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LSD1 inhibition promotes CD8+ T cells mediated anti-tumor immunity by MHC-I upregulation and enhanced infiltration of dendritic cells through IFNγ-CXCL9-CXCR3 axis

GSE278349 Mus musculus (mouse) Single-cell RNA-seq 10 samples Submitted 2025/09/26 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 10 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
10 samples
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)
Poor infiltration of CD8+ cells and dysregulated MHC-I provide resistance to anti-cancer clinical therapies. This study aimed to uncover mechanisms of lysine-specific demethylase 1 (Kdm1a or LSD1) in antitumor immunity in Head and Neck Squamous cell carcinoma (HNSCC). LSD1 inhibition in syngeneic and chronic tobacco carcinogen-induced HNSCC mice recruited an activated dendritic cell (DCs), CD4+, and CD8+ T cells, enriched IFNγ in T cells, CXCL9 in DCs, and CXCR3 in T cells, evaluated by single cell RNA-seq analysis.
Read the full summary ↓
Humanized HNSCC mice and TCGA data validate inverse correlation of Kdm1a with markers of DCs, CD8+ T cells, and their activating chemokines. LSD1 knockout or inhibitors in mouse HNSCC and co-culture showed MHC-I upregulation in tumors for efficient antigen presentation. Overall, LSD1 inhibition promoted DCs through IFNγ-CXCL9-CXCR3 axis and MHC-I upregulation in tumors to induce CD8+ T cell medicated antitumor immunity, which has implications for poorly immunogenic and immunotherapy-resistant cancers therapy.
This dataset
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.

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 GSE278349_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1166694 and SRA study SRP535499. Searching any of these in the dataset finder brings you back here.

Samples in this study

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