← BioTransfer GEO Dataset Finder
GEO series

Remodeling of T and endothelial cells during total neoadjuvant therapy in rectal cancer

GSE254249 Homo sapiens (human) Single-cell RNA-seq 92 samples Submitted 2025/10/02 Platform GPL30209Platform GPL30173
At a glance

Single-cell RNA-seq · human · 92 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)
Topic
Colorectal cancerThis series is part of our curated briefing on it.
Samples
92 samples: 92 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
Remodeling of T and endothelial cells during total neoadjuvant therapy in rectal cancerCancer cell · 2025
Summary (written by the submitters)
Total neoadjuvant therapy (TNT) is a standard care for locally advanced rectal cancer (LARC), yet the immune remodeling mechanisms underlying its efficacy remain unclear. Using single-cell RNA, T cell receptor, and spatial transcriptome sequencing of matched pre- and post-treatment samples, we depicted tumor microenvironment (TME) dynamics induced by different neoadjuvant therapies.
Read the full summary ↓
TNT is associated with reduced regulatory T cells and increased IFNG+CD8+ effector memory T cells with high IFNG expression, potentially contributing to improved complete response rates. The abundance of tumor-infiltrating CD8+ T cells is correlated with the enrichment of the ACKR1+ endothelial subset after TNT. We further validated that endothelial cells (ECs), when stimulated by IFNγ, potentially released by CD8+ T cells, acquire an enhanced ability for presenting antigens and activating CD8+ T cells. Together, our study systematically characterizes the TME dynamics and uncovers the unique interaction between activated CD8+ T cells and ECs after TNT.
Published in
Remodeling of T and endothelial cells during total neoadjuvant therapy in rectal cancer
Gao Q, Ling X, Liao L et al. · Cancer cell 2025 · PMID 41202810 · doi:10.1016/j.ccell.2025.10.008
This dataset
Open GSE254249 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 GSE254249_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 PRJNA1069074. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41202810) ↗
Study design
92 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 92 sample titles: 92 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

+ 52 more — browse all 92 samples with per-sample file links →

More Colorectal cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.