Fatty Liver Disease

Review article: Hepatic steatosis and its associations with acute and chronic liver diseases

BACKGROUND

Hepatic steatosis is a common finding in liver histopathology and the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), whose global prevalence is rising.

AIMS

To review the histopathology of hepatic steatosis and its mechanisms of development and to identify common and rare disease associations.

METHODS

We reviewed literature on the basic science of lipid droplet (LD) biology and clinical research on acute and chronic liver diseases associated with hepatic steatosis using the PubMed database.

RESULTS

A variety of genetic and environmental factors contribute to the development of chronic hepatic steatosis or steatotic liver disease, which typically appears macrovesicular. Microvesicular steatosis is associated with acute mitochondrial dysfunction and liver failure. Fat metabolic processes in hepatocytes whose dysregulation leads to the development of steatosis include secretion of lipoprotein particles, uptake of remnant lipoprotein particles or free fatty acids from blood, de novo lipogenesis, oxidation of fatty acids, lipolysis and lipophagy. Hepatic insulin resistance is a key feature of MASLD. Seipin is a polyfunctional protein that facilitates LD biogenesis. Assembly of hepatitis C virus takes place on LD surfaces. LDs make important, functional contact with the endoplasmic reticulum and other organelles.

CONCLUSIONS

Diverse liver pathologies are associated with hepatic steatosis, with MASLD being the most important contributor. The biogenesis and dynamics of LDs in hepatocytes are complex and warrant further investigation. Organellar interfaces permit co-regulation of lipid metabolism to match generation of potentially toxic lipid species with their LD depot storage.

An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology

Conventional histopathology involves expensive and labor-intensive processes that often consume tissue samples, rendering them unavailable for other analyses. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to nondestructively image the autofluorescence of unstained tissue sections. We then trained a deep learning model to use autofluorescence to generate virtual histologic stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histologic features present in the real-stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study, where both real and virtual stains are scored by pathologists (D.T., A.D.B., R.K.P.). The test showed moderate-to-good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH Clinical Research Network score prediction. We showed that the end-to-end automated pathology platform is comparable with an independent panel of pathologists for NASH Clinical Research Network scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology and enable novel approaches to spatial biology research.