Active aldehydes accelerate CD8+ T cell exhaustion by metabolic alteration in the tumor microenvironment
Single-cell RNA-seq · mouse · 3 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
- Mus musculus (mouse)
- Samples
- 3 samples: 3 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
- Active aldehydes accelerate CD8(+) T cell exhaustion by metabolic alteration in the tumor microenvironmentNature immunology · 2026
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE307051_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1314190 and SRA study SRP616597. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 3 sample titles: 3 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.
- GSM9214853 CD8+ T cells in MC38 tumor, VDJ
- GSM9214854 CD8+ T cells in MC38 tumor, GEX
- GSM9214855 CD8+ T cells in MC38 tumor, ADT
- GSE223164 Osr2 functions as a biomechanical checkpoint to aggravate CD8+ T cell exhaustion in tumor 49 samples shares cd8, exhaustion
- GSE316541 α2,6-Sialoglycan-regulated anti-tumor immunity of tumor-infiltrating CD8+ T cells in the tumor microenvironment [CD8+T] 27 samples shares cd8, microenvironment
- GSE269676 3D genome of CD8+ T cells reveals IRF8-mediated exhaustion in cancer 18 samples shares cd8, exhaustion
- GSE277610 Endothelial PD-L1 brakes CD8+ T cell exhaustion, empowering anti-tumor immunity in non-small cell lung cancer 17 samples shares cd8, exhaustion
- GSE310452 Sustained A2AR expression and loss paradoxically promote CD8+ T cell exhaustion 12 samples shares cd8, exhaustion
- GSE320177 All -trans retinoic acid suppresses CD8+ T cell terminal exhaustion which enhances anti–PD-1 efficacy against glioblastoma [RNA-Seq] 12 samples shares cd8, exhaustion
- GSE223157 Osr2 functions as a mechanical checkpoint to augment CD8+ T cell exhaustion [YODA1_vs_DMSO_ATAC_CD8] 12 samples shares cd8, exhaustion
- GSE316540 α2,6-Sialoglycan-regulated anti-tumor immunity of tumor-infiltrating CD8+ T cells in the tumor microenvironment 9 samples shares cd8, microenvironment
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.