GEO series

CRISPR Screening Uncovers a Long-Range Enhancer for ONECUT1 in Pancreatic Differentiation and Links a Diabetes Risk Variant [RNA-seq]

GSE261390 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/03/29 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 18 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 18 samples: 6 conditions, mostly in triplicateGroups 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: CRISPR screening uncovers a long-range enhancer for ONECUT1 in pancreatic differentiation and links a diabetes risk variantCell reports · 2024

Summary (written by the submitters)

Functional enhancer annotation is a valuable first step for understanding tissue-specific transcriptional regulation and prioritizing disease-associated non-coding variants for investigation. However, unbiased enhancer discovery in physiologically relevant contexts remains a major challenge. To discover regulatory elements pertinent to diabetes, we conducted a CRISPR interference (CRISPRi) screen in the human pluripotent stem cell (hPSC) pancreatic differentiation system.

Read the full summary ↓

Among the enhancers uncovered, we focused on a long-range enhancer ~664 kb from the ONECUT1 promoter, as coding mutations in ONECUT1 cause pancreatic hypoplasia and neonatal diabetes. Homozygous enhancer deletion in hESCs was associated with a near-complete loss of ONECUT1 gene expression and compromised pancreatic differentiation. We then identified a type 2 diabetes (T2D) associated variant (rs528350911) in the enhancer which disrupts a GATA motif. Introduction of the risk variant into hESCs revealed substantially reduced binding of key pancreatic transcription factors (GATA4, GATA6 and FOXA2) on the edited allele, accompanied by a subtle reduction of ONECUT1 transcription, supporting a causal role for this risk variant in metabolic disease. This work expands our knowledge about transcriptional regulation in pancreatic development through the characterization of a long-range enhancer and highlights the utility of enhancer discovery in disease-relevant settings for understanding monogenic and complex disease.

Published in

CRISPR screening uncovers a long-range enhancer for ONECUT1 in pancreatic differentiation and links a diabetes risk variant

Kaplan SJ, Wong W, Yan J et al. · Cell reports 2024 · PMID 39163202 · doi:10.1016/j.celrep.2024.114640

This dataset

Open GSE261390 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 GSE261390_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1086827 and SRA study SRP494732. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39163202) ↗

Study design

6 conditions, mostly in triplicate

WT_GT_RNAseq ×3 WT_PFG_RNAseq ×3 Het2_GT_RNAseq ×3 Het2_PFG_RNAseq ×3 Homo_GT_RNAseq ×3 Homo_PFG_RNAseq ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM8141919 WT_GT_RNAseq_rep1 WT_GT_RNAseq
GSM8141920 WT_GT_RNAseq_rep2 WT_GT_RNAseq
GSM8141921 WT_GT_RNAseq_rep3 WT_GT_RNAseq
GSM8141922 WT_PFG_RNAseq_rep1 WT_PFG_RNAseq
GSM8141923 WT_PFG_RNAseq_rep2 WT_PFG_RNAseq
GSM8141924 WT_PFG_RNAseq_rep3 WT_PFG_RNAseq
GSM8141925 Het2_GT_RNAseq_rep1 Het2_GT_RNAseq
GSM8141926 Het2_GT_RNAseq_rep2 Het2_GT_RNAseq
GSM8141927 Het2_GT_RNAseq_rep3 Het2_GT_RNAseq
GSM8141928 Het2_PFG_RNAseq_rep1 Het2_PFG_RNAseq
GSM8141929 Het2_PFG_RNAseq_rep2 Het2_PFG_RNAseq
GSM8141930 Het2_PFG_RNAseq_rep3 Het2_PFG_RNAseq
GSM8141931 Homo_GT_RNAseq_rep1 Homo_GT_RNAseq
GSM8141932 Homo_GT_RNAseq_rep2 Homo_GT_RNAseq
GSM8141933 Homo_GT_RNAseq_rep3 Homo_GT_RNAseq
GSM8141934 Homo_PFG_RNAseq_rep1 Homo_PFG_RNAseq
GSM8141935 Homo_PFG_RNAseq_rep2 Homo_PFG_RNAseq
GSM8141936 Homo_PFG_RNAseq_rep3 Homo_PFG_RNAseq

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