GEO series

An early precursor CD8 T cell that adapts to acute or chronic viral infection [bulk RNA-seq]

GSE284012 Mus musculus (mouse) RNA-seq (gene expression) 29 samples Submitted 2025/01/11 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 29 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: 29 samples: 29 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: An early precursor CD8(+) T cell that adapts to acute or chronic viral infectionNature · 2025

Summary (written by the submitters)

This study examines the origin and differentiation of stem-like CD8+ T cells that are essential for sustained T cell immunity in chronic viral infections and cancer and also play a key role in PD-1 directed immunotherapy. These PD-1+ TCF-1+ TOX+ stem-like CD8+ T cells, also referred to as precursors of exhausted T cells, have a distinct program that allows them to adapt to chronic antigen stimulation.

Read the full summary ↓

Using the mouse model of chronic LCMV infection we found that virus specific stem-like CD8+ T cells are generated early (day 5) during chronic infection suggesting that this crucial fate commitment occurs irrespective of infection outcome. Indeed, we found that nearly identical populations of stem-like CD8+ T cells were generated early after acute or chronic LCMV infection and that antigen was essential for maintaining the stem-like phenotype. We next performed reciprocal adoptive transfer experiments to determine the fate of these early stem-like CD8+ T cells after viral clearance versus persistence. Following transfer of day 5 stem-like CD8+ T cells from chronically infected into acutely infected mice, these cells downregulated canonical markers of the chronic stem-like CD8+ T cells and expressed markers (CD127 and CD62L) associated with central memory CD8+ T cells. Reciprocally, when day 5 stem-like cells from acutely infected mice were transferred into chronically infected mice these CD8+ T cells functioned like chronic resource cells and responded effectively to PD-1 therapy. These findings highlight the ability of these early PD-1+ TCF-1+ TOX+ stem-like CD8+ T cells to adapt their differentiation trajectory to either an acute or chronic viral infection. Most importantly, our study shows that the host is prepared a priori to deal with a potential chronic infection.

Published in

An early precursor CD8(+) T cell that adapts to acute or chronic viral infection

McManus DT, Valanparambil RM, Medina CB et al. · Nature 2025 · PMID 39778710 · doi:10.1038/s41586-024-08562-y

This dataset

Open GSE284012 in finder → Find more mouse 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 GSE284012_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 PRJNA1196833 and SRA study SRP550930. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39778710) ↗

Study design

29 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 29 sample titles: 29 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

GSM8676140 Sample1_GK day 5.5 stem (CD73+) 1
GSM8676141 Sample2_GK day 5.5 stem (CD73+) 2
GSM8676142 Sample3_GK day 5.5 stem (CD73+) 3
GSM8676143 Sample4_GK day 5.5 TD (Tim-3+) 1
GSM8676144 Sample5_GK day 5.5 TD (Tim-3+) 2
GSM8676145 Sample6_GK day 5.5 TD (Tim-3+) 3
GSM8676146 Sample7_day 48 stem (CD73+) 1
GSM8676147 Sample8_day 48 stem (CD73+) 2
GSM8676148 Sample9_day 48 stem (CD73+) 3
GSM8676149 Sample10_day 48 TD (Tim-3+) 1
GSM8676150 Sample11_day 48 TD (Tim-3+) 2
GSM8676151 Sample12_day 48 TD (Tim-3+) 3
GSM8676152 Sample13_Naïve (CD44lo PD-1- uninfected mouse) 1
GSM8676153 Sample14_Naïve (CD44lo PD-1- uninfected mouse) 2
GSM8676154 Sample15_Naïve (CD44lo PD-1- uninfected mouse) 3
GSM8676155 Sample16_Clone 13 Day5 Stem 1
GSM8676156 Sample17_Clone 13 Day5 Stem 2
GSM8676157 Sample18_Clone 13 Day5 Stem 3
GSM8676158 Sample19_Clone 13 Day5 TD 1
GSM8676159 Sample20_Clone 13 Day5 TD 3
GSM8676160 Sample21_Arm hi Day5 Stem 1
GSM8676161 Sample22_Arm hi Day5 Stem 2
GSM8676162 Sample23_Arm hi Day5 Stem 3
GSM8676163 Sample24_Arm hi Day5 TD 1
GSM8676164 Sample25_Arm hi Day5 TD 2
GSM8676165 Sample26_Arm hi Day5 TD 3
GSM8676166 Sample27_Naïve 1
GSM8676167 Sample28_Naïve 2
GSM8676168 Sample29_Naïve 3

Datasets on a similar topic

GSE255887 HDAC1 controls the generation and maintenance of effector-like CD8+ T cells during chronic viral infection 76 samples shares cd8, chronic, viral
GSE215759 Mettl3-dependent m6A modification is essential for effector differentiation and memory formation of CD8+ T cells [RNA-seq] 15 samples shares cd8, rna-seq
GSE275974 SATB1 is a key regulator of quiescence in stem-like CD8+ T cells [RNA-seq] 10 samples shares cd8, rna-seq
GSE261250 Gfi1 controls the formation of effector CD8+ T cells during chronic infection and cancer 47 samples shares cd8, chronic
GSE317032 Non-cytolytic viral vaccine vectors induce durable effector-memory CD8 T cell immunity by triggering type I interferon 18 samples shares cd8, viral
GSE330728 Single-nucleus RNA-seq atlas of 17 ASD-risk mouse lines under early-postnatal fluoxetine or lithium exposure (4-plex On-Chip Multiplexed) 53 samples shares rna-seq, early
GSE330674 Glucocorticoid resistance during chronic social stress in mice: RNA-seq of prefrontal cortex 24 samples shares chronic, rna-seq
GSE297298 Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer 18 samples shares rna-seq, early

Search all mouse 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.