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

Single-cell RNA transcriptome landscape of hepatocytes and non-parenchymal cells in healthy and NAFLD mouse liver

Nonalcoholic fatty liver disease (NAFLD) is a global health-care problem with limited therapeutic options. To obtain a cellular resolution of pathogenesis, 82,168 single-cell transcriptomes (scRNA-seq) across different NAFLD stages were profiled, identifying hepatocytes and 12 other non-parenchymal cell (NPC) types. scRNA-seq revealed insights into the cellular and molecular mechanisms of the disease. We discovered a dual role for hepatic stellate cells in gene expression regulation and in the potential to trans-differentiate into myofibroblasts. We uncovered distinct expression profiles of Kupffer cells versus monocyte-derived macrophages during NAFLD progression. Kupffer cells showed stronger immune responses, while monocyte-derived macrophages demonstrated a capability for differentiation. Three chimeric NPCs were identified including endothelial-chimeric stellate cells, hepatocyte-chimeric endothelial cells, and endothelial-chimeric Kupffer cells. Our work identified unanticipated aspects of mouse with NAFLD at the single-cell level and advanced the understanding of cellular heterogeneity in NAFLD livers.

GeneMarkeR: A Database and User Interface for scRNA-seq Marker Genes

Single-cell sequencing (scRNA-seq) has enabled researchers to study cellular heterogeneity. Accurate cell type identification is crucial for scRNA-seq analysis to be valid and robust. Marker genes, genes specific for one or a few cell types, can improve cell type classification; however, their specificity varies across species, samples, and cell subtypes. Current marker gene databases lack standardization, cell hierarchy consideration, sample diversity, and/or the flexibility for updates as new data become available. Most of these databases are derived from a single statistical analysis despite many such analyses scattered in the literature to identify marker genes from scRNA-seq data and pure cell populations. An R Shiny web tool called GeneMarkeR was developed for researchers to retrieve marker genes demonstrating cell type specificity across species, methodology and sample types based on a novel algorithm. The web tool facilitates online submission and interfaces with MySQL to ensure updatability. Furthermore, the tool incorporates reactive programming to enable researchers to retrieve standardized public data supporting the marker genes. GeneMarkeR currently hosts over 261,000 rows of standardized marker gene results from 25 studies across 21,012 unique genomic entities and 99 unique cell types mapped to hierarchical ontologies.

Genetic and Cellular Contributions to Liver Regeneration

The regenerative capabilities of the liver represent a paradigm for understanding tissue repair in solid organs. Regeneration after partial hepatectomy in rodent models is well understood, while regeneration in the context of clinically relevant chronic injuries is less studied. Given the growing incidence of fatty liver disease, cirrhosis, and liver cancer, interest in liver regeneration is increasing. Here, we will review the principles, genetics, and cell biology underlying liver regeneration, as well as new approaches being used to study heterogeneity in liver tissue maintenance and repair.

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis

Fibrosis is characterized by the excessive production of collagen and other extracellular matrix (ECM) components and represents a leading cause of morbidity and mortality worldwide. Previous studies of nonalcoholic steatohepatitis (NASH) with fibrosis were largely restricted to bulk transcriptome profiles. Thus, our understanding of this disease is limited by an incomplete characterization of liver cell types in general and hepatic stellate cells (HSCs) in particular, given that activated HSCs are the major hepatic fibrogenic cell population. To help fill this gap, we profiled 17,810 non-parenchymal cells derived from six healthy human livers. In conjunction with public single-cell data of fibrotic/cirrhotic human livers, these profiles enable the identification of potential intercellular signaling axes (e.g., ITGAV-LAMC1, TNFRSF11B-VWF and NOTCH2-DLL4) and master regulators (e.g., RUNX1 and CREB3L1) responsible for the activation of HSCs during fibrogenesis. Bulk RNA-seq data of NASH patient livers and rodent models for liver fibrosis of diverse etiologies allowed us to evaluate the translatability of candidate therapeutic targets for NASH-related fibrosis. We identified 61 liver fibrosis-associated genes (e.g., AEBP1, PRRX1 and LARP6) that may serve as a repertoire of translatable drug target candidates. Consistent with the above regulon results, gene regulatory network analysis allowed the identification of CREB3L1 as a master regulator of many of the 61 genes. Together, this study highlights potential cell-cell interactions and master regulators that underlie HSC activation and reveals genes that may represent prospective hallmark signatures for liver fibrosis.

Liver Zonation - Revisiting Old Questions With New Technologies

Despite the ever-increasing prevalence of non-alcoholic fatty liver disease (NAFLD), the etiology and pathogenesis remain poorly understood. This is due, in part, to the liver's complex physiology and architecture. The liver maintains glucose and lipid homeostasis by coordinating numerous metabolic processes with great efficiency. This is made possible by the spatial compartmentalization of metabolic pathways a phenomenon known as liver zonation. Despite the importance of zonation to normal liver function, it is unresolved if and how perturbations to liver zonation can drive hepatic pathophysiology and NAFLD development. While hepatocyte heterogeneity has been identified over a century ago, its examination had been severely hindered due to technological limitations. Recent advances in single cell analysis and imaging technologies now permit further characterization of cells across the liver lobule. This review summarizes the advances in examining liver zonation and elucidating its regulatory role in liver physiology and pathology. Understanding the spatial organization of metabolism is vital to further our knowledge of liver disease and to provide targeted therapeutic avenues.

Towards a Systems Immunology Approach to Understanding Correlates of Protective Immunity against HCV

Over the past decade, tremendous progress has been made in systems biology-based approaches to studying immunity to viral infections and responses to vaccines. These approaches that integrate multiple facets of the immune response, including transcriptomics, serology and immune functions, are now being applied to understand correlates of protective immunity against hepatitis C virus (HCV) infection and to inform vaccine development. This review focuses on recent progress in understanding immunity to HCV using systems biology, specifically transcriptomic and epigenetic studies. It also examines proposed strategies moving forward towards an integrated systems immunology approach for predicting and evaluating the efficacy of the next generation of HCV vaccines.

[An optimized protocol of meniscus cell extraction for single-cell RNA sequencing]

OBJECTIVE

To optimize the protocol of meniscus cell extraction to enhance the efficiency of cell suspension preparation and maintain a high cell viability for single-cell RNA sequencing.

METHODS

We compared the efficiency of the routine cell extraction methods (short-time digestion and long-time digestion) and the optimized protocol for obtaining meniscus cell suspensions by evaluating the cell number obtained and the cell viability. Single-cell RNA sequencing datasets were analyzed to evaluate the stability of the cell suspension prepared using the optimized protocol. The reliability of the optimized protocol was assessed by comparing the single-cell RNA sequencing dataset obtained by the optimized protocol with published single-cell RNA sequencing datasets of the meniscus.

RESULTS

The optimized protocol harvested a greater number of cells (over 1×10⁵) than the routine protocols. The cell suspension prepared with the optimized protocol showed a cell viability higher than 80%, the highest among the 3 methods. Analysis of single-cell RNA sequencing datasets showed that the ratio of the mitochondrial genes was below 20% in over 80% of the cells. CD34⁺ cells, MCAM⁺ cells and COL1A1⁺ cells were identified in the datasets. Comparison with the publish datasets showed that the optimized protocol was capable of harvesting COL3A1⁺, COL1A1⁺, MYLK⁺, BMP2⁺, CD93⁺ and CDK1⁺ cells.

CONCLUSION

Single-cell suspension prepared from the meniscus can be stably obtained using the optimized protocol for single-cell RNA sequencing using the 10× Genomics platform.

Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision

RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.

Single-cell RNA sequencing of human liver reveals hepatic stellate cell heterogeneity

Background & Aims

The multiple vital functions of the human liver are performed by highly specialised parenchymal and non-parenchymal cells organised in complex collaborative sinusoidal units. Although crucial for homeostasis, the cellular make-up of the human liver remains to be fully elucidated. Here, single-cell RNA-sequencing was used to unravel the heterogeneity of human liver cells, in particular of hepatocytes (HEPs) and hepatic stellate cells (HSCs).

Method

The transcriptome of ~25,000 freshly isolated human liver cells was profiled using droplet-based RNA-sequencing. Recently published data sets and RNA in situ hybridisation were integrated to validate and locate newly identified cell populations.

Results

In total, 22 cell populations were annotated that reflected the heterogeneity of human parenchymal and non-parenchymal liver cells. More than 20,000 HEPs were ordered along the portocentral axis to confirm known, and reveal previously undescribed, zonated liver functions. The existence of 2 subpopulations of human HSCs with unique gene expression signatures and distinct intralobular localisation was revealed (i.e. portal and central vein-concentrated GPC3⁺ HSCs and perisinusoidally located DBH⁺ HSCs). In particular, these data suggest that, although both subpopulations collaborate in the production and organisation of extracellular matrix, GPC3⁺ HSCs specifically express genes involved in the metabolism of glycosaminoglycans, whereas DBH⁺ HSCs display a gene signature that is reminiscent of antigen-presenting cells.

Conclusions

This study highlights metabolic zonation as a key determinant of HEP transcriptomic heterogeneity and, for the first time, outlines the existence of heterogeneous HSC subpopulations in the human liver. These findings call for further research on the functional implications of liver cell heterogeneity in health and disease.

Lay summary

This study resolves the cellular landscape of the human liver in an unbiased manner and at high resolution to provide new insights into human liver cell biology. The results highlight the physiological heterogeneity of human hepatic stellate cells.

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A cell atlas from the near-native transcriptome of >25,000 human liver cells is presented.

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Hepatocytes were ordered along the portocentral axis to reveal previously undescribed gene expression patterns and zonated liver functions.

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Two subpopulations of human hepatic stellate cells (HSCs) are reported, characterised by different spatial distribution in the native tissue.

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Characteristic gene signatures of HSC subpopulations are suggestive of far-reaching functional differences.

Epigenetic Biomarkers for the Diagnosis and Treatment of Liver Disease

Research in the last decades has demonstrated the relevance of epigenetics in controlling gene expression to maintain cell homeostasis, and the important role played by epigenome alterations in disease development. Moreover, the reversibility of epigenetic marks can be harnessed as a therapeutic strategy, and epigenetic marks can be used as diagnosis biomarkers. Epigenetic alterations in DNA methylation, histone post-translational modifications (PTMs), and non-coding RNA (ncRNA) expression have been associated with the process of hepatocarcinogenesis. Here, we summarize epigenetic alterations involved in the pathogenesis of chronic liver disease (CLD), particularly focusing on DNA methylation. We also discuss their utility as epigenetic biomarkers in liquid biopsy for the diagnosis and prognosis of hepatocellular carcinoma (HCC). Finally, we discuss the potential of epigenetic therapeutic strategies for HCC treatment.

Leveraging Single-Cell RNA-seq Data to Uncover the Association Between Cell Type and Chronic Liver Diseases

BACKGROUND

Components of liver microenvironment is complex, which makes it difficult to clarify pathogenesis of chronic liver diseases (CLD). Genome-wide association studies (GWASs) have greatly revealed the role of host genetic background in CLD pathogenesis and prognosis, while single-cell RNA sequencing (scRNA-seq) enables interrogation of the cellular diversity and function of liver tissue at unprecedented resolution. Here, we made integrative analysis on the GWAS and scRNA-seq data of CLD to uncover CLD-related cell types and provide clues for understanding on the pathogenesis.

METHODS

We downloaded three GWAS summary data and three scRNA-seq data on CLD. After defining the cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate the GWAS and scRNA-seq. In addition, we analyzed one scRNA-seq data without association to CLD to validate the specificity of our findings.

RESULTS

After processing the scRNA-seq data, we obtain about 19,002-32,200 cells and identified 10-17 cell types. For the HCC analysis, we identified the association between B cell and HCC in two datasets. RolyPoly also identified the association, when we integrated the two scRNA-seq datasets. In addition, we also identified natural killer (NK) cell as HCC-associated cell type in one dataset. In specificity analysis, we identified no significant cell type associated with HCC. As for the cirrhosis analysis, we obtained no significant related cell type.

CONCLUSION

In this integrative analysis, we identified B cell and NK cell as HCC-related cell type. More attention and verification should be paid to them in future research.

Inhibition of Mitochondrial ATP Synthesis and Regulation of Oxidative Stress Based on {SbW8 O30 } Determined by Single-Cell Proteomics Analysis

The 10-nuclear heteroatom cluster modified {SbW₈ O₃₀ } was successfully synthesized and exhibited inhibitory activity (IC₅₀ =0.29 μM). Based on proteomics analysis, Na₄ Ni₂ Sb₂ W₂ -SbW₈ inhibited ATP production by affecting the expression of 16 related proteins, hindering metabolic functions in vivo and cell proliferation due to reactive oxygen species (ROS) stress. In particular, the low expression of FAD/FMN-binding redox enzymes (relative expression ratio of the experimental group to the control=0.43843) could be attributed to the redox mechanism of Na₄ Ni₂ Sb₂ W₂ -SbW₈ , which was consistent with the effect of polyoxometalates (POMs) and FMN-binding proteins on ATP formation. An electrochemical study showed that Na₄ Ni₂ Sb₂ W₂ -SbW₈ combined with FMN to form Na₄ Ni₂ Sb₂ W₂ -SbW₈ -2FMN complex through a one-electron process of the W atoms. Na₄ Ni₂ Sb₂ W₂ -SbW₈ acted as catalase and glutathione peroxidase to protect the cell from ROS stress, and the inhibition rates were 63.3 % at 1.77 μM of NADPH and 86.06 % at 10.62 μM of 2-hydroxyterephthalic acid. Overall, our results showed that POMs can be specific oxidative/antioxidant regulatory agents.

Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment

Understanding tumor immune microenvironments is critical for identifying immune modifiers of cancer progression and developing cancer immunotherapies. Recent applications of single-cell RNA sequencing (scRNA-seq) in dissecting tumor microenvironments have brought important insights into the biology of tumor-infiltrating immune cells, including their heterogeneity, dynamics, and potential roles in both disease progression and response to immune checkpoint inhibitors and other immunotherapies. This review focuses on the advances in knowledge of tumor immune microenvironments acquired from scRNA-seq studies across multiple types of human tumors, with a particular emphasis on the study of phenotypic plasticity and lineage dynamics of immune cells in the tumor environment. We also discuss several imminent questions emerging from scRNA-seq observations and their potential solutions on the horizon.

Nucleic Acid-Induced Signaling in Chronic Viral Liver Disease

A hallmark for the development and progression of chronic liver diseases is the persistent dysregulation of signaling pathways related to inflammatory responses, which eventually promotes the development of hepatic fibrosis, cirrhosis and hepatocellular carcinoma (HCC). The two major etiological agents associated with these complications in immunocompetent patients are hepatitis B virus (HBV) and hepatitis C virus (HCV), accounting for almost 1.4 million liver disease-associated deaths worldwide. Although both differ significantly from the point of their genomes and viral life cycles, they exert not only individual but also common strategies to divert innate antiviral defenses. Multiple virus-modulated pathways implicated in stress and inflammation illustrate how chronic viral hepatitis persistently tweaks host signaling processes with important consequences for liver pathogenesis. The following review aims to summarize the molecular events implicated in the sensing of viral nucleic acids, the mechanisms employed by HBV and HCV to counter these measures and how the dysregulation of these cellular pathways drives the development of chronic liver disease and the progression toward HCC.

Holistic characterization of single-hepatocyte transcriptome responses to high-fat diet

During nutritional overload and obesity, hepatocyte function is grossly altered, and a subset of hepatocytes begins to accumulate fat droplets, leading to nonalcoholic fatty liver disease (NAFLD). Recent single-cell studies revealed how nonparenchymal cells, such as macrophages, hepatic stellate cells, and endothelial cells, heterogeneously respond to NAFLD. However, it remains to be characterized how hepatocytes, the major constituents of the liver, respond to nutritional overload in NAFLD. Here, using droplet-based, single-cell RNA sequencing (Drop-seq), we characterized how the transcriptomic landscape of individual hepatocytes is altered in response to high-fat diet (HFD) and NAFLD. We showed that the entire hepatocyte population undergoes substantial transcriptome changes upon HFD, although the patterns of alteration were highly heterogeneous, with zonation-dependent and -independent effects. Periportal (zone 1) hepatocytes downregulated many zone 1-specific marker genes, whereas a small number of genes mediating gluconeogenesis were upregulated. Pericentral (zone 3) hepatocytes also downregulated many zone 3-specific genes; however, they upregulated several genes that promote HFD-induced fat droplet formation, consistent with findings that zone 3 hepatocytes accumulate more lipid droplets. Zone 3 hepatocytes also upregulated ketogenic pathways as an adaptive mechanism to HFD. Interestingly, many of the top HFD-induced genes, which encode proteins regulating lipid metabolism, were strongly co-expressed with each other in a subset of hepatocytes, producing a variegated pattern of spatial co-localization that is independent of metabolic zonation. In conclusion, our data set provides a useful resource for understanding hepatocellular alteration during NAFLD at single cell level.

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis

As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, we find gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq's higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.