One of the challenges of exploiting extracellular vesicles (EVs) as a disease biomarker is to differentiate EVs released by similar cell types or phenotypes. Researchers at Iowa State University have developed a high-throughput and label-free EV microarray technology to differentiate EVs by simultaneous characterization of a panel of EV membrane proteins. The EsupplV microarray platform, which consists of an array of antibodies printed on a photonic crystal biosensor and a microscopic hyperspectral imaging technique, can rapidly assess the binding of the EV membrane proteins with their corresponding antibodies. The EV microarray assay requires only a 2 μL sample volume and a detection time of less than 2 h. The EV microarray assay was validated by not only quantifying seven membrane proteins carried by macrophage-derived EVs but also distinguishing the EVs secreted by three macrophage phenotypes. In particular, the EV microarray technology can generate a molecular fingerprint of target EVs that can be used to identify the EVs’ parental cells, and thus has utility for basic science research as well as for point-of-care disease diagnostics and therapeutics.
Hyperspectral imaging of the label-free EV microarray
a Schematic diagram of the hyperspectral imaging-based detection setup. b Intensity images captured at nine different wavelengths ranging from 830 nm to 870 nm. Each image consists of 1000 × 1000 pixels with a spatial pixel resolution of 1.85 μm. c Reconstructed transmission spectrum at a given pixel in the area of interest. The resonance wavelength of this pixel, λr(x, y), is determined by a curve fitting algorithm. d Label-free image around one microwell. e Label-free images of the EV microarray. The top panel shows the microarray before printing EVs. The lower panel shows the microarray after printing different concentrations of EVs. Scale bar: 60 μm. f Profile plot before and after EV treatment (black dashed line and red dashed line in e). Δλ represents the wavelength shift induced by the binding of EVs. g Dose–response curve for the detection of EVs using a CD63 coated PC sensor. The Δλr values were fitted using a linear function.