Acetylcyssteine synergizes PD-1 blockers against colorectal cancer progression by promoting TCF1+PD1+CD8+ T cell differentiation in mice
RNA-seq (gene expression) · mouse · 10 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
- Mus musculus (mouse)
- Samples
- 10 samples: 5 × C vs 5 × NGroups 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.
Read the full summary ↓
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 GSE252358_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: 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 PRJNA1060282 and SRA study SRP480912. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 2 replicated groups read from 10 sample titles; they account for 10 of them. 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.
- GSM8000136 C1 C
- GSM8000137 C2 C
- GSM8000138 C3 C
- GSM8000139 C4 C
- GSM8000140 C5 C
- GSM8000141 N1 N
- GSM8000142 N2 N
- GSM8000143 N3 N
- GSM8000144 N4 N
- GSM8000145 N5 N
- GSE306744 Pre-division TCF1 downregulation determines long-term effector and memory CD8 T cell fates [RNA-Seq] 31 samples shares tcf1, cd8
- GSE234974 Pan-cancer mapping of single CD8+ T cell profiles reveals a TCF1:CXCR6 axis regulating CD28 co-stimulation and anti-tumor immunity I 24 samples shares tcf1, cd8
- GSE261642 PLPP1-deficiency-mediated ferroptosis induced by PD-1 signaling impairs antitumor activity of intratumoral CD8+ T cells I 23 samples shares pd-1, cd8
- GSE290839 PD-1 regulates tumor-infiltrating CD8+ T cells in both a cell-intrinsic and cell-extrinsic fashion [scRNA-seq] 20 samples shares pd-1, cd8
- GSE269347 Daunorubicin-induced T cell remodeling facilitates a high antitumor response to PD-1 blockade therapy by promoting the expansion and effector function of CD8+ prolifeating T cells [scRNA-Seq] 16 samples shares pd-1, cd8
- GSE320177 All -trans retinoic acid suppresses CD8+ T cell terminal exhaustion which enhances anti–PD-1 efficacy against glioblastoma [RNA-Seq] 12 samples shares pd-1, cd8
- GSE281719 PD-1 Mediated Regulation of Unique Activated CD8+ T Cells by NK Cells in the Submandibular Gland [I15, I16] 60 samples shares pd-1, cd8
- GSE328207 Arid3b suppresses CD8+ T cell infiltration and function in Microsatellite-stable colorectal cancer via Runx3 24 samples shares cd8, colorectal
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.