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Single-cell RNA-seq reveals immune landscape of pancreatic cancer

GSE212966 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2022/09/13 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 12 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
12 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)
Pancreatic ductal adenocarcinoma (PDAC) has complex tumor immune microenvironment (TIME), the clinical values of which remains to be explored. This study aimed to delineate the immune landscape of PDAC and determine the clinical value of immune features in TIME. There was a significant difference in immune profiles between PDAC and adjacent normal pancreatic tissues.
Read the full summary ↓
Several novel immune features were captured by quantitative pathology analysis on mIHC, some of which were significantly correlated to the prognosis of PDAC patients. A risk score-based prognostic model was developed according to these immune features. We also drew a user-friendly nomogram plot to predict the overall survival of patients by combining risk score and clinicopathologic features. Both mIHC and scRNA-seq analyses showed the expression of PD-L1 was scarce in PDAC. We found that PD1+ cells were distributed in different T cell subpopulations, not enriched in a specific subpopulation. In addition, there were other conserved receptor-ligand pairs (CCL5-SDC1/4) besides PD1-PD-L1 interaction between PD1+ T cells and PD-L1+ tumor cells. Our findings reveal the immune landscape of PDAC and highlight the significant value of combined application of mIHC and scRNA-seq in uncovering TIME, which might provide new clues for developing immunotherapy strategies.
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 GSE212966_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 PRJNA878527 and SRA study SRP396295. Searching any of these in the dataset finder brings you back here.

Samples in this study

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