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Systematic characterization of single-cell full-length RNA isoforms in human colorectal cancer

GSE248094 Homo sapiens (human) Single-cell RNA-seq 51 samples Submitted 2025/07/29 Platform GPL26167
At a glance

Single-cell RNA-seq · human · 51 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
51 samples: 36 distinct titles, 8 of them repeatedGroups 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
Systematic characterization of full-length RNA isoforms in human colorectal cancer at single-cell resolutionProtein & cell · 2025
Summary (written by the submitters)
Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations.
Read the full summary ↓
Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest potentially novel targets for splicing-based therapeutic strategies.
Published in
Systematic characterization of full-length RNA isoforms in human colorectal cancer at single-cell resolution
Lu P, Zhang Y, Cui Y et al. · Protein & cell 2025 · PMID 40702779 · doi:10.1093/procel/pwaf049
This dataset
Open GSE248094 in finder → Find more human RNA-seq 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 GSE248094_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 PRJNA1041770. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40702779) ↗
Study design
36 distinct titles, 8 of them repeated
CRC01_L ×4 CRC07_L ×3 HCT116, NC ×3 HCT116, si1 ×3 HCT116, si2 ×3 HCT116, si3 ×3 CRC09_L ×2 HTCRC05_PT ×2

How we read this: Read from 51 sample titles: 36 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

+ 11 more — browse all 51 samples with per-sample file links →

More Colorectal cancer datasets

Search all human RNA-seq datasets in GEO →

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