GEO series

Single-cell lineage tracing uncovers unique skin adipose hierarchy and Sox9-mediated differentiation of adipocyte precursors

GSE241627 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2024/08/23 Platform GPL19057

At a glance

Single-cell RNA-seq · mouse · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 12 samples: 4 × P32 Naive biol vs 2 × P32 Sox9 OE prog biol vs 2 × P32 No Cebpa_DN biol vs 2 × P32 Inguinal transp biolGroups 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)

White adipose tissue is essential to various physiological functions, with adipocyte precursor cells (APCs)—comprised of progenitors and preadipocytes—playing a pivotal role in generating mature adipocytes during the expansion of adipose depots. Here, using single-cell RNA sequencing-based lineage tracing, we characterize APC populations in the skin, a depot distinguished by its uniquely rapid and efficient adipogenesis relative to other sites, such as inguinal adipose.

Read the full summary ↓

We identify a previously uncharacterized population of immature preadipocytes and reveal a biased differentiation potential among APCs. Contrary to the step-wise differentiation paradigm proposed to yield mature adipocytes, progenitor cells represent the primary source of committed preadipocytes, while preexisting preadipocytes accumulate in immature states with distinct potential. We use this framework of cell heterogeneity and lineage in skin adipose to investigate the mechanisms of APC differentiation, identifying Sox9 as a crucial regulator of precursor proliferation and differentiation. Finally, via cross-depot transplantation, we define intrinsic and extrinsic factors that influence the behavior of skin progenitors, highlighting the contrasting dynamics between skin and inguinal depots. These findings provide a deeper understanding of the specific dynamics and molecular mechanisms underlying rapid adipogenesis in skin adipose tissue.

This dataset

Open GSE241627 in finder → Find more mouse 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 GSE241627_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 PRJNA1009010 and SRA study SRP456911. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

4 × P32 Naive biol vs 2 × P32 Sox9 OE prog biol vs 2 × P32 No Cebpa_DN biol vs 2 × P32 Inguinal transp biol

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 10 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

GSM7732265 P32 Naive biol rep 1 P32 Naive biol
GSM7732266 P32 Naive biol rep 2 P32 Naive biol
GSM7732267 P32 Naive biol rep 3 P32 Naive biol
GSM7732268 P32 Naive biol rep 4 P32 Naive biol
GSM7732269 P32 Sox9 OE prog biol rep 1 P32 Sox9 OE prog biol
GSM7732270 P32 Sox9 OE prog biol rep 2 P32 Sox9 OE prog biol
GSM7732271 P32 Sox9 OE preadip
GSM7732272 P21 Naive
GSM8341547 P32 No Cebpa_DN biol rep 1 P32 No Cebpa_DN biol
GSM8341548 P32 No Cebpa_DN biol rep 2 P32 No Cebpa_DN biol
GSM8341549 P32 Inguinal transp biol rep 1 P32 Inguinal transp biol
GSM8341550 P32 Inguinal transp biol rep 2 P32 Inguinal transp biol

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