GEO series

Single-cell RNA-seq and spatial transcriptomics characterize CD8+ exhausted T cells in pancreatic ductal adenocarcinoma

GSE335452 Homo sapiens (human) Spatial transcriptomics 15 samples Submitted 2026/06/16 Platform GPL24676

At a glance

Spatial transcriptomics · human · 15 samples

What it is: Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism: Homo sapiens (human)
Topic: Pancreatic cancerThis series is part of our curated briefing on it.
Samples: 15 samples: 15 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.

Summary (written by the submitters)

Pancreatic cancer resists immunotherapy due to a suppressive immune microenvironment where CD8⁺ T cells play a key role. Using single‑cell RNA sequencing and spatial transcriptomics, we characterized CD8⁺ exhausted T (Tex) cells in pancreatic ductal adenocarcinoma (PDAC). We generated single‑cell profiles from PDAC tumors and matched peripheral blood mononuclear cells, and performed T cell sub‑analysis.

Read the full summary ↓

We found CXCL13 upregulated and GZMK downregulated in CD8⁺ Tex cells. Cell‑cell interaction analysis showed that T cells most frequently interacted with myeloid cells and cancer cells via ligand‑receptor pairs; INHBA⁺ macrophages and cancer cells communicated most with CD8⁺ Tex cells. Two key LR pairs (SPP1–integrin α4β1 and PLAUR–integrin α4β1) mediated crosstalk between cancer cells and CD8⁺ Tex cells, confirmed by immunofluorescence, spatial mapping, and protein docking. High SPP1 and PLAUR expression correlated with poor prognosis in TCGA‑PAAD. These findings provide a resource for understanding CD8⁺ T cell exhaustion in PDAC.

This dataset

Open GSE335452 in finder → Find more human RNA-seq datasets → Pancreatic cancer landscape →

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 GSE335452_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: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA1478580 and SRA study SRP709703. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

15 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 15 sample titles: 15 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

GSM9813919 wbq001-t
GSM9813920 xlc002-t
GSM9813921 cgq003-t
GSM9813922 lsy004-t
GSM9813923 zxq005-t
GSM9813924 wbq001_p
GSM9813925 xlc002_p
GSM9813926 cgq003_p
GSM9813927 lsy004_p
GSM9813928 zxq005_p
GSM9813929 wbq001_v
GSM9813930 xlc002_v
GSM9813931 cgq003_v
GSM9813932 lsy004_v
GSM9813933 zxq005_v

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GSE71729 Virtual Microdissection of Pancreatic Ductal Adenocarcinoma Reveals Tumor and Stroma Subtypes. 357 samples same disease briefing · shares adenocarcinoma, pancreatic
GSE102238 Gene expression signatures associated with Perineural Invasion in pancreatic ductal adenocarcinoma 100 samples same disease briefing · shares adenocarcinoma, pancreatic
GSE179351 Radiation Therapy Inhances Immunotherapy Response in Microsatellite-stable Colorectal and Pancreatic Adenocarcinoma in a Phase II Trial 54 samples same disease briefing · shares adenocarcinoma, pancreatic
GSE118694 Application of the High-throughput TAB-Array for the Discovery of Novel 5-Hydroxymethylcytosine Biomarkers in Pancreatic Ductal Adenocarcinoma 34 samples same disease briefing · shares adenocarcinoma, pancreatic
GSE288221 RNA sequencing human melanoma, glioblastoma and pancreatic adenocarcinoma cell lines 32 samples same disease briefing · shares adenocarcinoma, pancreatic
GSE125179 microRNA modulated networks of adaptive and innate immune response in pancreatic ductal adenocarcinoma 28 samples same disease briefing · shares adenocarcinoma, pancreatic

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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.