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Adipocyte death drives fat redistribution from adipocytes to hepatocytes in steatotic liver disease via S100A8+ macrophages [RNA-seq]

GSE285614 Mus musculus (mouse) Single-cell RNA-seq 34 samples Submitted 2025/08/07 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 34 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
34 samples: 8 conditions, mostly with about 5 replicates eachGroups 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)
Both adipocytes and hepatocytes have the capacity to store fat, but the factor(s) that determine fat distribution between these cell types remain unknown. In mice fed a high-fat diet, fat initially accumulates predominantly in adipocytes, while hepatic fat accumulation mainly emerges after the onset of epididymal adipocyte death that results in elevated free fatty acids to promote lipid accumulation in hepatocytes.
Read the full summary ↓
However, it remains unclear whether other signals following adipocyte death are required to direct and/or promote hepatocytes to store fat and subsequently trigger metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as nonalcoholic fatty liver disease). Using genetically modified mouse models combined with bulk and single-cell RNA sequencing analysis, we demonstrated that adipocyte death induces an accumulation of S100A8+ macrophages in the liver, which is partially induced by fatty acids. Macrophage-specific deletion of the S100a8 gene reduced hepatic fat accumulation and MASLD severity in mice. Mechanistically, S100A8+ macrophages suppress cellular communication network factor (CCN3), a negative regulator of CD36, thereby enhancing CD36 expression in hepatocytes. In conclusion, adipocyte death promotes hepatic infiltration of S100A8+ macrophages, which drive hepatocyte lipid storage and subsequently promote MASLD progression through CD36 upregulation, partially mediated by CCN3 suppression.
This dataset
Open GSE285614 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 GSE285614_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 PRJNA1204963 and SRA study SRP554696. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly with about 5 replicates each
HFD_WT ×5 HFD_Bcl2TG ×5 S100a8f/fCx3cr1Cre_Chow ×5 S100a8f/f_HFD ×5 S100a8f/f_Chow ×4 S100a8f/fCx3cr1Cre_HFD ×4 Chow_WT ×3 Chow_Bcl2TG ×3

Supports a between-group comparison across 34 samples.

How we read this: 8 replicated groups read from 34 sample titles; they account for 34 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
Datasets on a similar topic

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