Phenotypic clustering: a novel method for microglial morphology analysis. Issue 1 (December 2016) - Verdonk, Franck; Roux, Pascal; Flamant, Patricia; Fiette, Laurence; Bozza, Fernando; Simard, Sébastien; Lemaire, Marc; Plaud, Benoit; Shorte, Spencer; Sharshar, Tarek; Chrétien, Fabrice; Danckaert, Anne
Abstract Background Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex "dendritic-like" aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters. Methods Using the knock-in mouse model CX3CR1GFP/+, we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations. Results Our results on more than 20, 000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge. Conclusions Our results counteract the classical view of a homogenous regional resting state of the microglial cells within the brain. Microglial cells are distributed in different defined sub-populations that present specific behaviour after pathological challenge, allowing postulating for a cellular and functional specialization. Moreover, this new experimental approach will provide a support not only to neuropathological diagnosis but also to study microglial function in various disease models while reducing the number of animals needed to approach the international ethical statements.