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

Predicting prognosis and clinical efficacy of immune checkpoint blockade therapy via interferon-alpha response in muscle-invasive bladder cancer

Background: Immune checkpoint blockade (ICB) can prompt durable and robust responses in multiple cancers, involving muscle-invasive bladder cancer (MIBC). However, only a limited fraction of patients received clinical benefit. Clarifying the determinants of response and exploring corresponding predictive biomarkers is key to improving outcomes. Methods: Four independent formerly published cohorts consisting of 641 MIBC patients were enrolled in this study. We first analyzed the associations between various cancer hallmarks and ICB therapy response in two immunotherapeutic cohorts to identify the leading prognostic hallmark in MIBC. Furthermore, advanced machine learning methods were performed to select robust and promising predictors from genes functioning in the above leading pathway. The predictive ability of selected genes was also validated in multiple MIBC cohorts. Results: We identified and verified IFNα response as the leading cancer hallmark indicating better treatment responses, favorable overall survival, and an inflamed tumor microenvironment with higher infiltration of immune effector cells in MIBC patients treated with ICB therapy. Subsequently, two commonly selected genes, CXCL10 and LAMP3, implied better therapy response and the CXCL10^highLAMP3^high patients would benefit more from ICB therapy, which was comprehensively validated from the perspective of gene expression, clinical response, patient survival and immune features. Conclusion: Higher IFNα response primarily predicted better ICB therapeutic responses and reflected an inflamed microenvironment in MIBC. A composite of CXCL10 and LAMP3 expression could serve as promising predictive biomarkers for ICB therapeutic responses and be beneficial for clinical decision-making in MIBC.

Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation

T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.

Promising immunotherapeutic targets in lung cancer based on single-cell RNA sequencing

Immunotherapy has made great strides in the treatment of lung cancer, but a significant proportion of patients still do not respond to treatment. Therefore, the identification of novel targets is crucial to improving the response to immunotherapy. The tumor microenvironment (TME) is a complex niche composed of diverse pro-tumor molecules and cell populations, making the function and mechanism of a unique cell subset difficult to understand. However, the advent of single-cell RNA sequencing (scRNA-seq) technology has made it possible to identify cellular markers and understand their potential functions and mechanisms in the TME. In this review, we highlight recent advances emerging from scRNA-seq studies in lung cancer, with a particular focus on stromal cells. We elucidate the cellular developmental trajectory, phenotypic remodeling, and cell interactions during tumor progression. Our review proposes predictive biomarkers and novel targets for lung cancer immunotherapy based on cellular markers identified through scRNA-seq. The identification of novel targets could help improve the response to immunotherapy. The use of scRNA-seq technology could provide new strategies to understand the TME and develop personalized immunotherapy for lung cancer patients.

Recent advances in high-throughput single-cell transcriptomics and spatial transcriptomics

Single-cell RNA sequencing (scRNA-seq) has been developed for characterizing the transcriptome of cells that are rare but of biological significance. With cell barcoding and microchip technologies, a suite of high-throughput scRNA-seq protocols enable transcriptome profiling in thousands of individual cells at single-cell resolution for classifying cell types, discovering novel cell populations, investigating cellular heterogeneity and elucidating lineage trajectories. Microchip technologies including microfluidics- and microwell-based platforms play a major role in high-throughput scRNA-seq. As the emerging technology, spatial transcriptomics integrates cellular transcriptomics with their spatial coordinates within tissues for spatially deciphering cellular composition, heterogeneity and cell-cell communications. Spatial transcriptomics has been increasingly recognized as one of the most powerful tools for discovering new biology and advancing precision medicine. Microfluidics as an enabling technology plays an increasingly important role in spatial transcriptomics. We review the technological spectrum and advances in high-throughput scRNA-seq and spatial transcriptomics, discuss their advantages and limitations, and pitch into new biology learned from these new tools.

Dissection of cellular and molecular mechanisms of aristolochic acid-induced hepatotoxicity via single-cell transcriptomics

BACKGROUND

Aristolochic acids (AAs), a class of carcinogenic and mutagenic natural products from Aristolochia and Asarum plants, are well-known to be responsible for inducing nephrotoxicity and urothelial carcinoma. Recently, accumulating evidence suggests that exposure to AAs could also induce hepatotoxicity and even hepatocellular carcinoma, though the mechanisms are poorly defined.

METHODS

Here, we aimed to dissect the underlying cellular and molecular mechanisms of aristolochic acid I (AAI)-induced hepatotoxicity by using advanced single-cell RNA sequencing (scRNA-seq) and proteomics techniques. We established the first single-cell atlas of mouse livers in response to AAI.

RESULTS

In hepatocytes, our results indicated that AAI activated NF-κB and STAT3 signaling pathways, which may contribute to the inflammatory response and apoptosis. In liver sinusoidal endothelial cells (LSECs), AAI activated multiple oxidative stress and inflammatory associated signaling pathways and induced apoptosis. Importantly, AAI induced infiltration of cytotoxic T cells and activation of proinflammatory macrophage and neutrophil cells in the liver to produce inflammatory cytokines to aggravate inflammation.

CONCLUSIONS

Collectively, our study provides novel knowledge of AAs-induced molecular characteristics of hepatotoxicity at a single-cell level and suggests future treatment options for AAs associated hepatotoxicity.

The role of multi-omics in the diagnosis of COVID-19 and the prediction of new therapeutic targets

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, has led to more than 170 million confirmed cases in 223 countries and regions, claiming 3,872,457 lives. Some patients with COVID-19 have mild clinical symptoms despite severe respiratory failure, which greatly increases the difficulty of diagnosis and treatment. It is therefore necessary to identify biological characteristics of SARS-CoV-2, screen novel diagnostic and prognostic biomarkers, as well as to explore potential therapeutic targets for COVID-19. In this comprehensive review, we discuss the current published literature on COVID-19. We find that the comprehensive application of genomics, transcriptomics, proteomics and metabolomics is becoming increasingly important in the treatment of COVID-19. Multi-omics analysis platforms are expected to revolutionize the diagnosis and classification of COVID-19. This review aims to provide a reference for diagnosis, surveillance and clinical decision making related to COVID-19.

Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review

Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.

Determination of the nanoparticle- and cell-specific toxicological mechanisms in 3D liver spheroids using scRNAseq analysis

Engineered nanomaterials (ENMs) are commonly used in consumer products, allowing exposure to target organs such as the lung, liver, and skin that could lead to adverse health effects in humans. To better reflect on toxicological effects in liver cells, it is important to consider the contribution of hepatocyte morphology, function, and intercellular interactions in a dynamic 3D microenvironment. Herein, we used a 3D liver spheroid model containing hepatocyte and Kupffer cells (KCs) to study the effects of three different material compositions, namely vanadium pentoxide (V2O5), titanium dioxide (TiO2), or graphene oxide (GO). Additionally, we used single-cell RNA sequencing (scRNAseq) to determine the nanoparticle (NP) and cell-specific toxicological responses. A general finding was that hepatocytes exhibit more variation in gene expression and adaptation of signaling pathways than KCs. TNF-α production tied to the NF-κB pathway was a commonly affected pathway by all NPs while impacts on the metabolic function of hepatocytes were unique to V2O5. V2O5 NPs also showed the largest number of differentially expressed genes in both cell types, many of which are related to pro-inflammatory and apoptotic response pathways. There was also evidence of mitochondrial ROS generation and caspase-1 activation after GO and V2O5 treatment, in association with cytokine production. All considered, this study provides insight into the impact of nanoparticles on gene responses in key liver cell types, providing us with a scRNAseq platform that can be used for high-content screening of nanomaterial impact on the liver, for use in biosafety and biomedical applications.

In Vitro Models for the Study of Liver Biology and Diseases: Advances and Limitations

In vitro models of liver (patho)physiology, new technologies, and experimental approaches are progressing rapidly. Based on cell lines, induced pluripotent stem cells or primary cells derived from mouse or human liver as well as whole tissue (slices), such in vitro single- and multicellular models, including complex microfluidic organ-on-a-chip systems, provide tools to functionally understand mechanisms of liver health and disease. The International Society of Hepatic Sinusoidal Research (ISHSR) commissioned this working group to review the currently available in vitro liver models and describe the advantages and disadvantages of each in the context of evaluating their use for the study of liver functionality, disease modeling, therapeutic discovery, and clinical applicability.

SpaceX: gene co-expression network estimation for spatial transcriptomics

MOTIVATION

The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.

RESULTS

We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

AVAILABILITY AND IMPLEMENTATION

The SpaceX R-package is available at github.com/bayesrx/SpaceX.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of Intestinal Microbes in Chronic Liver Diseases

With the recent availability and upgrading of many emerging intestinal microbes sequencing technologies, our research on intestinal microbes is changing rapidly. A variety of investigations have found that intestinal microbes are essential for immune system regulation and energy metabolism homeostasis, which impacts many critical organs. The liver is the first organ to be traversed by the intestinal portal vein, and there is a strong bidirectional link between the liver and intestine. Many intestinal factors, such as intestinal microbes, bacterial composition, and intestinal bacterial metabolites, are deeply involved in liver homeostasis. Intestinal microbial dysbiosis and increased intestinal permeability are associated with the pathogenesis of many chronic liver diseases, such as alcoholic fatty liver disease (AFLD), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), chronic hepatitis B (CHB), chronic hepatitis C (CHC), autoimmune liver disease (AIH) and the development of hepatocellular carcinoma (HCC). Intestinal permeability and dysbacteriosis often lead to Lipopolysaccharide (LPS) and metabolites entering in serum. Then, Toll-like receptors activation in the liver induces the exposure of the intestine and liver to many small molecules with pro-inflammatory properties. And all of these eventually result in various liver diseases. In this paper, we have discussed the current evidence on the role of various intestinal microbes in different chronic liver diseases. As well as potential new therapeutic approaches are proposed in this review, such as antibiotics, probiotics, and prebiotics, which may have an improvement in liver diseases.

Intra-tumor heterogeneity and prognostic risk signature for hepatocellular carcinoma based on single-cell analysis

Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the "Enricher" function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.

Immune mechanisms linking metabolic injury to inflammation and fibrosis in fatty liver disease - novel insights into cellular communication circuits

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease and is emerging as the leading cause of cirrhosis, liver transplantation and hepatocellular carcinoma (HCC). NAFLD is a metabolic disease that is considered the hepatic manifestation of the metabolic syndrome; however, during the evolution of NAFLD from steatosis to non-alcoholic steatohepatitis (NASH), to more advanced stages of NASH with liver fibrosis, the immune system plays an integral role. Triggers for inflammation are rooted in hepatic (lipid overload, lipotoxicity, oxidative stress) and extrahepatic (gut-liver axis, adipose tissue, skeletal muscle) systems, resulting in unique immune-mediated pathomechanisms in NAFLD. In recent years, the implementation of single-cell RNA-sequencing and high dimensional multi-omics (proteogenomics, lipidomics) and spatial transcriptomics have tremendously advanced our understanding of the complex heterogeneity of various liver immune cell subsets in health and disease. In NAFLD, several emerging inflammatory mechanisms have been uncovered, including profound macrophage heterogeneity, auto-aggressive T cells, the role of unconventional T cells and platelet-immune cell interactions, potentially yielding novel therapeutics. In this review, we will highlight the recent discoveries related to inflammation in NAFLD, discuss the role of immune cell subsets during the different stages of the disease (including disease regression) and integrate the multiple systems driving inflammation. We propose a refined concept by which the immune system contributes to all stages of NAFLD and discuss open scientific questions arising from this paradigm shift that need to be unravelled in the coming years. Finally, we discuss novel therapeutic approaches to target the multiple triggers of inflammation, including combination therapy via nuclear receptors (FXR agonists, PPAR agonists).

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Hepatic Stellate Cell Modulates the Immune Microenvironment in the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a major cause of increases in the mortality rate due to cancer that usually develops in patients with liver fibrosis and impaired hepatic immunity. Hepatic stellate cells (HSCs) may directly or indirectly crosstalk with various hepatic cells and subsequently modulate extracellular remodeling, cell invasion, macrophage conversion, and cancer deterioration. In this regard, the tumor microenvironment created by activated HSC plays a critical role in mediating pathogenesis and immune escape during HCC progression. Herein, intermediately differentiated human liver cancer cell line (J5) cells were co-cultured with HSC-conditioned medium (HSC-CM); changes in cell phenotype and cytokine profiles were analyzed to assess the impact of HSCs on the development of hepatoma. The stage of liver fibrosis correlated significantly with tumor grade, and the administration of conditioned medium secreted by activated HSC (aHSC-CM) could induce the expression of N-cadherin, cell migration, and invasive potential, as well as the activity of matrix metalloproteinases in J5 cells, implying that aHSC-CM could trigger the epithelial-mesenchymal transition (EMT). Next, the HSC-CM was further investigated and network analysis indicated that specific cytokines and soluble proteins, such as activin A, released from activated HSCs could remarkably affect the tumor-associated immune microenvironment involved in macrophage polarization, which would, in turn, diminish a host’s immune surveillance and drive hepatoma cells into a more malignant phenotype. Together, our findings provide a novel insight into the integral roles of HSCs to enhance hepatocarcinogenesis through their immune-modulatory properties and suggest that HSC may serve as a potent target for the treatment of advanced HCC.

A reference single-cell regulomic and transcriptomic map of cynomolgus monkeys

Non-human primates are attractive laboratory animal models that accurately reflect both developmental and pathological features of humans. Here we present a compendium of cell types across multiple organs in cynomolgus monkeys (Macaca fascicularis) using both single-cell chromatin accessibility and RNA sequencing data. The integrated cell map enables in-depth dissection and comparison of molecular dynamics, cell-type compositions and cellular heterogeneity across multiple tissues and organs. Using single-cell transcriptomic data, we infer pseudotime cell trajectories and cell-cell communications to uncover key molecular signatures underlying their cellular processes. Furthermore, we identify various cell-specific cis-regulatory elements and construct organ-specific gene regulatory networks at the single-cell level. Finally, we perform comparative analyses of single-cell landscapes among mouse, monkey and human. We show that cynomolgus monkey has strikingly higher degree of similarities in terms of immune-associated gene expression patterns and cellular communications to human than mouse. Taken together, our study provides a valuable resource for non-human primate cell biology.

Non-human primates are attractive laboratory animal models that can accurately reflect some developmental and pathological features of humans. Here the authors chart a reference cell map of cynomolgus monkeys using both scATAC-seq and scRNA-seq data across multiple organs, providing insights into the molecular dynamics and cellular heterogeneity of this organism.

Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively

Sepsis-associated acute kidney injury (SA-AKI) is a common complication in critically ill patients with high morbidity and mortality. SA-AKI varies considerably in disease presentation, progression, and response to treatment, highlighting the heterogeneity of the underlying biological mechanisms. In this review, we briefly describe the pathophysiology of SA-AKI, biomarkers, reference databases, and available omics techniques. Advances in omics technology allow for comprehensive analysis of SA-AKI, and the integration of multiple omics provides an opportunity to understand the information flow behind the disease. These approaches will drive a shift in current paradigms for the prevention, diagnosis, and staging and provide the renal community with significant advances in precision medicine in SA-AKI analysis.

Spatial Transcriptomics for Tumor Heterogeneity Analysis

The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes. Although single-cell sequencing has become an important tool for investigating tumor heterogeneity recently, it lacks the spatial information of analyzed cells. In this regard, spatial transcriptomics holds great promise in deciphering the complex heterogeneity of cancer by providing localization-indexed gene expression information. This study reviews the applications of spatial transcriptomics in the study of tumor heterogeneity, discovery of novel spatial-dependent mechanisms, tumor immune microenvironment, and matrix microenvironment, as well as the pathological classification and prognosis of cancer. Finally, future challenges and opportunities for spatial transcriptomics technology’s applications in cancer are also discussed.

Personalized treatment for hepatocellular carcinoma: Current status and future perspectives

Systemic treatment for hepatocellular carcinoma (HCC) has been advancing rapidly over the last decade. More novel agents, including both targeted agents and immune checkpoint inhibitors, are available for physicians to use sequentially or concurrently for patients with advanced HCC. Despite more options, only a proportion of patients benefit from each regimen. Therefore, clinicians are facing challenges on how to choose the right regimen for the right patient with HCC, which raises the importance of personalized treatment approach. To advance personalized treatment for HCC, one approach relies on the acquisition of biomarker data from clinical trials to evaluate clinical parameters or genotypes in association with outcomes of selected drugs. This approach has led to finding of high baseline alpha-fetoprotein levels in association with benefits of ramucirumab. Cumulative findings from multiple clinical trials and translational studies also suggest that selected etiology and/or genotype of HCC could predict resistance to immune checkpoint inhibitors. The second approach is to decipher the tumor heterogeneity of HCC with an aim to identify clinically relevant patterns to guide clinical decisions. Tumor heterogeneity could exist within a single tumor (intra-tumoral heterogeneity), among different tumors in the same patient (inter-tumoral heterogeneity) or between primary and recurrent tumors (temporal tumor heterogeneity). The analyses of tumor heterogeneity have also been powered by coverage of tumor immune environment and incorporation of circulating tumor nucleic acid technology. Emerging publications have been reported above tumor heterogeneity exist in HCC, which is potentially clinically impactful.

Liver regeneration: Cellular origin and molecular mechanisms

The liver is known as an organ with high proliferation potential. Clarifying the cellular origin and deepening the understanding of liver regeneration mechanisms will help provide new directions for the treatment of liver disease. With the development and application of lineage tracing technology, the specific distribution and dynamic changes of hepatocyte subpopulations in homeostasis and liver injury have been illustrated. Self-replication of hepatocytes is responsible for the maintenance of liver function and mass under homeostasis. The compensatory proliferation of remaining hepatocytes is the main mechanism of liver regeneration following acute and chronic liver injury. Transdifferentiation between hepatocytes and cholangiocytes has been recognized upon severe chronic liver injury. Wnt/β-catenin, Hippo/YAP and Notch signalling play essential roles in the maintenance of homeostatic liver and hepatocyte-to-cholangiocyte conversion under liver injury. In this review, we summarized the recent studies on cell origin of newly generated hepatocytes and the underlying mechanisms of liver regeneration in homeostasis and liver injury.

Spatial transcriptomics identifies enriched gene expression and cell types in human liver fibrosis

Liver fibrosis and cirrhosis have limited therapeutic options and represent a serious unmet patient need. Recent use of single‐cell RNA sequencing (scRNAseq) has identified enriched cell types infiltrating cirrhotic livers but without defining the microanatomical location of these lineages thoroughly. To assess whether fibrotic liver regions specifically harbor enriched cell types, we explored whether whole‐tissue spatial transcriptomics combined with scRNAseq and gene deconvolution analysis could be used to localize cell types in cirrhotic explants of patients with end‐stage liver disease (total n = 8; primary sclerosing cholangitis, n = 4; primary biliary cholangitis, n = 2, alcohol‐related liver disease, n = 2). Spatial transcriptomics clearly identified tissue areas of distinct gene expression that strongly correlated with the total area (Spearman r = 0.97, p = 0.0004) and precise location (parenchyma, 87.9% mean congruency; range, 73.1%–97.1%; fibrosis, 68.5% mean congruency; range, 41.0%–91.7%) of liver regions classified as parenchymal or fibrotic by conventional histology. Deconvolution and enumeration of parenchymal and fibrotic gene content as measured by spatial transcriptomics into distinct cell states revealed significantly higher frequencies of ACTA2+ FABP4+ and COL3A1+ mesenchymal cells, IL17RA+ S100A8+ and FCER1G+ tissue monocytes, VCAM1+ SDC3+ Kupffer cells, CCL4+ CCL5+ KLRB1+ and GZMA+ IL17RA+ T cells and HLA‐DR+, CD37+ CXCR4+ and IGHM+ IGHG+ B cells in fibrotic liver regions compared with parenchymal areas of cirrhotic explants. Conclusion: Our findings indicate that spatial transcriptomes of parenchymal and fibrotic liver regions express unique gene content within cirrhotic liver and demonstrate proof of concept that spatial transcriptomes combined with additional RNA sequencing methodologies can refine the localization of gene content and cell lineages in the search for antifibrotic targets.

SpatialMap: Spatial Mapping of Unmeasured Gene Expression Profiles in Spatial Transcriptomic Data Using Generalized Linear Spatial Models

Recent advances in various single-cell RNA sequencing (scRNA-seq) technologies have enabled profiling the gene expression level with the whole transcriptome at a single-cell resolution. However, it lacks the spatial context of tissues. The image-based transcriptomics in situ studies (e.g., MERFISH and seqFISH) maintain the cell spatial context at individual cell levels but can only measure a limited number of genes or transcripts (up to roughly 1,000 genes). Therefore, integrating scRNA-seq data and image-based transcriptomics data can potentially gain the complementary benefits of both. Here, we develop a computational method, SpatialMap, to bridge the gap, which primarily facilitates spatial mapping of unmeasured gene profiles in spatial transcriptomic data via integrating with scRNA-seq data from the same tissue. SpatialMap directly models the count nature of spatial gene expression data through generalized linear spatial models, which accounts for the spatial correlation among spatial locations using conditional autoregressive (CAR) prior. With a newly developed computationally efficient penalized quasi-likelihood (PQL)-based algorithm, SpatialMap can scale up to performing large-scale spatial mapping analysis. Finally, we applied the SpatialMap to four publicly available tissue-paired studies (i.e., scRNA-seq studies and image-based transcriptomics studies). The results demonstrate that the proposed method can accurately predict unmeasured gene expression profiles across various spatial and scRNA-seq dataset pairs of different species and technologies.

The Single-Cell Level Perspective of the Tumor Microenvironment and Its Remodeling by CAR-T Cells

The tumor microenvironment (TME) is a complex milieu consisting of lymphoid cells, myeloid cells, fibroblasts, and multiple molecules, which play a key role in tumor progression and immunotherapy. TME is characterized by immune-suppressive features, which release anti-inflammatory cytokines such as IL-4 and TGFβ to skew the T cells to a Th2 state as well to polarize tumor-associated macrophages (TAMs) to an anti-inflammatory phenotype to curb the immunotherapy. Considering the heterogeneity of the TME and its role in determining response to chimeric antigen receptor (CAR)-T cells, delineating TME at a single-cell level will provide useful information for cancer treatment. First, we discuss cellular and molecular features that curb the response to CAR-T cells, for example, high expression of immune checkpoint molecules (PD-1, LAG3) and anti-inflammatory cytokines (IL-4, TGFb) that block CAR-T cell function. Then, we summarize how newly invented single-cell technologies such as spatial multi-omics would benefit the understanding of cancer immunotherapy. Finally, we will further describe recent attempts of CAR-T to remodel TME by arming the CAR-T with anti-PD-1 single-chain variants or Th1 triggering cytokines (such as IL-7, IL-12) to remodel TME into a pro-inflammatory state. Herein, we review the single-cell-level signatures of TME and the strategies of CAR-T to remodel TME.

High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways

High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods.

FibROAD: a manually curated resource for multi-omics level evidence integration of fibrosis research

Organ fibrosis represents a vital health threat that substantially contributes to yearly mortality rates. While a considerable amount of research has been conducted on fibrosis, these reports have only focused on specific organs as affected within distinct disorders. Accordingly, results from such studies have been unable to provide a comprehensive understanding of the pathological processes involved. Here, we describe the development of FibROAD, an open-access database that integrates evidence from fibrosis-associated disorders as obtained from both the literature and multi-omics data. This resource will greatly assist both researchers and clinicians in the comprehension and treatment of this condition. FibROAD currently involves an assembly of 232 strong evidence-based fibrosis-related genes (FRGs) as garnered from 909 PubMed publications and contains lists of multi-omics data from > 4000 samples including RNA-seq, single-cell RNA-seq, miRNA-seq, ChIP-seq, ATAC-seq MeDIP-seq and MBD-seq as obtained from 17 different organs in 5 species. Results from integrative analyses as obtained using FibROAD have demonstrated that FRGs can be indicators for a wide range of organ fibrosis and reveal potential pro-fibrotic candidate genes for fibrosis research. In conclusion, FibROAD serves as a convenient platform where researchers can acquire integrated evidence and a more comprehensive understanding of fibrosis-related disorders.

Database URL  https://www.fibroad.org

Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues

Single-cell RNA sequencing (RNA-seq) techniques can perform analysis of transcriptome at the single-cell level and possess an unprecedented potential for exploring signatures involved in tumor development and progression. These techniques can perform sequence analysis of transcripts with a better resolution that could increase understanding of the cellular diversity found in the tumor microenvironment and how the cells interact with each other in complex heterogeneous cancerous tissues. Identifying the changes occurring in the genome and transcriptome in the spatial context is considered to increase knowledge of molecular factors fueling cancers. It may help develop better monitoring strategies and innovative approaches for cancer treatment. Recently, there has been a growing trend in the integration of RNA-seq techniques with contemporary omics technologies to study the tumor microenvironment. There has been a realization that this area of research has a huge scope of application in translational research. This review article presents an overview of various types of single-cell RNA-seq techniques used currently for analysis of cancer tissues, their pros and cons in bulk profiling of transcriptome, and recent advances in the techniques in exploring heterogeneity of various types of cancer tissues. Furthermore, we have highlighted the integration of single-cell RNA-seq techniques with other omics technologies for analysis of transcriptome in their spatial context, which is considered to revolutionize the understanding of tumor microenvironment.

Museum of spatial transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .

Computational elucidation of spatial gene expression variation from spatially resolved transcriptomics data

Recent advances in spatially resolved transcriptomics (SRT) have revolutionized biological and medical research and enabled unprecedented insight into the functional organization and cell communication of tissues and organs in situ. Identifying and elucidating gene spatial expression variation (SE analysis) is fundamental to elucidate the SRT landscape. There is an urgent need for public repositories and computational techniques of SRT data in SE analysis alongside technological breakthroughs and large-scale data generation. Increasing efforts to use in silico techniques in SE analysis have been made. However, these attempts are widely scattered among a large number of studies that are not easily accessible or comprehensible by both medical and life scientists. This study provides a survey and a summary of public resources on SE analysis in SRT studies. An updated systematic overview of state-of-the-art computational approaches and tools currently available in SE analysis are presented herein, emphasizing recent advances. Finally, the present study explores the future perspectives and challenges of in silico techniques in SE analysis. This study guides medical and life scientists to look for dedicated resources and more competent tools for characterizing spatial patterns of gene expression.

Cell-Type Resolved Insights into the Cis-Regulatory Genome of NAFLD

The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing rapidly, and unmet treatment can result in the development of hepatitis, fibrosis, and liver failure. There are difficulties involved in diagnosing NAFLD early and for this reason there are challenges involved in its treatment. Furthermore, no drugs are currently approved to alleviate complications, a fact which highlights the need for further insight into disease mechanisms. NAFLD pathogenesis is associated with complex cellular changes, including hepatocyte steatosis, immune cell infiltration, endothelial dysfunction, hepatic stellate cell activation, and epithelial ductular reaction. Many of these cellular changes are controlled by dramatic changes in gene expression orchestrated by the cis-regulatory genome and associated transcription factors. Thus, to understand disease mechanisms, we need extensive insights into the gene regulatory mechanisms associated with tissue remodeling. Mapping cis-regulatory regions genome-wide is a step towards this objective and several current and emerging technologies allow detection of accessible chromatin and specific histone modifications in enriched cell populations of the liver, as well as in single cells. Here, we discuss recent insights into the cis-regulatory genome in NAFLD both at the organ-level and in specific cell populations of the liver. Moreover, we highlight emerging technologies that enable single-cell resolved analysis of the cis-regulatory genome of the liver.

Macrophages in cholangiopathies

PURPOSE OF REVIEW

Cholangiopathies are a heterogeneous class of liver diseases where cholangiocytes are the main targets of liver injury. Although available and emerging therapies mainly target bile acids (ursodeoxycholic acid/UDCA, 24-Norursodeoxycholic acid/norUDCA) and related signaling pathways (obeticholic acid, fibrates, FXR, and PPAR agonists), the mechanisms underlying inflammation, ductular reaction and fibrosis in cholestatic liver diseases remain poorly understood.

RECENT FINDINGS

Data from patients with cholestatic diseases, such as primary biliary cholangitis (PBC) or primary sclerosing cholangitis (PSC) as well as mouse models of biliary injury emphasize the role of immune cells in the pathogenesis of cholestatic disorders and indicate diverse functions of hepatic macrophages. Their versatile polarization phenotypes and their capacity to interact with other cell types (e.g. cholangiocytes, other immune cells) make macrophages central actors in the progression of cholangiopathies.

SUMMARY

In this review, we summarize recent findings on the response of hepatic macrophages to cholestasis and biliary injury and their involvement in the progression of cholangiopathies. Furthermore, we discuss how recent discoveries may foster the development of innovative therapies to treat patients suffering from cholestatic liver diseases, in particular, treatments targeting macrophages to limit hepatic inflammation.

TJ-M2010-5, A self-developed MyD88 inhibitor, attenuates liver fibrosis by inhibiting the NF-κB pathway

Liver fibrosis is the result of most chronic inflammatory liver damage and seriously endangers human health. However, no drugs have been approved to treat this disease. Previous studies showed that the Toll-like receptors (TLRs)/myeloid differentiation factor-88 (MyD88)/nuclear factor-κB (NF-κB) pathway plays a key role in liver fibrosis. TJ-M2010-5 is a self-developed small molecule MyD88 inhibitor, which has been proven to have a good protective effect in a variety of inflammatory disease models. In the present study, to investigate the anti-fibrotic effect of TJ-M2010-5, mice were injected with carbon tetrachloride (CCl4) in vivo and LX2 cells (a human hepatic stellate cell line) were treated with TGF-β1 in vitro to induce liver fibrosis. In vivo studies showed that TJ-M2010-5 attenuated the CCl4-induced liver damage, collagen accumulation, and the activation of hepatic stellate cells by inhibiting the nuclear transfer of NF-κB. Moreover, in vitro experiments of LX2 cells stimulated with TGF-β1 further indicated that the NF-κB pathway is involved in the development of liver fibrosis. TJ-M2010-5 significantly inhibited the proliferation and activation of LX2 cells. In addition, TJ-M2010-5 upregulated the expression of bone morphogenetic protein and membrane-bound inhibitor (BAMBI) in LX2 cells by blocking the activation of MyD88/NF-κB, thereby inhibiting the phosphorylation of Smad2/3 and the expression of collagen I (COL1A1) induced by TGF-β1. In conclusion, this study illustrates the anti-hepatic fibrosis effect of TJ-M2010-5 and provides a new treatment method for liver fibrosis.

Spatial components of molecular tissue biology

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.

Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

Steady progress over four decades toward understanding the pathogenesis and clinical consequences of hepatic fibrosis has led to the expectation of effective antifibrotic drugs, yet none has been approved. Thus, an assessment of the field is timely, to clarify priorities and accelerate progress. Here, we highlight the successes to date but, more importantly, identify gaps and unmet needs, both experimentally and clinically. These include the need to better define cell-cell interactions and etiology-specific elements of fibrogenesis and their link to disease-specific drivers of portal hypertension. Success in treating viral hepatitis has revealed the remarkable capacity of the liver to degrade scar in reversing fibrosis, yet we know little of the mechanisms underlying this response. Thus, there is an exigent need to clarify the cellular and molecular mechanisms of fibrosis regression in order for therapeutics to mimic the liver's endogenous capacity. Better refined and more predictive in vitro and animal models will hasten drug development. From a clinical perspective, current diagnostics are improving but not always biologically plausible or sufficiently accurate to supplant biopsy. More urgently, digital pathology methods that leverage machine learning and artificial intelligence must be validated in order to capture more prognostic information from liver biopsies and better quantify the response to therapies. For more refined treatment of NASH, orthogonal approaches that integrate genetic, clinical, and pathological data sets may yield treatments for specific subphenotypes of the disease. Collectively, these and other advances will strengthen and streamline clinical trials and better link histologic responses to clinical outcomes.

Stiffness is associated with hepatic stellate cell heterogeneity during liver fibrosis

The fibrogenic wound-healing response in liver increases stiffness. Stiffness mechanotransduction, in turn, amplifies fibrogenesis. Here, we aimed to understand the distribution of stiffness in fibrotic liver, how it impacts hepatic stellate cell (HSC) heterogeneity, and identify mechanisms by which stiffness amplifies fibrogenic responses. Magnetic resonance elastography and atomic force microscopy demonstrated a heterogeneous distribution of liver stiffness at macroscopic and microscopic levels, respectively, in a carbon tetrachloride (CCl₄) mouse model of liver fibrosis as compared with controls. High stiffness was mainly attributed to extracellular matrix dense areas. To identify a stiffness-sensitive HSC subpopulation, we performed single-cell RNA sequencing (scRNA-seq) on primary HSCs derived from healthy versus CCl₄-treated mice. A subcluster of HSCs was matrix-associated with the most upregulated pathway in this subpopulation being focal adhesion signaling, including a specific protein termed four and a half LIM domains protein 2 (FHL2). In vitro, FHL2 expression was increased in primary human HSCs cultured on stiff matrix as compared with HSCs on soft matrix. Moreover, FHL2 knockdown inhibited fibronectin and collagen 1 expression, whereas its overexpression promoted matrix production. In summary, we demonstrate stiffness heterogeneity at the whole organ, lobular, and cellular level, which drives an amplification loop of fibrogenesis through specific focal adhesion molecular pathways.NEW & NOTEWORTHY The fibrogenic wound-healing response in liver increases stiffness. Here, macro and microheterogeneity of liver stiffness correlate with HSC heterogeneity in a hepatic fibrosis mouse model. Fibrogenic HSCs localized in stiff collagen-high areas upregulate the expression of focal adhesion molecule FHL2, which, in turn, promotes extracellular matrix protein expression. These results demonstrate that stiffness heterogeneity at the whole organ, lobular, and cellular level drives an amplification loop of fibrogenesis through specific focal adhesion molecular pathways.

An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis

Hepatocellular carcinoma is a primary liver cancer caused by the accumulation of genetic mutation patterns associated with epidemiological conditions. This lethal malignancy exhibits tumor heterogeneity, which is considered as one of the main reasons for drug resistance development and failure of clinical trials. Recently, single-cell technology (SCT), a new advanced sequencing technique that analyzes every single cell in a tumor tissue specimen, aids complete insight into the genetic heterogeneity of cancer. This helps in identifying and assessing rare cell populations by analyzing the difference in gene expression pattern between individual cells of single biopsy tissue which normally cannot be identified from pooled cell gene expression pattern (traditional sequencing technique). Thus, SCT improves the clinical diagnosis, treatment, and prognosis of hepatocellular carcinoma as the limitations of other techniques impede this cancer research progression. Application of SCT at the genomic, transcriptomic, and epigenomic levels to promote individualized hepatocellular carcinoma diagnosis and therapy. The current review has been divided into ten sections. Herein we deliberated on the SCT, hepatocellular carcinoma diagnosis, tumor microenvironment analysis, single-cell genomic sequencing, single-cell transcriptomics, single-cell omics sequencing for biomarker development, identification of hepatocellular carcinoma origination and evolution, limitations, challenges, conclusions, and future perspectives.

Spatially resolved transcriptomics in immersive environments

Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

Single-Cell RNA Sequencing Reveals CXCLs Enriched Fibroblasts Within Odontogenic Keratocysts

Purpose

We aimed to define cell subpopulations of odontogenic keratocyst (OKC), particularly relating to angiogenesis and explored the potential regulation mechanism for angiogenesis.

Materials and Methods

Single-cell RNA sequencing (scRNA-seq) analysis was investigated on 14,072 cells from 3 donors with OKC. The differential expressed genes, cell trajectory and intercellular communications were evaluated by bioinformatic analysis. Hydrostatic pressure (80 mmHg, 6h) was applied to the primary fibroblasts of OKC and the supernatant was collected for cytokines detection by cytokine antibody array. The chemokine (C-X-C motif) ligand 12 (CXCL12) and CD31 expressions were explored by immunohistochemistry in tissue microarray of OKC.

Results

Five different cell types were identified in the epithelium of OKC and 3 different cell types in the OKC fibroblasts were characterized, indicating high intra-lesional heterogeneity. CXCLs were highly enriched in the subset of fibroblasts and showed close interactions with endothelial cells. Hydrostatic pressure (80mmHg) significantly increased CXCL12 secretions in OKC fibroblasts. Stromal CXCL12 expressions were closely related to CD31 expressions of tissue microarray of OKC.

Conclusion

CXCLs enriched fibroblasts are crucial for angiogenesis of OKCs which could be partially regulated by hydrostatic pressure.

Clinical challenges of tissue preparation for spatial transcriptome

Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para-cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in 'dry' status. The direct cryo-preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi-orientations.

Hepatic Stellate Cell-Immune Interactions in NASH

Nonalcoholic fatty liver disease (NAFLD) is the dominant cause of liver disease worldwide. Nonalcoholic steatohepatitis (NASH), a more aggressive presentation of NAFLD, is characterized by severe hepatocellular injury, inflammation, and fibrosis. Chronic inflammation and heightened immune cell activity have emerged as hallmark features of NASH and key drivers of fibrosis through the activation of hepatic stellate cells (HSCs). Recent advances in our understanding of the molecular and cellular pathways in NASH have highlighted extensive crosstalk between HSCs and hepatic immune populations that strongly influences disease activity. Here, we review these findings, emphasizing the roles of HSCs in liver immunity and inflammation, key cell-cell interactions, and exciting areas for future investigation.

Single Cell RNA Sequencing in NASH

Single cell RNA sequencing (scRNA-seq) allows to uncover cellular heterogeneity and the identification of novel subpopulations. In non-alcoholic steatohepatitis (NASH), scRNA-seq is particularly powerful to understand non-parenchymal cell heterogeneity in the liver, e.g. for inflammatory cells. Myeloid immune cells, particularly macrophages, play a critical role in response of the innate immune system and significantly contribute to the progression of fatty liver disease. Due to their high heterogeneity and complex phenotypes, their functional role in health and disease is difficult to analyze. Here, we describe the isolation and analysis of myeloid cell populations from mouse liver using microdroplet-based scRNA-seq. This approach allows the identification and characterization of different hepatic cell types, exemplified here by hepatic macrophage populations, as well as analyses of differentially expressed genes between samples (e.g., cells from healthy or NASH livers).

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

Small-Cell Lung Cancer Long-Term Survivor Patients: How to Find a Needle in a Haystack?

Small-cell lung cancer (SCLC) is an aggressive malignancy characterized by a rapid progression and a high resistance to treatments. Unlike other solid tumors, there has been a scarce improvement in emerging treatments and survival during the last years. A better understanding of SCLC biology has allowed for the establishment of a molecular classification based on four transcription factors, and certain therapeutic vulnerabilities have been proposed. The universal inactivation of TP53 and RB1, along with the absence of mutations in known targetable oncogenes, has hampered the development of targeted therapies. On the other hand, the immunosuppressive microenvironment makes the success of immune checkpoint inhibitors (ICIs), which have achieved a modest improvement in overall survival in patients with extensive disease, difficult. Currently, atezolizumab or durvalumab, in combination with platinum–etoposide chemotherapy, is the standard of care in first-line setting. However, the magnitude of the benefit is scarce and no predictive biomarkers of response have yet been established. In this review, we describe SCLC biology and molecular classification, examine the SCLC tumor microenvironment and the challenges of predictive biomarkers of response to new treatments, and, finally, assess clinical and molecular characteristics of long-term survivor patients in order to identify possible prognostic factors and treatment vulnerabilities.

Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the mouse liver

Reconstruction of heterogeneity through single cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we apply Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology.

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis

Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. Results: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. Conclusions: In our opinion, SCA represents the bioinformatics version of a universal “Swiss-knife” for the extraction of hidden knowledgeable features from single cell omics data.

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.