Importance of between and within Subject Variability in Extracellular Vesicle Abundance and Cargo when Performing Biomarker Analyses.

Lauren A Newman,Michael J Sorich,Andrew Rowland,Alia Fahmy,Oliver G Best,Zivile Useckaite

doi:10.3390/cells10030485

Abstract

Small extracellular vesicles (sEV) have emerged as a potential rich source of biomarkers in human blood and present the intriguing potential for a ‘liquid biopsy’ to track disease and the effectiveness of interventions. Recently, we have further demonstrated the potential for EV derived biomarkers to account for variability in drug exposure. This study sought to evaluate the variability in abundance and cargo of global and liver-specific circulating sEV, within (diurnal) and between individuals in a cohort of healthy subjects (n = 10). We present normal ranges for EV concentration and size and expression of generic EV protein markers and the liver-specific asialoglycoprotein receptor 1 (ASGR1) in samples collected in the morning and afternoon. EV abundance and cargo was generally not affected by fasting, except CD9 which exhibited a statistically significant increase (p = 0.018). Diurnal variability was observed in the expression of CD81 and ASGR1, which significantly decreased (p = 0.011) and increased (p = 0.009), respectively. These results have potential implications for study sampling protocols and normalisation of biomarker data when considering the expression of sEV derived cargo as a biomarker strategy. Specifically, the novel finding that liver-specific EVs exhibit diurnal variability in healthy subjects should have broad implications in the study of drug metabolism and development of minimally invasive biomarkers for liver disease.

Highlights

Small extracellular vesicles, in particular exosomes, have emerged as a rich source of circulating biomarkers with applications including tracking variability in disease, intervention efficacy, and drug exposure [1,2,3]. sEVs are heterogeneous membrane encapsulated particles of less than 150 nm in diameter that are secreted into the blood and other biofluids by virtually all cell types [4]
In order to assess the purity of EV isolations, a few samples were selected at random and imaged by Transmission Electron Microscopy (TEM) to evaluate the background, composition, and EV structu6reof. 1R8epresentative images from TEM analysis (Figure 2, Figure S1) indicated that EV populations obtained by qEV70 size exclusion chromatography (SEC) columns had limited non-vesicular contamination and the majority 2o.f12E.VEsV-wTeRrAeC4K0–140 nm in size
Data presented here indicate that accounting for diurnal variability in EV expression may be important for the analysis of liver-specific biomarkers

Summary

Introduction

Small extracellular vesicles (sEVs), in particular exosomes, have emerged as a rich source of circulating biomarkers with applications including tracking variability in disease, intervention efficacy, and drug exposure [1,2,3]. sEVs are heterogeneous membrane encapsulated particles of less than 150 nm in diameter that are secreted into the blood and other biofluids by virtually all cell types [4]. The sEV class comprises multiple specific EV types including exosomes (classical and non-classical), arrestin-domain-containing protein 1-mediated microvesicles (ARMM), small apoptotic EV, and small autophagic EV; multiple larger EV classes exist [5]. These vesicles contain a complement of nucleic acid (microRNA (miRNA), mRNA, and non-coding RNA), protein, and small molecule cargo that are derived from their cell of origin [6]. EVs derived from the same cell have been shown to (e.g., CD9, CD63, CD81, TSG101, and Calnexin). EVs derived from the same cell have been shown to vary in molecular composition [7]; yet, the degree of normal variability in the abunvdarnyceinomf coilreccuulalatirncgomsEpVosaintidonth[e7i]r; ycaert,gtoheredmeagirnese poof onrolrymdaelfivnaerdia[b8i]l.itIyninortdheeratbournod- ance bustlyofdcierfcinuelattihnrgesshEoVldasndofthaecicrucraarcgyo, rpermecaiisniosnpoaonrdlysdeenfisinteivdit[y8]f.oIrn aonrdEerVtodreorbivuesdtlybdi-efine omartkherre,sihtoilsdessosfenacticaulrtaocyu, npdreecrisstiaonndanthdesnenosrimtivalitydefgoreaenoEfVvadreiraibvielditybiionmcairckuelra, tiitnisg eEsVsential and ttoouunnddeerrssttaanndd pthaettneronrms (ael.gd.e,gcrierceaodfivanar)iasbsiolictyiaitnedcirwcuitlhatEinVgaEbVunanddantcoeu. nderstand patterns

Methods

Results

Conclusion