Rosenberger Florian A, Mädler 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 Sönke, Boor Peter, Fabián Ondřej, Fraňková Soňa, Krag Aleksander, Strnad Pavel, Mann Matthias
Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
Department of Gastroenterology and Hepatology, Centre for Liver Research, Odense, Denmark.
Nature. 2025 Apr 16. doi: 10.1038/s41586-025-08885-4.
Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood. 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 analysis, 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.
蛋白质错误折叠疾病，包括α1-抗胰蛋白酶缺乏症（AATD），对健康构成了重大挑战，其细胞进展仍知之甚少。我们通过质谱和机器学习的空间蛋白质组学技术来绘制人类肝脏组织中的AATD图谱。将深度视觉蛋白质组学（DVP）与单细胞分析相结合，我们对完整的患者活检组织进行探测，以解析纤维化各阶段中假时间内肝细胞应激过程中的分子事件。在福尔马林固定、石蜡包埋的组织中，我们从单个细胞的三分之一中实现了高达4300种蛋白质的蛋白质组深度覆盖。该数据集揭示了一种潜在的可在临床上采取行动的过氧化物酶体上调现象，它先于经典的未折叠蛋白反应出现。我们的单细胞蛋白质组学数据表明，α1-抗胰蛋白酶的积累在很大程度上是细胞内在的，肝细胞之间的应激传播极少。我们将蛋白质组学数据与基于人工智能引导的跨多个疾病阶段的图像表型分析相结合，揭示了一种晚期肝细胞表型，其特征为球状蛋白质聚集体和独特的蛋白质组特征，尤其包括肿瘤坏死因子超家族成员10（TNFSF10，也称为TRAIL）含量升高。这种表型可能代表了一个关键的疾病进展阶段。我们的研究为AATD发病机制提供了新见解，并引入了一种强大的方法用于复杂组织的高分辨率原位蛋白质组分析。这种方法有潜力揭示各种蛋白质错误折叠疾病中的分子机制，为在人类组织单细胞水平上理解疾病进展设定了新的标准。
