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A single-nucleus dataset sampling subcutaneous white adipose from living donors to study familial partial lipodystrophy 2

GSE310542 Homo sapiens (human) Single-nucleus RNA-seq 30 samples Submitted 2025/11/22 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 30 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).
Organism
Homo sapiens (human)
Samples
30 samples: 30 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
Altered lipid metabolism and inflammatory programs associate with adipocyte loss in familial partial lipodystrophy 2The Journal of clinical investigation · 2026
Summary (written by the submitters)
Familial partial lipodystrophy 2 (FPLD2) is a rare disease characterized by adipose tissue loss and redistribution, and metabolic dysfunction. FPLD2 is caused by pathogenic variants in the LMNA gene, encoding nuclear lamins A/C, structural proteins that control nuclear function and gene expression. However, the mechanisms driving adipocyte loss in FPLD2 remain poorly defined.
Read the full summary ↓
In this study, we recruited eight families with developing or established FPLD2 and performed clinical, histological, and transcriptomic analyses of subcutaneous adipose tissue biopsies. Bulk and single-nuclei RNA-sequencing revealed suppression of lipid metabolism and mitochondrial pathways, alongside increased inflammation. These signatures were mirrored in tamoxifen-inducible adipocyte-specific Lmna knockout mice, in which lamin A/C-deficient adipocytes shrank and disappeared. Lmna-deficient fibroblasts shared similar gene expression changes, linked to altered chromatin accessibility, underscoring lamin A/C’s potential regulatory role in lipid metabolism and inflammatory programs. By directly comparing atrophic and hypertrophic adipose depots in FPLD2, and integrating human, mouse, and in vitro models, this study provides new insights into disease progression and potential therapeutic targets.
Published in
Altered lipid metabolism and inflammatory programs associate with adipocyte loss in familial partial lipodystrophy 2
Maung JN, Schill RL, Nishii A et al. · The Journal of clinical investigation 2026 · PMID 41217838 · doi:10.1172/JCI198387
This dataset
Open GSE310542 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 GSE310542_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 PRJNA1366488 and SRA study SRP647011. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41217838) ↗
Study design
30 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 30 sample titles: 30 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
Datasets on a similar topic

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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.