Mechanical property estimation of sarcoma-relevant extracellular vesicles using transmission electron microscopy

Area of Science:

• Biophysics
• Nanotechnology
• Biomaterials

Background:

• Accurate measurement of extracellular vesicle (EV) mechanical properties is essential for understanding their biological roles and therapeutic potential.
• Current methods for analyzing nanoscale particle mechanics are limited by low throughput and inconsistent results.
• Extracellular vesicles (EVs) are nanoscale particles with diverse functions, but their mechanical properties remain poorly characterized.

Purpose of the Study:

• To develop and validate a high-throughput, flow-based method for quantifying the mechanical properties of single extracellular vesicles (EVs).
• To estimate the elastic modulus of liposarcoma-derived EVs (LPS-EVs) and investigate the relationship between EV size and mechanical properties.
• To provide a reliable method for label-free analysis of EV mechanical characteristics.

Main Methods:

• A flow-based deformation analysis technique was developed and combined with transmission electron microscopy (TEM) imaging.
• Morphological data from 432 TEM images of single and multiple EVs were analyzed.
• Thin-shell deformation theory was applied to estimate the elastic modulus (E) of EVs based on their size.

Main Results:

• The elastic modulus for small EVs (sEVs; 30-150 nm) was estimated at E = 0.16 ± 0.02 MPa.
• The elastic modulus for large EVs (lEVs; >150 nm) was estimated at E = 0.17 ± 0.03 MPa.
• This study presents the first reported mechanical property estimation for liposarcoma-derived EVs, showing no significant difference between small and large EVs.

Conclusions:

• The developed flow-based method enables high-throughput, quantitative analysis of EV mechanical properties.
• The findings suggest that EV size does not significantly influence the elastic modulus in liposarcoma-derived EVs.
• This research provides a foundation for establishing correlations between EV size and mechanical properties, crucial for EV-based diagnostics and therapeutics.