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Oncogenic KRAS-dependent stromal interleukin-33 directs the pancreatic microenvironment to promote tumor growth [scRNA-seq]

GSE269888 Mus musculus (mouse) Single-cell RNA-seq 11 samples Submitted 2024/06/26 Platform GPL21103Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 11 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
11 samples: 2 × KC PanIN vs 2 × iKrasG12D;Trp53R172H/+ 14 weeks ON vs 2 × iKrasG12D;Trp53R172H/+ 15 weeks ON + 1 week OFFGroups 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
Oncogenic KRAS-Dependent Stromal Interleukin-33 Directs the Pancreatic Microenvironment to Promote Tumor GrowthCancer discovery · 2024
Summary (written by the submitters)
Here, we sought to understand the function of stromal interleukin-33 (IL-33) in PanIN and PDA. We collected pancreatic tissues from various mouse models of PanIN and PDA for single cell RNA sequencing to assess IL-33 expression across the microenviornment. We also implanted syngeneic orthotopic pancreatic tumors into mice harboring a conditional stromal IL-33 knockout and sequenced the resulting tumors.
Published in
Oncogenic KRAS-Dependent Stromal Interleukin-33 Directs the Pancreatic Microenvironment to Promote Tumor Growth
Donahue KL, Watkoske HR, Kadiyala P et al. · Cancer discovery 2024 · PMID 38958646 · doi:10.1158/2159-8290.CD-24-0100
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 GSE269888_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 PRJNA1124072 and SRA study SRP514012. Searching any of these in the dataset finder brings you back here.

Study design
2 × KC PanIN vs 2 × iKrasG12D;Trp53R172H/+ 14 weeks ON vs 2 × iKrasG12D;Trp53R172H/+ 15 weeks ON + 1 week OFF

Supports a between-group comparison across 6 samples.

How we read this: 3 replicated groups read from 11 sample titles; they account for 6 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
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