Han Kyu Sang, Sander Inbal B, Kumer Jacqueline, Resnick Eric, Booth Clare, Cheng Guoqing, Im Yebin, Starich Bartholomew, Kiemen Ashley L, Phillip Jude M, Reddy Sashank, Joshu Corrine E, Sunshine Joel C, Walston Jeremy D, Wirtz Denis, Wu Pei-Hsun
Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.
The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.
bioRxiv. 2024 Jul 6:2024.04.03.588011. doi: 10.1101/2024.04.03.588011.
Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales.
衰老在人类疾病中是一个主要驱动因素。识别与衰老相关的特征对于设计有效的干预策略和发现新的衰老生物标志物至关重要。在分子和器官/全身生理尺度上的广泛研究有助于确定与衰老相关的特征。然而，缺乏中尺度研究，尤其是在组织水平上的研究，限制了将分子尺度上的发现转化为与衰老相关的组织功能受损的能力。在这项工作中，我们建立了一种组织图像分析工作流程——定量显微解剖表型分析（qMAP），该流程利用深度学习和机器视觉对组织切片中的组织和细胞区域进行全面标记。完整映射的组织图像解决了寻找可解释的特征集以定量描述与年龄相关的显微解剖变化的挑战。我们针对皮肤组织优化了qMAP，并将其应用于99名年龄在14至92岁之间的捐赠者队列。我们提取了914个显微解剖特征，发现其中广泛的这些特征，以10个核心过程为代表，与衰老密切相关。我们的分析表明，皮肤的显微解剖特征能够以7.7年的平均绝对误差（MAE）预测衰老，与最先进的表观遗传时钟相当。我们的研究表明，组织水平的结构变化与衰老密切相关，代表了一类新的衰老生物标志物，可补充分子标志物。我们的结果突出了分子和组织显微解剖尺度之间复杂且未被充分探索的多尺度关系。
