← BioTransfer GEO Dataset Finder
GEO series

RNA-seq analysis of human CD8 CAR-EGFR T cells in expansion and repeated hypoxic coculture

GSE263349 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/11/15 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
24 samples: 20 distinct titles, 4 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
Targeting P4HA1 promotes CD8(+) T cell progenitor expansion toward immune memory and systemic anti-tumor immunityCancer cell · 2025
Summary (written by the submitters)
Human CD8 EGFR CART cells during expansion and repeated hypoxic coculture were collected at different time points and subject to RNA-seq to identify key players in T cell differentiation and hypoxia-induced exhaustion. The RNA-seq data together with other functional analysis identified P4HA1 as an important enzyme that regulates T cell early differentiation and terminal exhaustion. To investigate the mechanism, P4HA1 inhibitor DPCA was used to treat the human CD8 cells during early expansion and repeated coculture and the cells were harvested for RNA-seq analysis.
Published in
Targeting P4HA1 promotes CD8(+) T cell progenitor expansion toward immune memory and systemic anti-tumor immunity
Ma S, Ong LT, Jiang Z et al. · Cancer cell 2025 · PMID 39729997 · doi:10.1016/j.ccell.2024.12.001
This dataset
Open GSE263349 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 GSE263349_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1096749 and SRA study SRP499980. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39729997) ↗
Study design
20 distinct titles, 4 of them repeated
Donor 1_Norm_CoX ×2 Donor 2_Norm_CoX ×2 Donor 1_Hyp_CoX ×2 Donor 2_Hyp_CoX ×2

How we read this: Read from 24 sample titles: 20 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
Datasets on a similar topic

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.