Multi-marker profiling of extracellular vesicles using streaming current and sequential electrostatic labeling.

High heterogeneity in the membrane protein expression of small extracellular vesicles (sEVs) means that bulk methods relying on antibody-based capture for expression analysis have a drawback that each type of antibody may capture a different sub-population. An improved approach is to capture a representative sEV population, without any bias, and then perform a multiplexed protein expression analysis on this population. However, such a possibility has been largely limited to fluorescence-based methods. Here, we present a novel electrostatic labelling strategy and a microchip-based all-electric method for membrane protein analysis of sEVs. The method allows us to profile multiple surface proteins on the captured sEVs using alternating charge labels. It also permits the comparison of expression levels in different sEV-subtypes. The proof of concept was tested by capturing sEVs both non-specifically (unbiased) as well as via anti-CD9 capture probes (biased), and then profiling the expression levels of various surface proteins using the charge labelled antibodies. The method is the first of its kind, demonstrating an all-electrical and microchip based method that allows for unbiased analysis of sEV membrane protein expression, comparison of expression levels in different sEV subsets, and fractional estimation of different sEV sub-populations. These results were also validated in parallel using a single-sEV fluorescence technique.

Label-Free Surface Protein Profiling of Extracellular Vesicles by an Electrokinetic Sensor.

Small extracellular vesicles (sEVs) generated from the endolysosomal system, often referred to as exosomes, have attracted interest as a suitable biomarker for cancer diagnostics, as they carry valuable biological information and reflect their cells of origin. Herein, we propose a simple and inexpensive electrical method for label-free detection and profiling of sEVs in the size range of exosomes. The detection method is based on the electrokinetic principle, where the change in the streaming current is monitored as the surface markers of the sEVs interact with the affinity reagents immobilized on the inner surface of a silica microcapillary. As a proof-of-concept, we detected sEVs derived from the non-small-cell lung cancer (NSCLC) cell line H1975 for a set of representative surface markers, such as epidermal growth factor receptor (EGFR), CD9, and CD63. The detection sensitivity was estimated to be ∼175000 sEVs, which represents a sensor surface coverage of only 0.04%. We further validated the ability of the sensor to measure the expression level of a membrane protein by using sEVs displaying artificially altered expressions of EGFR and CD63, which were derived from NSCLC and human embryonic kidney (HEK) 293T cells, respectively. The analysis revealed that the changes in EGFR and CD63 expressions in sEVs can be detected with a sensitivity in the order of 10% and 3%, respectively, of their parental cell expressions. The method can be easily parallelized and combined with existing microfluidic-based EV isolation technologies, allowing for rapid detection and monitoring of sEVs for cancer diagnosis.