Flow Cytometry and EVs: from MIFlowCyt-EV to calibration of fluorescence and light scatter.
Kenneth Witwer, PhD. The Johns Hopkins University School of Medicine, Baltimore, MD, USA
Edwin van der Pol  & Joshua Welsh ,
Amsterdam University Medical Centers, Netherlands 
National Institutes of Health, USA. 
This webinar was hosted by the ISEV Extracellular Vesicle Club focusing on the role of MIFlowCyt-EV in increasing the reliability of EV flow cytometry literature. The practical aspects of fluorescence and light scatter calibration were also addressed.
Interpreting EV Flow Cytometry Data: Do You Have What It Takes?
Vera Tang  & John Tigges 
 University of Ottawa, Canada.
 Beth Israel Deaconess Medical Center, USA
Joanne Lannigan, Flow Cytometry Support Services, LLC, USA
The generation of EV flow cytometry data is becoming more and more common place as the field continues to expand and the desire to characterize EVs in a high dimensional high throughput manner. Many new flow cytometers are entering the market which claim to be suitable for measuring EVs. It therefore becomes critically important to understand and interpret the data being generated. As there are many factors and variables which can impact the results of EV flow cytometry data, the only reasonable way to accurately understand and evaluate the data is with proper controls. In this webinar we will discuss how, through the use of proper controls, we can interpret EV data generated using flow cytometry and understand the limitations of the accuracy of the results.
Influence of lipoprotein particles in extracellular vesicle analysis by single particle flow cytometry.
Jennifer Jones  & André Görgens ,
National Institutes of Health, USA ,
Karolinska Institute, Sweden .
Estefanía Lozano-Andrés, University of Utrecht, Netherlands
Flow cytometry (FC) allows for the detection of single extracellular vesicles (EV) and enables their quantitative and qualitative characterization. EV in plasma have been associated with diseases, making them attractive for diagnosis and prognosis of patients. However, the presence of lipoprotein particles (LPP) in plasma, which overlap with EV in size and density, may hamper robust scatter-based flow cytometric analysis of EV. The use of fluorescence-based FC allows for a more selective way to discriminate fluorescently stained particles from other non-fluorescent particles. Therefore, we here investigated the interference of these particles when generic fluorescent dyes are used for staining and detection of EV by FC. To define the impact of LPP on fluorescence-based FC–detection of EV, commercially available LPP preparations and EV isolated from conditioned media of the mouse 4T1 mammary carcinoma cell line were stained with PKH67. Stained LPP and 4T1 EV were then succumbed to density gradient floatation, after which FC-analysis was performed using a BD Influx that was optimized for detection of submicron-sized particles (Van der Vlist et al, Nat protoc 2012, PMID: 22722367). We found that PKH67 has the capacity to label various types of LPP. When analysed by FC, fluorescently stained LPP and EV are hard to discriminate based on fluorescent and light scatter signals.. In addition, we demonstrated that LPP show certain sensitivity to detergent lysis when compared to EV and as such detergent sensitivity is not an ‘exclusive’ EV-feature. Finally, by performing spike-in experiments with LPP before EV-staining we found that the presence of LPP can obscure the analysis of fluorescently stained EV. This indicates the need for proper EV isolation and purification when LPP containing samples, such as human plasma, are used for FC-detection of EV.
EV Calibration Tools Tutorial
Edwin van der Pol , Joshua Welsh , John Nolan
Amsterdam University Medical Centers, Netherlands 
National Institutes of Health, USA. 
Scintillon Institute, USA. 
The following video was recorded for educational purposes to show currently available
calibration tools for small particle analysis as part of the virtual CYTO 2020 tutorial.
Products and methods used are presented as examples of available tools. The National Institutes
of Health and the ISEV-ISAC-ISTH EV flow cytometry working group do not endorse or promote any
specific commercial products or resources presented in these recordings. General use of the
MIFlowCyt-EV reporting framework  for performing and reporting EV flow cytometry experiments
 Welsh J A, van der Pol E, Arkesteijn G, Bremer M, Brisson A, Coumans F, Dignat-George F, Duggan E, Ghiran I, Giebel B, Görgens A, Hendrix A, Lacroix R, Lannigan J, Libregts S, Lozano-Andrés E, Morales-Kastresana A, Robert S, de Rond L, Tertel T, Tigges J, de Wever O, Yan X, Nieuwland R, Wauben M, Nolan J, Jones, J C., MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments, Journal of Extracellular Vesicles. doi: 10.1080/20013078.2020.1713526. https://doi.org/10.1080/20013078.2020... EV Tutorial: Instrument calibration/characterization techniques
Calibrated flow cytometry to measure the concentration of extracellular vesicles in patients with myocardial infarction.
André Görgens  & Paul Harrison 
Karolinska Institute, Sweden ,
University of Birmingham, UK 
Aleksandra Gąsecka, Medical University of Warsaw, Poland & Amsterdam University Medical Centers, Netherlands
Acute myocardial infarction (AMI) is a major cause of human death and disability, but early
biomarkers for AMI are lacking. AMI is due to atherothrombosis, i.e. formation of platelet
aggregates (thrombi) on ruptured atherosclerotic plaques. Because platelets release
extracellular vesicles (EVs) during thrombus formation, we hypothesized that EVs are a biomarker
of atherothrombosis and an early biomarker of AMI.
Venous blood was collected 24 hours (acute phase), 72 hours (hospital discharge) and 6 months (late phase) after AMI from fasting patients (n=60, age 64.5±10.8 years, 68% male), and once from fasting healthy volunteers (n=30, mean age 57.7±6.6 years, 62% male). Flow cytometry (Apogee A60-Micro) was used to determine concentrations of plasma EVs labelled with markers for endothelial cells (CD146), leukocytes (CD45), phosphatidylserine (lactadherin), platelets (CD31, CD61, CD62p), and fibrinogen. Analysis of the 1,224 flow cytometry data files was fully automated with in-house developed software (MATLAB R2018a), enabling automatic flow rate stabilization, application of Rosetta Calibration (Exometry) and Flow-SR for diameter and refractive index determination, size distribution fitting, MESF calibration, fluorescent gate determination, and statistics reporting. To differentiate between EVs and small platelets, only particles < 1,000 nm were included. Populations for which an unpaired t-test or one-way ANOVA with Bonferroni correction resulted in p < 0.05 were considered significant.
EV concentrations from leukocytes and endothelial cells were lower in patients in the acute AMI phase, compared to healthy volunteers (p < 0.05 for both), and increased to the level observed in healthy volunteers in the late phase (p < 0.05 for both). Concentrations of PS-exposing EVs and platelet EVs (CD31, CD61) were decreased in the acute AMI phase, compared to the late phase (p < 0.05 for all).
We identified decreased concentrations of EVs from leukocytes and endothelial cells as new candidate biomarkers to differentiate patients with atherothrombosis from healthy volunteers. Further, we identified decreased concentrations of PS-exposing EVs and EVs from platelets as new candidate biomarkers to differentiate patients in the acute and late AMI phase. Because the flow rate, fluorescence detectors and scatter detectors of our flow cytometer were calibrated, our data can be compared to future clinical studies, which essential to confirm the clinical utility of EVs as biomarkers of atherothrombosis.
Size and refractive index determination of sub-micrometer particles using the flow cytometry scatter ratio [Flow-SR]
Joshua Welsh  & John Nolan 
National Institutes of Health, USA. 
Scintillon Institute, La Jolla, USA 
Edwin van der Pol, Amsterdam University Medical Centers, Netherlands
Flow cytometers measure light scattering signals in arbitrary units, which hampers data
interpretation and comparison. Light scattering signals, however, contain valuable information
about the size and composition of particles. This information can be assessed by calibration,
which means conversion of arbitrary units to standardized measurement units, like diameter in
nm. One scatter detector can be calibrated by measuring beads and describing the signals with
Mie theory, taking into account the optical configuration of the flow cytometer and the sizes
and refractive indices [RIs] of the beads. The RI is an intrinsic property of a particle that
depends on the composition and that determines how efficient a particle scatters light. When the
RI is known and one scatter detector is calibrated, Mie theory can be used to derive the
diameter of extracellular vesicles [EVs] . In this presentation, I will explain that by
taking the ratio between side scatter and forward scatter signals, which we named the flow
cytometry scatter ratio [Flow-SR], it becomes possible to determine both the diameter and RI of
submicrometer particles . Flow-SR enables label-free discrimination between EVs [RI <
1.42] and lipoprotein particles [RI > 1.45] in plasma and can be used to evaluate the
specificity of antibody labels. We have automated Flow-SR and are applying it to analyze data in
clinical research studies on EVs [next talk] .
 De Rond et al. Deriving extracellular vesicle size from scatter intensities measured by flow cytometry. Curr. Protoc. Cytom. e43, 1-14 
 van der Pol et al. Absolute sizing and label-free identification of extracellular vesicles by flow cytometry. Nanomedicine 14, 801-10 
 Gasecka et al. Ticagrelor attenuates the increase of extracellular vesicles concentrations in plasma after acute myocardial infarction compared to clopidogrel. J. Thromb. Haemost. 18, 609-623 
MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments
Joanne Lannigan  & Joshua Welsh   Flow Cytometry Support Services, LLC, USA  National Institutes of Health, USA.
John Nolan, Scintillon Institute, La Jolla, USA.
Extracellular vesicles (EVs) are small, heterogeneous and difficult to measure. Flow cytometry (FC) is a key technology for the measurement of individual particles, but its application to the analysis of EVs and other submicron particles has presented many challenges and has produced a number of controversial results, in part due to limitations of instrument detection, lack of robust methods and ambiguities in how data should be interpreted. The MIFlowCyt-EV framework is an initiative that was developed by a flow cytometry working group of researchers from the international societies for extracellular vesicles, advancement of cytometry and thrombosis and haemostatisis (ISEV-ISAC-ISTH). The MIFlowCyt-EV framework was designed to be assay agnostic and layout a standard reporting framework for experiments using single extracellular vesicle flow cytometry analysis. This work was recently published as a position paper in the Journal of Extracellular Vesicles.
Using a Combination of Bead-based and Single EV Imaging Flow Cytometry To Understand EV Heterogeneity
Joshua Welsh, National Institutes of Health, USA.
André Görgens, Karolinska Institutet, Stockholm, Sweden.
Extracellular vesicles (EVs) are secreted by all cell types and can be found in all body fluids. They are roughly classified based on their size and origin as exosomes (70-150 nm) and microvesicles (100 nm to 1 µm). However, it is nowadays commonly accepted in the field that there is a much higher degree of EV heterogeneity. The content, protein composition and surface signature of EVs is likely dependent on multiple parameters like the cell’s metabolic or immunological status, and on the cell type releasing them. Accordingly, EVs secreted by different normal cell types or malignant cells also will display distinct surface profiles. We have recently optimized two flow cytometry based methods for EV surface marker analysis, a multiplex bead-based approach which allows robust identification of co-expressed surface marker combinations (Wiklander et al, Front Immunol 2018) and a method using imaging flow cytometry to quantify EV subsets at the single vesicle level (Görgens et al, JEV 2019). Both methods will be briefly introduced and examples for applications in context of heterogeneity will be given.