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Single-cell multi-omics characterize colorectal tumors, adjacent healthy tissue and matched organoids identifying CRC-unique features

GSE294559 Homo sapiens (human) Single-cell RNA-seq 72 samples Submitted 2025/07/18 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 72 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)
Topic
Colorectal cancerThis series is part of our curated briefing on it.
Samples
72 samples: 72 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 multi-omics characterize colorectal tumors, adjacent healthy tissue and matched (tumor) organoids identifying CRC-unique featuresInternational journal of cancer · 2025
Summary (written by the submitters)
Colorectal cancer (CRC) arises in the colorectal tissue driven by genetic disorder or the accumulation of somatic mutations, leading to abnormal epithelial cell growth. In this study, we employed single-nucleus multi-omics analysis, including single-nucleus RNA-seq and single-nucleus ATAC-seq, on over 100,000 high-quality nuclei to investigate the molecular landscape of both primary tissue and patient-derived organoids (PDOs).
Read the full summary ↓
Our analysis showed that normal PDOs (N-PDOs) derived from tissue adjacent to tumors replicate the cellular composition and differentiation trajectory of colorectal crypts. In contrast, tumor PDOs (T-PDOs) showed patient-specific transcriptomic and epigenomic heterogeneity yet consistently maintained a stem cell-like state. T-PDOs retained the somatic mutation profile of the primary tumor while also exhibiting de novo mutations not detected in either the primary tumor or N-PDOs. Notably, inferred cell-cell interaction analysis highlighted the activin signaling pathway as a potential unique feature of fibroblast-epithelial interactions within tumor microenvironment. This study provides a comprehensive view of the transition from normal to malignant colorectal epithelium and underscores the utility of PDOs as faithful model for capturing both conserved and patient-specific features of colorectal cancer.
Published in
Single-cell multi-omics characterize colorectal tumors, adjacent healthy tissue and matched (tumor) organoids identifying CRC-unique features
Yu Z, Derksen M, Te Pas BM et al. · International journal of cancer 2025 · PMID 40847690 · doi:10.1002/ijc.70103
This dataset
Open GSE294559 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Colorectal cancer 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 GSE294559_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 PRJNA1250499. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40847690) ↗
Study design
72 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 36 samples read as cases, 36 as controls.

How we read this: Read from 72 sample titles: 72 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

+ 32 more — browse all 72 samples with per-sample file links →

More Colorectal cancer datasets

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.