A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping

• Published in: Brain sciences 2023-04, Vol.13 (5), p.711
• Main Authors: Wang, Tianyi, Shi, Peiyao, Luo, Dingsan, Guo, Jun, Liu, Hui, Yuan, Jinyun, Jin, Haiqun, Wu, Xiaolong, Zhang, Yueyi, Xiong, Zhiwei, Zhu, Jinlong, Zhou, Renjie, Zhang, Ruobing
• Format: Article
• Language: English
• Subjects: Automation; Brain mapping; Carbon; cell classification; Coatings; Contamination; correlative light and electron microscopy; Costs; Electron microscopes; Electron microscopy; Localization; Mapping; Methods; Neural networks; Neuroimaging; Neurons; Neurophysiology; optical multilayer interference tomography; Protective coatings; Scanning electron microscopy; Silicon wafers; Structure-function relationships; Tomography
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci13050711

The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light-electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.