Harnessing extracellular vesicle heterogeneity for diagnostic and therapeutic applications

Abstract

Extracellular vesicles (EVs) are diverse nanoparticles with large heterogeneity in size and molecular composition. Although this heterogeneity provides high diagnostic value for liquid biopsy and confers many exploitable functions for therapeutic applications in cancer detection, wound healing and neurodegenerative and cardiovascular diseases, it has also impeded their clinical translation-hence heterogeneity acts as a double-edged sword. Here we review the impact of subpopulation heterogeneity on EV function and identify key cornerstones for addressing heterogeneity in the context of modern analytical platforms with single-particle resolution. We outline concrete steps towards the identification of key active biomolecules that determine EV mechanisms of action across different EV subtypes. We describe how such knowledge could accelerate EV-based therapies and engineering approaches for mimetic artificial nanovesicle formulations. This approach blunts one edge of the sword, leaving only a single razor-sharp edge on which EV heterogeneity can be exploited for therapeutic applications across many diseases.

Fig. 1 |. EVs exhibit major heterogeneity that currently limits their effective application in drug delivery.

The first cornerstone by which to address EV heterogeneity is to assess it using multiple, complementary single-particle characterization techniques. a, EVs are heterogeneous in size, from ~30 nm to more than 150 nm. The smallest and largest EVs are unlikely to exhibit similar functions due to large differences in their volume, surface area and composition (for example, membrane components and soluble cargo). The relative volume, surface area and number of membrane proteins scale for a given vesicle size. b, Single-particle sizing (via negative-stained TEM, cryo-EM, resistive pulse sensing (RPS) and nanoparticle tracking analysis (NTA)) reveals high polydispersity, with as much as 70% of EVs by count (and 25% of EVs by surface area) falling below 80 nm, that is, the size detection threshold of NTA. TEM, transmission electron microscopy. c, Molecular information measured using single-particle immunofluorescence indicates that common membrane proteins (that is, tetraspanins CD9, CD63 and CD81) vary with diameter.

Fig. 2 |. Generalized experimental workflow for determining the EV MOA in the context of single-vesicle heterogeneity.

A second cornerstone by which to address EV heterogeneity is to develop in vitro functional studies towards identifying an MOA. EV-enriched inputs should be dosed in complementary functional assays with defined quantifiable metrics. Bold arrows along the left and centre of the schematic illustrate an idealized path towards defining potential biomolecules (ideally defining their distribution across single particles), enriching target subpopulations with those biomolecules and repeating the assay against EV sources without the target. Various controls that depend on the context of the assay are summarized along the right-hand side of the schematic. Input controls, such as purified EV subpopulations, EV-depleted biofluids or liposomes loaded with the target biomolecule or active pharmaceutical ingredient (API) should be used if possible. To aid in identification of the MOA, functional blocking or other manipulation of the target biomolecule should be performed.

Fig. 3 |. Engineering ANVs to address long-standing challenges that are associated with EVs.

A third cornerstone for addressing inherent EV heterogeneity is to use synthetic ANVs. Whereas native EVs exist in highly entangled mixed populations that vary in, for example, size, density and molecular composition, ANVs can in theory be tailored with highly controlled molecular content (for example, a desired protein ‘X’), size and density. Whereas such materials could benchmark or replace/complement EV therapeutics, it remains a challenge to assess ANV heterogeneity, and technical capabilities for synthesizing vesicles that match the complexity of native EVs (for example, multiple membrane proteins per vesicle, complex lipid and/or glycan decoration) lag behind.