| Literature DB >> 33326739 |
Jiajia Zhao1,2, Hei Ming Lai3, Yuwei Qi1,2, Dian He1,2, Haitao Sun1,2,4,5.
Abstract
Due to the complexity and limited availability of human brain tissues, for decades, pathologists have sought to maximize information gained from individual samples, based on which (patho)physiological processes could be inferred. Recently, new understandings of chemical and physical properties of biological tissues and multiple chemical profiling have given rise to the development of scalable tissue clearing methods allowing superior optical clearing of across-the-scale samples. In the past decade, tissue clearing techniques, molecular labeling methods, advanced laser scanning microscopes, and data visualization and analysis have become commonplace. Combined, they have made 3D visualization of brain tissues with unprecedented resolution and depth widely accessible. To facilitate further advancements and applications, here we provide a critical appraisal of these techniques. We propose a classification system of current tissue clearing and expansion methods that allows users to judge the applicability of individual ones to their questions, followed by a review of the current progress in molecular labeling, optical imaging, and data processing to demonstrate the whole 3D imaging pipeline based on tissue clearing and downstream techniques for visualizing the brain. We also raise the path forward of tissue-clearing-based imaging technology, that is, integrating with state-of-the-art techniques, such as multiplexing protein imaging, in situ signal amplification, RNA detection and sequencing, super-resolution imaging techniques, multiomics studies, and deep learning, for drawing the complete atlas of the human brain and building a 3D pathology platform for central nervous system disorders.Entities:
Keywords: 3D neuropathology; Brain atlas; Data analysis; Fluorescent labeling; Optical sectioning microscopy; Tissue clearing
Mesh:
Year: 2020 PMID: 33326739 DOI: 10.1021/acschemneuro.0c00563
Source DB: PubMed Journal: ACS Chem Neurosci ISSN: 1948-7193 Impact factor: 4.418