Murray Melissa E, Smith Colin, Menon Vilas, Keene C Dirk, Lein Ed, Hawrylycz Michael, Aguzzi Adriano, Benedetti Brett, Brose Katja, Caetano-Anolles Kelsey, Sillero Maria Inmaculada Cobos, Crary John F, De Jager Philip L, Faustin Arline, Flanagan Margaret E, Gokce Ozgun, Grant Seth G N, Grinberg Lea T, Gutman David A, Hillman Elizabeth M C, Huang Zhi, Irwin David J, Jones David T, Kapasi Alifiya, Karch Celeste M, Kukull Walter T, Lashley Tammaryn, Lee Edward B, Lehner Thomas, Parkkinen Laura, Pedersen Maria, Pritchett Dominique, Rutledge Matthew H, Schneider Julie A, Seeley William W, Shepherd Claire E, Spires-Jones Tara L, Steen Judith A, Sutherland Margaret, Vickovic Sanja, Zhang Bin, Stewart David J, Keiser Michael J, Vogel Jacob W, Dugger Brittany N, Phatnani Hemali
Department of Neuroscience, Mayo Clinic Jacksonville, Jacksonville, FL, USA; Department of Laboratory Medicine and Pathology, Mayo Clinic Jacksonville, Jacksonville, FL, USA.
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Neuron. 2025 Jul 15. doi: 10.1016/j.neuron.2025.06.014.
Transformative neuropathology is redefining human brain research by integrating foundational descriptive pathology with advanced methodologies. These approaches, spanning multi-omics studies and machine learning applications, will drive discovery for the identification of biomarkers, therapeutic targets, and complex disease patterns through comprehensive analyses of postmortem human brain tissue. Yet critical challenges remain, including the sustainability of brain banks, expanding donor participation, strengthening training pipelines, enabling rapid autopsies, supporting collaborative platforms, and integrating data across modalities. Innovations in digital pathology, tissue quality enhancement, harmonization of data standards, and machine learning integration offer opportunities to accelerate tissue-level "pathomics" research in brain health through cross-disciplinary collaborations. Lessons from neuroimaging, particularly in establishing common data frameworks and multi-site collaborations, offer a valuable roadmap for streamlining innovations. In this perspective, we outline actionable solutions for leveraging existing resources and strengthening collaboration -where we envision future opportunities to drive translational discoveries stemming from transformative neuropathology.
变革性神经病理学通过将基础描述性病理学与先进方法相结合，正在重新定义人类大脑研究。这些方法涵盖多组学研究和机器学习应用，将通过对死后人类脑组织的全面分析，推动发现生物标志物、治疗靶点和复杂疾病模式。然而，关键挑战依然存在，包括脑库的可持续性、扩大捐赠者参与度、加强培训渠道、实现快速尸检、支持协作平台以及整合跨模态数据。数字病理学、组织质量提升、数据标准协调和机器学习整合方面的创新，为通过跨学科合作加速大脑健康方面的组织水平“病理组学”研究提供了机会。神经影像学的经验教训，尤其是在建立通用数据框架和多中心合作方面，为简化创新提供了宝贵的路线图。从这个角度出发，我们概述了利用现有资源和加强合作的可行解决方案——我们设想未来有机会推动源于变革性神经病理学的转化发现。
