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The liver is an important organ in vertebrates that plays an essential role in metabolism. It is also responsible for storing and redistributing nutrients such as carbohydrates, fats, and vitamins in the body. Additionally, the liver releases bile salts which are critical for digesting food and eliminating toxic metabolites from the body.
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Updated: May 29, 2025

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Published on: December 9, 2022

uniLIVER: a human liver cell atlas for data-driven cellular state mapping.

Yanhong Wu¹, Yuhan Fan¹, Yuxin Miao¹

¹MOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao

|February 1, 2025

Summary

This study introduces uniLIVER, a comprehensive human liver cell atlas, and LiverCT, a machine learning tool for analyzing cellular states in liver disease. LiverCT identifies novel biomarkers for hepatocellular carcinoma prognosis.

Keywords:

Abnormal state identification Hepatic zonation Liver atlas Patient stratification Reference mapping

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Area of Science:

Cellular and Molecular Biology
Bioinformatics
Genomics

Background:

The liver's complex cellular composition is crucial for its metabolic and detoxification functions.
Single-cell RNA sequencing (sc/snRNA-seq) offers unprecedented resolution for mapping tissue atlases.
Understanding cellular heterogeneity is key to deciphering liver physiology and pathology.

Purpose of the Study:

To construct a unified, high-resolution reference atlas of the normal human liver.
To develop a novel computational method (LiverCT) for analyzing cellular states in query datasets.
To investigate the prognostic implications of cellular states and zonation in liver cancer.

Main Methods:

Integrative analysis of a large-scale dataset comprising 331,125 cells from 6 sc/snRNA-seq studies of normal human liver.
Development and application of LiverCT, a machine learning approach for mapping query data to the reference atlas.
Analysis of liver cancer datasets to identify "variant" cellular states and their correlation with clinical outcomes.

Main Results:

Creation of uniLIVER, a comprehensive single-cell reference map of the human liver.
Identification of "deviated" T cell states associated with stress pathways in hepatocellular carcinoma.
Correlation of hepatocyte-cholangiocyte intermediate states and tumor cell zonation tendencies with poor prognosis.

Conclusions:

The uniLIVER atlas provides a valuable resource for liver research.
LiverCT enables the identification of clinically relevant cellular states and deviations from normal tissue.
This framework offers a scalable approach for creating tissue-specific atlases and analyzing disease states.