GEO series

Y chromosome gene KDM5D coordinates epigenetic and metabolic control of sex differences in CD8⁺ T cell immunity [RNA-seq]

GSE330655 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/07/23 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 samples: 3 × Female vs 3 × Male vs 3 × WT vs 3 × KDGroups 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.

Summary (written by the submitters)

Sex differences in anti-tumor immunity remain incompletely understood yet have important implications for tumor biology and immunotherapy. Here, we combine murine colorectal cancer (CRC) models, primary CD8+ T-cell perturbation studies, and single-cell transcriptomic analyses of human CRC to define a male-biased regulatory axis that limits CD8+ T-cell effector function.

Read the full summary ↓

Male CD8+ T cells displayed reduced cytokine production, proliferation, cytotoxicity, TCR abundance, and TCR signaling relative to female CD8+ T-cells. Mechanistically, depletion of histone demethylase KDM5D, encoded by the Y chromosome, enhanced TCR expression and clustering, increased proximal TCR signaling, reduced cholesterol biosynthetic programs, and lowered expression of exhaustion-associated genes. In human CRC single-cell datasets, male tumor-infiltrating CD8+ T-cells showed enrichment for exhaustion and cholesterol-associated programs, supporting the translational relevance of this axis. Consistent with the known role of cholesterol in TCR clustering and activation, lovastatin reduced exhaustion-associated marker expression in male CD8+ T-cells and delayed CRC tumor growth in vivo – findings which align with the male-specific CRC risk reduction role of statins. Together, these findings identify KDM5D as a sex-chromosome-linked regulator of male CD8+ T-cell dysfunction and validate cholesterol metabolism as an actionable target for sex-specific immunotherapeutic modulation in cancer.

This dataset

Open GSE330655 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 GSE330655_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 PRJNA1465169 and SRA study SRP699606. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × Female vs 3 × Male vs 3 × WT vs 3 × KD

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 of them. 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

GSM9729901 Female_rep1 Female
GSM9729902 Female_rep2 Female
GSM9729903 Female_rep3 Female
GSM9729904 Male_rep1 Male
GSM9729905 Male_rep2 Male
GSM9729906 Male_rep3 Male
GSM9729907 WT_rep1 WT
GSM9729908 WT_rep2 WT
GSM9729909 WT_rep3 WT
GSM9729910 KD_rep1 KD
GSM9729911 KD_rep2 KD
GSM9729912 KD_rep3 KD

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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.