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

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 b...

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Bibliographic Details
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
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Summary: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.
ISSN:2076-3425
2076-3425
DOI:10.3390/brainsci13050711