GEO series

Dissecting tissue-specific microenvironments in acute myeloid leukemia (RNA-Seq VecadAML)

GSE269040 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/05/22 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: Mus musculus (mouse)
Samples: 12 samples: 12 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.

Summary (written by the submitters)

Acute myeloid leukemia (AML) is a blood cancer with poor survival outcomes. Limited studies have reported lethal complications in patients due to leukemia infiltration into solid tissues such as the lung, but the related mechanisms remain understudied. Here, we map the transcriptional landscape of the mouse lung microenvironment at single-cell resolution in the AML-infiltrated lungs.

Read the full summary ↓

Our analysis demonstrated a considerable remodeling of the lung microenvironment under leukemia conditions, as indicated by expansion of stromal myofibroblasts and reduction of endothelial capillary aerocytes. Those changes were accompanied by changes in immune resident cells of the lung microenvironment, as supported by reduction of B- and T- cell proportions, and increased frequencies of activated neutrophils and monocyte populations. We show an induction of inflammatory response within the microenvironment of the leukemic lung. We dissect the spatial interplay between AML and the lung microenvironment, and we identify previously unappreciated interactions driven by inflammatory mediators within the leukemic milieu. Of note, we show that Lgals9 (encoding for Galectin-9) mediates most of cell-cell interactions within the microenvironment of the AML-infiltrated lungs. LGALS9 is highly expressed in AML patients and its role in AML remains elusive. Targeting Galectin-9 reduces the AML burden in the lungs and reverses the infiltration phenotype. Combining single-cell, spatial transcriptomics approaches and functional experimentsin vivo, our study is the first to characterize the pulmonary infiltration phenotype in AML, opening the route for novel treatment strategies in acute leukemia.

This dataset

Open GSE269040 in finder → Find more mouse 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 GSE269040_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 PRJNA1120013 and SRA study SRP511647. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 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 12 sample titles: 12 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

GSM8305431 Vecad_BM_Ctrl1, RNA-seq
GSM8305432 Vecad_BM_Ctrl2, RNA-seq
GSM8305433 Vecad_BM_Ctrl3, RNA-seq
GSM8305434 Vecad_BM_AML1, RNA-seq
GSM8305435 Vecad_BM_AML2, RNA-seq
GSM8305436 Vecad_BM_AML3, RNA-seq
GSM8305437 Vecad_Lung_Ctrl1, RNA-seq
GSM8305438 Vecad_Lung_Ctrl2, RNA-seq
GSM8305439 Vecad_Lung_Ctrl3, RNA-seq
GSM8305440 Vecad_Lung_AML1, RNA-seq
GSM8305441 Vecad_Lung_AML2, RNA-seq
GSM8305442 Vecad_Lung_AML3, RNA-seq

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