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Related Experiment Video

Updated: Jan 16, 2026

Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells

Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells

Published on: May 23, 2025

Cell-Type Deconvolution Reveals Dynamic Changes in MASLD.

Jeff J H Kim¹, Yang Dai¹

¹Department of Biomedical Engineering, University of Illinois Chicago, Chicago IL USA.

Liver International Communications

|October 6, 2025

Summary

This summary is machine-generated.

This study reveals significant cellular changes during metabolic-associated steatotic liver disease (MASLD) progression to metabolic-associated steatohepatitis (MASH). Analyzing cell type proportions improves fibrosis staging, offering new therapeutic insights.

Keywords:

Cell-Type Deconvolution Disease Progression Fibrosis Staging MASH MASLD

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Last Updated: Jan 16, 2026

Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells

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Published on: February 18, 2022

Area of Science:

Hepatology
Genomics
Immunology

Background:

Metabolic-associated steatotic liver disease (MASLD) is a prevalent liver disorder.
Progression to metabolic-associated steatohepatitis (MASH) involves fibrosis and inflammation, lacking effective treatments.
Understanding cellular dynamics is crucial for elucidating MASH mechanisms.

Purpose of the Study:

To conduct a comprehensive analysis of cell type proportions across the MASLD to MASH spectrum.
To investigate cellular dynamics in relation to fibrosis and Non-Alcoholic Fatty Liver Disease Activity Score (NAS) staging.
To explore the utility of cell type proportions in improving fibrosis staging classification.

Main Methods:

Utilized MuSiC deconvolution on two bulk RNA sequencing datasets.
Analyzed cell type proportions across MASLD and MASH spectrum, including fibrosis and NAS staging.
Integrated cell type proportion data with transcriptomic profiles for machine learning models.

Main Results:

Identified distinct proportional trends in 10 cell types, including hepatocytes, cholangiocytes, hepatic stellate cells, endothelial cells, Kupffer cells, TREM2+ macrophages, and plasma B cells.
Demonstrated that integrating cell type proportions with transcriptomic data significantly enhanced random forest model performance for fibrosis staging.
Revealed specific cellular dynamics associated with MASLD progression.

Conclusions:

Cellular dynamics play a critical role in MASLD progression to MASH.
Comprehensive cell type analysis provides deeper insights into disease mechanisms.
Findings may inform the development of novel therapeutic strategies for MASH.