Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg.
Swiss Data Science Center, ETH Zürich, Zürich, Switzerland.
Glia. 2019 Aug;67(8):1496-1509. doi: 10.1002/glia.23623. Epub 2019 Apr 14.
The phenotypic changes of microglia in brain diseases are particularly diverse and their role in disease progression, beneficial, or detrimental, is still elusive. High-throughput molecular approaches such as single-cell RNA-sequencing can now resolve the high heterogeneity in microglia population for a specific physiological condition, however, the relation between the different microglial signatures and their surrounding brain microenvironment is barely understood. Thus, better tools to characterize the phenotypic variations of microglia in situ are needed, particularly for human brain postmortem samples analysis. To address this challenge, we developed MIC-MAC, a Microglia and Immune Cells Morphologies Analyser and Classifier pipeline that semiautomatically segments, extracts, and classifies all microglia and immune cells labeled in large three-dimensional (3D) confocal image stacks of mouse and human brain samples. Our imaging-based approach enables automatic 3D-morphology characterization and classification of thousands of individual microglia in situ and revealed species- and disease-specific morphological phenotypes in mouse aging, human Alzheimer's disease, and dementia with Lewy Bodie's samples. MIC-MAC is a precision diagnostic tool that allows a rapid, unbiased, and large-scale analysis of microglia morphological states in mouse models and patient brain samples.
脑疾病中小胶质细胞的表型变化特别多样,其在疾病进展中的作用是有益还是有害仍难以捉摸。现在,高通量分子方法,如单细胞 RNA 测序,可以解析特定生理条件下小胶质细胞群体的高度异质性,然而,不同小胶质细胞特征与其周围脑微环境之间的关系还鲜为人知。因此,需要更好的工具来对原位小胶质细胞的表型变化进行特征描述,特别是对人类大脑尸检样本的分析。为了解决这一挑战,我们开发了 MIC-MAC,这是一个小胶质细胞和免疫细胞形态分析器和分类器的管道,它可以半自动地分割、提取和分类在小鼠和人类大脑样本的大型三维(3D)共聚焦图像堆栈中标记的所有小胶质细胞和免疫细胞。我们的基于成像的方法能够对数千个原位单个小胶质细胞进行自动 3D 形态特征描述和分类,并在小鼠衰老、人类阿尔茨海默病和路易体痴呆的样本中揭示了物种和疾病特异性的形态表型。MIC-MAC 是一种精准诊断工具,允许对小鼠模型和患者大脑样本中的小胶质细胞形态状态进行快速、无偏和大规模分析。