Deep Visual Proteomics maps proteotoxicity in a genetic liver disease - Rosenberger, Florian A; Mädler, Sophia C; Thorhauge, Katrine Holtz; Steigerwald, Sophia; Fromme, Malin; Lebedev, Mikhail; Weiss, Caroline A M; Oeller, Marc; Wahle, Maria; Metousis, Andreas; Zwiebel, Maximilian; Schmacke, Niklas A; et al. (Nature, 2025)
Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood1-3. We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudotime across fibrosis stages. We achieve proteome depth of up to 4,300 proteins from one-third of a single cell in formalin-fixed, paraffin-embedded tissue. This dataset reveals a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show α1-antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with artificial intelligence-guided image-based phenotyping across several disease stages, revealing a late-stage hepatocyte phenotype characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10 (also known as TRAIL) amounts. This phenotype may represent a critical disease progression stage. Our study offers new insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.
Keywords: hepatocyty; Liver; analýza jednotlivých buněk; Female; Phenotype; játra; Liver Cirrhosis; proteom; ženské pohlaví; Proteome; strojové učení; jaterní cirhóza; lidé; proteomika; Hepatocytes; Proteomics; Male; progrese nemoci; alpha 1-Antitrypsin Deficiency; Machine Learning; Disease Progression; alpha 1-Antitrypsin; deficit alfa1-antitrypsinu; signální dráha UPR; mužské pohlaví; fenotyp; alfa-1-antitrypsin; Single-Cell Analysis; Unfolded Protein Response; Humans