Aging compromises terminal differentiation program of cytotoxic effector lineage and promotes exhaustion in CD8+ T cells responding to coronavirus infection [RNA-seq]
Single-cell RNA-seq · mouse · 16 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
- 16 samples
- 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
- Aging Compromises Terminal Differentiation Program of Cytotoxic Effector Lineage and Promotes Exhaustion in CD8(+) T Cells Responding to Coronavirus InfectionAging cell · 2025
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 GSE284244_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 PRJNA1198063 and SRA study SRP551351. Searching any of these in the dataset finder brings you back here.
The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.
+ 16 more — browse all 16 samples with per-sample file links →
- 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, terminal
- GSE302682 Sleeping Beauty mutagenesis identifies BACH2 and other regulators promoting CD8+ T cell persistence and effector function under tumor-associated chronic antigen stimulation - RNAseq 66 samples shares cd8, effector
- GSE306744 Pre-division TCF1 downregulation determines long-term effector and memory CD8 T cell fates [RNA-Seq] 31 samples shares cd8, effector
- GSE248272 Therapeutically targeting perivascular macrophage effector function improves CD8+ T-cell infiltration and response to chemotherapeutic drugs in cancer 30 samples shares cd8, effector
- GSE302683 Sleeping Beauty mutagenesis identifies BACH2 and other regulators promoting CD8+ T cell persistence and effector function under tumor-associated chronic antigen stimulation - esTAG 29 samples shares cd8, effector
- GSE277610 Endothelial PD-L1 brakes CD8+ T cell exhaustion, empowering anti-tumor immunity in non-small cell lung cancer 17 samples shares cd8, exhaustion
- 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 cd8, effector
- GSE252337 Dysfunction of infiltrating cytotoxic CD8+ T cells within the graft promotes murine kidney allotransplant tolerance 16 samples shares cd8, cytotoxic
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.