From Sample Preparation to Data Analysis: A Comprehensive Workflow for Imaging EVs with SMLM

Introduction

Extracellular vesicles (EVs) including exosomes, microvesicles, and apoptotic bodies are lipid bilayer-delimited nanoparticles. They are shed by all the living cells and can be collected from any body fluid including blood, urine, and saliva. Even though their main duty is always regulating intercellular communication, they express different lipids, proteins, and nucleic acids on their surface and within their cargo (Figure 1). This discrepancy is determined mainly by their parental cell and reflects its pathological state. Hence, they have been a center of interest in many different fields ranging from diagnostics and therapeutics to cosmetics and even the food industry.

Why Single Molecule Localization Microscopy?

The multi-layered heterogeneity of EVs includes their varying size, membrane composition, and cargo content. This makes the resolution of such heterogeneity a critical aspect of EV-based application development, for which single EV characterization methods are highly critical. The main techniques employed for single EV characterization include flow cytometry for surface marker profiling, electron microscopy for morphology and sizing, and nanoparticle tracking analysis (NTA) for size and concentration analysis (Welsh, 2024). Among these, flow cytometry is mainly limited by its spatial resolution that misses the detection of small EVs. Electron microscopy techniques, despite their high resolution, suffer from the lack of molecular profiling capabilities.

Single molecule localization microscopy (SMLM), on the other hand, brings the advantages of flow cytometry and electron microscopy together. It works by exciting a small, random subset of fluorescent molecules in a sample, so their signals do not overlap. This helps to pinpoint the position of each single molecule with high precision. The repetition of this process stochastically for thousands of times then provides the user with a detailed, high-resolution image.

This way, SMLM enables simultaneous characterization of EVs in terms of their phenotypes and morphology. Its super-resolution at the nanoscale allows for visualization of single surface molecules on EVs. This way, SMLM offers unique advantages for comprehensive EV characterization for detailed mapping of specific EV markers as well as structural information capture.

Materials and Methods

In this study, Abbelight's SAFe MN360 platform attached to an inverted microscope together with Smart Kit EVs is employed to analyze our FLuoEVs to develop an end-to-end workflow for EV imaging with SMLM.

FLuoEVs, products of genetic EV engineering to HEK293 cells, carry EGFP, BFP, or mCherry proteins in fusion with the common tetraspanin EV markers, namely CD9, CD63, and CD81. This way, they fluoresce stably without any photobleaching or non-specific background signal problem.

The study employed CD63-EGFP FLuoEVs (Product code: HBM-HEK-EGFP63). The sample is stained for two markers, CD63 and CD81 (with CF680 and AF647, respectively), and imaged in simultaneous multicolor mode with SMLM. Using Abbelight’s acquisition software NEO as well as ultra-wide field of view technology ASTER, super-resolution images of EV samples are obtained.

Data Analysis

Using DBSCAN algorithm to segment each EV automatically, single vesicle analysis is performed (see Figure 3) (Martin Ester, 1996). Key properties such as EV size, composition, and morphology are calculated for each vesicle.

This visualization and data analysis not only acts as a quality control for our FLuoEVs that are stably fluorescent, but also sets a good example in how they can be exploited as a reference material in single vesicle imaging studies.

References

[1] Martin Ester, H.-P. K. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, (pp. 226-231).

[2] Welsh, J. A.-P.-L. (2024). Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. Journal of Extracellular Vesicles, 13(2). doi:https://doi.org/10.1002/jev2.12404.