GEO series

The Cd4+ T cell population Partners with Tpex CD8 T cells to mediate Antitumor Immunity in the Tumor Microenvironment. [P6]

GSE295601 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/02/05 Platform GPL28038

At a glance

Single-cell RNA-seq · human · 18 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: Homo sapiens (human)
Samples: 18 samples: 18 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: The CD4(+) T cell population partners with Tpex CD8(+) T cells to mediate antitumor immunity in the tumor microenvironmentNature communications · 2026

Summary (written by the submitters)

The priming, expansion, and function of CD8⁺ T cells are helped by CD4⁺ T cells via dendritic cells. Precursor exhausted T cells (Tpex) maintain self-renewal and supply cytotoxic CD8⁺ T cells in the tumor microenvironment (TME), but the identity of their CD4⁺ T-cell partners remains unclear. Here, we perform scRNA-seq, scTCR-seq, and mass cytometry analysis on peripheral blood, tumor, and lymph nodes derived from lung cancer patients and identify a population of IL-7Rhigh CCR6⁺ Th1-like CD4⁺ T cells, namedTh7R, that is numerically and spatially partnered with Tpex cells.

Read the full summary ↓

Th7R cells express lymphotoxin-β and CXCL13, correlate with high endothelial venules, and co-localize with Tpex in tertiary lymphoid structures. Furthermore, Th7R cell abundance correlates with Tpex numbers in the TME and lymph nodes, and adoptive transfer of Th7R increases Tpex in a preclinical mouse model of skin cancer. In lung cancer patients, Th7R and Tpex in TME are associated with better response to neoadjuvant PD-1 blockade therapy. Thus, these results suggest that Th7R cells act as partners of Tpex CD8 T cells to sustain antitumor T-cell immunity.

Published in

The CD4(+) T cell population partners with Tpex CD8(+) T cells to mediate antitumor immunity in the tumor microenvironment

Takei S, Yamasaki S, Yamaguchi O et al. · Nature communications 2026 · PMID 41904165 · doi:10.1038/s41467-026-71161-0

This dataset

Open GSE295601 in finder → Find more human 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 GSE295601_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: 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 PRJNA1255286 and SRA study SRP581142. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41904165) ↗

Study design

18 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 18 sample titles: 18 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

GSM8953623 P6 TIL, scRNA-seq
GSM8953624 P6 TIL, Feature Barcoding(ADT)
GSM8953625 P6 TIL, scTCR-seq
GSM8953626 P6 PBMC pre-operative, scRNA-seq
GSM8953627 P6 PBMC pre-operative, Feature Barcoding(ADT)
GSM8953628 P6 PBMC pre-operative, scTCR-seq
GSM8953629 P7 TIL, scRNA-seq
GSM8953630 P7 TIL, Feature Barcoding(ADT)
GSM8953631 P7 TIL, scTCR-seq
GSM8953632 P7 PBMC pre-operative, scRNA-seq
GSM8953633 P7 PBMC pre-operative, Feature Barcoding(ADT)
GSM8953634 P7 PBMC pre-operative, scTCR-seq
GSM8953635 P8 TIL, scRNA-seq
GSM8953636 P8 TIL, Feature Barcoding(ADT)
GSM8953637 P8 TIL, scTCR-seq
GSM8953638 P8 PBMC pre-operative, scRNA-seq
GSM8953639 P8 PBMC pre-operative, Feature Barcoding(ADT)
GSM8953640 P8 PBMC pre-operative, scTCR-seq

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