Deep Visual Proteomics maps proteotoxicity in a genetic liver disease

• 成果类型: Article
• 署名作者: Rosenberger, Florian A.; Maedler, 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.; Detlefsen, Soenke; Boor, Peter; Fabian, Ondrej; Frankova, Sona; Krag, Aleksander; Strnad, Pavel; Mann, Matthias
• 署名单位: Max Planck Society; University of Southern Denmark; RWTH Aachen University; RWTH Aachen University Hospital; University of Munich; University of Munich; University of Southern Denmark; Odense University Hospital; RWTH Aachen University; RWTH Aachen University Hospital; Institute for Clinical & Experimental Medicine (IKEM); Charles University Prague; Thomayer Hospital; Institute for Clinical & Experimental Medicine (IKEM); University of Southern Denmark; Aarhus University; University of Copenhagen
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2357
• DOI: 10.1038/s41586-025-08885-4
• 发表日期: 2025-06-12
• 关键词: spectrometry-based proteomics; accumulation; deficiency

摘要

Protein misfolding diseases, including alpha 1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood1, 2-3. 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 alpha 1-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.