Identification of mouse microglial cell subpopulations by high dimentional flow cytometry analysis in LPS-induced neuroinflammation

MANZONE RODRIGUEZ, CLARISA; PERALTA RAMOS JAVIER MARIA ; DANIELA SOLEDAD ARROYO; CECILIA MARÍA RODRÍGUEZ; NATALIA SOLEDAD BÁEZ; JI MING WANG; PABLO IRIBARREN

Congreso; Reunión Anual de Sociedades de Biociencias 2020; 2020

Sociedad Argentina de Inmunología (SAI), Sociedad Argentina de Investigación Clínica (SAIC) y la Sociedad Argentina de Fisiología (SAFIS)

IDENTIFICATION OF MOUSE MICROGLIAL CELL SUBPOPULATIONS BY HIGH DIMENSIONAL FLOW CYTOMETRY ANALYSIS IN LPS-INDUCED NEUROINFLAMMATIONIntroduction: Brain-resident microglia and peripheral leukocytes play essential roles in shaping the immune response in the CNS. These cells activate and migrate during active immune responses and may contribute to the progression of neuroinflammation.We previously found that systemic lipopolysaccharide (LPS) challenge induced glial activation and an active recruitment of CD45hi leukocytes, close to vascular endothelial cells, in circumventricular organs. However, the phenotype of microglial cells and the recruited leukocytes were not fully characterized.Methods: In this study, we assessed the phenotype of microglial cells and the recruited leukocytes to the brain, in response to systemic TLR4 stimulation, by applying high dimensional flow cytometry analysis. We used machine learning algorithms to detect changes in morphology and marker expression in microglia, due to activation by systemic administration of LPS (LPS - 1.6 mg/kg, i.p. injections, n=4). After perfusion, we obtained brain cells suspensions, stained the cells and eight parameters where simultaneously analyzed by conventional flow cytometry and bioinformatics algorithms implemented in R. Results: We detected three populations of microglial cells based on CD45 expression and cell size. After LPS-induced systemic inflammation we observed changes in the microglial cell phenotype and size (p<0.01 and p<0.05). In addition, we observed increased frequency of CD45hi inflammatory monocytes (p<0.001). Dimensional reduction (viSNE) and clustering confirmed these results and suggested additional heterogeneity in the recruited cell populations. Conclusions: These preliminary results suggest the presence of microglial cell subpopulations that responded to peripheral inflammatory stimulation. Further research is required to better define these populations either by increasing the number of cell markers studied or by morphological and tissular characterization, to identify their pathophysiological relevance. microglia, LPS, inflammation, TLR4, algorithms