Single-cell genomics and spatial transcriptomi.pdf

Journal Pre-proofSingle-cell genomics and spatial transcriptomics:discovery of novel cell states andcellular interactions in liver physiology and disease biologyAntonio Saviano,Neil C.Henderson,Thomas F.BaumertPII:S0168-8278(20)30372-XDOI:https:/doi.org/10.1016/j.jhep.2020.06.004Reference:JHEPAT 7794To appear in:Journal of HepatologyReceived Date:31 January 2020Revised Date:28 May 2020Accepted Date:2 June 2020Please cite this article as:Saviano A,Henderson NC,Baumert TF,Single-cell genomics and spatialtranscriptomics:discovery of novel cell states and cellular interactions in liver physiology and diseasebiology,Journal of Hepatology(2020),doi:https:/doi.org/10.1016/j.jhep.2020.06.004.This is a PDF file of an article that has undergone enhancements after acceptance,such as the additionof a cover page and metadata,and formatting for readability,but it is not yet the definitive version ofrecord.This version will undergo additional copyediting,typesetting and review before it is publishedin its final form,but we are providing this version to give early visibility of the article.Please note that,during the production process,errors may be discovered which could affect the content,and all legaldisclaimers that apply to the journal pertain.2020 European Association for the Study of the Liver.Published by Elsevier B.V.1 Single-cell genomics and spatial transcriptomics:discovery of novel cell states and cellular interactions in liver physiology and disease biology Antonio Saviano1,2,3,Neil C.Henderson4,5,Thomas F.Baumert1,2,3,6 1Inserm,U1110,Institut de Recherche sur les Maladies Virales et Hpatiques,Strasbourg,France 2Universit de Strasbourg,Strasbourg,France 3Ple Hpato-digestif,Institut-Hospitalo-Universitaire,Hpitaux Universitaires de Strasbourg,Strasbourg,France 4Centre for Inflammation Research,University of Edinburgh,Edinburgh EH16 4TJ,UK 5MRC Human Genetics Unit,Institute of Genetics and Molecular Medicine,University of Edinburgh,Edinburgh EH4 2XU,UK 6Institut Universitaire de France,Paris,France.Correspondence should be addressed to:Prof.Thomas F.Baumert,MD,Inserm U1110,Institut de Recherche sur les Maladies Virales et Hpatiques,Universit de Strasbourg,3 rue Koeberl,67000 Strasbourg,France.Email:thomas.baumertunistra.fr Prof.Neil C.Henderson,MD,PhD,Centre for Inflammation Research,The Queens Medical Research Institute,University of Edinburgh,Edinburgh EH16 4TJ,UK.Email:Neil.Hendersoned.ac.uk Keywords:single-cell,single-cell RNA sequencing,spatial transcriptomics,zonation,liver diseases,hepatocellular carcinoma,microenvironment,cirrhosis,fibrosis,non-parenchymal cells.2 Word count:summary 198 words,main text only 4998 words.Figures:7 Tables:1 Authors contribution:AS performed literature research and data collection and analysis,designed the figures and wrote the original draft.AS,NCH,TFB reviewed and wrote the manuscript.NCH and TFB supervised the research.Financial support:A.S.is the recipient of a fellowship of Region-Alsace,IHU and LabEx HEPSYS.N.C.H.acknowledges the support of the Wellcome Trust(Senior Research Fellowship in Clinical Science,ref.103749),Medical Research Council and the Chan Zuckerberg Initiative.T.F.B.acknowledges support by the European Union(ERC-AdG2014 HEPCIR#671231,H2020 HEPCAR#667273,ERC PoC-HEPCAN#862551),Fondation ARC Paris and IHU Strasbourg(TheraHCC2.0,IHU201901299),the Foundation of the University of Strasbourg and Roche Institute(HEPKIN),the Agence Nationale de Recherches sur le Sida(ANRS)and the US National Institutes of Health(R21CA209940,R03AI131066,R01CA233794,U19AI12386).This work has been published under the framework of the LABEX ANR-10-LABX-0028_HEPSYS and PLAN CANCER 2014-2019 HCCMICTAR and benefits from a funding from the state managed by the French National Research Agency as part of the Investments for the Future Program,INCa(National Institute for Cancer)and Inserm.The authors do not declare any conflict of interest.3 KEY POINTS-Single-cell RNA sequencing(scRNA-seq)and spatial transcriptomics are revolutionary techniques which allow the study of liver cell composition,physiology and disease development in unprecedented detail.-ScRNA-seq comprises multiple technologies and the choice of platform used should be guided by the biological question,the study design and endpoints required.-Gathering spatial information from single-cell data is challenging and several sequencing strategies and computational frameworks have been developed to overcome this issue.-ScRNA-seq has uncovered substantial functional heterogeneity within the main liver cell lineages in health and disease,identifying zonation of multiple lineages across the liver lobule,and identification of novel progenitor populations.-Liver zonation is not restricted to hepatocytes,but it is extended to non-parenchymal cells such as liver sinusoidal endothelial cells and stellate cells.-ScRNA-seq of cirrhotic liver samples has allowed investigation of the cellular interactome regulating the human liver fibrotic niche.-Notch signaling is a central pathway involved in cell interactions in the human liver fibrotic niche.-ScRNA-seq has uncovered cellular heterogeneity within the tumor microenvironment of primary liver cancers.SUMMARY Transcriptome analysis allows the study of gene expression of human tissues and it is a valuable tool to characterize liver function,gene expression changes during liver disease,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 to the discovery of previously unknown cell types and subtypes in normal and diseased liver,the study of rare cells such as liver progenitor cells as well as the functional role of non-parenchymal cells in chronic liver 4 disease and cancer.By adding spatial information to scRNA-seq data,spatial transcriptomics transforms understanding of tissue functional organization and cell-to-cell interactions in their native environment.These approaches have recently been applied to investigate liver regeneration,organization and division of labor of hepatocytes and non-parenchymal cells,and to profile the single cell landscape of chronic liver diseases and cancer.Here we review the principles and technologies behind scRNA-seq and spatial transcriptomics approaches,highlighting the recent discoveries and novel insights these methodologies have yielded in both liver physiology and disease biology.5 INTRODUCTION Sequencing technologies are increasingly used to study phenotypes and drivers of liver disease.Whole tissue RNA sequencing has been primarily used to identify major differences in gene expression between normal and diseased conditions.Advanced computational analyses have established gene signatures to predict patients prognosis and classify primary liver cancers1,2 but these tools have yet to be fully integrated into clinical practice.Whole tissue RNA sequencing provides an average readout of the RNA content of a sample,which represents mixed RNA signals from the different cells present within the tissue and,therefore,it is significantly influenced by cell type prevalence.This approach is unable to study rare cell populations,cellular heterogeneity(i.e.cell subsets among major cell types),specific pathogenic cell subpopulations,or to dissect cancer clonal evolution and microenvironment.In the era of immunotherapy and precision medicine,higher resolution sequencing data are required to characterize heterogeneous tissues and complex diseases such as chronic liver disease and cancer.Recent technological advances enabled genome-wide RNA profiling in individual cells,a technique termed single-cell RNA sequencing(scRNA-seq)3-6.In scRNA-seq,liver tissue is dissociated,single cells captured,and RNA sequencing is performed using several workflows(Figure 1,2).ScRNA-seq generates very large datasets of thousands of gene transcripts per cell.These datasets are usually represented in a compressed 2D space,e.g.t-distributed stochastic neighbor embedding(t-SNE)map7,where each cell is a dot and the distance between cells is a function of their similarity(Figure 1A).In this 2D space,cells can be clustered according to their similarity and single or multiple genes can be plotted on separate t-SNE maps.ScRNA-seq allows discovery,identification and/or study of rare cell types,cell subtypes,disease-specific cell-types and cell-to-cell interactions via ligand-6 receptor expression analysis(Figure 1A).Furthermore,computational analyses,such as pseudo-time diffusion mapping8 or RNA velocity9,allow in silico lineage tracing and analysis of development trajectories between cell types(e.g.from progenitor cells to differentiated hepatocytes)or among cell subtypes(e.g.from cytotoxic to exhausted T cells)(Figure 1B).A major challenge of scRNA-seq data is to match the cell RNA profile with cell position within the tissue(i.e.spatial information).This is particularly important in liver biology because the liver is spatially organized in functional lobules and acini10.To address this need,spatially-resolved RNA sequencing,paired-cell sequencing,complex computational algorithms and direct spatial transcriptomic techniques in which scRNA-seq is performed on tissues sections using spatially organized RNA capture probes have recently been developed.Here,we summarize and discuss the technical principles of scRNA-seq and spatial transcriptomic approaches and present their application and discoveries regarding liver organization,regeneration,and cell-cell interactions in chronic liver disease and cancer.FROM LIVER TISSUE TO SINGLE-CELL RNA SEQUENCING The initial steps in a scRNA-seq experiment involve tissue dissociation and isolation of single cells which can be obtained by a variety of methods such as fluorescence-activated cell sorting(FACS),magnetic separation using specific antibodies,chip-based or microdroplet-based microfluidic technologies,micromanipulation using an inverted microscope and a motorized micromanipulation platform or laser microdissection11.FACS is one of the most widely used techniques and allows the selection of specific cell populations from heterogeneous tissues.High-throughput microdroplet-based microfluidic technologies 7 (e.g.10X Chromium)are increasingly used because of high capture efficiency and low costs.Microfluidic technologies are based on the dispersion of single cells into water-in-oil droplets,containing uniquely barcoded beads and primers,using a continuous oil flow as depicted in Figure 2.The choice of single-cell capture method greatly depends on the cell types of interests,their prevalence in the tissue,and costs.After cell isolation,scRNA-seq libraries are generated by cell lysis,reverse transcription into complementary DNA(cDNA),second-strand synthesis and cDNA amplification by polymerase chain reaction(PCR)or in vitro transcription(IVT)followed by deep sequencing.These steps vary across the different scRNA-seq protocols(Figure 2).Smart-Seq2 is a protocol which uses template-switching technologies for the reverse transcription and PCR technologies for the amplification allowing the sequencing of full-length transcripts and the study of splicing events and allele-specific expression6,12,13.Smart-Seq2 is limited by high costs and,therefore,different protocols have evolved to allow 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(Figure 2).The presence of both cellular barcodes and UMIs in each single cDNA allow pooling of cDNAs from different cells for the amplification and sequencing steps which reduces significantly the costs per run.The cell of origin is inferred using the cellular barcodes and gene expression is quantified by counting and normalizing UMIs per single cells.In terms of performance,Smart-seq2 and CEL-seq2 showed the highest sensitivity,while Drop-seq has reduced costs but capture efficiency and resolution are lower3.Among the different microdroplet-based microfluidic technologies,10X Chromium results in higher sensitivity and less technical noise14.Finally,the combination of two or more scRNA-seq techniques,e.g.a microdroplet-based system and Smart-Seq2,can 8 be synergistic,increasing the probability of capturing both rare cell types and low abundance transcripts15.ScRNA-seq comprises multiple technologies and the choice of platform used should be guided by the biological question.The appropriate technique or combination of techniques should be chosen in the context of the study design and endpoints required(e.g.study of rare cell types or lowly expressed genes or splicing variant analysis).Smart-seq2 is preferred when analyzing splicing,transcriptome annotations or genome integrations while high-throughput microdroplet-based microfluidic technologies are preferred for broader cell coverage at shallower sequencing read depths.LIVER PHYSIOLOGY AT SINGLE-CELL LEVEL REWIND THE TAPE:GATHER SPATIAL INFORMATION FROM SINGLE-CELL DATA TO STUDY LIVER ZONATION One of the first applications of scRNA-seq has been the study of liver zonation in mice and humans.The liver is a highly organized tissue,and the porto-central axis of the acinus is a fundamental functional unit during homeostasis and disease development.Hepatocyte function varies along this axis,with hepatocytes classically divided into three zones.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(Figure 3).Halpern et al.studied liver zonation in mice combining scRNA-seq with single-molecule RNA fluorescence in situ hybridization(smRNA-FISH)to perform spatially-resolved RNA-sequencing16.At first,they used smRNA-FISH to 9 assess at high-resolution the spatial distribution of known zonated landmark genes allowing their fine porto-central profiling.Secondly,scRNA-seq of mouse hepatocytes was performed and the porto-central profile of landmark genes was used to assign a porto-central position to each single cell(for review see17).Spatially-resolved scRNA-seq data of the mouse liver discovered that(1)major determinants of liver zonation were not only oxygen gradient and WNT signaling18,but also RAS signaling,which activates periportal genes,and pituitary signals which inhibits periportal genes(Figure 3B);(2)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(Figure 3A);(3)genes encoding for biliary acid metabolism enzymes are differently expressed alo