Diurnal Variations Of Circulating Extracellular Vesicles Measured By Nano Flow Cytometry

The identification of extracellular vesicles (EVs) as intercellular conveyors of biological information has recently emerged as a novel paradigm in signaling, leading to the exploitation of EVs and their contents as biomarkers of various diseases. However, whether there are diurnal variations in the size, number, and tissue of origin of blood EVs is currently not known, and could have significant implications when using EVs as biomarkers for disease progression. Currently available technologies for the measurement of EV size and number are either time consuming, require specialized equipment, or lack sufficient accuracy across a range of EV sizes. Flow cytometry represents an attractive alternative to these methods; however, traditional flow cytometers are only capable of measuring particles down to 500 nm, which is significantly larger than the average and median sizes of plasma EVs. Utilizing a Beckman Coulter MoFlo XDP flow cytometer with NanoView module, we employed nanoscale flow cytometry (termed nanoFCM) to examine the relative number and scatter distribution of plasma EVs at three different time points during the day in 6 healthy adults. Analysis of liposomes and plasma EVs proved that nanoFCM is capable of detecting biologically-relevant vesicles down to 100 nm in size. With this high resolution configuration, we observed variations in the relative size (FSC/SSC distributions) and concentration (proportions) of EVs in healthy adult plasma across the course of a day, suggesting that there are diurnal variations in the number and size distribution of circulating EV populations. The use of nanoFCM provides a valuable tool for the study of EVs in both health and disease; however, additional refinement of nanoscale flow cytometric methods is needed for use of these instruments for quantitative particle counting and sizing. Furthermore, larger scale studies are necessary to more clearly define the diurnal variations in circulating EVs, and thus further inform their use as biomarkers for disease.