Unsupervised Machine Learning‐Based Clustering of Nanosized Fluorescent Extracellular Vesicles

Abstract

Extracellular vesicles (EV) are biological nanoparticles that play an important role in cell‐to‐cell communication. The phenotypic profile of EV populations is a promising reporter of disease, with direct clinical diagnostic relevance. Yet, robust methods for quantifying the biomarker content of EV have been critically lacking, and require a single‐particle approach due to their inherent heterogeneous nature. Here, multicolor single‐molecule burst analysis microscopy is used to detect multiple biomarkers present on single EV. The authors classify the recorded signals and apply the machine learning‐based t‐distributed stochastic neighbor embedding algorithm to cluster the resulting multidimensional data. As a proof of principle, the authors use the method to assess both the purity and the inflammatory status of EV, and compare cell culture and plasma‐derived EV isolated via different purification methods. This methodology is then applied to identify intercellular adhesion molecule‐1 specific EV subgroups released by inflamed endothelial cells, and to prove that apolipoprotein‐a1 is an excellent marker to identify the typical lipoprotein contamination in plasma. This methodology can be widely applied on standard confocal microscopes, thereby allowing both standardized quality assessment of patient plasma EV preparations, and diagnostic profiling of multiple EV biomarkers in health and disease.

Ultrasensitive Single Extracellular Vesicle Detection Using High Throughput Droplet Digital Enzyme-Linked Immunosorbent Assay

“…Among CD81 + EVs, we found that 67% (64.3%–69.8%) of EVs coexpressed another copy of CD81 that could be labeled with a fluorophore-conjugated antibody. Consistent with the observation of others, we found heterogeneity among EVs’ surface protein “pan-EV” markers even though they were derived from the same cell line. ,, We chose to use CD81 for capture and labeling, rather than neuron specific surface markers, because this protein could be characterized using established Nanoview assay kits, allowing us to quantify and benchmark the performance of DEVA. Improving the performance of DEVA assay required optimization in a multidimensional parameter space, including the concentration of several reagents and blocking conditions.…”

From Conventional to Microfluidic: Progress in Extracellular Vesicle Separation and Individual Characterization

“…Multidimensional single burst analysis spectroscopy combining with t-SNE is a powerful tool to multidimensional characterize single EVs with high accuracy and speed, which will significantly improve the diagnostic ability of EV in disease-related biomarker studies. [283] Similarly, Yan et al described a platform based on SERS in combination with multivariate analysis to characterize single exosomes derived from different biological sources (Figure 13a). The Raman substrate was made of a graphene-covered Au surface containing quasi-periodic pyramidal arrays.…”

Clinical Translation of Extracellular Vesicles

“…Recent studies have employed machine learning in different stages of development of EV based therapeutics including identification of EVs [166] and single EV analysis. [167][168][169] Use of above mentioned high throughput techniques such as microfluidics in combination with machine learning based EV analysis could accelerate the clinical translation of EV-based therapeutics.…”