Abstract
SummaryTranscriptome analysis enables the study of gene expression in human tissues and is a valuable tool to characterise liver function and gene expression dynamics during liver disease, as well as to identify prognostic markers or signatures, and to facilitate discovery of new therapeutic targets. In contrast to whole tissue RNA sequencing analysis, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics enables the study of transcriptional activity at the single cell or spatial level. ScRNA-seq has paved the way for the discovery of previously unknown cell types and subtypes in normal and diseased liver, facilitating the study of rare cells (such as liver progenitor cells) and the functional roles of non-parenchymal cells in chronic liver disease and cancer. By adding spatial information to scRNA-seq data, spatial transcriptomics has transformed our understanding of tissue functional organisation and cell-to-cell interactions in situ. These approaches have recently been applied to investigate liver regeneration, organisation and function of hepatocytes and non-parenchymal cells, and to profile the single cell landscape of chronic liver diseases and cancer. Herein, we review the principles and technologies behind scRNA-seq and spatial transcriptomic approaches, highlighting the recent discoveries and novel insights these methodologies have yielded in both liver physiology and disease biology.
Highlights
Sequencing technologies are increasingly used to study phenotypes and drivers of liver disease
Smart-seq[2] is limited by high costs, so different protocols have evolved to allow for adequate RNA coverage and reduced costs. These protocols involve the capture of the RNA poly(A) tail with the insertion into the complementary DNA (cDNA) of random unique molecular identifiers (UMIs) and pre-specified cellular barcodes (Fig. 2)
WNT-related genes Scavenger functions and platelet activation data from the mouse liver demonstrated that i) major determinants of liver zonation were oxygen gradient and WNT signalling,[18] and RAS signalling, which activates periportal genes, and pituitary signals, which inhibit periportal genes (Fig. 3B); ii) zonation is not always monotonic and some genes, e.g. Hamp encoding for hepcidin, have the highest expression in the mid-layers of the lobule (Fig. 3A); iii) genes encoding for enzymes involved in bile acid metabolism are differently expressed along the porto-central axis, suggesting the spatial zonation of entire metabolic processes; iv) metabolites produced in periportal areas are taken up by pericentral hepatocytes in a process called spatial recycling
Summary
Sequencing technologies are increasingly used to study phenotypes and drivers of liver disease. Smart-seq[2] is a protocol which uses template-switching technologies for the reverse transcription and PCR technologies for the amplification, enabling the sequencing of full-length transcripts and the study of splicing events and allele-specific expression.[6,12,13] Smart-seq[2] is limited by high costs, so different protocols have evolved to allow for adequate RNA coverage and reduced costs These protocols involve the capture of the RNA poly(A) tail with the insertion into the cDNA of random unique molecular identifiers (UMIs) and pre-specified cellular barcodes (Fig. 2). A major challenge in the use of scRNA-seq for the study of liver physiology is the integration of individual cell RNA data with spatial information To overcome this hurdle, specific sequencing strategies and bioinformatic analyses have been developed (Table 1), allowing new insights into liver zonation (Fig. 3). ScRNA-seq comprises multiple technologies and the choice of platform used should be guided by the biological question, the study design and endpoints required
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