Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215125, China.
Commun Biol. 2023 Feb 3;6(1):148. doi: 10.1038/s42003-023-04456-3.
Dissection of the anatomical information at the single-cell level is crucial for understanding the organization rule and pathological mechanism of biological tissues. Mapping the whole organ in numerous groups with multiple conditions brings the challenges in imaging and analysis. Here, we describe an approach, named array fluorescent micro-optical sectioning tomography (array-fMOST), to identify the three-dimensional information at single-cell resolution from multi-samples. The pipeline contains array embedding, large-scale imaging, post-imaging staining and data analysis, which could image over 24 mouse brains simultaneously and collect the slices for further analysis. With transgenic mice, we acquired the distribution information of neuropeptide somatostatin neurons during natural aging and compared the changes in the microenvironments by multi-component labeling of serial sections with precise co-registration of serial datasets quantitatively. With viral labeling, we also analyzed the input circuits of the medial prefrontal cortex in the whole brain of Alzheimer's disease and autism model mice. This pipeline is highly scalable to be applied to anatomical alterations screening and identification. It provides new opportunities for combining multi-sample whole-organ imaging and molecular phenotypes identification analysis together. Such integrated high-dimensional information acquisition method may accelerate our understanding of pathogenesis and progression of disease in situ at multiple levels.
在单细胞水平上对解剖学信息进行剖析对于理解生物组织的组织规则和病理机制至关重要。对具有多种条件的大量组进行整个器官的映射在成像和分析方面带来了挑战。在这里,我们描述了一种名为阵列荧光微光学切片层析成像(array-fMOST)的方法,用于从多个样本中以单细胞分辨率识别三维信息。该流水线包含阵列嵌入,大规模成像,成像后染色和数据分析,可同时对 24 只老鼠的大脑进行成像,并收集切片进行进一步分析。通过转基因小鼠,我们获得了在自然衰老过程中神经肽生长抑素神经元的分布信息,并通过对连续切片进行多组分标记和对连续数据集进行精确配准的定量分析,比较了微环境的变化。通过病毒标记,我们还分析了阿尔茨海默病和自闭症模型小鼠全脑的内侧前额叶皮质的输入回路。该流水线具有高度的可扩展性,可用于解剖结构改变的筛选和识别。它为结合多样本全器官成像和分子表型识别分析提供了新的机会。这种集成的高维信息获取方法可能会加速我们对多个层面原位疾病发病机制和进展的理解。