GEO series

Human subcutaneous and visceral adipocyte atlases uncover classical and non-classical adipocytes and depot-specific patterns

GSE281356 Homo sapiens (human) Single-nucleus RNA-seq 15 samples Submitted 2024/11/11 Platform GPL24676Platform GPL18573

At a glance

Single-nucleus RNA-seq · human · 15 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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: 15 samples: 15 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: Human subcutaneous and visceral adipocyte atlases uncover classical and nonclassical adipocytes and depot-specific patternsNature genetics · 2025

Summary (written by the submitters)

Human adipose depots are functionally distinct. Yet, recent single-nucleus RNA-sequencing (snRNA-seq) analyses largely uncovered overlapping/similar cell-type landscapes. We hypothesized that adipocytes subtypes, differentiation trajectories, and/or intercellular communication patterns could illuminate this depot similarity-difference gap.

Read the full summary ↓

For this, we performed snRNA-seq of human subcutaneous/visceral adipose tissues (5/10 samples, respectively). Of 27,665 adipocyte nuclei in both depots, the majority were “classical”, namely- enriched in lipid metabolism pathways. However, we also observed “non-classical” adipocyte subtypes, enriched in immune-related, extracellular matrix deposition (fibrosis), vascularization/angiogenesis, or ribosomal processes. Pseudo-temporal analysis showed a developmental trajectory from adipose progenitor cells to classical adipocytes via non-classical adipocytes, suggesting that the classical state stems from loss, rather than gain, of specialized functions. Lastly, intercellular communication routes were consistent with the different inflammatory tone of the two depots. Jointly, these findings provide a high-resolution view into the contribution of cellular composition, differentiation, and intercellular communication patterns to human fat depot differences.

Published in

Human subcutaneous and visceral adipocyte atlases uncover classical and nonclassical adipocytes and depot-specific patterns

Lazarescu O, Ziv-Agam M, Haim Y et al. · Nature genetics 2025 · PMID 39856219 · doi:10.1038/s41588-024-02048-3

This dataset

Open GSE281356 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 GSE281356_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 PRJNA1183538 and SRA study SRP543910. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39856219) ↗

Study design

15 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 15 sample titles: 15 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

GSM8619160 donor_4263_visceral fat
GSM8619161 donor_3427_subcutaneous adipose tissue
GSM8619162 donor_3382_subcutaneous adipose tissue
GSM8619163 donor_3431_visceral fat
GSM8619164 donor_3384_subcutaneous adipose tissue
GSM8619165 donor_3427_visceral fat
GSM8619166 donor_3387_visceral fat
GSM8619167 donor_3382_visceral fat
GSM8619168 donor_3313_visceral fat
GSM8619169 donor_3384_visceral fat
GSM8619170 donor_3357_visceral fat
GSM8619171 donor_3387_subcutaneous adipose tissue
GSM8619172 donor_3399_visceral fat
GSM8619173 donor_3399_subcutaneous adipose tissue
GSM8619174 donor_3350_visceral fat

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Search all human RNA-seq datasets in GEO →

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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.