Visualizing murine placental extracellular vesicle data with tidyNano: a computational framework for analyzing and visualizing nanoparticle data in R

Abstract

Extracellular vesicles (EVs) are increasingly recognized as important mediators of intercellular communication, and in mammals are generally classified as ~50–150nm exosomes, ~100–1000nm microvesicles, and apoptotic bodies, each arising as a result of different biological processes. EVs carry protein, lipids, and nucleic acids within the circulation, to target cells whereupon they mediate physiological changes. Due to their small size, quantification and characterization by conventional microscopy is not possible. However, nanoparticle tracking analysis (NTA) has provided a method to determine the fluid concentration and size of extracellular vesicles and other nanoparticles. While NTA provides statistical summaries of samples in an experiment, a recurring difficulty is the organization, manipulation, and management of raw particle count data because of the large size of datasets, and resultant vulnerability to user error. To address these limitations, we developed tidyNano, an R package that provides functions to import, clean, and quickly summarize raw NanoSight (Malvern Panalytical) data for efficient calculation of statistics and visualization. Here, we provide a framework for importing raw nanoparticle data and provide functions to facilitate rapid and efficient analysis, visualization and calculation of summary statistics. tidyNano was used to analyze murine plasma extracellular vesicles across gestation by aggregating and summarizing samples based on technical, biological and gestational parameters. In addition, we developed shinySIGHT, a Shiny web application that allows for interactive exploration and visualization of EV data. Using this package, we analyzed data generated from 36 samples of EV derived from the plasma of mice across gestation.

Peripheral EV concentration increased linearly across pregnancy, with trending increases as early as gestation day (GD) 5.5 and significant rises at GD14.5, and 17.5 relative to EV concentrations in nonpregnant females. Thus, the data highlight the utility of the mouse as a model of EV biology in pregnancy. Further, the package provides a mechanism for seamless analysis of EV data generated by NanoSight. Importantly, this package provides a straightforward framework by which diverse types of large datasets can be simply and efficiently analyzed. tidyNano and shinySIGHT are freely available under the MIT license and is hosted on GitHub ( https://nguyens7.github.io/tidyNano/).