Date Published: June 18, 2019
Publisher: Public Library of Science
Author(s): Sean L. Nguyen, Jacob W. Greenberg, Hao Wang, Benjamin W. Collaer, Jianrong Wang, Margaret G. Petroff, Shang-Chun Guo.
Extracellular vesicles (EVs) are increasingly recognized as important mediators of intercellular communication that carry protein, lipids, and nucleic acids via the circulation to target cells whereupon they mediate physiological changes. In pregnancy, EVs are released in high quantities from the placenta and have been postulated to target multiple cell types, including those of the vascular and immune systems. However, most studies of pregnancy-associated EVs have used clinical samples and in vitro models; to date, few studies have taken advantage of murine models in which pregnancy can be precisely timed and manipulated. In this study, we used a murine model to determine whether the quantity of EVs is altered during healthy pregnancy and during inflammation-associated preterm birth. To facilitate data analysis, we developed a novel software package, tidyNano, an R package that provides functions to import, clean, and quickly summarize raw data generated by the nanoparticle tracking device, NanoSight (Malvern Panalytical). We also developed shinySIGHT, a Shiny web application that allows for interactive exploration and visualization of EV data. In mice, EV concentration in blood increased linearly across pregnancy, with significant rises at GD14.5 and 17.5 relative to EV concentrations in nonpregnant females. Additionally, lipopolysaccharide treatment resulted in a significant reduction in circulating EV concentrations relative to vehicle-treated controls at GD16.5 within 4 hours. Use of tidyNano facilitated rapid analysis of EV data; importantly, this package provides a straightforward framework by which diverse types of large datasets can be simply and efficiently analyzed, is freely available under the MIT license, and is hosted on GitHub (https://nguyens7.github.io/tidyNano/). Our data highlight the utility of the mouse as a model of EV biology in pregnancy, and suggest that placental dysfunction is associated with reduced circulating EVs.
Extracellular vesicles (EVs) encompass a broad class of membrane-enclosed structures secreted by cells, and are classified based on their size and subcellular origin. Exosomes, which range from 40-150nm, arise from the inward budding of late endosomes, and microvesicles, which range from 100-1000nm, develop as a result of outward budding of the plasma membrane. Because EVs have the capacity to induce physiological responses in recipient/target cells, they are of immense interest for many life science disciplines including immunology, cancer biology, and medicine [1–4]. Molecular contents of EVs, which include lipids, proteins, and nucleic acids, serve as the basis for intercellular communication . Indeed, EVs have been shown to be effective and important in mediating processes including antigen cross-presentation , establishing local ‘niches’ for metastasis of cancer cells , delivery of microRNAs for suppression of gene expression in target tissues , and even transfer and subsequent translation of mRNAs into target cells .
To demonstrate the utility of the package, we used tidyNano to analyze three NanoSight datasets. The first set consisted NanoSight data from polystyrene fluorescent and non-fluorescent bead standards (S2 Fig) and a second set consisted of peripheral exosomes from C57Bl/6 female mice across six time points of pregnancy. The third dataset consisted of peripheral exosome data from a lipopolysaccharide (LPS) model of preterm birth in GD16.5 C57 Bl/6 mice.
In this study, we quantified the murine plasma EVs across normal gestation, as well as in a model of inflammation-induced preterm birth. The principle findings were that plasma EVs concentrations increased with advancing gestational age in mice in comparison to nonpregnant females, and that LPS-induced fetal loss was associated with a striking reduction in circulating EV. Additionally, we report a novel pipeline in the R platform for rapid exploration and visualization of data generated from NanoSight analysis of EVs.