德国马克斯·普朗克生物化学研究所Matthias Mann小组的研究显示，深度视觉蛋白质组学绘制遗传性肝病的蛋白质毒性。这一研究成果于2025年4月16日发表在国际顶尖学术期刊《自然》上。

该课题组将空间蛋白质组学应用于质谱和机器学习来绘制人类肝脏组织中的AATD。将深度视觉蛋白质组学（Deep Visual Proteomics， DVP）与单细胞分析相结合，该研究团队探测了完整的患者活检，以解决肝细胞在纤维化阶段的假时间应激过程中的分子事件。该研究组从福尔马林固定的石蜡包埋组织中三分之一的单个细胞中获得高达4300个蛋白质的蛋白质组深度。该数据集揭示了一种潜在的临床可操作的过氧化物酶体上调，这种上调发生在规范的未折叠蛋白反应之前。他们的单细胞蛋白质组学数据显示α1-抗胰蛋白酶积累主要是细胞内在的，肝细胞之间的应激传播最小。

课题组将蛋白质组学数据与人工智能引导的基于图像的表型整合在几个疾病阶段，揭示了以球状蛋白聚集和独特的蛋白质组学特征为特征的晚期肝细胞表型，特别是包括TNFSF10（也称为TRAIL）量的升高。这种表型可能代表一个关键的疾病进展阶段。他们的研究为AATD的发病机制提供了新的见解，并为复杂组织的高分辨率原位蛋白质组学分析引入了一种强大的方法。这种方法有可能揭示各种蛋白质错误折叠障碍的分子机制，为理解人类组织单细胞水平的疾病进展设定新的标准。

研究人员表示，蛋白质错误折叠疾病，包括α1-抗胰蛋白酶缺乏症（AATD），对健康构成重大挑战，其细胞进展仍知之甚少。

附：英文原文

Title: Deep Visual Proteomics maps proteotoxicity in a genetic liver disease

Author: Rosenberger, Florian A., Mdler, 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, Snke, Boor, Peter, Fabin, Ondej, Frakov, Soa, Krag, Aleksander, Strnad, Pavel, Mann, Matthias

Issue&Volume: 2025-04-16

Abstract: Protein misfolding diseases, including α1-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 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.

DOI: 10.1038/s41586-025-08885-4

Source: https://www.nature.com/articles/s41586-025-08885-4