RESUMEN
Microglia, the immune resident cells of the central nervous system (CNS), are now recognized as performing crucial roles for maintaining homeostasis and determining the outcomes of various pathological challenges across life. While brightfield microscopy is a powerful and established tool to study microglia-mediated mechanisms underlying neurological diseases, microglial density and distribution are some of the most frequently investigated parameters. Their quantitative assessment provides relevant clues regarding dynamic densitometric changes in the microglial population across various CNS regions. Investigators often rely on a manual identification and analysis of these cells within key regions of interest, which can be time-consuming and introduce an experimenter bias. Automation of this process, which has been gaining popularity in recent years, represents a potential solution to minimize both experimenter's bias and time investment, thus increasing the efficacy of the experiment and uniformity of the collected data. We aimed to compare manual versus automatic analysis methods to determine whether an automatic analysis is efficient and accurate enough to replace a manual analysis in both homeostatic and pathological contexts (i.e., adult healthy and lipopolysaccharide-challenged adolescent male mice, respectively). To do so, we used a script that runs on the ImageJ software to perform microglial density analysis by automatic detection of microglial cells from brightfield microscopy images. The main core of the macro script consists in an automatic cell selection step using a threshold followed by a spatial analysis for each selected cell. The resulting data were then compared with the values obtained using a well-established manual method. Overall, the evaluation of the established automatic densitometry method with manual density and distribution analysis revealed similar results for the density and nearest neighbor distance in healthy adult mice, as well as density and distribution in lipopolysaccharide-challenged adolescent mice. Applying machine learning to the automatic process could further improve the accuracy and robustness of the method.