GEO series

Defining the vascular niche of human adipose tissue across metabolic conditions

GSE268904 Homo sapiens (human) Single-nucleus RNA-seq 14 samples Submitted 2025/12/31 Platform GPL28038

At a glance

Single-nucleus RNA-seq · human · 14 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: 14 samples: 14 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: Defining the vascular niche of human adipose tissue across metabolic statesbioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

White adipose tissue requires a well-maintained vascular network to function properly. Although advances in single-cell transcriptomics have allowed the development of comprehensive human white adipose tissue atlases, there has been a little focus on deciphering the heterogeneity and the functional states of adipose vascular cells, including blood adipose endothelial cells (AdECs), adipose lymphatic endothelial cells (AdLECs), and mural cells (pericytes and vascular smooth muscle cells).

Read the full summary ↓

Using single nucleus RNA sequencing (snRNA-seq), we mapped human subcutaneous adipose tissue cellular clusters, and focused on cells comprising the vascular niche. We further integrated our in-house generated cohort with seven publicly available datasets of human SAT single cell RNA-seq (scRNA-seq) and snRNA-seq. The generated atlas comprised nearly 70,000 vascular cells from 65 donors, which allowed us to explore the adipose vascular niche. By mining this rich resource, we identify eight endothelial populations, comprising seven canonical subtypes and one additional heterogeneous population of sub-endothelial AdECs. We also provide detailed analyses comparing the transcriptomes of vascular cells from individuals living with or without obesity and type 2 diabetes. These data provide an extensive atlas for the exploration of the adipose vascular niche and its modulation of adipose tissue biology in health and disease.

Published in

Defining the vascular niche of human adipose tissue across metabolic states

AlZaim I, Hassan MN, Schröter M et al. · bioRxiv : the preprint server for biology 2025 · PMID 41040169 · doi:10.1101/2024.09.22.610444

This dataset

Open GSE268904 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 GSE268904_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 PRJNA1119437 and SRA study SRP511352. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41040169) ↗

Study design

14 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 14 sample titles: 14 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

GSM8302405 SAT, sample_9S, snRNAseq
GSM8302406 SAT, sample_10S, snRNAseq
GSM8302407 SAT, sample_13S, snRNAseq
GSM8302408 SAT, sample_17S, snRNAseq
GSM8302409 SAT, sample_SAT22, snRNAseq
GSM8302410 SAT, sample_SAT54, snRNAseq
GSM8302411 SAT, sample_SAT1, snRNAseq
GSM8302412 SAT, sample_SAT18, snRNAseq
GSM8302413 SAT, sample_SAT34, snRNAseq
GSM8302414 SAT, sample_SAT39, snRNAseq
GSM8302415 SAT, sample_SAT4, snRNAseq
GSM8302416 SAT, sample_SAT51, snRNAseq
GSM8302417 SAT, sample_SAT58, snRNAseq
GSM8302418 SAT, sample_SAT60, snRNAseq

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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.