Deep Visual Proteomics maps proteotoxicity in a genetic liver disease
Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
L40 TR001095
NCATS NIH HHS - United States
PubMed
40240610
PubMed Central
PMC12158776
DOI
10.1038/s41586-025-08885-4
PII: 10.1038/s41586-025-08885-4
Knihovny.cz E-zdroje
- MeSH
- alfa-1-antitrypsin metabolismus MeSH
- analýza jednotlivých buněk MeSH
- deficit alfa1-antitrypsinu * patologie metabolismus genetika MeSH
- fenotyp MeSH
- hepatocyty metabolismus patologie MeSH
- jaterní cirhóza patologie metabolismus MeSH
- játra patologie metabolismus MeSH
- lidé MeSH
- progrese nemoci MeSH
- proteom * analýza metabolismus MeSH
- proteomika * metody MeSH
- signální dráha UPR MeSH
- strojové učení MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- alfa-1-antitrypsin MeSH
- proteom * MeSH
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.
Danish Institute of Advanced Study University of Southern Denmark Odense Denmark
Department of Gastroenterology and Hepatology Centre for Liver Research Odense Denmark
Department of Pathology Odense University Hospital Odense Denmark
Gene Center and Department of Biochemistry Ludwig Maximilians Universität München Munich Germany
Institute of Pathology University Hospital Aachen RWTH Aachen University Aachen Germany
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