FCMPASS Software Aids Extracellular Vesicle Light Scatter Standardization.
Joshua A WelshPeter HorakJames S WilkinsonVerity J FordJennifer C JonesDavid SmithJudith A HollowayNicola A EnglystPublished in: Cytometry. Part A : the journal of the International Society for Analytical Cytology (2019)
The study of extracellular vesicles (EVs) is a rapidly growing field due to their great potential in many areas of clinical medicine including diagnostics, prognostics, theranostics, and therapeutics. Flow cytometry is currently one of the most popular methods of analyzing EVs due to it being a high-throughput, multiparametric technique, that is readily available in the majority of research labs. Despite its wide use, few commercial flow cytometers are designed specifically for the detection of EVs. Many flow cytometers used for EV analysis are working at their detection limits and are unable to detect the majority of EVs. Currently, very little standardization exists for EV flow cytometry, which is an issue because flow cytometers vary considerably in the way they collect scattered or fluorescent light from particles being interrogated. This makes published research hard to interpret, compare, and in some cases, impossible to reproduce. Here we demonstrate a method of flow cytometer light scatter standardization, utilizing flow cytometer postacquisition analysis software (FCMPASS ). FCMPASS is built upon Mie theory and enables the approximation of flow cytometer geometric parameters either by analyzing beads of known diameter and refractive index or by inputting the collection angle if known. The software is then able to create a scatter-diameter curve and scatter-refractive index curve that enables researchers to convert scattering data and instrument sensitivity into standardized units. Furthermore, with the correct controls, light scatter data can be converted to diameter distributions or refractive index distributions. FCMPASS therefore offers a freely available and ergonomic method of standardizing and further extending EV characterization using flow cytometry.