Unraveling Molecular Mechanisms of Human Liver Fibrosis Resolution Using Spatial Proteomics

Objective

Alcoholic liver disease (ALD) is a major global health burden, with liver fibrosis representing a critical stage. Although fibrosis can regress after treatment, the molecular drivers and predictors of patient response remain unknown. RESOLiVER aims to bridge this knowledge gap using powerful and novel approaches and a unique cohort of ALD biopsies collected pre- and post-treatment from both responders and non-responders. Macrophages and fibroblasts are critical for fibrosis regression. Deciphering their dynamics necessitates sensitive protein measurement with high spatial resolution, enabling precise quantification of molecular changes undetectable by other modalities. The host laboratory of Professor Matthias Mann is ideally suited for this investigation, given their pioneering method development, state-of-the-art instrumentation, and unparalleled proteomics expertise. Using insights from spatial proteomics, I will develop machine learning models to predict treatment responses and identify protein signatures for clinical application. Additionally, I will employ proteomics and phosphoproteomics approaches to capture dynamic extracellular matrix (ECM) changes - the hallmark of liver fibrosis - and active cross-regulation between ECM and adjacent cells. This is enabled by recent host lab developments in ultra-sensitive phosphoproteomics from low input amounts. The convergence of clinical expertise and distinctive patient cohorts from collaborators, unmatched proteomics capabilities of the host laboratory, and my profound background in liver research and computational analysis provides RESOLiVER with complementary skillsets to tackle this challenge. By elucidating molecular drivers of fibrosis regression and progression, RESOLiVER will advance fundamental understanding of liver disease and provide actionable insights to guide prognosis, inform therapeutic strategies, and improve ALD management.