GEO series

Single-Cell Multi-omics Profiling of Mouse Hepatic Immune Microenvironment Reveals Neutrophil-Kupffer Cell Crosstalk via Thrombospondin-1/CD36 after Surgical Stress

GSE315482 Mus musculus (mouse) Single-cell RNA-seq 10 samples Submitted 2026/07/15 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 10 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: 10 samples: 10 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)

The hepatic immune microenvironment comprises both innate and adaptive immune responses, to surgical stress during liver surgeries, leading to ischemia/reperfusion (I/R) injury. Traditional approaches to treat I/R injury have predominantly targeted single, known injury mechanisms, often overlooking the complexity of the hepatic immune microenvironment.

Read the full summary ↓

Consequently, pharmaceutical interventions focusing on isolated pathways have yielded minimal therapeutic success, underscoring an urgent need for a more comprehensive understanding of the underlying cellular and molecular dynamics. By leveraging single-cell multi-omics technologies, we dissected the multifaceted cellular interactions and molecular alterations within the hepatic immune microenvironment post-surgery. Importantly, we demonstrate that targeting the interaction between infiltrating neutrophils and hepatic resident macrophages, Kupffer cells, through the thrombospondin-1/CD36 pathway during liver I/R. Together, these findings unveil extensive reprogramming of immune cell populations and activation of multiple signaling pathways in response to surgical stress, emphasizing the significance of myeloid cell interactions. This research opens avenues for investigating interactions among different hepatic immune cell types in response to liver damage.

This dataset

Open GSE315482 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 GSE315482_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 PRJNA1394454. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

10 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 10 sample titles: 10 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

GSM9429339 Sham_1 single-cell RNA-seq library
GSM9429340 Sham_2 single-cell RNA-seq library
GSM9429341 SedIR_1 single-cell RNA-seq library
GSM9429342 SedIR_2 single-cell RNA-seq library
GSM9429343 SedIR_3 single-cell RNA-seq library
GSM9429344 SedIR_4 single-cell RNA-seq library
GSM9429345 Spatial_SedSham_1 Spatial RNA-seq library
GSM9429346 Spatial_SedSham_2 Spatial RNA-seq library
GSM9429347 Spatial_SedIR_1 Spatial RNA-seq library
GSM9429348 Spatial_SedIR_2 Spatial RNA-seq library

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GSE318498 Gene expression differences between LPS, anti-CD36 Abs, or LPS plus anti-CD36 Abs treated RAW264.7 cells. 12 samples shares cd36

Search all mouse 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.