Deep Visual Proteomics maps proteotoxicity in a genetic liver disease

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023001%3A_____%2F25%3A00085671" target="_blank" >RIV/00023001:_____/25:00085671 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11120/25:43928371

Výsledek na webu

<a href="https://www.nature.com/articles/s41586-025-08885-4" target="_blank" >https://www.nature.com/articles/s41586-025-08885-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41586-025-08885-4" target="_blank" >10.1038/s41586-025-08885-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Visual Proteomics maps proteotoxicity in a genetic liver disease

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep Visual Proteomics maps proteotoxicity in a genetic liver disease

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10700 - Other natural sciences

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Nature

ISSN

0028-0836

e-ISSN

1476-4687

Svazek periodika

642

Číslo periodika v rámci svazku

June 2025

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

"484–491"

Kód UT WoS článku

001468265300001

EID výsledku v databázi Scopus

2-s2.0-105002643795