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A platform for multisite immune profiling of premetastatic pancreatic cancer at single cell resolution

GSE300304 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2025/09/02 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 30 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
30 samples: 30 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
A platform for multisite immune profiling of premetastatic pancreatic cancer at single-cell resolutionCancer immunology, immunotherapy : CII · 2025
Summary (written by the submitters)
Pancreatic ductal adenocarcinoma (PDAC) is characterized by exceedingly high rates of metastatic progression, with the liver representing the most common site of distant spread. Here, we established a platform for multisite immune profiling of human PDAC encompassing the tumor, peripheral circulation, and premetastatic liver, to more comprehensively study how various immune subsets might contribute to patient outcomes.
Read the full summary ↓
METHODS: Tumor, liver, and blood samples were obtained from patients undergoing resection for non-metastatic PDAC. Derived immune cells underwent paired single-cell RNA and TCR sequencing. Immune composition, cell type functional profiles, and T cell clonal expansion patterns were evaluated across tissue sites. RESULTS: In total, 106,539 immune cells were sequenced, of which 85,748 met criteria for analysis. We identified 32 cell populations, of which seven demonstrated significant enrichment within a particular tissue. Functional profiling revealed tissue-specific differences in cell phenotypes. This included terminally differentiated exhausted CD8 T cells within the tumor, highly active Tregs within the premetastatic liver and tumor, and M1 versus M2 polarization of liver and tumor macrophage populations, respectively. Clonal expansion of T cells was greatest in the liver, which comprised predominantly mature CD8 T cells and MAIT cells. Within the tumor, expanded Treg clones were uniquely abundant, and while expanded clones could be tracked to the blood and premetastatic liver, many of these mapped back to known viral antigens. Leveraging previously validated gene sets, we predicted the tumor reactivity of intratumoral T cells using transcriptional signatures. We demonstrated a high degree of concordance between multiple independent signatures and tracked high-priority TCRs within the blood and liver. CONCLUSION: This study provides a platform, which has already been implemented into ongoing clinical protocols, for immune profiling of human PDAC across the sites most relevant to metastatic progression. Future applications of this work can monitor immune populations throughout metastatic progression to build a temporal database of immune phenotypes and track association with clinical outcomes.
Published in
A platform for multisite immune profiling of premetastatic pancreatic cancer at single-cell resolution
Kanu EN, Fletcher AA, Bao J et al. · Cancer immunology, immunotherapy : CII 2025 · PMID 40848148 · doi:10.1007/s00262-025-04146-5
This dataset
Open GSE300304 in finder → Find more human 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 GSE300304_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 PRJNA1277284 and SRA study SRP592338. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40848148) ↗
Study design
30 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 30 sample titles: 30 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
More Pancreatic cancer datasets

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