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Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease [ATAC-seq]

GSE269929 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2024/07/11 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
24 samples: 6 conditions, mostly with about 4 replicates eachGroups 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 nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver diseaseEBioMedicine · 2024
Summary (written by the submitters)
Abdominal obesity increases the risk for non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated steatotic liver disease (MASLD). To elucidate the directional cell-type level biological mechanisms underlying the association between abdominal obesity and MASLD, we integrated adipose and liver single nucleus RNA-sequencing and bulk cis-expression quantitative trait locus (eQTL) data with the UK Biobank genome-wide association study (GWAS) data using colocalization.
Read the full summary ↓
Then we used colocalized cis-eQTL variants as instrumental variables in Mendelian randomization (MR) analyses, followed by functional validation experiments on the target genes of the cis-eQTL variants. We identified 17 colocalized abdominal obesity GWAS variants, regulating 17 adipose cell-type marker genes. Incorporating these 17 variants into MR discovers a putative tissue-of-origin, cell-type-aware causal effect of abdominal obesity on MASLD consistently with multiple MR methods without significant evidence for pleiotropy or heterogeneity. Single cell data confirm the adipocyte-enriched mean expression of the 17 genes. Our cellular experiments across human adipogenesis identify risk variant -specific epigenetic and transcriptional mechanisms. Knocking down two of the 17 genes, PPP2R5A and SH3PXD2B, shows a marked decrease in adipocyte lipidation and significantly alters adipocyte function and adipogenesis regulator genes, including DGAT2, LPL, ADIPOQ, PPARG, and SREBF1. Furthermore, the 17 genes capture a characteristic MASLD expression signature in subcutaneous adipose tissue. Overall, we discover a significant cell-type level effect of abdominal obesity on MASLD and trace its biological effect to adipogenesis (Lee et. al, eBioMedicine 2024).
Published in
Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease
Lee SHT, Garske KM, Arasu UT et al. · EBioMedicine 2024 · PMID 38991381 · doi:10.1016/j.ebiom.2024.105232
This dataset
Open GSE269929 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE269929_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1124130 and SRA study SRP514096. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38991381) ↗
Study design
6 conditions, mostly with about 4 replicates each
preadipocytes, Sample 1, Day 0, ×4 preadipocytes, Sample 1, Day 1, ×4 preadipocytes, Sample 1, Day 2, ×4 preadipocytes, Sample 1, Day 4, ×4 preadipocytes, Sample 1, Day 7, ×4 preadipocytes, Sample 1, Day 14, ×4

Supports a between-group comparison across 24 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 24 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
Datasets on a similar topic

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