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High-resolution and noninvasive profiling of the tumor microenvironment with spatial ecotypes [RNA-seq]

GSE320038 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2026/05/07 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 14 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
14 samples: 4 × CRC; Intact tissue; WU vs 4 × CRC; Dissociated tissue; WU vs 3 × Melanoma; Intact tissue; WU vs 3 × Melanoma; Dissociated tissue; WUGroups 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
Non-invasive profiling of the tumour microenvironment with spatial ecotypesNature · 2026
Summary (written by the submitters)
Multicellular programs in the tumor microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically. Here, we present a machine learning framework for multi-analyte profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs).
Read the full summary ↓
By integrating 10M single-cell and spot-level spatial transcriptomes from diverse human carcinomas and melanomas, we identified nine SEs with broad conservation—each with unique biology, geospatial features, and clinical outcome associations, including several linked to immunotherapy response. Notably, SEs were distinguishable by DNA methylation profiling and recoverable from plasma cell-free DNA (cfDNA) using deep learning. In cfDNA from nearly 100 melanoma patients, SE levels exhibited striking associations with immunotherapy response. Our data reveal fundamental units of TME organization and demonstrate a multimodal platform for solid and liquid TME profiling, with implications for improved risk stratification and therapy personalization.
Published in
Non-invasive profiling of the tumour microenvironment with spatial ecotypes
Zhang W, Brown EL, Usmani A et al. · Nature 2026 · PMID 42092150 · doi:10.1038/s41586-026-10452-4
This dataset
Open GSE320038 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 GSE320038_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 PRJNA1426037. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42092150) ↗
Study design
4 × CRC; Intact tissue; WU vs 4 × CRC; Dissociated tissue; WU vs 3 × Melanoma; Intact tissue; WU vs 3 × Melanoma; Dissociated tissue; WU

Supports a between-group comparison across 14 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 14 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
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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.