Ertürk Ali
Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
Nat Methods. 2024 Jul;21(7):1153-1165. doi: 10.1038/s41592-024-02327-1. Epub 2024 Jul 12.
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
为了全面理解组织和生物体的生理学与病理生理学，创建完整的三维（3D）细胞图谱至关重要。这些图谱需要结构数据，如组织和细胞的三维构型与定位，以及每个细胞组成的分子数据，范围从DNA序列到蛋白质表达。虽然单细胞转录组学正在揭示跨物种和组织的细胞与分子多样性，但这些分子数据的三维空间背景常常被忽视。在此，我将探讨新兴的3D组织组织学技术，这些技术为生物医学研究增添了缺失的第三个空间维度。通过组织透明化化学、标记和体积成像方面的创新，增强3D重建及其与分子技术的协同作用，这些技术将在细胞水平提供整个器官或生物体的详细蓝图。机器学习，尤其是深度学习，对于从海量数据中提取有意义的见解至关重要。综合结构、分子和计算方法的进一步发展将释放下一代3D组织学的全部潜力。
