Addressing heterogeneity in direct analysis of Extracellular Vesicles and analogues using Membrane-Sensing Peptides as Pan-Affinity Probes

Extracellular vesicles (EVs), crucial mediators of cell-to-cell communication, hold immense potential for diagnostic applications due to their ability to enrich protein biomarkers in body fluids. However, challenges in isolating EVs from complex biological specimens hinder their widespread use. In this frame, integrated isolation-and-analysis workflows are the go-to strategy, most of which see the prevalence of immunoaffinity methods. Yet, the high heterogeneity of EVs poses challenges, as proposed ubiquitous markers are less homogenously prevalent than believed, raising concerns about the reliability of downstream biomarker discovery programs. This issue extends to the burgeoning field of engineered EV-mimetics and bio-nanoparticles, where conventional immune-affinity methods may lack applicability. Addressing these challenges, we introduce the use Membrane Sensing Peptides (MSP) as "universal" affinity ligands for both EVs and EV-analogues. Employing a streamlined process integrating on-bead capture and vesicle phenotyping through Single Molecule Array (SiMoA) technology, we showcase the application of MSP ligands in the integrated analysis of circulating EVs in blood derivatives, eliminating the need for prior EV isolation. Demonstrating the possible clinical translation of MSP technology, we directly detect an EV-associated epitope signature in serum and plasma samples, demonstrating its potential for distinguishing patients with myocardial infarction versus stable angina. At last, notably, MSP exhibits a unique capability to enable the analysis of tetraspanin-lacking Red Blood Cell derived EVs (RBC-EVs). Overall, unlike traditional antibody-based methods, MSP probes work agnostically, overcoming limitations associated with surface protein abundance or scarcity. This highlights the potential of MSP in advancing EV analysis for clinical diagnostics and beyond. Of note, this represents also the first-ever peptide-based application in SiMoA technology.