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Single cell and spatial sequencing analysis of cancer associated fibroblasts in the brain metastasis tumor microenvironment [Multiome]

GSE322964 Homo sapiens (human) Single-cell RNA-seq 22 samples Submitted 2026/03/09 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 22 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
22 samples: 22 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
Single cell and spatial sequencing analysis of cancer associated fibroblasts in the brain metastasis tumor microenvironmentCommunications biology · 2026
Summary (written by the submitters)
Brain metastasis (BM) remains largely incurable. Cancer-associated fibroblasts (CAFs) can either support or inhibit tumor growth in the tumor microenvironment (TME), yet their roles in BM remain under-described. Here we report a single-cell and spatial sequencing analysis of human BM tissues and define four transcriptionally distinct CAF subpopulations.
Read the full summary ↓
BM CAF subpopulations are characterized by either extracellular matrix (ECM), immune, contractile, or neural features, and show distinct spatial distributions within the BM TME. Further analyses reveal that BM CAFs engage extensively in cell-cell communication and adopt distinct cell states, including an ECM CAF cell state marked by high levels of immunoglobulin superfamily containing leucine rich repeat expression (ISLR-CAFs). Functionally, ISLR-CAFs reduce BM tumor cell viability in vitro, consistent with a tumor-inhibitory role. These findings highlight the heterogeneity and plasticity of CAFs in BM, emphasizing the importance of precision in defining stromal contributions to disease progression and therapeutic response.
Published in
Single cell and spatial sequencing analysis of cancer associated fibroblasts in the brain metastasis tumor microenvironment
Simon T, Buckley DN, Yang Z et al. · Communications biology 2026 · PMID 41917370 · doi:10.1038/s42003-026-09915-1
This dataset
Open GSE322964 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 GSE322964_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 PRJNA1432021 and SRA study SRP680958. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41917370) ↗
Study design
22 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 22 sample titles: 22 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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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.