Effect of exercise on the plasma vesicular proteome: a methodological study comparing acoustic trapping and centrifugation

Abstract

Extracellular vesicles (EVs) are a heterogeneous group of actively released vesicles originating from a wide range of cell types. Characterization of these EVs and their proteomes in the human plasma provides a novel approach in clinical diagnostics, as they reflect physiological and pathological states. However, EV isolation is technically challenging with the current methods having several disadvantages, requiring large sample volumes, and resulting in loss of sample and EV integrity. Here, we use an alternative, non-contact method based on a microscale acoustic standing wave technology. Improved coupling of the acoustic resonator increased the EV recovery from 30% in earlier reports to 80%, also displaying long term stability between experiment days. We report a pilot study, with 20 subjects who underwent physical exercise. Plasma samples were obtained before and 1 h after the workout. Acoustic trapping was compared to a standard high-speed centrifugation protocol, and the method was validated by flow cytometry (FCM). To monitor the device stability, the pooled frozen plasma from volunteers was used as an internal control. A key finding from the FCM analysis was a decrease in CD62E+ (E-selectin) EVs 1 h after exercise that was consistent for both methods. Furthermore, we report the first data that analyse differential EV protein expression before and after physical exercise. Olink-based proteomic analysis showed 54 significantly changed proteins in the EV fraction in response to physical exercise, whereas the EV-free plasma proteome only displayed four differentially regulated proteins, thus underlining an important role of these vesicles in cellular communication, and their potential as plasma derived biomarkers. We conclude that acoustic trapping offers a fast and efficient method comparable with high-speed centrifugation protocols. Further, it has the advantage of using smaller sample volumes (12.5 μL) and rapid contact-free separation with higher yield, and can thus pave the way for future clinical EV-based diagnostics.