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Lipid droplet-mediated danger signaling balances Trem2+ macrophage induction and NLRP3 inflammasome activation in NASH

GSE230700 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2024/04/26 Platform GPL28457Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 14 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: Mus musculus (mouse)
Samples: 14 samples: 3 × RNA-seq WT vs 3 × RNA-seq KO vs 3 × RNA-seq PBS vs 3 × RNA-seq LDGroups 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)

Macrophages are exquisitely capable of sensing danger-associated molecular patterns (DAMPs) and orchestrating inflammatory response during tissue injury. Nonalcoholic steatohepatitis (NASH) represents an advanced stage of metabolic fatty liver disease that increases the risk for cirrhosis and liver cancer. Pathogenic mechanisms of NASH center on hepatocyte injury and the ensuing immune response within the liver microenvironment.

Read the full summary ↓

However, the nature of DAMPs released by injured hepatocytes and how they shape the liver immune milieu remain largely unknown. Here we show that lipid droplets (LDs) released by injured fatty hepatocytes provide a potent signal that triggers monocyte infiltration and maturation into Trem2+ macrophages, recently described as NASH-associated macrophages (NAMs) or lipid- associated macrophages. LD treatment exacerbated liver injury in mice with diet-induced NASH. We identified Membrane spanning 4-domains a7 (Ms4a7) as a NAM-specific pathogenic factor that was strongly induced in mouse and human NASH. Ms4a7 inactivation ameliorated key aspects of diet-induced NASH pathologies in mice. At the mechanistic level, Ms4a7 physically interacts with NLR family pyrin domain containing 3 (NLRP3) and is required for endosomal NLRP3 inflammasome activation. These findings illustrate a crucial role of Ms4a7 in driving pro-inflammatory signaling in NASH liver. Surprisingly, LD treatments attenuated Ms4a7 expression and NLRP3 inflammasome activation in cultured macrophages. As such, LDs serve as a DAMP signal that balances the induction of Trem2+ macrophages and NLRP3 inflammasome activation in NASH liver

This dataset

Open GSE230700 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 GSE230700_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 PRJNA962145 and SRA study SRP434740. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × RNA-seq WT vs 3 × RNA-seq KO vs 3 × RNA-seq PBS vs 3 × RNA-seq LD

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 12 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

GSM7232760 RNA-seq WT-1 RNA-seq WT
GSM7232761 RNA-seq WT-2 RNA-seq WT
GSM7232762 RNA-seq WT-3 RNA-seq WT
GSM7232763 RNA-seq KO-1 RNA-seq KO
GSM7232764 RNA-seq KO-2 RNA-seq KO
GSM7232765 RNA-seq KO-3 RNA-seq KO
GSM7232766 RNA-seq PBS-1 RNA-seq PBS
GSM7232767 RNA-seq PBS-2 RNA-seq PBS
GSM7232768 RNA-seq PBS-3 RNA-seq PBS
GSM7232769 RNA-seq LD-1 RNA-seq LD
GSM7232770 RNA-seq LD-2 RNA-seq LD
GSM7232771 RNA-seq LD-3 RNA-seq LD
GSM7232772 snRNA-seq WT
GSM7232773 snRNA-seq KO

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