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Publications > Journals > Journal of Clinical and Translational Hepatology > Article Full Text

  • Review Article
  • OPEN ACCESS

Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology

  • Guanzi Chen#,
  • Wenli Xu#,
  • Zhicong Long,
  • Yutian Chong,
  • Bingliang Lin*  and
  • Yusheng Jie* 
 Author information  Cite
Journal of Clinical and Translational Hepatology   2024;12(1):79-90

doi: 10.14218/JCTH.2023.00224

Abstract

The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.

Graphical Abstract

Keywords

Single-cell technology, Normal liver, Chronic liver disease, Hepatocellular carcinoma, Acute-on-chronic liver failure

Introduction

Liver diseases have become a global health burden in recent decades. For example, cirrhosis causes 1.16 million deaths, and liver cancer causes 0.788 million deaths annually, making them the 11th and 16th most common causes of death.1 More than one-fifth of the Chinese population is affected by liver diseases, particularly hepatitis B viral infections, while cases of nonalcoholic hepatitis and autoimmune hepatitis are increasing.2 Persistent chronic inflammation in the liver is closely associated with cirrhosis and ultimately leads to liver cancer or liver failure, and the main cause of acute liver failure in developing countries is viral hepatitis.3,4 However, effective treatment options for liver diseases are so limited that liver transplantation is regarded as the only viable solution, but its demand fairly exceeds the supply.1 Therefore, better tools are urgently required to elucidate the pathological mechanism and development of liver diseases with a clearer perspective. Their application may contribute to precise, personalized therapy and a decrease in mortality.

Each cell is unique for it occupies an exclusive position in space, carries distinct errors in its copied genome and is subject to programmed and induced changes in gene expression. Yet traditional sequencing is performed on tissue samples or cell populations, in which biological differences between cells can be obscured by averaging or mistaken for technical noise. In contrast, single-cell sequencing technology can isolate several thousand single cells at one time and has the advantage of detecting heterogeneity among individual cells then depicting cellular profiles, which can help us identify the rare populations of cells that have derived significance for disease progression with unprecedented high resolution.5 The workflow of single-cell sequencing experiments and analyses generally includes single-cell suspension preparation, single-cell sorting, amplification and library preparation, high-throughput sequencing, and bioinformatics analysis (Fig. 1). Tang et al.6 proposed the first single-cell transcriptome sequencing (scRNA-seq) technology in 2009. Since then, the single-cell field has advanced rapidly, and several new approaches have been developed, including genome, transcriptome, proteome, spatial transcriptome, epigenome. Single-cell sequencing can not only analyze the heterogeneity of cells with the same phenotype but also obtain the genetic information of hard-to-culture microorganisms and valuable clinical samples.

Flow diagram of different single-cell approaches in clinical liver samples.
Fig. 1  Flow diagram of different single-cell approaches in clinical liver samples.

Single-cell suspension preparation: single-cell comes from fresh samples including liquid (peripheral blood, ascites) and tissue (liver cancer, adjacent tumor, normal liver) by dissociation; frozen samples can be used to snRNA-seq and ST. ST: frozen liver tissue samples are sectioned, fixed, and permeated, then transcripts are barcoded according to their location based on a matrix of spots. These barcodes are then used to show the spatial gene signatures across the tissue section. SCoPE-MS: surviving single-cell is lysed by ultrasound, proteins are extracted from the lysate then trypsinized, and peptides from multiple samples are identified and quantified using TMTs. CITE-seq: a technology consisting of mRNA and tagged antibody sequencing, which enables the simultaneous determination of unbiased transcriptional profiles and protein markers based on oligo-tagged antibody detection in a cell. ScATAC-seq: a technique for studying open chromatin regions with epigenetic and gene regulatory roles at the single-cell level. Tn5 transposase is used to cleave accessible DNA regions of open chromatin in the nuclear suspension of sorted cells, followed by post-PCR sequencing using tags of known sequence. Cell sorting: the platforms used for cell sorting are mainly based on microfluidic or cellular plate technologies, both of which allow for unbiased quantification of gene expression with the aid of unique molecular identifier and cell label technology, where mRNA expression profiles of TCR and BCR can be determined in T and B cells for adaptive immune response analysis. 10×,10× Genomics; BCR, B-cell receptor; CITE-seq, cellular indexing of transcriptome and epitopes by sequencing; PCR, polymerase chain reaction; scATAC-seq, single-cell assay for transposase-accessible chromatin with high throughput sequencing; SCoPE-MS, single-cell proteomics by mass spectrometry; scRNA-seq, single-cell RNA sequencing; snRNA-seq, single nuclear sequencing; ST, spatial transcriptome technology; TMTs, tandem mass spectrometry tags; TCR, T-cell receptor.

In this article, we review the current single-cell approaches and discussed their key principles along with their advantages and disadvantages to understand which method is more suitable under what kind of circumstances (Table 1).7–23 We also discuss the novel application of those techniques in studies on liver physiology and pathology and the application prospects and technical improvements in single-cell technology to find effective individualized treatment strategies for patients with liver diseases and reduce the overall morbidity and mortality related to liver diseases.

Table 1

Summary of single-cell analysis methods

Single-cell histologyApplicationSingle-cell sequencingAdvantagesDisadvantagesRef.
TranscriptomeCell type discovery and characterization in diseases; cell development trajectory and plasticitySmart-seqFull-length coverage across transcriptsUnstable; transcript bias20
Smart-seq2Captures rare genes; commercial off-the-shelf SMARTer kitCan not analyze nonpolyA RNAs; nonstrand specificity8
Drop-seqHigh throughput; low costLow sensitivity21
10× Genomics ChromiumHigh throughput; high sensitivity; short cycle time; low technical noiseHigh cost22
snRNA-seqAllow for frozen samples; relatively simple to operateFew genes detected; cellular preference7
Spatial transcriptomeSpatial distribution of heterogeneous cells and spatial patterns of gene expression in different tissues; tumor microenvironment10× VisiumCommercial platform is mature and stable; widely used; with supporting verification platformLow spatial resolution; non-single cell level9
GeoMx DSPCompatible with FFPE samples and fresh frozen samples; for in-depth analysis of local tissuesLow throughput10
DBiT-seqHigh spatial resolution; co-localization of mRNA and protein; simplified workflowInability to readily identify cell types11
ProteomeDisease heterogeneity and treatment resistance; cancer immunotherapy; high throughput drug screeningscWesternHigh specificity;Low protein flux23
SCoPE-MSAccurately differentiate the proteome of different cellsLow protein flux Complex workflow12
nanoPOTSMicroprotein identification; high quantitative accuracy and protein coverage13
EpigenomeGenetic characteristics of disease; deeper understanding of gene regulation from open chromatin; infer development trajectories and identify regulatory elementsscCGI-seqHigh accuracy; simplified workflowLow coverage14
CUT & RUN techniqueLow sample requirement; high resolution and accuracy; commercial kitsHighly antibody specificity-dependent17
scATAC-seqHigh resolution, sensitivity, and accuracyLow recovery of DNA fragments15
scChIP-seqHigh throughput, high coverageHighly antibody-dependent16
MultiomicsIntegrated analysis different histological techniques to characterize the state of gene regulation more comprehensively within cells and reveal the mechanisms of disease regulationscTrio-seqSimultaneous analysis of genome,transcriptome, and epigenomeLow throughput18
CITE-seqSimultaneous analysis of transcriptome and protein expression; high throughputOnly surface proteins can be measured19

CITE-seq, cellular indexing of transcriptomes and epitopes by sequencing; CUT & RUN, cleavage under targets and release using nuclease; FFPE, formalin-fixed and paraffin embedded; nanoPOTS, nanodroplet processing in one pot for trace samples; scATAC-seq, single-cell assay for transposase-accessible chromatin using sequencing; scCGI-seq, single-cell CpG island methylation sequencing; scChIP-seq, single-cell chromatin immune-precipitation sequencing; scCOOL-seq, single-cell chromatin overall omic-scale landscape sequencing; SCoPE-MS, single-cell proteomics by mass spectrometry; scTrio-seq, single-cell triple omics sequencing; scWestern, single-cell western blotting; snRNA-seq, single-nucleus RNA sequencing.

An overview of single-cell technologies

scRNA-seq

scRNA-seq measures gene expression at the single-cell level and reveals heterogeneity of gene expression in individual cells or homologous cell types, its core principle refers to tag-based identification of single cells. A unique cell tag attached to a barcode is added to each cell when reverse transcription is performed prior to RNA sequencing. Therefore, even if all cells are mixed and sequenced, RNA fragments carrying the same barcode can be considered as coming from the same cell. By building a library once, we can measure the information of tens of thousands of single cells (Fig. 1). A challenge in conducting research using scRNA-seq is that high quality suspensions based on fresh tissue are not available for all tissues. In contrast, single-nucleus RNA sequencing (snRNA-seq)7 is not limited by dissociation conditions and can be used for analyzing cell and gene expression of primary tissues obtained from frozen samples. Thus, this process complements scRNA-seq, but the nuclear sequencing depth is low. Some studies have compared the differences in sample type, study purpose, and target cell population between scRNA-seq and snRNA-seq for various tissues and diseases.24–26

In addition, immune cell receptor sequencing, which focuses on immune T and B cells, can comprehensively resolve T-cell receptor/B-cell receptor (TCR/BCR) gene rearrangement base sequences and the abundance of each sequence to reveal the mechanism of T/B cell-mediated humoral immune response. Single-cell analysis was first used for examining immune cells around 2013 and early 2014; specifically, researchers used scRNA-seq to generate immune cell profiles from tissues.27,28 Recent advancements in scRNA-seq have enabled cellular transcriptome data and immune repertoire information to be combined for the coverage of full-length mRNA,29 which can be used to determine the functional changes and interactions of subsets of immune cells during pathogen invasion and the infiltration landscape of T cells in the tumor microenvironment.30,31 The most widely used transcriptome sequencing technologies in the market today are droplet-based 10× Genomics32 and Smart-seq28 a single-cell full-length transcriptome sequencing technology (Table 1).

Spatial transcriptome technology (ST)

Compared with bulk RNA-seq and scRNA-seq, ST can be used to analyze the information of tissue sections in space without cell enzymatic hydrolysis and maximum restoration of the spatial specificity of gene expression in individual cells in human organs.33 It has advanced considerably in the past 30 years, initially, transcriptome technology could be used to analyze only a small number of genes, but now it can be used for analyzing the complete transcriptome. From the early technologies, such as single-molecule fluorescence in situ hybridization (smFISH)34 and laser capture microdissection (LCM), to the current technologies, such as 10× Visium,9 GeoMx DSP,10 and DBiT-seq (Table 1).11 More details on ST can be found in a recent review.35 Among all ST, for now, the 10× Visium ST is the most widely used.

Single-cell proteomic analysis

Qualitative and quantitative analysis of the single-cell proteome can avoid processing information on the expression level changes caused by post transcriptional and post translational modifications and efficaciously obtain information on the regulatory nature of cells. No tools are available for analyzing the whole proteome at the single-cell level, but various single-cell proteome analysis methods are available in different subsegments, including methods based on antibody-based strategies, mass spectrometry (SCoPE-MS),12 Proseek multiplex technology based on polymerase chain reaction (commonly known as PCR) immunoassay,36 and others. Recently, nanodroplet processing in one pot for trace samples (nanoPOTS) microsample processing13 combined with LCM and mass spectrometry found that in mouse uterine tissue sections prepared for blastocyst implantation, quantitative cell type-specific images of >2,000 proteins could be generated with a high spatial resolution.37 Protein profiling characterized by captured ion mobility has led to the development of four-dimensional (4D) proteomics, which can perform high-depth quantitative analysis 4D proteomics quantification (4D-LFQ, 4D-DIA), and 4D protein modification omics.38,39

Single-cell epigenomic analysis

Cellular epigenetic heterogeneity at the single-cell level can be used to explain the pathological regulatory mechanisms of diseases in the absence of altered genetic information. The detection of DNA methylation in single cells was first conducted in 2015 when Farlik et al.40 developed genome-wide bisulfite sequencing, which was used to analyze genome-wide methylation sites at the single-cell level, but required many DNA samples. Currently, methylation sequencing targeting genomic regions with high CpG content (scCGI-seq)14 and DNA methylation analysis using indexing methods (sci-MET)41 at the single-cell level are also available. There are significant differences in chromatin accessibility between cells, which often indicate gene regulation functions. For analyzing open chromatin conformations with loose features at the single-cell level, sensitive enzymes, such as the Tn5 transposase-based single-cell assay for transposase-accessible chromatin using sequencing (also referred to as scATAC-seq)15 and the DNase I-based single-cell DNase sequencing (also referred to as scDNase-seq)42 are required. The latter can detect a much wider range of chromatin, up to 350,000 reads/cell. To determine the link between the diversity of histone modification sites and changes in the chromatin state, single-cell histone modification, single-cell chromatin immune-precipitation sequencing (also referred to as scChIP-seq), was used in 2015.16 Subsequently, the cleavage under targets and release using nuclease (CUT & RUN) technique, indexing single-cell immunocleavage (also referred to as iscChICseq), and antibody-guided chromatin tagmentation (also referred to as ACT-seq)17,43,44 were developed to overcome the low throughput characteristics of cells. More information on single-cell epigenetic techniques is summarized in a previous review.45

Single-cell multiomics analysis

In the near future, with the help of single-cell multiomics analysis, the genomic information of cells and the regulation of transcription and translation in a cell can be presented in a comprehensive manner. In 2016, Hou et al.18 developed single-cell triple omics sequencing (also referred to as scTrio-seq), and were the first to simultaneously achieve high-throughput sequencing of genomic copy number variation, DNA methylome, and transcriptome.18 Cellular indexing of transcriptomes and epitopes by sequencing (also referred to as CITE-seq) using oligonucleotide-tagged antibodies can be applied to target cell surface proteins and analyze differences between protein and transcript levels in individual cells.19 Presently, multimodal analysis has greatly improved our ability to determine cellular states. The single-cell approaches described above has been applied in the field of hepatology.

Application of findings from single-cell approaches in liver physiology and pathology

The liver is the largest digestive gland and detoxification organ in the human body and participates in the synthesis and decomposition of various substances.46 The incidence of hepatitis B virus (HBV) related to end-stage liver disease have become the main causes of mortality due to liver diseases in China,47 and early in vitro and in vivo studies have found that hepatitis B virus X protein (HBx) can induce malignant transformation and carcinogenesis in hepatocytes.48 In 2019, integrated proteomic genomics identified the key signaling and metabolic pathways in HBV-associated hepatocellular carcinoma (HCC), where mutant CTNNB1-associated ALDOA phosphorylation was identified to be involved in HCC metabolic reprogramming, and metabolic alterations may be the most important factor for advanced cancer and poor clinical outcomes.49 Studies using single-cell technology have provided valuable information on the characteristics of single cells and their gene-expression profile of normal liver and liver diseases from multiple perspectives (Table 2).50–62 As Wang et al.63 provided a comprehensive single-cell immune profile of HBV-infected patients, revealing that increased Treg and Tex cells are associated with the degree of liver damage, and that immune cell interactions networks may promote persistent HBV infection and liver histopathology. Thus, HBV infection is a dynamic process, and a deeper understanding of cellular interactions and the changes of immune microenvironment will help develop more effective therapeutic targets and biomarkers for immunotherapy in chronic hepatitis B (CHB) patients, thereby slowing down the occurrence of end-stage liver disease (Fig. 2).

Table 2

Overview of recent studies on human liver disease at the single-cell level

Liver diseasesSamplesPatientsMethodsSynopsisYearRef.
HepatitisHuh7.5-NTCP cellsnonescRNA-seq(Illumina HiSeq PE150); CRISPR-Cas9Rare transcripts from HBV-infected hepatocytes are preferentially enriched; HBV infection did not significantly alter gene expression but treatment with IFN-α altered gene expression in Huh7.5-NTCP cells202150
Peripheral blood12scATAC-seqIdentified 12 leukocytic clusters corresponding to five cell types; specific cell type of CHB was B-0 and T-3202251
NASH liver tissue10scRNA-seq (10×)Cell type-specific transcriptional features of NASH; two distinct populations of activated hepatic stellate cells associated with fibrosis levels202256
Liver fibrosis / CirrhosisHealthy, cirrhotic liver tissue; peripheral blood10scRNA-seq (10×)Characterizing and comparing the functional diversity of liver biopsy cells from human fibrotic and normal livers; identified scar-associated TREM2+CD9+macrophages, ACKR1+ and PLVAP+ endothelial cells201952
Healthy, HBV-related cirrhosis, alcoholic cirrhosis liver tissue15scRNA-seq (Illumina HiSeq X)Different immune profiles between alcoholic and HBV cirrhotic patients; Galectin-9 may be a potential therapeutic target for alcoholic cirrhosis treatment202353
ACLFHealthy, cirrhosis, ACLF liver tissue25scRNA-seq (Illumina HiSeq X)Revealed the state of hepatic lymphatic endothelial cells in hepatitis B virus-associated ACLF; monocytes/macrophages infiltrate into ACLF202254
HCCHCC tumor tissue8scRNA-seq (10×)Cellular heterogeneity in HCC; intertumoral heterogeneity higher than intratumor heterogeneity; immunocyte characteristics202157
HCC tumor tissue1scRNA-seq (SMART- seq)Heterogeneity of CSCs in human HCC; identified CSCs as CD24+/CD133+/EpCAM+/CD45- cells201858
HCC tumor tissue; normal liver tissue; peripheral blood8scRNA-seq (Illumina HiSeq)Characteristics of immune cell in HCC; revealed specific cytokines/compartmental factors201959
HCC, iCCA tumor tissue7scRNA-seq (10×)Similar developmental trajectories in the same tumor, but heterogeneity in stemness gene expression202260
HCC Tumor tissue; normal liver tissue; peripheral blood6scRNA-seq (Illumina HiSeq)Identified 11 populations of T cells in HCC; TME; exhausted CD8+T cells and Tregs preferentially enriched in HCC201755
HCC tumor tissue; normal liver tissue18scRNA-seq (10×)Immune characteristics in early-relapse HCC; early-relapse tumors have less regulatory T cells, DCs and increased infiltrating CD8+T cells and higher expressed CD161+CD8+T cell compared to the primary tumors202161
(HCC, iCCA, HH, CHC, ASC, SAR, SLC) Tumor tissue; normal liver tissue; peripheral blood124scRNA-seq (Illumina HiSeq)Identified six tumor-associated neutrophils, among them CCL4+ TAN and PD-L1+ TAN could promote tumor growth202262

ACLF, acute-on-chronic liver failure; ACKR1, atypical chemokine receptor 1; ASC, adenosquamous carcinoma; CCL4, C-C motif chemokine ligand 4; CHB, chronic hepatitis B; CHC, combined hepatocellular-cholangiocarcinoma; CSCs, cancer stem cells; DCs, dendritic cells; EpCAM, epithelial cell adhesion molecule; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HH, hepatic hemangioma; iCCA, intrahepatic cholangiocarcinoma; IFN-α, interferon-α; NASH, nonalcoholic steatohepatitis; PD-L1, programmed cell death-ligand 1; PLVAP, plasmalemma vesicle-associated protein; SAR, sarcomatoid carcinoma; scATAC-seq, single-cell assay for transposase-accessible chromatin with high throughput sequencing; scRNA-seq, single-cell transcriptome sequencing; SLC, secondary liver cancer; TAN, tumors-associated neutrophil; TME, tumor microenvironment; TREM2, triggering receptor expressed on myeloid cells-2.

Insights provided by single-cell approaches into the normal liver and the process of chronic liver disease.
Fig. 2  Insights provided by single-cell approaches into the normal liver and the process of chronic liver disease.

(A) scRNA-seq with ST can reveal spatial zonation profiles of gene expression in the normal liver and a detailed cell landscape of the human liver, showing that 50% of liver genes have partitioning rules. Intrahepatic resident immune cells are demonstrated their unique distribution and functions. The receptor tyrosine kinase Tie1 is the key to regulate vascular activity signals and liver regeneration. (B) The most important feature of the CHB process is immune dysregulation, with CD8+T at the center of the process; B cell reduction impairs specific T-cell subsets and decreases the clearance of HBV and the decreased cell population in hepatitis B samples results in poor IFN-α response but activated B-0 subpopulation. (C) TREM2+/CD9+macrophages and PDGFRα+collagen-producing myofibroblasts subpopulations involve in fibrosis process; ACKR1+ and PLVAP+ endothelial cells are found to accumulate in the fibrotic ecotone and participate in immune cell migration; Galectin-9 on Kupffer cells is associated with specific immunity and could be a potential therapeutic target for cirrhosis treatment. (D) ACLF samples show high expression of the genes FGF19, ADCY8, KRT17 and SPP1 secreted by macrophages resulting in reduced LVs and dysfunctional LyECs. The pro-inflammatory monocyte subtype THBS1 may reflect ACLF progression and poor prognosis. (E) HCC with different prognosis shows differential immune cell microenvironment changes, with increasing PD-1+/CD4+T & CD8+T cells and CD8+T diversity suggesting a good prognosis; high heterogeneity in HCC as well as the increasing subtypes of TAMs, exhausted CD8+T/Tregs, CD161+/CD8+T, CCL4+/PD-L1+/TAN, ILCs/ILC2a, CD24+/CD133+/EpCAM+/CD45- cells suggesting a poor prognosis. The Layilin gene is functionally associated with tumor-characteristic CD8+T cells and may serve as a new target for immunotherapy in HCC. ACLF, acute-on-chronic liver failure; ACKR1, atypical chemokine receptor 1; CHB, chronic hepatitis B; EpCAM, epithelial cell adhesion molecule; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; IFN-α, interferon-α; LVs, lymphatic vessels; LyECs, lymphatic vessel endothelial cells; PD-1, programmed cell death-1; PD-L1, programmed cell death-ligand 1; PDGFRα, recombinant human platelet-derived growth factor-α; PLVAP, plasmalemma vesicle-associated protein; scRNA-seq, single-cell transcriptome sequencing; SPP1, secretory phosphoprotein 1; ST, spatial transcriptome technology; TAMs, tumor-associated macrophages; TAN, tumors-associated neutrophil; THBS1, thrombospondin-1; Tie 1, tyrosine kinase with immunoglobulin like and EGF like domains 1; TREM2, triggering receptor expressed on myeloid cells-2.

Normal liver

scRNA-seq can be used to construct the complete cellular map of the normal liver based on the information of human liver regarding zonation, development, regeneration, and immune cell heterogeneity. In 2017, Halpern et al.64 performed scRNA-seq to measure the entire transcriptome of thousands of mouse hepatocytes, combining it with marker gene function by smFISH to determine the positional coordinate characteristic of liver lobe positions. The researchers found that about 50% of liver genes had partitioning rules and obtained the spatial zonation profiles of the liver.64 In 2019, a group of researchers performed scRNA-seq to analyze approximately 10,000 cells in normal liver tissue from nine human donors and constructed a detailed cellular landscape of the human liver,47 which covered almost all important liver cell types, including hepatocytes, major liver metabolic cells, vascular endothelial cells, liver macrophages, and other immune cell types. A subsequent study integrated and analyzed scRNA-seq datasets from five independent studies of healthy livers from individuals of different sexes and ages and developed an interactively accessible online browser that could be used to access information on the expression of the gene of interest for each cell (sub) type from 28 healthy livers.65 ScRNA-seq combined with ST has provided a comprehensive understanding of the differentiation of all major cell types in the human fetal/embryonic liver, including the function of fetal liver hematopoietic stem cells.66,67 Such information can be used for developing effective strategies for treating liver diseases and enhancing liver regeneration. Other than that the developmental trajectories of various cell types in the human liver at the single-cell level have been described.68 In 2021, Inverso et al.69 combined transcriptomics and proteomics to analyze and obtain high-resolution data on liver vascular endothelial cells and their hepatic vascular signaling regulation. They found that the receptor tyrosine kinase Tie1 (a protein activation region) regulates vascular activity signals and strongly influences the maintenance of liver regeneration.

Immune cell proteomic analysis associated with liver damage and regeneration were developed later.70,71 In 2018, MacParland et al.72 isolated 8,444 parenchymal cells from five healthy human livers and then performed scRNA-seq analysis to obtain the macrophage population distribution of the human liver and mapped the human liver cell landscape. In addition, researchers also used comprehensive transcriptomes of unbiased scRNA-seq to study liver-resident immune cells (LrICs).73 That study showed the tissue distribution, gene expression, and functional modules of specific immune cell populations (natural killer cells, T cells, B cells, and plasma cells) in human liver and presented a landscape of LrICs to explain how the liver promotes immune defense and tolerance in vivo. Liver sinusoidal endothelial cells can function as host immune defenses by coordinating the proper distribution of intrahepatic resident immune cells.74 The integrated information on the liver and immunity by single-cell analysis can promote the development of new immunotherapies and targeted therapies, such as programmed cell death-1 (PD-1) for antitumor treatment.75 Thus, a complete intrahepatic immune signature is essential for understanding the development of liver disease and conditions under which intrahepatic compartmentalization extends to immune cells (Fig. 2).

Hepatitis B

Hepatitis, for various reasons, can lead to adverse outcomes, including cirrhosis, liver failure, or HCC. Among them, for viral hepatitis B, which is very common in China, which affects 257 million people worldwide, a suitable functional cure is lacking, even though effective viral suppression regimens have been developed.76,77 A study used scRNA-seq to analyze crosstalk between B cell and T cell subpopulations and blood samples from interferon-α (IFN-α)-treated CHB patients.78 The results showed that the immune profile of B cells differed across patients, and the effect of IFN-α treatment also differed. Based on the antigen-presenting and costimulatory functions of B cells in cellular immunity, B cell depletion was found to impair functional T-cell subsets, such as specific CD8+T cells, leading to the ineffective clearance of HBV.78 The ability of different subgroups of CD8+T cells to control the virus in patients with chronic viral hepatitis is closely related to the prognosis of the disease.79,80 The CRISPR-Cas9 technology integrated with scRNA-seq analysis can be combined to interpret host gene expression in HBV-infected hepatocytes, including sequence information of low-abundance genes.50 Interestingly, Mederacke et al.81 analyzed the TCR repertoire of CD4+T, CD8+T, and regulatory T cells from peripheral blood and the transplanted livers of liver transplant recipients and showed that their TCR pools were nearly distinct, and the activated T cells might be released by the graft in an acute cellular response to rejection. The latest study on the application of scATAC-seq in the pathophysiology of hepatitis B infection reported specific immune cell populations and chromatin-accessible specific molecules associated with the HBV infection through the detection of peripheral blood mononuclear cells (PBMCs); for example, the B-0 subgroup was found to be over activated in CHB patients.51

Liver fibrosis and cirrhosis

Liver fibrosis involves complex interactions between multiple mesenchymal cells located in scarred areas. These cells include mesenchymal cells, immune cells, vascular endothelial cells, etc. To determine the specific mechanisms of human liver fibrosis, Ramachandran et al.52 used scRNA-seq to analyze the cellular diversity of more than 105 human cells in normal and fibrotic livers of humans and found that TREM2+/CD9+macrophages and PDGFRα+collagen-producing myofibroblast subpopulations were involved in fibrosis. Additionally, profibrotic atypical chemokine receptor 1 (referred to herein as ACKR1) and plasmalemma vesicle-associated protein (referred to herein as PLVAP) endothelial cells were found to accumulate in the fibrotic ecotone and participate in the migration of immune cells, which present several profibrotic signaling pathways through receptor and ligand interactions. Such interactions, combined with newly identified fibrogenic markers, facilitate the development of rational therapeutic targets.

Cirrhosis is a major factor associated with mortality in nonalcoholic steatohepatitis (NASH). The application of scRNA-seq by Schwabe et al.82 provided new insights into the cellular networks involved in the development of NASH fibrosis. The cellular heterogeneity and intercellular transitions involved in the injurious fibrotic response are becoming clearer at the single-cell level.83 To investigate the immune characteristics of cirrhosis due to different causes, a recent study in China performed scRNA-seq on 15 liver specimens (five each from healthy controls, alcoholic cirrhotic (ALC) patients, and HBV-related cirrhotic patients) and found that the proportion of intrahepatic monocytes and macrophages was higher in the ALC group compared to that in the HBV group, with CD5L+Kupffer cells being the predominant subpopulation in ALC. The proportion of T and B cells was significantly lower, which might be related to the expression of Galectin-9 in Kupffer cells.53 Therefore, the immune profile of ALC patients differs from that of HBV cirrhotic patients, and Galectin-9 might be a therapeutic target for treating ALC.

Acute-on-chronic liver failure (ACLF)

Liver failure occurs due to serious liver damage caused by various factors, especially cirrhosis, which leads to severe impairment or loss of liver function. Thus, a comprehensive and systematic understanding of liver failure and the development of new treatment strategies are extremely important. In 2022, a study on the hepatic inflammatory milieu in ACLF found that hepatic lymphatic vessels (LVs) and lymphatic vessel endothelial cells (LyECs) might be new therapeutic targets for treating ACLF.54 That study used scRNA-seq to analyze nonparenchymal cells from three groups of human liver tissue (healthy participants and cirrhotic and ACLF patients) and found the development of apoptotic and dysfunctional LyECs caused by the release of secretory phosphoprotein 1 (also known as SPP1) from infiltrating monocytes/macrophages in the liver. The study also found that the number of LVs was significantly lower in the ACLF group than that in the cirrhotic group. A study on HBV- associated ACLF combined RNA-seq and differential gene analysis and identified key genes associated with immunometabolic disorders, including FGF19, ADCY8, and KRT17. The expression of these genes was significantly upregulated during the progression of ACLF, and thus, they might be sensitive biomarkers for HBV-associated ACLF.84 A transcriptomic study of PBMCs suggested that significant immune dysregulation leads to the development of ACLF from CHB.85 In a recent study, researchers performed scRNA-seq in 17,310 circulating monocytes from healthy & ACLF patients and found five monocyte subpopulations, including pro-inflammatory monocytes, CD16 monocytes, HLA monocytes, megakaryocyte-like monocytes, and natural killer-like monocytes; thrombospondin-1 (THBS1) from pro-inflammatory monocytes characterized specific types of cells and transcriptional signatures, and its production was speculated to reflect the progression of ACLF and a poor prognosis. A comparison between the ACLF survival group and the death group showed that inflammation-related cytokines were significantly higher in the ACLF death group. The results of that study indicated that a detailed analysis of the characteristic monocyte subpopulations and relevant proinflammatory molecular markers might provide therapeutic strategies to arrest the progression of ACLF.86 Shen et al.87 investigated the dynamics of T cells in ACLF longitudinally by collecting PBMCs from five HBV-ACLF patients and sorting total CD4+T and CD8+T cells. They performed unbiased high-throughput sequencing to analyze the TCRβ complementarity-determining regions 3 (also referred to as CDR3) and found that the diversity of the T cell pool decreased significantly as HBV-ACLF progressed. There was also a significant clonal expansion of CD8+T cells but not CD4+T cells. Thus, the diversity of the CD8+T-TCRβ pool might be a predictor of ACLF prognosis. To summarize, single-cell studies assisted by functional experiments might help in understanding the immunological aspect of liver failure and reveal new methods for treating this disease.

HBV-associated liver cancer

Tumorigenesis is a complex pathological process characterized by intratumoral and intertumor heterogeneity and changes in the immune microenvironment.88 HCC is the most prevalent liver tumor due to HBV infection. Here, we summarized the studies based on scRNA-seq related to human HCC in the last five years (Table 2). Tumor heterogeneity and the complexity of the immune microenvironment associated with HCC are the main reasons for its drug resistance and low clinical cure rate.89 To elucidate the immune landscape of HCC, Zheng et al.55 defined 11 functionally characterized T cell subsets and their characteristic genes by combining TCR-seq and scRNA-seq. The gene known as Layilin is functionally associated with tumor-characteristic CD8+T cells and might serve as a new target for immunotherapy in HCC. Wu et al.90 constructed a complete spatial ecological landscape of tumor nodules in a comprehensive genome-wide transcriptome analysis of primary HCC cells, including normal liver tissue, tumor margins, and tumor regions. They found that the tumor envelope might influence the spatial distribution and growth of tumor cells, and HCC heterogeneity can even exist in a single tumor nodule, which might contribute to the ineffectiveness of drug therapy for HCC. Tumor marginal cells are most responsive to their local microenvironment and are highly aggressive and metastatic.91 In 2022, the relationship between the immunophenotype of human liver-resident lymphocytes and HCC was determined by scRNA-seq.92 The researchers found that the changes in the HCC cell number and subpopulation were associated with the dysregulation of innate lymphoid cells (ILCs); seven characteristic ILCs were identified, including inducible T cell costimulator (ICOS)+ ILC2a, which were enriched in HCC and suggested a poor prognosis.

The accurate identification of specific cell subtypes and their targeted molecules is conducive to the diagnosis and treatment of liver diseases.93,94 For example, in the study of intrahepatic cholangiocarcinoma (iCCA), the tagging function of proteomics to complement other histological examinations was used to successfully identify three distinct functional molecular subtypes, of which POE+/C1QB+tumor-associated macrophages (commonly referred to as TAMs) represented a chronic inflammatory marker that might be developed as an immunotherapeutic target for iCCA.95 In a study on the association between HBV and iCCA, a proteomic analysis of the link between cells from HBV-iCCA patients found that a decrease in intercellular adhesion was more likely to lead to an increase in the epithelial-mesenchymal transition signaling.96 Additionally, abnormal methylation was found to be a marker for the progression of cirrhosis to HCC, and altered epigenetic modifications were found to affect chromatin structure and function in HCC patients. For example, the viral protein HBx was found to be an epigenetic regulator, playing an important role in HBV-associated HCC.97–99

Regarding metastatic liver cancer, an adequate understanding of the immune status of the tumor is needed to detect immune checkpoint suppression. By combining transcriptomic, epigenomic, and proteomic data from five HCC cell lines, Wang et al.100 showed that the metastatic potential of HCC is related to the mesenchymal state of tissues and the proliferation ability of cells. They also found that the hypoxic subtype increases the metastatic ability of cancer cells. The combination of multiomics can help identify the regulatory factors of tumor growth and metastasis and reveal the multifaceted nature of tumor cells and their interaction with the immune microenvironment, which might be used to find therapeutic targets.101,102 Yang et al.103 combined transcriptome and whole exome sequencing in primary and metastatic HCC and identified an immunosuppressive target known as SLC2A1, which acts by regulating SPP1+macrophages and T cells. To summarize, single-cell technologies provide valuable information for the diagnosis and treatment of liver cancers, and some of these strategies might be implemented soon in the clinic.

Challenges and future perspectives

The rapid advancement in single-cell sequencing has promoted the identification of the gene structure and gene expression state of single cells at extremely high resolution. With scRNA-seq having a key role, newly developed single-cell approaches have combined genome, epigenome, proteome, spatial transcriptome, and even multiomics to demonstrate the heterogeneity between cells and the changes in the microenvironment of cell populations. Such information might be used for the individualized diagnosis and treatment of diseases and to develop better precision medicine.104 By focusing on the progression of CHB to end-stage liver disease, our single-cell perspective on the mechanism of disease development can help identify relevant sensitive biomarkers and, thus, arrest disease progression on time.

Advances in technology and analytical techniques have encouraged further development in the field of single-cell technology, but research in this field is still at an early and has not been clinically accessible because of many challenges. First, single-cell studies require fresh liver tissue samples, and the availability and dissociation of samples greatly limit the ability to expand the spectrum of liver diseases.105 Although snRNA-seq can reduce the effects of enzymatic hydrolysis and mechanical pressure and is suitable for sequencing frozen samples and hard-to-dissociate samples,24,26,106 its sequencing depth and cell diversity are limited. Thus, snRNA-seq might not be suitable for studying immune cells. In 2023, Yu et al.107 developed a new technology known as “scONE-seq”, which can simultaneously sequence the DNA and RNA of single cells in frozen and fresh tissues. This technology was used for identifying one kind of cancer cell in the human brain with genomic mutations and near-normal transcriptomes. Another challenge involves the complexity of data analysis; specifically, defining quality control criteria, removing technical artifacts, and interpreting results are performed by a few technical staff members. Additionally, open source software tools are unsuitable for most clinical analyses. Therefore, in the future, data visualization and computational methods might be performed by artificial intelligence, which can be designed to analyze complex datasets quickly, thus allowing the networked dissemination of the results of single-cell analysis to a wider field. In addition, the current single-cell sequencing data visualization mainly applies principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection dimensionality reduction methods to project the cell population into two-dimensional space,108 but in fact, it is difficult for each dimension to correspond to the last biological structure after dimensionality reduction, which will lead to the loss of spatial information in a certain direction of the cell population, so mapping cells to three-dimensional space one by one is the future improvement of single-cell sequencing. Recently, in order to visualize different dataset types in a unified spatial framework, Voytek109 used a pure data approach of neurological mapping to study neuroscience, which opens new doors for other fields to study the spatial three-dimensional mapping of cells. More computational tools and related methods for ST data analysis and visualization are summarized in this review.110 It must be mentioned that the cost of sequencing is an important factor. Therefore, addressing common issues related to single-cell sequencing technologies, such as reducing sequencing costs, simplifying the sequencing process, enhancing sensitivity, and improving resolution, are areas that need to be focused on to further elucidate the molecular mechanisms of the development and progression of different liver diseases.

Novel single-cell techniques combined with multimodal, integrated analytical approaches have expanded the perspective of analysis at different levels within a cell. For example, the newly described technique for studying histone modifications in single cells, known as single-cell chromatin immune cleavage and unmixing sequencing (or scChIX-seq),111 overcomes the limitation that only one histone modification can be studied in a cell. This technology combines and compares the modification patterns of different histones in a cell to generate corresponding maps. The spatial information of gene expression with high resolution provides accurate information for analyzing the native environment of the lesion. The newly described Spatial Tumor Cell Estimator (commonly referred to as SpaCET)112 was used in 2023 to identify different types of immune and stromal cells from information on the spatial abundance of tumor cells. Thus, it can be used to study the role of immune cells in the progression of tumors. In addition, full-length transcriptome sequencing technology represented by single-molecule real-time sequencing can be used to obtain the full-length information of transcripts without splicing. Such information can be used to accurately identify gene mutations and find new mutants quickly.112 Surprisingly, the recently developed droplet-based technology detecting single nucleus information (also referred to as snRandom-seq) allows to capture full-length total RNAs with random primers and detect those noncoding RNA without polyA tails and nascent RNAs.113

The application of single-cell approaches for identifying and assessing HCC is the most prominent use of this technology. However, HCC is often the end-stage manifestation of chronic liver disease, and its prognosis is extremely poor; also, only a few patients are suitable for liver transplantation.114 In China, CHB, cirrhosis-HCC are part of a typical axis of chronic liver disease. Advancements in single-cell technologies are needed for detecting abnormal biomolecules in the early stages of chronic liver disease, especially in the early stages of hepatitis, to arrest the progression of the disease. Some of the latest studies show promise. For example, studies have shown that core transcription factors and pre-mRNA splicing regulators of the hepatocyte phenotype can predict the regression of chronic liver disease.115 Single-cell analysis in the liver involving parenchymal cells, immune cells, and stromal cells has elucidated the pathological mechanisms of viral infections, parasites, cancers, and other liver injuries.74 A more in-depth single-cell analysis of macrophages from patients with liver metastasis was performed to identify two markers associated with cancer.116 In the future, single-cell techniques should be combined with general clinical examinations, and such techniques should be applied beyond basic research to analyze the pathology and physiology of liver disease in patients with different characteristics. Such steps can facilitate precise and individualized treatment of patients with liver disease.

Conclusions

With scRNA-seq as a core sequencing technology, single-cell approaches have yielded significant discoveries in the physiology and pathology of the liver at an unprecedented high resolution. We comprehensively discuss the application and findings of the single-cell approach in the progression of CHB to liver cancer or liver failure. These continuously optimized single-cell techniques provide invaluable tools for exploring subpopulations of liver cells and developing new biomarkers. At present, experience in single-cell technology is most extensive in HCC, and we look forward to increased use in other liver diseases, reduced cost, and simplifying the procedures. One day, single-cell technology will become a basic tool for clinical diagnosis, achieving precise treatment of patients with liver disease.

Abbreviations

4D: 

four-dimensional

10×: 

10× Genomics

ACLF: 

acute-on-chronic liver failure

ACKR1: 

atypical chemokine receptor 1

ALC: 

alcoholic cirrhotic

CCL4: 

C-C motif chemokine ligand 4

CHB: 

chronic hepatitis B

CHC: 

combined hepatocellular-cholangiocarcinoma

CITE-seq: 

cellular indexing of transcriptomes and epitopes by sequencing

CSCs: 

cancer stem cells

CUT & RUN: 

cleavage under targets and release using nuclease

DCs: 

dendritic cells

EpCAM: 

epithelial cell adhesion molecule

FFPE: 

formalin-fixed and paraffin embedded

HBV: 

hepatitis B virus

HCC: 

hepatocellular carcinoma

HH: 

hepatic hemangioma

iCCA: 

intrahepatic cholangiocarcinoma

IFN-α: 

interferon-α

ILCs: 

innate lymphoid cells

LVs: 

lymphatic vessels

LyECs: 

lymphatic vessel endothelial cells

nanoPOTS: 

nanodroplet processing in one pot for trace samples

NASH: 

nonalcoholic steatohepatitis

PBMCs: 

peripheral blood mononuclear cells

PD-1: 

programmed cell death-1

PDGFRα: 

recombinant human platelet-derived growth factor-α

PD-L1: 

programmed cell death-ligand 1

PLVAP: 

plasmalemma vesicle-associated protein

SAR: 

sarcomatoid carcinoma

scATAC-seq: 

single-cell assay for transposase-accessible chromatin using sequencing

scCGI-seq: 

single-cell CpG island methylation sequencing

scChIP-seq: 

single-cell chromatin immune-precipitation sequencing

scChIX-seq: 

single-cell chromatin immune cleavage and unmixing sequencing

scCOOL-seq: 

single-cell chromatin overall omic-scale landscape sequencing

scDNase-seq: 

single-cell DNase sequencing

sci-MET: 

single-cell combinatorial indexing for methylation analysis

SCoPE-MS: 

single-cell proteomics by mass spectrometry

scRNA-seq: 

single-cell RNA sequencing

scTrio-seq: 

single-cell triple omics sequencing

scWestern: 

single-cell western blotting

SLC: 

secondary liver cancer

smFISH: 

single-molecule fluorescence in situ hybridization

snRNA-seq: 

single-nucleus RNA sequencing

SPP1: 

secretory phosphoprotein 1

ST: 

spatial transcriptome technology

TAMs: 

tumor-associated macrophages

TAN: 

tumors-associated neutrophil

THBS1: 

thrombospondin-1

Tie 1: 

tyrosine kinase with immunoglobulin like and EGF like domains 1

TREM2: 

triggering receptor expressed on myeloid cells-2

Declarations

Funding

This work was supported in part by the National Natural Science Foundation of China (32000892); the Guangdong Basic and Applied Basic Research Foundation (2023A1515010143) and Science and Technology Program of Guangzhou, China (202102010155).

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Made substantial contributions to conceptualizing, visualizing, drafting, reviewing, and editing the review article (GC, WX), made substantial contributions to reviewing of and funding acquisitions for the article (YJ, BL, WX), contributed to discussion and conceptualization (ZL, YC). All authors approved the final version of this manuscript.

References

  1. Asrani SK, Devarbhavi H, Eaton J, Kamath PS. Burden of liver diseases in the world. J Hepatol 2019;70(1):151-171 View Article PubMed/NCBI
  2. Xiao J, Wang F, Wong NK, He J, Zhang R, Sun R, et al. Global liver disease burdens and research trends: Analysis from a Chinese perspective. J Hepatol 2019;71(1):212-221 View Article PubMed/NCBI
  3. Kisseleva T, Brenner D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nat Rev Gastroenterol Hepatol 2021;18(3):151-166 View Article PubMed/NCBI
  4. Stravitz RT, Lee WM. Acute liver failure. Lancet 2019;394(10201):869-881 View Article PubMed/NCBI
  5. Wen L, Tang F. Boosting the power of single-cell analysis. Nat Biotechnol 2018;36(5):408-409 View Article PubMed/NCBI
  6. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009;6(5):377-382 View Article PubMed/NCBI
  7. Grindberg RV, Yee-Greenbaum JL, McConnell MJ, Novotny M, O’Shaughnessy AL, Lambert GM, et al. RNA-sequencing from single nuclei. Proc Natl Acad Sci U S A 2013;110(49):19802-19807 View Article PubMed/NCBI
  8. Picelli S, Faridani OR, Björklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 2014;9(1):171-181 View Article PubMed/NCBI
  9. Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat Neurosci 2021;24(3):425-436 View Article PubMed/NCBI
  10. Van TM, Blank CU. A user’s perspective on GeoMx(TM) digital spatial profiling. Immunooncol Technol 2019;1:11-18 View Article PubMed/NCBI
  11. Liu Y, Yang M, Deng Y, Su G, Enninful A, Guo CC, et al. High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue. Cell 2020;183(6):1665-1681.e18 View Article PubMed/NCBI
  12. Budnik B, Levy E, Harmange G, Slavov N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol 2018;19(1):161 View Article PubMed/NCBI
  13. Ouldali H, Sarthak K, Ensslen T, Piguet F, Manivet P, Pelta J, et al. Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore. Nat Biotechnol 2020;38(2):176-181 View Article PubMed/NCBI
  14. Han L, Wu HJ, Zhu H, Kim KY, Marjani SL, Riester M, et al. Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells. Nucleic Acids Res 2017;45(10):e77 View Article PubMed/NCBI
  15. Chen X, Miragaia RJ, Natarajan KN, Teichmann SA. A rapid and robust method for single cell chromatin accessibility profiling. Nat Commun 2018;9(1):5345 View Article PubMed/NCBI
  16. Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol 2015;33(11):1165-1172 View Article PubMed/NCBI
  17. Bartosovic M, Kabbe M, Castelo-Branco G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat Biotechnol 2021;39(7):825-835 View Article PubMed/NCBI
  18. Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 2016;26(3):304-319 View Article PubMed/NCBI
  19. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 2017;14(9):865-868 View Article PubMed/NCBI
  20. Goetz JJ, Trimarchi JM. Transcriptome sequencing of single cells with Smart-Seq. Nat Biotechnol 2012;30(8):763-765 View Article PubMed/NCBI
  21. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 2015;161(5):1202-1214 View Article PubMed/NCBI
  22. Gao C, Zhang M, Chen L. The Comparison of Two Single-cell Sequencing Platforms: BD Rhapsody and 10× Genomics Chromium. Curr Genomics 2020;21(8):602-609 View Article PubMed/NCBI
  23. Quadri SM. Single-cell western blotting. Methods Mol Biol 2015;1312:455-464 View Article PubMed/NCBI
  24. Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of Single-Nucleus over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis. J Am Soc Nephrol 2019;30(1):23-32 View Article PubMed/NCBI
  25. Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, Wakiro I, et al. Author Correction: A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med 2020;26(8):1307 View Article PubMed/NCBI
  26. Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC, Marjanovic ND, et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 2020;38(6):737-746 View Article PubMed/NCBI
  27. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 2013;498(7453):236-240 View Article PubMed/NCBI
  28. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014;343(6172):776-779 View Article PubMed/NCBI
  29. Stubbington MJT, Lönnberg T, Proserpio V, Clare S, Speak AO, Dougan G, et al. T cell fate and clonality inference from single-cell transcriptomes. Nat Methods 2016;13(4):329-332 View Article PubMed/NCBI
  30. Lyu M, Wang S, Gao K, Wang L, Zhu X, Liu Y, et al. Dissecting the Landscape of Activated CMV-Stimulated CD4+ T Cells in Humans by Linking Single-Cell RNA-Seq With T-Cell Receptor Sequencing. Front Immunol 2021;12:779961 View Article PubMed/NCBI
  31. Zheng L, Qin S, Si W, Wang A, Xing B, Gao R, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 2021;374(6574):abe6474 View Article PubMed/NCBI
  32. Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, et al. Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell 2017;65(4):631-643.e4 View Article PubMed/NCBI
  33. Marx V. Method of the Year: spatially resolved transcriptomics. Nat Methods 2021;18(1):9-14 View Article PubMed/NCBI
  34. Kwon S. Single-molecule fluorescence in situ hybridization: quantitative imaging of single RNA molecules. BMB Rep 2013;46(2):65-72 View Article PubMed/NCBI
  35. Piñeiro AJ, Houser AE, Ji AL. Research Techniques Made Simple: Spatial Transcriptomics. J Invest Dermatol 2022;142(4):993-1001.e1 View Article PubMed/NCBI
  36. Darmanis S, Gallant CJ, Marinescu VD, Niklasson M, Segerman A, Flamourakis G, et al. Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells. Cell Rep 2016;14(2):380-389 View Article PubMed/NCBI
  37. Piehowski PD, Zhu Y, Bramer LM, Stratton KG, Zhao R, Orton DJ, et al. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-µm spatial resolution. Nat Commun 2020;11(1):8 View Article PubMed/NCBI
  38. Deng Y, Xie M, Li Q, Xu X, Ou W, Zhang Y, et al. Targeting Mitochondria-Inflammation Circuit by β-Hydroxybutyrate Mitigates HFpEF. Circ Res 2021;128(2):232-245 View Article PubMed/NCBI
  39. Wu M, Chen Y, Xia H, Wang C, Tan CY, Cai X, et al. Transcriptional and proteomic insights into the host response in fatal COVID-19 cases. Proc Natl Acad Sci U S A 2020;117(45):28336-28343 View Article PubMed/NCBI
  40. Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schönegger A, Klughammer J, et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 2015;10(8):1386-1397 View Article PubMed/NCBI
  41. Mulqueen RM, Pokholok D, Norberg SJ, Torkenczy KA, Fields AJ, Sun D, et al. Highly scalable generation of DNA methylation profiles in single cells. Nat Biotechnol 2018;36(5):428-431 View Article PubMed/NCBI
  42. Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 2015;528(7580):142-146 View Article PubMed/NCBI
  43. Carter B, Ku WL, Kang JY, Hu G, Perrie J, Tang Q, et al. Mapping histone modifications in low cell number and single cells using antibody-guided chromatin tagmentation (ACT-seq). Nat Commun 2019;10(1):3747 View Article PubMed/NCBI
  44. Ku WL, Pan L, Cao Y, Gao W, Zhao K. Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing. Genome Res 2021;31(10):1831-1842 View Article PubMed/NCBI
  45. Preissl S, Gaulton KJ, Ren B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat Rev Genet 2023;24(1):21-43 View Article PubMed/NCBI
  46. Ben-Moshe S, Itzkovitz S. Spatial heterogeneity in the mammalian liver. Nat Rev Gastroenterol Hepatol 2019;16(7):395-410 View Article PubMed/NCBI
  47. Aizarani N, Saviano A, Sagar, Mailly L, Durand S, Herman JS, et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 2019;572(7768):199-204 View Article PubMed/NCBI
  48. Zhang T, Zhang J, You X, Liu Q, Du Y, Gao Y, et al. Hepatitis B virus X protein modulates oncogene Yes-associated protein by CREB to promote growth of hepatoma cells. Hepatology 2012;56(6):2051-2059 View Article PubMed/NCBI
  49. Gao Q, Zhu H, Dong L, Shi W, Chen R, Song Z, et al. Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell 2019;179(2):561-577.e22 View Article PubMed/NCBI
  50. Le C, Liu Y, López-Orozco J, Joyce MA, Le XC, Tyrrell DL. CRISPR Technique Incorporated with Single-Cell RNA Sequencing for Studying Hepatitis B Infection. Anal Chem 2021;93(31):10756-10761 View Article PubMed/NCBI
  51. Xu H, Yu H, Zheng F, Zhang C, Cai W, Zhang X, et al. Analyzing the gene regulatory network in hepatitis B patients by single-cell ATAC sequencing. Clin Rheumatol 2022;41(11):3513-3524 View Article PubMed/NCBI
  52. Ramachandran P, Dobie R, Wilson-Kanamori JR, Dora EF, Henderson BEP, Luu NT, et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 2019;575(7783):512-518 View Article PubMed/NCBI
  53. Zhang P, Li H, Peng B, Zhang Y, Liu K, Cheng K, et al. Single-cell RNA transcriptomics reveals differences in the immune status of alcoholic and hepatitis B virus-related liver cirrhosis. Front Endocrinol (Lausanne) 2023;14:1132085 View Article PubMed/NCBI
  54. Zhang P, Li H, Zhou C, Liu K, Peng B, She X, et al. Single-Cell RNA Transcriptomics Reveals the State of Hepatic Lymphatic Endothelial Cells in Hepatitis B Virus-Related Acute-on-Chronic Liver Failure. J Clin Med 2022;11(10):2910 View Article PubMed/NCBI
  55. Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, et al. Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell 2017;169(7):1342-1356.e16 View Article PubMed/NCBI
  56. Fred RG, Steen Pedersen J, Thompson JJ, Lee J, Timshel PN, Stender S, et al. Single-cell transcriptome and cell type-specific molecular pathways of human non-alcoholic steatohepatitis. Sci Rep 2022;12(1):13484 View Article PubMed/NCBI
  57. Ho DW, Tsui YM, Chan LK, Sze KM, Zhang X, Cheu JW, et al. Single-cell RNA sequencing shows the immunosuppressive landscape and tumor heterogeneity of HBV-associated hepatocellular carcinoma. Nat Commun 2021;12(1):3684 View Article PubMed/NCBI
  58. Zheng H, Pomyen Y, Hernandez MO, Li C, Livak F, Tang W, et al. Single-cell analysis reveals cancer stem cell heterogeneity in hepatocellular carcinoma. Hepatology 2018;68(1):127-140 View Article PubMed/NCBI
  59. Zhang Q, Lou Y, Yang J, Wang J, Feng J, Zhao Y, et al. Integrated multiomic analysis reveals comprehensive tumour heterogeneity and novel immunophenotypic classification in hepatocellular carcinomas. Gut 2019;68(11):2019-2031 View Article PubMed/NCBI
  60. Ma L, Heinrich S, Wang L, Keggenhoff FL, Khatib S, Forgues M, et al. Multiregional single-cell dissection of tumor and immune cells reveals stable lock-and-key features in liver cancer. Nat Commun 2022;13(1):7533 View Article PubMed/NCBI
  61. Sun Y, Wu L, Zhong Y, Zhou K, Hou Y, Wang Z, et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell 2021;184(2):404-421.e16 View Article PubMed/NCBI
  62. Xue R, Zhang Q, Cao Q, Kong R, Xiang X, Liu H, et al. Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 2022;612(7938):141-147 View Article PubMed/NCBI
  63. Zhang C, Li J, Cheng Y, Meng F, Song JW, Fan X, et al. Single-cell RNA sequencing reveals intrahepatic and peripheral immune characteristics related to disease phases in HBV-infected patients. Gut 2023;72(1):153-167 View Article PubMed/NCBI
  64. Halpern KB, Shenhav R, Matcovitch-Natan O, Toth B, Lemze D, Golan M, et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 2017;542(7641):352-356 View Article PubMed/NCBI
  65. Brancale J, Vilarinho S. A single cell gene expression atlas of 28 human livers. J Hepatol 2021;75(1):219-220 View Article PubMed/NCBI
  66. Hou X, Yang Y, Li P, Zeng Z, Hu W, Zhe R, et al. Integrating Spatial Transcriptomics and Single-Cell RNA-seq Reveals the Gene Expression Profling of the Human Embryonic Liver. Front Cell Dev Biol 2021;9:652408 View Article PubMed/NCBI
  67. Gao S, Shi Q, Zhang Y, Liang G, Kang Z, Huang B, et al. Identification of HSC/MPP expansion units in fetal liver by single-cell spatiotemporal transcriptomics. Cell Res 2022;32(1):38-53 View Article PubMed/NCBI
  68. Wesley BT, Ross ADB, Muraro D, Miao Z, Saxton S, Tomaz RA, et al. Single-cell atlas of human liver development reveals pathways directing hepatic cell fates. Nat Cell Biol 2022;24(10):1487-1498 View Article PubMed/NCBI
  69. Inverso D, Shi J, Lee KH, Jakab M, Ben-Moshe S, Kulkarni SR, et al. A spatial vascular transcriptomic, proteomic, and phosphoproteomic atlas unveils an angiocrine Tie-Wnt signaling axis in the liver. Dev Cell 2021;56(11):1677-1693.e10 View Article PubMed/NCBI
  70. Ölander M, Wiśniewski JR, Artursson P. Cell-type-resolved proteomic analysis of the human liver. Liver Int 2020;40(7):1770-1780 View Article PubMed/NCBI
  71. Guilliams M, Bonnardel J, Haest B, Vanderborght B, Wagner C, Remmerie A, et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 2022;185(2):379-396.e38 View Article PubMed/NCBI
  72. MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun 2018;9(1):4383 View Article PubMed/NCBI
  73. Zhao J, Zhang S, Liu Y, He X, Qu M, Xu G, et al. Single-cell RNA sequencing reveals the heterogeneity of liver-resident immune cells in human. Cell Discov 2020;6:22 View Article PubMed/NCBI
  74. Ray K. Zones of immune defence in the liver. Nat Rev Gastroenterol Hepatol 2021;18(2):81 View Article PubMed/NCBI
  75. Bukhari S, Henick BS, Winchester RJ, Lerrer S, Adam K, Gartshteyn Y, et al. Single-cell RNA sequencing reveals distinct T cell populations in immune-related adverse events of checkpoint inhibitors. Cell Rep Med 2023;4(1):100868 View Article PubMed/NCBI
  76. Nguyen MH, Wong G, Gane E, Kao JH, Dusheiko G. Hepatitis B Virus: Advances in Prevention, Diagnosis, and Therapy. Clin Microbiol Rev 2020;33(2):e00046-19 View Article PubMed/NCBI
  77. Wang D, Fu B, Wei H. Advances in Immunotherapy for Hepatitis B. Pathogens 2022;11(10):1116 View Article PubMed/NCBI
  78. Zhong S, Li Q, Wen C, Li Y, Zhou Y, Jin Z, et al. Interferon α facilitates anti-HBV cellular immune response in a B cell-dependent manner. Antiviral Res 2022;207:105420 View Article PubMed/NCBI
  79. Wen C, Dong Z, Wang Y, Ye G, Ma Y, Yi X, et al. CTLA4(+)CD4(+)CXCR5(-)FOXP3(+) T cells associate with unfavorable outcome in patients with chronic HBV infection. BMC Immunol 2023;24(1):3 View Article PubMed/NCBI
  80. Li Y, Tang L, Guo L, Chen C, Gu S, Zhou Y, et al. CXCL13-mediated recruitment of intrahepatic CXCR5(+)CD8(+) T cells favors viral control in chronic HBV infection. J Hepatol 2020;72(3):420-430 View Article PubMed/NCBI
  81. Mederacke YS, Nienen M, Jarek M, Geffers R, Hupa-Breier K, Babel N, et al. T cell receptor repertoires within liver allografts are different to those in the peripheral blood. J Hepatol 2021;74(5):1167-1175 View Article PubMed/NCBI
  82. Schwabe RF, Tabas I, Pajvani UB. Mechanisms of Fibrosis Development in Nonalcoholic Steatohepatitis. Gastroenterology 2020;158(7):1913-1928 View Article PubMed/NCBI
  83. Terkelsen MK, Bendixen SM, Hansen D, Scott EAH, Moeller AF, Nielsen R, et al. Transcriptional Dynamics of Hepatic Sinusoid-Associated Cells After Liver Injury. Hepatology 2020;72(6):2119-2133 View Article PubMed/NCBI
  84. Yang L, Zhen L, Li Z, Zhu S, Xu W, Luo Q, et al. Human liver tissue transcriptomics revealed immunometabolic disturbances and related biomarkers in hepatitis B virus-related acute-on-chronic liver failure. Front Microbiol 2022;13:1080484 View Article PubMed/NCBI
  85. Li J, Liang X, Jiang J, Yang L, Xin J, Shi D, et al. PBMC transcriptomics identifies immune-metabolism disorder during the development of HBV-ACLF. Gut 2022;71(1):163-175 View Article PubMed/NCBI
  86. Yao J, Liu T, Zhao Q, Ji Y, Bai J, Wang H, et al. Genetic landscape and immune mechanism of monocytes associated with the progression of acute-on-chronic liver failure. Hepatol Int 2023;17(3):676-688 View Article PubMed/NCBI
  87. Shen G, Sun S, Huang J, Deng H, Xu Y, Wang Z, et al. Dynamic changes of T cell receptor repertoires in patients with hepatitis B virus-related acute-on-chronic liver failure. Hepatol Int 2020;14(1):47-56 View Article PubMed/NCBI
  88. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell 2018;175(4):984-997.e24 View Article PubMed/NCBI
  89. Aliya S, Lee H, Alhammadi M, Umapathi R, Huh YS. An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis. Int J Mol Sci 2022;23(3):1402 View Article PubMed/NCBI
  90. Wu R, Guo W, Qiu X, Wang S, Sui C, Lian Q, et al. Comprehensive analysis of spatial architecture in primary liver cancer. Sci Adv 2021;7(51):eabg3750 View Article PubMed/NCBI
  91. Smit MM, Feller KJ, You L, Storteboom J, Begce Y, Beerens C, et al. Spatially Annotated Single Cell Sequencing for Unraveling Intratumor Heterogeneity. Front Bioeng Biotechnol 2022;10:829509 View Article PubMed/NCBI
  92. He Y, Luo J, Zhang G, Jin Y, Wang N, Lu J, et al. Single-cell profiling of human CD127(+) innate lymphoid cells reveals diverse immune phenotypes in hepatocellular carcinoma. Hepatology 2022;76(4):1013-1029 View Article PubMed/NCBI
  93. Wernberg CW, Ravnskjaer K, Lauridsen MM, Thiele M. The Role of Diagnostic Biomarkers, Omics Strategies, and Single-Cell Sequencing for Nonalcoholic Fatty Liver Disease in Severely Obese Patients. J Clin Med 2021;10(5):930 View Article PubMed/NCBI
  94. Liu Z, Wang Y, Yao Y, Fang Z, Miao QR, Ye M. Quantitative proteomic and phosphoproteomic studies reveal novel 5-fluorouracil resistant targets in hepatocellular carcinoma. J Proteomics 2019;208:103501 View Article PubMed/NCBI
  95. Bao X, Li Q, Chen J, Chen D, Ye C, Dai X, et al. Molecular Subgroups of Intrahepatic Cholangiocarcinoma Discovered by Single-Cell RNA Sequencing-Assisted Multiomics Analysis. Cancer Immunol Res 2022;10(7):811-828 View Article PubMed/NCBI
  96. Shen Y, Xu S, Ye C, Li Q, Chen R, Wu W, et al. Proteomic and single-cell landscape reveals novel pathogenic mechanisms of HBV-infected intrahepatic cholangiocarcinoma. iScience 2023;26(2):106003 View Article PubMed/NCBI
  97. Nagaraju GP, Dariya B, Kasa P, Peela S, El-Rayes BF. Epigenetics in hepatocellular carcinoma. Semin Cancer Biol 2022;86(Pt 3):622-632 View Article PubMed/NCBI
  98. Wolinska E, Skrzypczak M. Epigenetic Changes Affecting the Development of Hepatocellular Carcinoma. Cancers (Basel) 2021;13(16):4237 View Article PubMed/NCBI
  99. Taniai M. Alcohol and hepatocarcinogenesis. Clin Mol Hepatol 2020;26(4):736-741 View Article PubMed/NCBI
  100. Wang S, Xie J, Zou X, Pan T, Yu Q, Zhuang Z, et al. Single-cell multiomics reveals heterogeneous cell states linked to metastatic potential in liver cancer cell lines. iScience 2022;25(3):103857 View Article PubMed/NCBI
  101. Chen S, Teichmann SA. Completing the cancer jigsaw puzzle with single-cell multiomics. Nat Cancer 2021;2(12):1260-1262 View Article PubMed/NCBI
  102. Wang H, Jiang W, Wang H, Wei Z, Li H, Yan H, et al. Identification of Mutation Landscape and Immune Cell Component for Liver Hepatocellular Carcinoma Highlights Potential Therapeutic Targets and Prognostic Markers. Front Genet 2021;12:737965 View Article PubMed/NCBI
  103. Yang S, Qian L, Li Z, Li Y, Bai J, Zheng B, et al. Integrated Multi-Omics Landscape of Liver Metastases. Gastroenterology 2023;164(3):407-423.e17 View Article PubMed/NCBI
  104. Gohil SH, Iorgulescu JB, Braun DA, Keskin DB, Livak KJ. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat Rev Clin Oncol 2021;18(4):244-256 View Article PubMed/NCBI
  105. van den Brink SC, Sage F, Vértesy Á, Spanjaard B, Peterson-Maduro J, Baron CS, et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods 2017;14(10):935-936 View Article PubMed/NCBI
  106. Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, Wakiro I, et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med 2020;26(5):792-802 View Article PubMed/NCBI
  107. Yu L, Wang X, Mu Q, Tam SST, Loi DSC, Chan AKY, et al. scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors. Sci Adv 2023;9(1):eabp8901 View Article PubMed/NCBI
  108. Kulkarni A, Anderson AG, Merullo DP, Konopka G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr Opin Biotechnol 2019;58:129-136 View Article PubMed/NCBI
  109. Voytek B. The data science future of neuroscience theory. Nat Methods 2022;19(11):1349-1350 View Article PubMed/NCBI
  110. Waylen LN, Nim HT, Martelotto LG, Ramialison M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun Biol 2020;3(1):602 View Article PubMed/NCBI
  111. Yeung J, Florescu M, Zeller P, de Barbanson BA, Wellenstein MD, van Oudenaarden A. scChIX-seq infers dynamic relationships between histone modifications in single cells. Nat Biotechnol 2023;41(6):813-823 View Article PubMed/NCBI
  112. Ru B, Huang J, Zhang Y, Aldape K, Jiang P. Estimation of cell lineages in tumors from spatial transcriptomics data. Nat Commun 2023;14(1):568 View Article PubMed/NCBI
  113. Xu Z, Zhang T, Chen H, Zhu Y, Lv Y, Zhang S, et al. High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq. Nat Commun 2023;14(1):2734 View Article PubMed/NCBI
  114. Acharya C, Bajaj JS. Chronic Liver Diseases and the Microbiome-Translating Our Knowledge of Gut Microbiota to Management of Chronic Liver Disease. Gastroenterology 2021;160(2):556-572 View Article PubMed/NCBI
  115. Berasain C, Arechederra M, Argemí J, Fernández-Barrena MG, Avila MA. Loss of liver function in chronic liver disease: An identity crisis. J Hepatol 2023;78(2):401-414 View Article PubMed/NCBI
  116. Cortese N, Carriero R, Barbagallo M, Putignano AR, Costa G, Giavazzi F, et al. High-Resolution Analysis of Mononuclear Phagocytes Reveals GPNMB as a Prognostic Marker in Human Colorectal Liver Metastasis. Cancer Immunol Res 2023;11(4):405-420 View Article PubMed/NCBI
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  • Journal of Clinical and Translational Hepatology
  • pISSN 2225-0719
  • eISSN 2310-8819

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Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology

Guanzi Chen, Wenli Xu, Zhicong Long, Yutian Chong, Bingliang Lin, Yusheng Jie
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