Adaptive immune system, principally governed by the T cells-dendritic cells (DCs) nexus, is an essential mediator of gestational fetal tolerance and protection against infection. However, the exact composition and dynamics of DCs and T cell subsets in gestational tissues are not well understood. These are controlled in human physiology by a complex interplay of alloantigen distribution and presentation, cellular/humoral active and passive tolerance, hormones/chemokines/angiogenic factors and their gradients, systemic and local microbial communities. Reductive discrimination of these factors in physiology and pathology of model systems and humans requires simplification of the model and increased resolution of interrogative technologies. As a baseline, we have studied the gestational tissue dynamics in the syngeneic C57BL/6 mice, as the simplest immunological environment, and focused on validating the approach to increased data density and computational analysis pipeline afforded by highly polychromatic flow cytometry and machine learning interpretation. We mapped DC and T cell subsets, and comprehensively examined their maternal (decidual)-fetal (placental) interface dynamics. Both frequency and composition of decidual DCs changed across gestation, with a dramatic increase in myeloid DCs in early pregnancy, and exclusion of plasmacytoid DCs. CD4+ T cells, in contrast, were lower at all gestational ages and an unusual CD4–CD8–TCRαβ+group was prominent at mid-pregnancy. Dimensionality reduction with machine learning-aided clustering revealed that CD4–CD8– T cells were phenotypically different from CD4+ and CD8+ T cells. Additionally, divergence between maternal decidual and fetal placental compartment was prominent, with absence of DCs from the placenta, but not decidua or embryo. These results provide a novel framework and a syngeneic baseline on which the specific role of alloantigen/tolerance, polymicrobial environment, and models of pregnancy pathology can be precisely modeled and analyzed.
Read the full article at: https://www.ncbi.nlm.nih.gov/pubmed/30283441