Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice

MitoTracker Red for isolation of zone-specific hepatocytes and characterization of hepatic sublobular metabolism

BACKGROUND

The liver lobule is divided into three zones or regions: periportal (PP or Zone 1) that is highly oxidative and active in ureagenesis, pericentral (PC or Zone 3) that is more glycolytic, and midzonal (MZ or Zone 2) with intermediate characteristics.

AIM

Our goal was to isolate and metabolically characterize hepatocytes from specific sublobular zones.

METHODS

Mice were administered rhodamine123 (Rh123) or MitoTracker Red (MTR) prior to intravital imaging, liver fixation, or hepatocyte isolation. After in vivo MTR, hepatocytes were isolated and sorted based on MTR fluorescence intensity. Alternatively, E-cadherin (Ecad) and cytochrome P450 2E1 (CYP2E1) immunolabeling was performed in fixed liver slices. Ecad and CYP2E1 gene expression in sorted hepatocytes was assessed by qPCR. Oxygen consumption rates (OCR) of sorted hepatocytes were also assessed.

RESULTS

Multiphoton microscopy showed Rh123 and MTR fluorescence distributed zonally, decreasing from PP to PC in a flow-dependent fashion. In liver cross-sections, Ecad was expressed periportally and CYP2E1 pericentrally in association with high and low MTR labeling, respectively. Based on MTR fluorescence, hepatocytes were sorted into PP, MZ, and PC populations with PP and PC hepatocytes enriched in Ecad and CYP2E1, respectively. OCR of PP hepatocytes was ∼4 times that of PC hepatocytes.

CONCLUSIONS

MTR treatment in vivo delineates sublobular hepatic zones and can be used to sort hepatocytes zonally. PP hepatocytes have substantially greater OCR compared to PC and MZ. The results also indicate a sharp midzonal demarcation between hepatocytes with PP characteristics (Ecad) and those with PC features (CYP2E1). This new method to sort hepatocytes in a zone-specific fashion holds the potential to shed light on sublobular hepatocyte metabolism and regulatory pathways in health and disease.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) elicited dose-dependent shifts in the murine urinary metabolome associated with hepatic AHR-mediated differential gene expression

Epidemiological evidence suggests an association between dioxin and dioxin-like compound (DLC) exposure and human liver disease. The prototypical DLC, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), has been shown to induce the progression of reversible hepatic steatosis to steatohepatitis with periportal fibrosis and biliary hyperplasia in mice. Although the effects of TCDD toxicity are mediated by aryl hydrocarbon receptor (AHR) activation, the underlying mechanisms of TCDD-induced hepatotoxicity are unresolved. In the present study, male C57BL/6NCrl mice were gavaged every 4 days for 28 days with 0.03 - 30 μg/kg TCDD and evaluated for liver histopathology and gene expression as well as complementary 1-dimensional proton magnetic resonance (1D- 1H NMR) urinary metabolic profiling. Urinary trimethylamine (TMA), trimethylamine N-oxide (TMAO), and 1-methylnicotinamide (1MN) levels were altered by TCDD at doses ≤ 3 μg/kg; other urinary metabolites, like glycolate, urocanate, and 3-hydroxyisovalerate, were only altered at doses that induced moderate to severe steatohepatitis. Bulk liver RNA-seq data suggested altered urinary metabolites correlated with hepatic differential gene expression corresponding to specific metabolic pathways. In addition to evaluating whether altered urinary metabolites were liver-dependent, published single-nuclear RNA-seq (snRNA-seq), AHR ChIP-seq, and AHR knockout gene expression datasets provide further support for hepatic cell-type and AHR-regulated dependency, respectively. Overall, TCDD-induced liver effects were preceded by and occurred with changes in urinary metabolite levels due to AHR-mediated changes in hepatic gene expression.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Unique challenges and best practices for single cell transcriptomic analysis in toxicology

The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose-response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.

Perturbed liver gene zonation in a mouse model of non-alcoholic steatohepatitis

Liver zonation characterizes the separation of metabolic pathways along the lobules and is required for optimal hepatic function. Wnt signaling is a master regulator of spatial liver zonation. A perivenous-periportal Wnt activity gradient orchestrates metabolic zonation by activating gene expression in perivenous hepatocytes, while suppressing gene expression in their periportal counterparts. However, the understanding as to the liver gene zonation and zonation regulators in diseases is limited. Non-alcoholic steatohepatitis (NASH) is a chronic liver disease characterized by fat accumulation, inflammation, and fibrosis. Here, we investigated the perturbation of liver gene zonation in a mouse NASH model by combining spatial transcriptomics, bulk RNAseq and in situ hybridization. Wnt-target genes represented a major subset of genes showing altered spatial expression in the NASH liver. The altered Wnt-target gene expression levels and zonation spatial patterns were in line with the up regulation of Wnt regulators and the augmentation of Wnt signaling. Particularly, we found that the Wnt activator Rspo3 expression was restricted to the perivenous zone in control liver but expanded to the periportal zone in NASH liver. AAV8-mediated RSPO3 overexpression in controls resulted in zonation changes, and further amplified the disturbed zonation of Wnt-target genes in NASH, similarly Rspo3 knockdown in Rspo3+/- mice resulted in zonation changes of Wnt-target genes in both chow and HFD mouse. Interestingly, there were no impacts on steatosis, inflammation, or fibrosis NASH pathology from RSPO3 overexpression nor Rspo3 knockdown. In summary, our study demonstrated the alteration of Wnt signaling in a mouse NASH model, leading to perturbed liver zonation.

Emerging Roles of Spatial Transcriptomics in Liver Research

Spatial transcriptomics, leveraging sequencing- and imaging-based techniques, has emerged as a groundbreaking technology for mapping gene expression within the complex architectures of tissues. This approach provides an in-depth understanding of cellular and molecular dynamics across various states of healthy and diseased livers. Through the integration of sophisticated bioinformatics strategies, it enables detailed exploration of cellular heterogeneity, transitions in cell states, and intricate cell-cell interactions with remarkable precision. In liver research, spatial transcriptomics has been particularly revelatory, identifying distinct zonated functions of hepatocytes that are crucial for understanding the metabolic and detoxification processes of the liver. Moreover, this technology has unveiled new insights into the pathogenesis of liver diseases, such as the role of lipid-associated macrophages in steatosis and endothelial cell signals in liver regeneration and repair. In the domain of liver cancer, spatial transcriptomics has proven instrumental in delineating intratumor heterogeneity, identifying supportive microenvironmental niches and revealing the complex interplay between tumor cells and the immune system as well as susceptibility to immune checkpoint inhibitors. In conclusion, spatial transcriptomics represents a significant advance in hepatology, promising to enhance our understanding and treatment of liver diseases.

In situ characterization of the tumor microenvironment

The development of new therapies for cancer is underpinned by an increasing need to comprehensively characterize the tumor microenvironment (TME). While traditional approaches have relied on bulk or single-cell approaches, these are limited in their ability to provide cellular context. Deconvolution of the complex TME is fundamental to understanding tumor dynamics and treatment resistance. Spatially resolved characterization of the TME is likely to provide greater insights into the cellular architecture, tumor-immune cell interactions, receptor-ligand interactions, and cell niches. In turn, these aid in dictating the optimal way in which to target each patient's individual cancer. In this review, we discuss a number of cutting-edge in situ spatial profiling methods giving us new insights into tumor biology.

Transcriptomic signatures of progressive and regressive liver fibrosis and portal hypertension

Persistent liver injury triggers a fibrogenic program that causes pathologic remodeling of the hepatic microenvironment (i.e., liver fibrosis) and portal hypertension. The dynamics of gene regulation during liver disease progression and early regression remain understudied. Here, we generated hepatic transcriptome profiles in two well-established liver disease models at peak fibrosis and during spontaneous regression after the removal of the inducing agents. We linked the dynamics of key disease readouts, such as portal pressure, collagen area, and transaminase levels, to differentially expressed genes, enabling the identification of transcriptomic signatures of progressive vs. regressive liver fibrosis and portal hypertension. These candidate biomarkers (e.g., Tcf4, Mmp7, Trem2, Spp1, Scube1, Islr) were validated in RNA sequencing datasets of patients with cirrhosis and portal hypertension, and those cured from hepatitis C infection. Finally, deconvolution identified major cell types and suggested an association of macrophage and portal hepatocyte signatures with portal hypertension and fibrosis area.

How single-cell transcriptomics provides insight on hepatic responses to TCDD

The prototypical aryl hydrocarbon receptor (AHR) ligand, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), has been a valuable model for investigating toxicant-associated fatty liver disease (TAFLD). TCDD induces dose-dependent hepatic lipid accumulation, followed by the development of inflammatory foci and eventual progression to fibrosis in mice. Previously, bulk approaches and in vitro examination of different cell types were relied upon to study the mechanisms underlying TCDD-induced liver pathologies. However, the advent of single-cell transcriptomic technologies, such as single-nuclei RNA sequencing (snRNAseq) and spatial transcriptomics (STx), has provided new insights into the responses of hepatic cell types to TCDD exposure. This review explores the application of these single-cell transcriptomic technologies and highlights their contributions towards unraveling the cell-specific mechanisms mediating the hepatic responses to TCDD.

TCDD dysregulation of lncRNA expression, liver zonation and intercellular communication across the liver lobule

The persistent environmental aryl hydrocarbon receptor agonist and hepatotoxin TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin) induces hepatic lipid accumulation (steatosis), inflammation (steatohepatitis) and fibrosis. Thousands of liver-expressed, nuclear-localized lncRNAs with regulatory potential have been identified; however, their roles in TCDD-induced hepatoxicity and liver disease are unknown. We analyzed single nucleus (sn)RNA-seq data from control and subchronic (4 wk) TCDD-exposed mouse liver to determine liver cell-type specificity, zonation and differential expression profiles for thousands of lncRNAs. TCDD dysregulated >4000 of these lncRNAs in one or more liver cell types, including 684 lncRNAs specifically dysregulated in liver non-parenchymal cells. Trajectory inference analysis revealed major disruption by TCDD of hepatocyte zonation, affecting >800 genes, including 121 lncRNAs, with strong enrichment for lipid metabolism genes. TCDD also dysregulated expression of >200 transcription factors, including 19 Nuclear Receptors, most notably in hepatocytes and Kupffer cells. TCDD-induced changes in cell-cell communication patterns included marked decreases in EGF signaling from hepatocytes to non-parenchymal cells and increases in extracellular matrix-receptor interactions central to liver fibrosis. Gene regulatory networks constructed from the snRNA-seq data identified TCDD-exposed liver network-essential lncRNA regulators linked to functions such as fatty acid metabolic process, peroxisome and xenobiotic metabolism. Networks were validated by the striking enrichments that predicted regulatory lncRNAs showed for specific biological pathways. These findings highlight the power of snRNA-seq to discover functional roles for many xenobiotic-responsive lncRNAs in both hepatocytes and liver non-parenchymal cells and to elucidate novel aspects of foreign chemical-induced hepatotoxicity and liver disease, including dysregulation of intercellular communication within the liver lobule.

The Ah Receptor from Toxicity to Therapeutics: Report from the 5th AHR Meeting at Penn State University, USA, June 2022

The aryl hydrocarbon receptor (AHR) is a sensor of low-molecular-weight molecule signals that originate from environmental exposures, the microbiome, and host metabolism. Building upon initial studies examining anthropogenic chemical exposures, the list of AHR ligands of microbial, diet, and host metabolism origin continues to grow and has provided important clues as to the function of this enigmatic receptor. The AHR has now been shown to be directly involved in numerous biochemical pathways that influence host homeostasis, chronic disease development, and responses to toxic insults. As this field of study has continued to grow, it has become apparent that the AHR is an important novel target for cancer, metabolic diseases, skin conditions, and autoimmune disease. This meeting attempted to cover the scope of basic and applied research being performed to address possible applications of our basic knowledge of this receptor on therapeutic outcomes.