Cytometry-based methods for sorting and characterization of Extracellular Vesicles: a Novel approach for Breast Cancer detection and stratification

In recent years Extracellular Vesicles (EVs) have become heavily studied due to their ability to regulate gene expression and alter the function of various cell types. There has been significant interest in the cargo and the cells of origin due to this effect. Isolation and molecular profiling of subsets of EVs (i.e., RNAs, proteins, lipids, metabolites) are critical for understanding the biogenesis of EVs and their potential utility as biomarkers. To this end, Flow Cytometry has been employed for the analysis, characterisation, and phenotyping of EV populations and their cargo in a technique called nano-FACS. While nano-FACS has become more prevalent as an analysis tool and undergone standardization formats, nano-sorting is neither well defined nor a commonly used technique. Historically, this is due to the complexity of the technique and instrumentation necessary for sorting EVs. In this study, the CytoFLEX SRT, a semiconductor-based benchtop cell sorter equipped with avalanche photodiodes and optimized signal detection, is utilized for the detection and nano-sorting of EV populations via Violet Side Scatter (VSSC) and fluorescent (FL) triggering. The SRT can be optimized to interrogate EV populations via VSSC, 405nm, to evaluate size > 100 nm, calculated by Mie Theory, and FL intensity of specific proteins on the EV surface. Moreover, multiple scattering profiles (VSSC vs Red SSC/640nm) can be analysed simultaneously to elucidate subpopulations for greater characterisation. Nano-FACS consists of minor hardware and software adaptations, a rigorous QC/QA component, and post-analysis via specialized software for standardization and accuracy of results. To prove the feasibility of nano-FACS as a tool for EV research, this work highlights the characterisation of SKBR3, a Breast Cancer (BC) cell line that overexpresses HER2 gene, EVs via nano-FACS methodology for the isolation and characterisation of HER2+ SKBR3 EVs. By fluorescently labelling the HER2 surface protein, one can sort subpopulations of SKBR3 EVs for further classification of subtypes and their cargo. This study will develop a standardised methodology, termed nano-FACS, to identify, isolate, and characterise EV subpopulations via Flow Cytometry. Furthermore, this study will show the robust nature of nano-FACS for the sizing, concentration, and identification of subpopulations in a standardised and straightforward method. In turn, it is the aim of this study to isolate and characterise cancer cell EVs for use in nanomedicine.