Spatial transcriptome profiling of normal human liver

Heterozygous GAA knockout is nonconsequential on metabolism and the spatial liver transcriptome in high-fat diet-induced obese and prediabetic mice

Glycophagy is the autophagic degradation of glycogen by the enzyme acid alpha-glucosidase (GAA). Although GAA inhibitors improve metabolic health by inhibiting GAA in the intestine, it is not clear if GAA inhibition in peripheral tissues such as the liver is metabolically beneficial. This study tested if the heterozygous knockout of GAA (HetKO-GAA) alters liver metabolism and metabolic health in mice fed a low-fat diet or a high-fat diet to induce obesity. HetKO-GAA mice fed either diet did not have altered body weight, glucose tolerance, insulin action, energy expenditure, substrate metabolism, liver glucose output, or liver triglycerides compared to control wildtype mice. A liver spatial transcriptomics analysis revealed that high-fat diet feeding reduced the gene abundance of predominantly metabolic pathways in both periportal and perivenous hepatocytes, and uniquely reduced ribosome gene abundance in perivenous hepatocytes. HetKO-GAA mice did not have significantly altered transcriptomes in periportal or perivenous hepatocytes compared to wildtype mice. In conclusion, heterozygous GAA knockout is nonconsequential on metabolism and metabolic health in high-fat diet induced obesity. Spatial transcriptomics revealed alterations in the transcriptome of periportal and perivenous hepatocytes from high-fat diet induced obese mice, highlighting novel targets that could be exploited to improve metabolic health in obesity.

From Omics to Multi-Omics: A Review of Advantages and Tradeoffs

Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. Conclusions: There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

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.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

3D microperfusion of mesoscale human microphysiological liver models improves functionality and recapitulates hepatic zonation

Hepatic in vitro models that accurately replicate phenotypes and functionality of the human liver are needed for applications in toxicology, pharmacology and biomedicine. Notably, it has become clear that liver function can only be sustained in 3D culture systems at physiologically relevant cell densities. Additionally, drug metabolism and drug-induced cellular toxicity often follow distinct spatial micropatterns of the metabolic zones in the liver acinus, calling for models that capture this zonation. We demonstrate the manufacture of accurate liver microphysiological systems (MPS) via engineering of 3D stereolithography printed hydrogel chips with arrays of diffusion open synthetic vasculature channels at spacings approaching in vivo capillary distances. Chip designs are compatible with seeding of cell suspensions or preformed liver cell spheroids. Importantly, primary human hepatocytes (PHH) and hiPSC-derived hepatocyte-like cells remain viable, exhibit improved molecular phenotypes compared to isogenic monolayer and static spheroid cultures and form interconnected tissue structures over the course of multiple weeks in perfused culture. 3D optical oxygen mapping of embedded sensor beads shows that the liver MPS recapitulates oxygen gradients found in the acini, which translates into zone-specific acet-ami-no-phen toxicity patterns. Zonation, here naturally generated by high cell densities and associated oxygen and nutrient utilization along the flow path, is also documented by spatial proteomics showing increased concentration of periportal- versus perivenous-associated proteins at the inlet region and vice versa at the outlet region. The presented microperfused liver MPS provides a promising platform for the mesoscale culture of human liver cells at phenotypically relevant densities and oxygen exposures. STATEMENT OF SIGNIFICANCE: A full 3D tissue culture platform is presented, enabled by massively parallel arrays of high-resolution 3D printed microperfusion hydrogel channels that functionally mimics tissue vasculature. The platform supports long-term culture of liver models with dimensions of several millimeters at physiologically relevant cell densities, which is difficult to achieve with other methods. Human liver models are generated from seeded primary human hepatocytes (PHHs) cultured for two weeks, and from seeded spheroids of hiPSC-derived human liver-like cells cultured for two months. Both model types show improved functionality over state-of-the-art 3D spheroid suspensions cultured in parallel. The platform can generate physiologically relevant oxygen gradients driven by consumption rather than supply, which was validated by visualization of embedded oxygen-sensitive microbeads, which is exploited to demonstrate zonation-specific toxicity in PHH liver models.

Like father, like daughter:Paternal cadmium exposure causes hepatic glucose metabolic disorder and phospholipids accumulation in adult female offspring

Cadmium (Cd), is a well-known reproductive toxicant. The impacts of paternal Cd exposure on offspring glucose and lipid metabolism remain unclear, despite the abundance of adverse reports following early exposure from the mother. Here, we assessed paternally acquired metabolic derailment using a mouse model. LC-MS/MS, transcriptomics and molecular experimental techniques were subsequently applied in this study to explore the potential mechanism. We found that paternal Cd exposure caused glucose intolerance, lower insulin sensitivity and abnormal hepatic glycogen storage in adult female offspring, but not in males. LC-MS/MS data showed that hepatic phospholipids accumulation was also only observed in adult female offspring after paternal Cd exposure. Gene expression data showed that the level of insulin signaling and lipid transport-related genes was decreased in Cd-treated adult female offspring livers. Meanwhile, AHR, a transcription factor that combines with phospholipids to promote insulin resistance, was increased in Cd-treated adult female offspring livers. In addition, the escalation of the afore-mentioned lipid metabolites in the liver occurred as early as fetal stages in the female pups following paternal Cd exposure, suggesting the potential for these lipid species to be selected as early markers of disease for metabolic derailment later in life. Altogether, paternal Cd exposure causes offspring glucose metabolism disorder and phospholipids accumulation in a sex-dependent manner. This study provides a theoretical framework for future understanding of paternal-originated metabolic diseases.