Deep Visual Proteomics maps proteotoxicity in a genetic liver disease.

Area of Science:

• Biochemistry
• Cell Biology
• Proteomics

Background:

• Protein misfolding diseases like alpha-1-antitrypsin deficiency (AATD) present significant health issues, with poorly understood cellular mechanisms.
• Current understanding of AATD pathogenesis at the cellular level is limited, hindering the development of effective treatments.

Purpose of the Study:

• To map the cellular progression of AATD in human liver tissue using spatial proteomics and machine learning.
• To resolve molecular events during hepatocyte stress in AATD using single-cell analysis and Deep Visual Proteomics (DVP).

Main Methods:

• Spatial proteomics by mass spectrometry and machine learning applied to human liver tissue.
• Deep Visual Proteomics (DVP) combined with single-cell analysis on intact patient biopsies.
• Proteomic profiling of up to 4,300 proteins from a fraction of a single cell in FFPE tissue.

Main Results:

• Identified a clinically relevant peroxisomal upregulation preceding the unfolded protein response in AATD.
• Demonstrated that alpha-1-antitrypsin accumulation is primarily cell-intrinsic with minimal intercellular stress propagation.
• Revealed a late-stage hepatocyte phenotype with globular aggregates and elevated TNFSF10 (TRAIL), potentially indicating critical disease progression.

Conclusions:

• The study provides novel insights into AATD pathogenesis at the single-cell level.
• Introduced a powerful methodology for high-resolution, in situ proteomic analysis of complex human tissues.
• This approach has the potential to advance the understanding of various protein misfolding disorders.