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Antigen-driven colonic inflammation is associated with development of dysplasia in primary sclerosing cholangitis [bulk RNASeq]

GSE230524 Homo sapiens (human) Single-cell RNA-seq 226 samples Submitted 2023/04/30 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 226 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
Homo sapiens (human)
Samples
226 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)
Primary sclerosing cholangitis (PSC) is an autoimmune-like disease of the bile ducts that co-occurs with inflammatory bowel disease (IBD) in almost 90% of cases. Colorectal cancer is a major complication of patients with both PSC and IBD, and these patients are at a much greater risk compared to patients with IBD without concomitant PSC.
Read the full summary ↓
Combining flow cytometry, bulk and single cell transcriptomics, and T- and B-cell receptor repertoire analysis of right colon tissue from PSC, IBD, and healthy controls we identified a unique adaptive inflammatory transcriptional signature associated with greater risk and shorter time to dysplasia specifically in patients with PSC. This inflammatory signature is characterized by antigen-driven IL-17A+ Foxp3+ CD4 T-cell and an expansion of IgG-secreting B-cell responses. These data results suggest that the mechanisms that drive the emergence of dysplasia in of PSC and IBD dysplasia are distinct and provide molecular insights to guide prevention of colorectal cancer in PSC.
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 GSE230524_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 PRJNA961704 and SRA study SRP434404. Searching any of these in the dataset finder brings you back here.

Samples in this study

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