GEO series

Functional gene regulatory networks and broad applications of the human expandable pancreatic progenitor-islet system [RNA-Seq]

GSE284159 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2026/01/12 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. 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: 12 samples: 3 × PB-C16-WT vs 3 × PB-C16-KO vs 3 × PB-N2-WT vs 3 × PB-N2-KOGroups 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 multi-omic analyses highlight the essential role of NKX2.2-CLEC16A/endosomal pathway for human pancreatic differentiation and functionCell reports · 2026

Summary (written by the submitters)

Investigating the precise gene regulatory programs directing pancreatic differentiation provides insights into the mechanisms of pancreatic development and diabetes progression. Here, we performed integrated single-cell multi-omic analyses of the expandable pancreatic progenitor (ePP)-islet system.

Read the full summary ↓

We defined the dynamic transcriptomic and chromatin landscapes of pancreatic differentiation, inferred the sophisticated gene regulatory networks (GRNs) that govern ePP self-renewal, endocrine specification and islet function, and identified the essential roles and interesting mechanisms of the NKX2.2-CLEC16A/endosomal pathway axis during cell-fate transitions. We have obtained interesting observations that are human specific not observed in mouse models (e.g., CLEC16A-related diabetes mechanisms), and further took advantage of the ePP-islet system to identify pharmacological rescuers. Notably, this study highlights the ePP-islet system as a very powerful platform for uncovering the molecular mechanisms of cell fate decision and rich information for further optimizing the differentiation process. With further optimization and better understanding, the ePP-islet system will undoubtedly have very broad applications, including disease modeling, drug discovery, and regenerative medicine.

Published in

Single-cell multi-omic analyses highlight the essential role of NKX2.2-CLEC16A/endosomal pathway for human pancreatic differentiation and function

Chen G, Ma X, Jiang L et al. · Cell reports 2026 · PMID 41689803 · doi:10.1016/j.celrep.2026.116990

This dataset

Open GSE284159 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 GSE284159_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 PRJNA1197473 and SRA study SRP551120. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41689803) ↗

Study design

3 × PB-C16-WT vs 3 × PB-C16-KO vs 3 × PB-N2-WT vs 3 × PB-N2-KO

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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

GSM8679090 PB-C16-WT-1 PB-C16-WT
GSM8679091 PB-C16-WT-2 PB-C16-WT
GSM8679092 PB-C16-WT-3 PB-C16-WT
GSM8679093 PB-C16-KO-1 PB-C16-KO
GSM8679094 PB-C16-KO-2 PB-C16-KO
GSM8679095 PB-C16-KO-3 PB-C16-KO
GSM8679096 PB-N2-WT-1 PB-N2-WT
GSM8679097 PB-N2-WT-2 PB-N2-WT
GSM8679098 PB-N2-WT-3 PB-N2-WT
GSM8679099 PB-N2-KO-1 PB-N2-KO
GSM8679100 PB-N2-KO-2 PB-N2-KO
GSM8679101 PB-N2-KO-3 PB-N2-KO

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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.