GEO series

The single-cell transcriptome defines an effector Vd1 gd T cell-dependent anti-tumor immunity in human gastrointestinal stromal tumor (GIST)

GSE300283 Mus musculus (mouse) Single-cell RNA-seq 6 samples Submitted 2026/08/07 Platform GPL17021

At a glance

Single-cell RNA-seq · mouse · 6 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 6 samples: 6 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: Effector Vδ1 γδ T cells mediate anti-tumor immunity and are reprogrammed by imatinib in human gastrointestinal stromal tumorResearch square · 2026

Summary (written by the submitters)

Gamma-delta (gd) T cells bridge innate and adaptive immunity and have the ability to recognize diverse tumor-associated antigens. However, the function of gd T cells in human gastrointestinal stromal tumor (GIST), the most common type of human sarcoma, is unknown. Here, we combined single-cell RNA-seq (scRNA-seq), immunophenotyping, and next-generation T-cell receptor (TCR) sequencing of sorted human circulating and intratumoral gd T cells to query their impact on tumor immunity in GIST.

Read the full summary ↓

We identified major subtypes of Vd1 and Vd2 cells, which were more differentiated in tumors. The effector Vd1 subset exhibited higher expression of cytotoxicity-related transcription and exhaustion molecules, and highly expressed the key transcription factors TBX21 and EOMES. Tyrosine kinase inhibitor (TKI) treatment induced the tumor-enriched Vd1 cells to undergo profound phenotype differentiation, alternative clonal expansion, and TCR diversity. Upon antigen exposure, the Vd1 subset maintained enhanced cytotoxicity properties through an innate-effector phenotype and adaptive-like activation. In a genetically engineered murine GIST model, combination therapy of imatinib with a checkpoint inhibitor increased the adaptive immune response of gd T cell subsets in GIST. Collectively, our findings highlight the importance of Vd1-dependent anti-tumor immunity and open the possibility of specific targeting of gd T subsets based on V gene usage for immunotherapy of GIST.

Published in

Effector Vδ1 γδ T cells mediate anti-tumor immunity and are reprogrammed by imatinib in human gastrointestinal stromal tumor

Zeng S, Sussman JH, Morris M et al. · Research square 2026 · PMID 42282009 · doi:10.21203/rs.3.rs-9557741/v1

This dataset

Open GSE300283 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 GSE300283_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 PRJNA1280034 and SRA study SRP593351. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42282009) ↗

Study design

6 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 6 sample titles: 6 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

GSM9057433 Veh-1_S1
GSM9057434 Veh-2_S2
GSM9057435 Veh-3_S3
GSM9057436 IM1_S4
GSM9057437 IM2_S5
GSM9057438 IM3_S6

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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.