Gestationally-Dependent Immune Organization at the Maternal-Fetal Interface

Abstract

The immune system and placenta have a dynamic relationship across gestation to accommodate fetal growth and development. High-resolution characterization of this maternal-fetal interface is necessary to better understand the immunology of pregnancy and its complications. We developed a single-cell framework to simultaneously immuno-phenotype circulating, endovascular, and tissue-resident cells at the maternal-fetal interface throughout gestation, discriminating maternal and fetal contributions. Our data reveal distinct immune profiles across the endovascular and tissue compartments with tractable dynamics throughout gestation that respond to a systemic immune challenge in a gestationally-dependent manner. We uncover that mononuclear phagocytes and neutrophils drive the temporal immune composition of the placenta with remarkably diverse populations, including PD-L1-expressing subsets having compartmental and early gestational bias. Our approach and accompanying datasets provide a resource for additional investigations into gestational immunology and evoke a more significant role for the innate immune system in establishing the microenvironment of early pregnancy.

Single-cell Spatial Proteomic Imaging for Human Neuropathology

“…We also used this approach to delineate features from individuals with cognitive impairment (Figure S2B). Taken together, much like multiplexed analysis of the immune system and other tissues (Glass et al, 2019; Hartmann and Bendall, 2020; Hartmann et al, 2021; Keren et al, 2018; Liu et al, 2021; McCaffrey et al, 2022; Moore et al, 2021), combinatorial protein expression patterns provided a snapshot of functional organization or proteopathy within brain regions. This low-level analysis of multiplexed proteomic data could serve as a guide for ‘fingerprinting’ human brain and be used to model progression in neurodegeneration.…”

Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA

“…LDA can also be applied to visualize biological units other than single cells to tease out differences in summary statistics between samples. 35 , 36 HSS-LDA may not perform well in situations in which a large degree of heterogeneity exists within a given class. For example, labeling PBMCs according to the time point of origin would likely fail to produce a visualization that segregates time points, as each PBMC sample would be composed of many cell types.…”