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      Single-cell genomics and spatial transcriptomics: Discovery of novel cell states and cellular interactions in liver physiology and disease biology

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          Summary

          Transcriptome 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.

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          Massively parallel digital transcriptional profiling of single cells

          Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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            Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

            Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
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              RNA velocity of single cells

              RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.
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                Author and article information

                Contributors
                Journal
                8503886
                J Hepatol
                J Hepatol
                Journal of hepatology
                0168-8278
                1600-0641
                17 September 2020
                01 November 2020
                10 June 2020
                01 November 2020
                : 73
                : 5
                : 1219-1230
                Affiliations
                [1 ]Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Strasbourg, France
                [2 ]Université de Strasbourg, Strasbourg, France
                [3 ]Pôle Hépato-digestif, Institut-Hospitalo-Universitaire, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
                [4 ]Centre for Inflammation Research, University of Edinburgh, Edinburgh EH16 4TJ, UK
                [5 ]MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh,Edinbnrgh EH4 2XU,UK
                [6 ]Institut Universitaire de France, Paris, France
                Author notes
                [* ]Corresponding authors. Addresses: Inserm U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Universite de Strasbourg, 3 rue Koeberlé, 67000 Strasbourg, France (T.F. Baumert), or Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK (N.C. Henderson).
                Article
                EMS95149
                10.1016/j.jhep.2020.06.004
                7116221
                32534107
                a9051e79-5c0b-4368-baaa-fc4925897ee2

                This is an open access article under the CC BY license https://creativecommons.org/licenses/by-nc-nd/4.0/.

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                Categories
                Article

                Gastroenterology & Hepatology
                single-cell,single-cell rna sequencing,spatial transcriptomics,zonation,liver diseases,hepatocellular carcinoma,microenvironment,cirrhosis,fibrosis,non-parenchymal cells

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