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Transcriptomic Profiling of Patients’ Human Brown and White Adipose Tissue Depots

GSE334145 Homo sapiens (human) RNA-seq (gene expression) 68 samples Submitted 2026/07/07 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 68 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
68 samples: 68 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.
Summary (written by the submitters)
Human adipose tissues are solid organs distributed throughout the body in specific locations and are thought to have dichotomous functions: white adipose tissue (WAT) stores triglycerides as an energy reservoir, and brown adipose tissue (BAT) uses metabolic fuels to generate heat. To define the transcriptional profile of six principal adipose depots, we analyzed tissue from 11 autopsies (3F/8M, 16-71y) of patients who had different underlying diseases.
Read the full summary ↓
Despite global similarities in terms of cell type proportions, each depot had a distinct transcriptomic signature and functional profile. Each human’s adipose tissues were more similar to each other than to the anatomically and functionally matching depots in other patients. The transcriptomic pattern of subcutaneous WAT could even be used to predict the human donor. This pattern suggests that individualized factors such as genetics, age, environment, diet, and disease play impactful roles in coordinating the behavior of the whole-body adipose tissue depot.
This dataset
Open GSE334145 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 GSE334145_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: 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 PRJNA1473913 and SRA study SRP706216. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
68 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 68 sample titles: 68 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

+ 28 more — browse all 68 samples with per-sample file links →

Datasets on a similar topic

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.