Epidemiological studies report that infarct size is decreased and stroke outcomes are improved in young females when compared to males. However, mechanistic insight is lacking. We posit that sex-specific differences in glial cell functions occurring immediately after ischemic stroke are a source of dichotomous outcomes. In this study we assessed astrocyte Ca2+ dynamics, aquaporin 4 (AQP4) polarity, Sloop expression pattern, as well as, microglia morphology and phagocytic marker CD11b in male and female mice following 60 min of middle cerebral artery (MCA) occlusion. We reveal sex differences in the frequency of intracellular astrocyte Ca2+ elevations (F-(1,F-86) = 8.19, P = 0.005) and microglia volume (F-(1,F-40) = 12.47, P = 0.009) immediately following MCA occlusion in acute brain slices. Measured in fixed tissue, AQP4 polarity was disrupted (F-(5,F-86) = 3.30, P = 0.009) and the area of non-S100 beta immunoreactivity increased in ipsilateral brain regions after 60 min of MCA occlusion (F-(5,F-86) = 4.72, P = 0.007). However, astrocyte changes were robust in male mice when compared to females. Additional sex differences were discovered regarding microglia phagocytic receptor CD11b. In sham mice, constitutively high CD11b immunofluorescence was observed in females when compared to males (P = 0.03). When compared to sham, only male mice exhibited an increase in CD11b immunoreactivity after MCA occlusion (P = 0.006). We posit that a sex difference in the presence of constitutive CD11b has a role in determining male and female microglia phagocytic responses to ischemia. Taken together, these findings are critical to understanding potential sex differences in glial physiology as well as stroke pathobiology which are foundational for the development of future sex-specific stroke therapies. (C) 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Microglia are the brain’s immunocompetent macrophages with a unique feature that allows surveillance of the surrounding microenvironment and subsequent reactions to tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking morphological plasticity is one of their prominent characteristics and the categorization of microglial cell function based on morphology is well established. Frequently, automated classification of microglial morphological phenotypes is performed by using quantitative parameters. As this process is typically limited to a few and especially manually chosen criteria, a relevant selection bias may compromise the resulting classifications. In our study, we describe a novel microglial classification method by morphological evaluation using a convolutional neuronal network on the basis of manually selected cells in addition to classical morphological parameters. We focused on four microglial morphologies, ramified, rod-like, activated and amoeboid microglia within the murine hippocampus and cortex. The developed method for the classification was confirmed in a mouse model of ischemic stroke which is already known to result in microglial activation within affected brain regions. In conclusion, our classification of microglial morphological phenotypes using machine learning can serve as a time-saving and objective method for post-mortem characterization of microglial changes in healthy and disease mouse models, and might also represent a useful tool for human brain autopsy samples.