Person:

Aikawa, Elena

Calcifying extracellular vesicles (EVs) released from cells within atherosclerotic plaques have received increased attention for their role in mediating vascular calcification, a major predictor of cardiovascular morbidity and mortality. However, little is known about the difference between this pathologic vesicle population and other EVs that contribute to physiological cellular processes. One major challenge that hinders research into these differences is the inability to selectively isolate calcifying EVs from other vesicle populations. In this study, we hypothesized that the formation of mineral within calcifying EVs would increase the density of the vesicles such that they would pellet at a faster rate during ultracentrifugation. We show that after 10 min of ultracentrifugation at 100,000×g, calcifying EVs are depleted from the conditioned media of calcifying coronary artery smooth muscle cells and are enriched in the pelleted portion. We utilized mass spectrometry to establish functional proteomic differences between the calcifying EVs enriched in the 10 min ultracentrifugation compared to other vesicle populations preferentially pelleted by longer ultracentrifugation times. The procedures established in this study will allow us to enrich the vesicle population of interest and perform advanced proteomic analyses to find subtle differences between calcifying EVs and other vesicle populations that may be translated into therapeutic targets for vascular calcification. Finally, we will show that the differences in ultracentrifugation times required to pellet the vesicle populations can also be used to estimate physical differences between the vesicles.

Objectives: As computing technology and image analysis techniques have advanced, the practice of histology has grown from a purely qualitative method to one that is highly quantified. Current image analysis software is imprecise and prone to wide variation due to common artifacts and histological limitations. In order to minimize the impact of these artifacts, a more robust method for quantitative image analysis is required. Methods and Results: Here we present a novel image analysis software, based on the hue saturation value color space, to be applied to a wide variety of histological stains and tissue types. By using hue, saturation, and value variables instead of the more common red, green, and blue variables, our software offers some distinct advantages over other commercially available programs. We tested the program by analyzing several common histological stains, performed on tissue sections that ranged from 4 µm to 10 µm in thickness, using both a red green blue color space and a hue saturation value color space. Conclusion: We demonstrated that our new software is a simple method for quantitative analysis of histological sections, which is highly robust to variations in section thickness, sectioning artifacts, and stain quality, eliminating sample-to-sample variation.