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A Novel EBF2 Nonsense Variant Drives Adipose Tissue Fibrosis and Dysfunction in Partial Lipodystrophy Syndrome

GSE288824 Homo sapiens (human) RNA-seq (gene expression) 11 samples Submitted 2025/02/17 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 11 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
11 samples: 3 × Human Pre-adipocytes, empty lentivirus+GFP, differentiated vs 3 × Human Pre-adipocytes, EBF2 (1-164)_lentivirus+GFP, differentiated vs 3 × Human Pre-adipocytes, control_no-lentivirus, differentiated vs 2 × Human Pre-adipocytes, EBF2-full_lentivirus+GFP, differentiatedGroups 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
EBF2 variant identified in a patient with atypical partial lipodystrophy causes adipose fibrosis and dysfunctionThe Journal of clinical investigation · 2026
Summary (written by the submitters)
In vitro disease modeling of an erly B cell factor 2 (EBF2) nonsense variant (EBF2 8:26033143 C>A, c.493G>T, p.E165X) demonstrates that the EBF2 variant impairs adipogenesis causing excess accumulation of extracelluar matrix protein and inflammatory myeloid cells.
Published in
EBF2 variant identified in a patient with atypical partial lipodystrophy causes adipose fibrosis and dysfunction
Foss-Freitas MC, Gilio D, Pais L et al. · The Journal of clinical investigation 2026 · PMID 41615236 · doi:10.1172/JCI192737
This dataset
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.

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 GSE288824_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1219568 and SRA study SRP561764. Searching any of these in the dataset finder brings you back here.

Study design
3 × Human Pre-adipocytes, empty lentivirus+GFP, differentiated vs 3 × Human Pre-adipocytes, EBF2 (1-164)_lentivirus+GFP, differentiated vs 3 × Human Pre-adipocytes, control_no-lentivirus, differentiated vs 2 × Human Pre-adipocytes, EBF2-full_lentivirus+GFP, differentiated

Supports a between-group comparison across 11 samples.

How we read this: 4 replicated groups read from 11 sample titles; they account for 11 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
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