Computational detection, characterization, and clustering of microglial cells in a mouse model of fat-induced postprandial hypothalamic inflammation.

Obesity is associated with brain inflammation, glial reactivity, and immune cells infiltration. Studies in rodents have shown that glial reactivity occurs within 24 h of high-fat diet (HFD) consumption, long before obesity development, and takes place mainly in the hypothalamus (HT), a crucial brain structure for controlling body weight. Understanding more precisely the kinetics of glial activation of two major brain cells (astrocytes and microglia) and their impact on eating behavior could prevent obesity and offer new prospects for therapeutic treatments. To understand the mechanisms pertaining to obesity-related neuroinflammation, we developed a fully automated algorithm, NutriMorph. Although some algorithms were developed in the past decade to detect and segment cells, they are highly specific, not fully automatic, and do not provide the desired morphological analysis. Our algorithm copes with these issues and performs the analysis of cells images (here, microglia of the hypothalamic arcuate nucleus), and the morphological clustering of these cells through statistical analysis and machine learning. Using the k-Means algorithm, it clusters the microglia of the control condition (healthy mice) and the different states of neuroinflammation induced by high-fat diets (obese mice) into subpopulations. This paper is an extension and re-analysis of a first published paper showing that microglial reactivity can already be seen after few hours of high-fat diet (Cansell et al., 2021 [5]). Thanks to NutriMorph algorithm, we unravel the presence of different hypothalamic microglial subpopulations (based on morphology) subject to proportion changes in response to already few hours of high-fat diet in mice.