Unbiased High-Dimensional Identification of Lymphocytes in Mouse Uterus

Abstract

Abstract Introduction Uterine lymphocytes play an essential role at the maternal-fetal interface, contributing to vascular remodeling, fetal tolerance and protection against infection. Despite intense research interest, simultaneous population assessment of diverse immune cells has been limited by the use of few concurrent markers and difficulty in assigning unambiguous cell identity. We present the first comprehensive, unbiased identification of mouse uterine immune subsets by application of dimensionality reduction of highly-polychromatic flow cytometry. Methods Uterine specimens were dissected from C57BL/6 mice and mononuclear cells (MCs) were isolated by mechanical and enzymatic disruption. MCs labeled by flurochrome-conjugate antibodies. Data acquisition and analysis were performed using BD Fortessa flow cytometer in an 18 parameter, 5-laser configuration and Flowjo, respectively. Dimensionality reduction by Barnes-Hut modification of t-distributed Stochastic Neighbor Embedding (t-SNE) and density-based k-means clustering was performed using the open-source ACCENSE package. Results Analysis revealed uterine B-, T-, Dendritic, and Innate Lymphoid cell subsets. Analysis with ACCENSE allowed unbiased mapping of these subsets onto 2-dimensional scaffolds, without the necessity for expert manual gating. Conclusion We present the first comprehensive analysis of uterine immune cells, demonstrating a complex mixture of innate and adaptive immune mediators. The use of dimensionality reduction and population clustering allows allocation of uterine immune cell phenotype in an unambiguous fashion, enhancing reproducibility and functional analysis across studies.