AI-based phenotyping of hepatic fiber morphology to inform molecular alterations in metabolic dysfunction-associated steatotic liver disease

Abstract

Background & Aims: Hepatic fiber morphology may significantly enhance our understanding of molecular alterations in metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to comprehensively characterize hepatic fiber morphological phenotypes in MASLD and their associated molecular alterations using multi-layer omics analyses. Approach & Results: To quantify the morphological phenotypes of hepatic fibers, the artificial intelligence-based FibroNest algorithm (PharmaNest) was applied to 94 MASLD-affected liver biopsies, among which 12 (13%) had concurrent hepatocellular carcinoma (HCC). FibroNest identified 327 fiber phenotypes that were summarized into eight major principal components, named FibroPC1–8. Next, molecular alterations captured by morphological fiber phenotypes were evaluated by comparison with genome-wide transcriptomics of paired liver samples. Pathway analyses revealed that FibroPCs more sensitively captured MASLD-related molecular alterations, such as upregulation of interleukin-6 and susceptibility to resmetirom, compared with the histological fibrosis stage. Among them, FibroPC4, which reflects reticular fibers, was associated with a gene signature predictive of incident HCC from MASLD. Furthermore, we used a spatial single-cell transcriptome, CosMx, to reveal the cell-cell interactions driving MASLD pathogenesis, as captured by FibroPC4. CosMx revealed that the FibroPC4-rich microenvironment contains HCC-promoting hepatic stellate cells (HSCs) located adjacent to periportal endothelial cells (ECs). Neighboring cell analyses suggested that the HCC-promoting phenotype of HSCs was acquired by insulin growth factor-binding protein 7 (IGFBP-7) secreted from senescent periportal ECs. Consistently, in vitro experiments showed that IGFBP-7 transformed HSCs into an HCC-promoting phenotype. Conclusions: Hepatic morphological fiber phenotyping can reveal the disease progression and underlying mechanisms of MASLD.