GEO series

Multi-omics analysis identifies progenitors of CD4-CTLs [bulk RNA-seq]

GSE288966 Homo sapiens (human) RNA-seq (gene expression) 70 samples Submitted 2025/08/26 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 70 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: 70 samples: 70 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: A distinct subset of stem-cell memory is poised for the cytotoxicity program in CD4(+) T cells in humansScience advances · 2026

Summary (written by the submitters)

The CD4+ T cells expressing cytolytic molecules such as granzymes and perforins have been detected in many diseases and are shown to be enriched in the effector memory expressing CD45RA (TEMRA) in humans, but their origin remains elusive. However, their gene expression profile in comparison to classically defined cytotoxic T lymphocytes (CTLs), the CD8-CTLs has not been well demonstrated, hence their relevance remains debatable.

Read the full summary ↓

Thus in this study by parallelly analysing the CD4-CTLs and CD8-CTLs, we demonstrate that they are indistinguishable for the cytolytic program with both showing similar gene expression profile and T cell antigen-receptor (TCR) clonal expansion. Further using an integrative multi-omics approach combining the transcriptome, TCR repertoire, and open chromatin profile of CD4+ naïve (CD4-TN) and memory subsets, we discovered a stem-cell memory subset that is pre-committed to CTL program within the CD4+ T cell lineage. Through an in vitro differentiation model we developed CD4+ T cells with cytolytic potential from CD4-TN cells. The in vitro differentiated cells followed the trajectory of different developmental memory subsets from ex vivo CD4+ T cells for transcriptomic patterns as well as open-chromatin landscape. Thus, through this model, we deciphered the molecular signatures of early commitment of CD4-TN cells to cytotoxicity program. Of particular interest was the expression of both longevity as well as cytotoxicity associated gene sets by the in vitro differentiated CD4-CTLs, hence generating long-lived CD4-CTL effectors. This specific property can be further explored for vaccine development as well as testing the efficacy and cell based therapies for precision medicine.

Published in

A distinct subset of stem-cell memory is poised for the cytotoxicity program in CD4(+) T cells in humans

Kar R, Sinha S, Khatun Z et al. · Science advances 2026 · PMID 41499515 · doi:10.1126/sciadv.ady6423

This dataset

Open GSE288966 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 GSE288966_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 PRJNA1220122 and SRA study SRP562081. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41499515) ↗

Study design

70 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 70 sample titles: 70 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

GSM8779962 bulkRNASeq_D1_CD4_Naive
GSM8779963 bulkRNASeq_D2_CD4_Naive
GSM8779964 bulkRNASeq_D3_CD4_Naive
GSM8779965 bulkRNASeq_D4_CD4_Naive
GSM8779966 bulkRNASeq_D5_CD4_Naive
GSM8779967 bulkRNASeq_D6_CD4_Naive
GSM8779968 bulkRNASeq_D7_CD4_Naive
GSM8779969 bulkRNASeq_D8_CD4_Naive
GSM8779970 bulkRNASeq_D9_CD4_Naive
GSM8779971 bulkRNASeq_D10_CD4_Naive
GSM8779972 bulkRNASeq_D1_CD4_TSCM
GSM8779973 bulkRNASeq_D2_CD4_TSCM
GSM8779974 bulkRNASeq_D3_CD4_TSCM
GSM8779975 bulkRNASeq_D4_CD4_TSCM
GSM8779976 bulkRNASeq_D5_CD4_TSCM
GSM8779977 bulkRNASeq_D6_CD4_TSCM
GSM8779978 bulkRNASeq_D7_CD4_TSCM
GSM8779979 bulkRNASeq_D8_CD4_TSCM
GSM8779980 bulkRNASeq_D9_CD4_TSCM
GSM8779981 bulkRNASeq_D10_CD4_TSCM
GSM8779982 bulkRNASeq_D1_CD4_TEMRA_P
GSM8779983 bulkRNASeq_D2_CD4_TEMRA_P
GSM8779984 bulkRNASeq_D3_CD4_TEMRA_P
GSM8779985 bulkRNASeq_D4_CD4_TEMRA_P
GSM8779986 bulkRNASeq_D5_CD4_TEMRA_P
GSM8779987 bulkRNASeq_D6_CD4_TEMRA_P
GSM8779988 bulkRNASeq_D7_CD4_TEMRA_P
GSM8779989 bulkRNASeq_D8_CD4_TEMRA_P
GSM8779990 bulkRNASeq_D9_CD4_TEMRA_P
GSM8779991 bulkRNASeq_D10_CD4_TEMRA_P
GSM8779992 bulkRNASeq_D1_CD4_TEMRA_E
GSM8779993 bulkRNASeq_D2_CD4_TEMRA_E
GSM8779994 bulkRNASeq_D3_CD4_TEMRA_E
GSM8779995 bulkRNASeq_D4_CD4_TEMRA_E
GSM8779996 bulkRNASeq_D5_CD4_TEMRA_E
GSM8779997 bulkRNASeq_D6_CD4_TEMRA_E
GSM8779998 bulkRNASeq_D7_CD4_TEMRA_E
GSM8779999 bulkRNASeq_D8_CD4_TEMRA_E
GSM8780000 bulkRNASeq_D9_CD4_TEMRA_E
GSM8780001 bulkRNASeq_D10_CD4_TEMRA_E

+ 30 more — browse all 70 samples with per-sample file links →

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