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Characterization of human pancreatic tissue and organoids reveals subpopulation of intercalated duct cells with progenitor traits

GSE263364 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2024/04/09 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 23 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
23 samples: 23 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
Single-cell transcriptomics reveals a role for pancreatic duct cells as potential mediators of inflammation in diabetes mellitusFrontiers in immunology · 2024
Summary (written by the submitters)
Pancreatic duct organoids have emerged as a valuable tool for investigating pancreas biology and physiology through in vitro modeling. Despite their widespread use, a comprehensive understanding of the cellular heterogeneity of organoids and their underlying tissue remains elusive. Here we performed single-cell transcriptomic profiling of human adult pancreatic exocrine tissue and their derived organoids, revealing a gradient of keratin expression within the ductal network that clustered cells into two distinct low- and high-keratin subpopulations.
Read the full summary ↓
Keratin-high cells display a higher capacity to form organoids while keratin-low cells express stemness associated markers. Finally, comparative gene expression analysis between ductal tissue and derived organoids revealed a partial recapitulation of the in vivo heterogeneity. These findings further our knowledge on pancreatic duct organoids and pave the way for future investigations into the heterogeneity of the pancreatic ductal syste
Published in
Single-cell transcriptomics reveals a role for pancreatic duct cells as potential mediators of inflammation in diabetes mellitus
Muñoz García A, Juksar J, Groen N et al. · Frontiers in immunology 2024 · PMID 38742118 · doi:10.3389/fimmu.2024.1381319
This dataset
Open GSE263364 in finder → Find more human RNA-seq datasets →
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 GSE263364_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 PRJNA1096822 and SRA study SRP500006. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38742118) ↗
Study design
23 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 23 sample titles: 23 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
Datasets on a similar topic

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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.