Design and Optimization of a Biosensor Surface Functionalization to Effectively Capture Urinary Extracellular Vesicles

High resolution imaging and analysis of extracellular vesicles using mass spectral imaging and machine learning

Extracellular vesicles (EVs) are potentially useful biomarkers for disease detection and monitoring. Development of a label-free technique for imaging and distinguishing small volumes of EVs from different cell types and cell states would be of great value. Here, we have designed a method to explore the chemical changes in EVs associated with neuroinflammation using Time-of-Flight Secondary Ion Mass spectrometry (ToF-SIMS) and machine learning (ML). Mass spectral imaging was able to identify and differentiate EVs released by microglia following lipopolysaccharide (LPS) stimulation compared to a control group. This process requires a much smaller sample size (1 µL) than other molecular analysis methods (up to 50 µL). Conspicuously, we saw a reduction in free cysteine thiols (a marker of cellular oxidative stress associated with neuroinflammation) in EVs from microglial cells treated with LPS, consistent with the reduced cellular free thiol levels measured experimentally. This validates the synergistic combination of ToF-SIMS and ML as a sensitive and valuable technique for collecting and analysing molecular data from EVs at high resolution.

Radiovesicolomics-new approach in medical imaging

This review introduce extracellular vesicles (EVs) to a molecular imaging field. The idea of modern analyses based on the use of omics studies, using high-throughput methods to characterize the molecular content of a single biological system, vesicolomics seems to be the new approach to collect molecular data about EV content, to find novel biomarkers or therapeutic targets. The use of various imaging techniques, including those based on radionuclides as positron emission tomography (PET) or single photon emission computed tomography (SPECT), combining molecular data on EVs, opens up the new space for radiovesicolomics—a new approach to be used in theranostics.

Study of the influence of hyperglycemia on the abundance of amino acids, fatty acids, and selected lipids in extracellular vesicles using TOF-SIMS

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) with the Bi₃⁺ liquid metal ion gun was used to investigate the content of lipids and amino acids (AAs) in extracellular vesicles (EVs). We induced metabolic changes in human pancreatic β-cells by stimulation with high glucose concentrations (35 mM) and tested the hypothesis of hyperglycemia (HG) has a detrimental effect on lipids and AAs in released EV subpopulations: ectosomes and exosomes. As a result of HG treatment, selected fatty acids (FAs) such as arachidonic, myristic and palmitic acids, changed their abundance in ectosomes and exosomes. Also, intensities of the characteristic peaks for cholesterol (m/z 95.09; 147.07; 161.11; 369.45) along with the molecular ion m/z 386.37 [C₂₇H₄₆O⁺] under HG conditions, both for ectosomes and exosomes, have changed significantly. Comparative analysis of HG EVs and normoglycemic (NG) ones showed statistically significant differences in the signal intensities of four AAs: valine (m/z 72.08 and 83.05), isoleucine (m/z 86.10), phenylalanine (m/z 120.08 and 132.05) and tyrosine (m/z 107.05 and 136.09). We confirmed that ToF-SIMS is a useful technique to study selected AAs and lipid profiles in various EV subpopulations. Our study is the first demonstration of changes in FAs and AAs in exosomes and ectosomes derived from β-cells under the influence of HG.