exchange factors (GEFs) function as negative and positive regulators of small GTPases, orchestrating GDP/GTP-bound binary activity switches. Yet their functional significance in hematopoietic malignancies remains largely uncharted. In our study, guided by single-cell transcriptomic analysis and functional CRISPR screens in primary AML specimens and their derived models, we uncover an unexpected and selective oncogenic role for ARHGAP45 in AML.
Read the full summary ↓
ARHGAP45 is upregulated in malignant cells while notably absent in healthy counterparts. Depletion of ARHGAP45 impedes AML growth and triggers myeloid differentiation but is dispensable for normal CD34+ hematopoietic stem/progenitor cells (HSPC) development. Using CRISPR-mediated epistasis screens, we further show that the ARHGEF1-RhoA-PKN1 axis is responsible for the ARHGAP45 phenotype. In addition, we further found that ARHGAP45 prevents RhoA overactivation, loss of cell polarity, and lineage commitment. On the other hand, our screens also unveil the synergistic effect of combinatorial ARHGAP45 and CDC42 inhibition, which we further validate in vitro and in vivo. Collectively, our studies reveal that ARHGAP45 is indispensable for leukemia stemness and aggressiveness and rationalize therapeutic strategies for modulating GAP and GTPase activity in AML.
Published in
Systematic Evaluation of GAPs and GEFs Identifies a Targetable Dependency for Hematopoietic Malignancies
Zhang P, Cao Z, Pan X et al. · Cancer discovery 2025 · PMID 40788260 · doi:10.1158/2159-8290.CD-25-0299
This dataset
Open GSE268874 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 GSE268874_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 PRJNA1119231 and SRA study SRP511255. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40788260) ↗
Study design
16 conditions, mostly in triplicate
MOLM13-SgGAP45_3-MBQ ×3 MOLM13-SgGAP45_3-veh ×3 MOLM13-SgGAP45_4-MBQ ×3 MOLM13-SgGAP45_4-veh ×3 MOLM13-SgNeg-MBQ ×3 MOLM13-SgNeg-veh ×3 PDX2263_day5_sgGAP45_3 ×3 PDX2263_day5_sgGAP45_4 ×3 +11 more

Supports a between-group comparison across 41 samples.

How we read this: 16 replicated groups read from 44 sample titles; they account for 41 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

+ 4 more — browse all 44 samples with per-sample file links →

Datasets on a similar topic

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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.