Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis

Abstract

Liver disease is a major global health-care problem, affecting an estimated 844 million people worldwide. Despite this substantial burden, therapeutic options for liver disease remain limited, in part owing to a paucity of detailed analyses defining the cellular and molecular mechanisms that drive these conditions in humans. Single-cell transcriptomic technologies are transforming our understanding of cellular diversity and function in health and disease. In this Review, we discuss how these technologies have been applied in hepatology, advancing our understanding of cellular heterogeneity and providing novel insights into fundamental liver biology such as the metabolic zonation of hepatocytes, endothelial cells and hepatic stellate cells, and the cellular mechanisms underpinning liver regeneration. Application of these methodologies is also uncovering critical pathophysiological changes driving disease states such as hepatic fibrosis, where distinct populations of macrophages, endothelial cells and mesenchymal cells reside within a spatially distinct fibrotic niche and interact to promote scar formation. In addition, single-cell approaches are starting to dissect key cellular and molecular functions in liver cancer. In the near future, new techniques such as spatial transcriptomics and multiomic approaches will further deepen our understanding of disease pathogenesis, enabling the identification of novel therapeutic targets for patients across the spectrum of liver diseases.

Key points

  • Single-cell RNA sequencing coupled with spatial mapping have demonstrated previously unknown molecular patterns of metabolic zonation of hepatocytes, endothelial cells and hepatic stellate cells across the human and mouse liver lobule.

  • Single-cell analysis of the adaptive immune compartment, specifically T cells, in hepatocellular carcinoma and intrahepatic cholangiocarcinoma has highlighted potential new approaches to prognostication and the development of immunotherapy strategies for affected patients.

  • The study of human liver macrophage subpopulations at the single-cell level has identified a distinct population of monocyte-derived macrophages that expand during liver fibrosis; these cells reside in the fibrotic niche and promote mesenchymal cell activation and scar deposition.

  • Distinct populations of endothelial cells and mesenchymal cells also expand in liver fibrosis and are topographically located in the fibrotic niche.

  • Interactome modelling of ligand–receptor pairs between subpopulations of scar-associated macrophages, and endothelial and mesenchymal cells, in liver fibrosis provides a molecular framework for the therapeutic targeting of key pathogenic cell populations.

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Fig. 1: Workflow of single-cell RNA sequencing experiments.
Fig. 2: Zonation of cells across the liver sinusoid.
Fig. 3: Macrophage dynamics in human liver fibrosis.
Fig. 4: Defining human endothelial cell heterogeneity.
Fig. 5: Dissecting the cellular interactome within the fibrotic niche.

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Acknowledgements

The authors acknowledge the support of the Wellcome Trust, Medical Research Council and the Chan Zuckerberg Initiative.

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Related links

A human liver cell atlas reveals heterogeneity and epithelial progenitors: http://human-liver-cell-atlas.ie-freiburg.mpg.de

Landscape and dynamics of single immune cells in hepatocellular carcinoma: http://cancer-pku.cn:3838/HCC/

Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing: http://hcc.cancer-pku.cn

Resolving the fibrotic niche of human liver cirrhosis at the single-cell level: http://www.livercellatlas.mvm.ed.ac.uk

Single-cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations: https://github.com/BaderLab/HumanLiver

Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis: http://livermesenchyme.hendersonlab.mvm.ed.ac.uk

Glossary

Functional zonation

The functional specialization of different spatial regions of liver cells, distinguished by their zonal distribution.

Trajectory analysis

By considering each single cell as representative of a snapshot along a continuous process, trajectory analysis attempts to reconstruct this path through cellular space by minimizing transcriptional changes between neighbouring cells.

Pathway enrichment analysis

Given a list of genes, for example, differentially expressed markers of a given cellular population, pathway enrichment analysis identifies biological pathways that are more enriched than would be expected by chance.

Fate-mapping computational algorithms

Fate-mapping algorithms quantify fate biases for progenitor cells along branching differentiation trajectories, to provide insight into cell fate choices and their regulation.

Transcriptome

The set of all RNA molecules from one cell or group of cells.

Gene regulatory network reconstruction

A computational approach that attempts to uncover the complex interplay of regulatory interactions that ultimately determine the expression level of a given gene, using such measurements as correlation.

Antibody-tagging techniques

Methodologies in which antibodies are tagged with a unique molecular barcode. Barcoded antibodies bind to target epitopes on cells and the unique molecular barcodes are sequenced alongside the cellular transcriptome, providing an indication of the degree of antibody binding and the level of the target antigen on each single cell.

Immunophenotyping

The analysis of heterogeneous populations of cells for the purpose of identifying the presence and proportions of the various populations of interest.

Canonical correlation analysis

A data integration methodology that attempts to identify shared correlation structures across datasets, which can then be used to align multiple datasets to one another in a way that minimizes batch effect.

RNA velocity analysis

This analysis uses the ratio of unspliced to spliced mRNA to infer directionality in single-cell data, by predicting the future state of individual cells and superimposing this prediction onto other cells in the dataset.

Deconvolution algorithms

Using reference gene expression profiles of cell types of interest, these algorithms estimate cell type composition within a bulk RNA sample containing an unknown mixture of cells.

Gene set enrichment

From a list of genes, for example, differentially expressed markers of a cellular population, gene set enrichment analysis identifies biological features such as pathways or functions that are more enriched than would be expected by chance.

Interactome

The molecular interactions between biological entities such as proteins within cells and organisms.

CellPhoneDB

This resource is a publicly available repository of curated receptors, ligands, and their interactions, integrated with a statistical framework that enables prediction of enriched cellular interactions between cell types from single-cell transcriptomic data.

Secretome

The set of molecules secreted by a cell or organism.

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Ramachandran, P., Matchett, K.P., Dobie, R. et al. Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis. Nat Rev Gastroenterol Hepatol 17, 457–472 (2020). https://doi.org/10.1038/s41575-020-0304-x

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