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A single-nucleus RNA-seq atlas of colorectal cancer across the age continuum reveals distinct features of early-onset colorectal cancer

GSE312983 Homo sapiens (human) Single-nucleus RNA-seq 189 samples Submitted 2026/09/23 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 189 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism
Homo sapiens (human)
Samples
189 samples
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)
We generated a single nucleus-RNA-Sequencing atlas from 189 primary CRC tumors, spanning eight decades of life with 20+ tumors in most decades, to enable identification of distinct transcriptomic features across the age continuum. Our atls has sufficient sample numbers to allow stratification of tumors by microsatellite status and sidedness and modeling of age as a continuous variable.
This dataset
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.

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 GSE312983_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1378049. Searching any of these in the dataset finder brings you back here.

Samples in this study

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