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A dynamic subcutaneous adipose transcriptome of human insulin resistance identifies a molecular predisposition shared with visceral adipose tissue

GSE245948 Homo sapiens (human) Single-cell RNA-seq 76 samples Submitted 2024/10/19 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 76 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
76 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)
Subcutaneous adipose tissue (SAT) is classically viewed as a metabolic buffer for lipid deposition during positive caloric balance, while visceral adipose tissue (VAT) is viewed as the dominant contributor and prime mediator of insulin resistance (IR) and cardiometabolic disease risk. Nevertheless, a growing body of data suggests that similar morphologic and molecular changes may occur in SAT as in VAT during obesity.
Read the full summary ↓
In addition, while pro-inflammatory immune changes within adipose are thought to drive IR there is increasing data implicating a role for adipocytes and stromal populations especially in humans. Here, we identified a transcriptional landscape of IR in SAT of 220 humans across the spectrum of obesity and IR states, highlighting a broad range of metabolic pathways central to IR. Using single cell and nucleus deconvolution and statistical learning techniques, we identified a 35-gene signature that (1) achieved high predictive accuracy for homeostatic model of IR (HOMA-IR) across BMI; (2) was expressed across a variety of non-immune cell populations (most prominently adipocytes and adipocyte stem and precursor cells [ASPCs]), with primarily “protective” IR associations for adipocyte transcripts and “deleterious” associations for macrophage transcripts; (3) displayed a high concordance between SAT and VAT (greater than non-IR associated genes). Multiple SAT genes exhibited dynamic expression 5-years after weight loss surgery and with insulin stimulation. Finally, using available expression quantitative trait loci in SAT and/or VAT, we demonstrate similar genetic effect sizes of SAT and VAT on type 2 diabetes and BMI, suggesting underlying similarities in genetic determinants of IR between adipose depots. These results implicate a dynamic transcriptional architecture of IR that resides in both immune and non-immune populations in SAT and that is shared with VAT, nuancing the current VAT-centric concept of IR in humans.
This dataset
Open GSE245948 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 GSE245948_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 PRJNA1030424 and SRA study SRP467602. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 76 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run P4244 … P4239 and carry no group information.

Samples in this study

+ 36 more — browse all 76 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.