Molecular profiling of individual extracellular vesicles with nanoFACS
J. C. Jones, A. Morales-Kastresana, J. A. Berzofsky
National Cancer Institute, Bethesda, MD
Objectives: Extracellular vesicles (EVs), including (~80-140 nm) exosomes, have tremendous potential as biomarkers and therapeutic targets, because different cells, under different conditions, release EVs with distinctive protein, lipid and RNA cargo. In order to delineate the molecular profiles of Extracellular Vesicles (EVs), we developed nano-scale flow cytometric sorting (nanoFACS) methods. The purpose of this study was to quantitate the molecular profiling limits of nanoFACS, and compare nanoFACS-based EV profiles to predicted profiles in circulating tumor cell studies.
Methods: We performed a high throughput screen and quantitated binding patterns of 350 directly conjugated monoclonal antibodies. Fluorescence sensitivity was cross-calibrated with Molecular Equivalents of Soluble Fluorescence (MESF) beads. To delineate the limits of RNA cargo profiling methods for EVs, we performed three different miRNA-omic profiling methods with titrations of EVs, ranging from 10,000 to 1 trillion.
Results: The limits of direct, antibody-based detection of epitopes on individual EVs, are <100 MESF units per single EV, with our nanoFACS jet-in-air sorting instrument, and <10 MESF units with a next-generation Avalanche photodiode-based instrument. Thus, for surface epitopes, next generation EV flow cytometric analysis provides single molecule detection potential.Next-generation RNAseq methods required as few as 0. 5-1 million EVs to produce a robust small RNA profile, whereas RNA analysis methods that do not use amplification failed to produce a robust profile with fewer than 1-10 billion EVs.
Conclusion: NanoFACS offers a unique platform for the identification and isolation of tumor- and treatment-associated EV subsets. Ongoing efforts to optimize single cell –omics methods are of critical importance to our efforts to achieve robust single EV –omic profiles.In this study, we built upon prior work that established a robust workflow for EV production, isolation, and staining methods, to establish benchmarks for the detection of different types of EV cargo. This provides a foundation for the future use of EV profiles to predict, monitor, and improve responses to treatment.