Quantitative flow cytometry enables end-to-end optimization of cross-platform extracellular vesicle studies.

Flow cytometry (FCM) is a common method for characterizing extracellular particles (EPs), including viruses and extracellular vesicles (EVs). Frameworks such as MIFlowCyt-EV exist to provide reporting guidelines for metadata, controls, and data reporting. However, tools to optimize FCM for EP analysis in a systematic and quantitative way are lacking. Here, we demonstrate a cohesive set of methods and software tools that optimize FCM settings and facilitate cross-platform comparisons for EP studies. We introduce an automated small-particle optimization (SPOT) pipeline to optimize FCM fluorescence and light scatter detector settings for EP analysis and leverage quantitative FCM (qFCM) as a tool to further enable FCM optimization of fluorophore panel selection, laser power, pulse statistics, and window extensions. Finally, we demonstrate the value of qFCM to facilitate standardized cross-platform comparisons, irrespective of instrument configuration, settings, and sensitivity, in a cross-platform standardization study utilizing a commercially available EV reference material.

Extracellular Vesicle Refractive Index Derivation Utilizing Orthogonal Characterization.

The analysis of small particles, including extracellular vesicles and viruses, is contingent on their ability to scatter sufficient light to be detected. These detection methods include flow cytometry, nanoparticle tracking analysis, and single particle reflective image sensing. To standardize measurements and enable orthogonal comparisons between platforms, a quantifiable limit of detection is required. The main parameters that dictate the amount of light scattered by particles include size, morphology, and refractive index. To date, there has been a lack of accessible techniques for measuring the refractive index of nanoparticles at a single-particle level. Here, we demonstrate two methods of deriving a small particle refractive index using orthogonal measurements with commercially available platforms. These methods can be applied at either a single-particle or population level, enabling the integration of diameter and scattering cross section values to derive the refractive index using Mie theory.

A compendium of single extracellular vesicle flow cytometry.

Flow cytometry (FCM) offers a multiparametric technology capable of characterizing single extracellular vesicles (EVs). However, most flow cytometers are designed to detect cells, which are larger than EVs. Whereas cells exceed the background noise, signals originating from EVs partly overlap with the background noise, thereby making EVs more difficult to detect than cells. This technical mismatch together with complexity of EV-containing fluids causes limitations and challenges with conducting, interpreting and reproducing EV FCM experiments. To address and overcome these challenges, researchers from the International Society for Extracellular Vesicles (ISEV), International Society for Advancement of Cytometry (ISAC), and the International Society on Thrombosis and Haemostasis (ISTH) joined forces and initiated the EV FCM working group. To improve the interpretation, reporting, and reproducibility of future EV FCM data, the EV FCM working group published an ISEV position manuscript outlining a framework of minimum information that should be reported about an FCM experiment on single EVs (MIFlowCyt-EV). However, the framework contains limited background information. Therefore, the goal of this compendium is to provide the background information necessary to design and conduct reproducible EV FCM experiments. This compendium contains background information on EVs, the interaction between light and EVs, FCM hardware, experimental design and preanalytical procedures, sample preparation, assay controls, instrument data acquisition and calibration, EV characterization, and data reporting. Although this compendium focuses on EVs, many concepts and explanations could also be applied to FCM detection of other particles within the EV size range, such as bacteria, lipoprotein particles, milk fat globules, and viruses.

Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison.

High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.

Panel Optimization for High-Dimensional Immunophenotyping Assays Using Full-Spectrum Flow Cytometry.

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large flow cytometry panels reaching up to 43 colors at the single-cell level. However, as panel size and complexity increase, so too does the detail involved in designing and optimizing successful high-quality panels fit for downstream high-dimensional data analysis. In contrast to conventional flow cytometers, full-spectrum flow cytometers measure the entire emission spectrum of each fluorophore across all lasers. This allows for fluorophores with very similar emission maxima but unique overall spectral fingerprints to be used in conjunction, enabling relatively straightforward design of larger panels. Although a protocol for best practices in full-spectrum flow cytometry panel design has been published, there is still a knowledge gap in going from the theoretically designed panel to the necessary steps required for panel optimization. Here, we aim to guide users through the theory of optimizing a high-dimensional full-spectrum flow cytometry panel for immunophenotyping using comprehensive step-by-step protocols. These protocols can also be used to troubleshoot panels when issues arise. A practical application of this approach is exemplified with a 24-color panel designed for identification of conventional T-cell subsets in human peripheral blood. © 2021 Malaghan Institute of Medical Research, Cytek Biosciences. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation and evaluation of optimal spectral reference controls Support Protocol 1: Antibody titration Support Protocol 2: Changing instrument settings Basic Protocol 2: Unmixing evaluation of fully stained sample Basic Protocol 3: Evaluation of marker resolution Support Protocol 3: Managing heterogeneous autofluorescence Basic Protocol 4: Assessment of data quality using expert gating and dimensionality reduction algorithms.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

T cell-derived extracellular vesicles are elevated in essential HTN.

Extracellular vesicles (EVs) are novel mediators of cell-to-cell communication and appear to mediate the pathogenesis of hypertension (HTN). However, the mechanisms underlying the involvement of EVs in HTN remain unclear. The adaptive and innate immune systems play an important role affecting the kidney and vasculature in animal models of HTN. Evolving evidence shows that immune cell-derived EVs can modulate the immune system in a paracrine fashion and therefore may mediate the effects of inflammation in the pathogenesis of HTN. Therefore, we aimed to understand if specific subtypes of leukocyte/immune cell-derived EVs are altered in essential HTN using an in vivo model of angiotensin II (ANG II)-induced HTN. After 4 wk of ANG II treatment, EVs were isolated from the blood and kidney. EV origin and counts were characterized with Imaging Flow Cytometry, antibody panels targeting platelets, endothelial cells, and leukocytes including B and T cells, monocytes, and neutrophils. Leukocyte-derived EVs (CD45+) were elevated in the circulation and kidney tissue in ANG II-induced HTN. Subgroup analysis depicted T cell-derived EVs (CD3+) to be significantly elevated in ANG II-induced HTN (3.50e+5 particles/mL) compared with control groups (9.16e+4 particles/mL, P = 0.0106). T cell-derived EVs also significantly correlated with systolic blood pressure levels (r2 = 0.898, P = 0.0012). In summary, leukocyte-derived EVs, and more specifically T cell-derived EVs (CD3+), are elevated in ANG II-induced HTN in the circulation and kidney tissue and correlate well with blood pressure severity. EVs from the circulation and kidney may be sensitive biomarkers for HTN and end-organ damage and may lead to new mechanistic insights in this silent disease.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

OMIP-069: Forty-Color Full Spectrum Flow Cytometry Panel for Deep Immunophenotyping of Major Cell Subsets in Human Peripheral Blood.

This 40-color flow cytometry-based panel was developed for in-depth immunophenotyping of the major cell subsets present in human peripheral blood. Sample availability can often be limited, especially in cases of clinical trial material, when multiple types of testing are required from a single sample or timepoint. Maximizing the amount of information that can be obtained from a single sample not only provides more in-depth characterization of the immune system but also serves to address the issue of limited sample availability. The panel presented here identifies CD4 T cells, CD8 T cells, regulatory T cells, γδ T cells, NKT-like cells, B cells, NK cells, monocytes and dendritic cells. For each specific cell type, the panel includes markers for further characterization by including a selection of activation and differentiation markers, as well as chemokine receptors. Moreover, the combination of multiple markers in one tube might lead to the discovery of new immune phenotypes and their relevance in certain diseases. Of note, this panel was designed to include only surface markers to avoid the need for fixation and permeabilization steps. The panel can be used for studies aimed at characterizing the immune response in the context of infectious or autoimmune diseases, monitoring cancer patients on immuno- or chemotherapy, and discovery of unique and targetable biomarkers. Different from all previously published OMIPs, this panel was developed using a full spectrum flow cytometer, a technology that has allowed the effective use of 40 fluorescent markers in a single panel. The panel was developed using cryopreserved human peripheral blood mononuclear cells (PBMC) from healthy adults (Table 1). Although we have not tested the panel on fresh PBMCs or whole blood, it is anticipated that the panel could be used in those sample preparations without further optimization. @ 2020 Cytek Biosciences, Inc. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

Is there a Pot of Gold at the End of the Spectrum?

Full spectrum flow cytometry: Is there a pot of gold at the end of the spectrum?

Rigorous characterization of urinary extracellular vesicles (uEVs) in the low centrifugation pellet - a neglected source for uEVs.

Urinary extracellular vesicles (uEVs) provide bio-markers for kidney and urogenital diseases. Centrifugation is the most common method used to enrich uEVs. However, a majority of studies to date have focused on the ultracentrifugation pellet, potentially losing a novel source of important biomarkers that could be obtained at lower centrifugation. Thus, the aim of this study is to rigorously characterize for the first time uEVs in the low speed pellet and determine the minimal volume of urine required for proteomic analysis (≥9.0 mL urine) and gene ontology classification identified 75% of the protein as extracellular exosomes. Cryo-Transmission Electron Microscopy (≥3.0 mL urine) provided evidence of a heterogeneous population of EVs for size and morphology independent of uromodulin filaments. Western blot detected several specific uEV kidney and EV markers (≥4.5 mL urine per lane). microRNAs quantification by qPCR was possible with urine volume as low as 0.5 mL. Particle enumeration with tunable resistive pulse sensing, nano particles tracking analysis and single EV high throughput imaging flow cytometry are possible starting from 0.5 and 3.0 mL of urine respectively. This work characterizes a neglected source of uEVs and provides guidance with regard to volume of urine necessary to carry out multi-omic studies and reveals novel aspects of uEV analysis such as autofluorescence of podocyte origin.

MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments.

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. These complications are exacerbated by the field's lack of a robust reporting framework, and many EV-FC manuscripts include incomplete descriptions of methods and results, contain artefacts stemming from an insufficient instrument sensitivity and inappropriate experimental design and lack appropriate calibration and standardization. To address these issues, a working group (WG) of EV-FC researchers from ISEV, ISAC and ISTH, worked together as an EV-FC WG and developed a consensus framework for the minimum information that should be provided regarding EV-FC. This framework incorporates the existing Minimum Information for Studies of EVs (MISEV) guidelines and Minimum Information about a FC experiment (MIFlowCyt) standard in an EV-FC-specific reporting framework (MIFlowCyt-EV) that supports reporting of critical information related to sample staining, EV detection and measurement and experimental design in manuscripts that report EV-FC data. MIFlowCyt-EV provides a structure for sharing EV-FC results, but it does not prescribe specific protocols, as there will continue to be rapid evolution of instruments and methods for the foreseeable future. MIFlowCyt-EV accommodates this evolution, while providing information needed to evaluate and compare different approaches. Because MIFlowCyt-EV will ensure consistency in the manner of reporting of EV-FC studies, over time we expect that adoption of MIFlowCyt-EV as a standard for reporting EV- FC studies will improve the ability to quantitatively compare results from different laboratories and to support the development of new instruments and assays for improved measurement of EVs.

Using Imaging Flow Cytometry to Quantify Neutrophil Phagocytosis.

Neutrophils are professional phagocytes that are important for innate host defenses against pathogens and resolution of inflammation. Traditionally, the phagocytic capacity of neutrophils was quantified by enumeration of cells containing either internalized or bound bacteria or other cargo from a series of microscopic images. Here we describe an imaging flow cytometry-based protocol and analysis method for quantifying the binding and uptake of Neisseria gonorrhoeae by primary adherent human neutrophils. Imaging flow cytometry combines the capacity for quantitative, high-throughput analysis of tens of thousands of cells per condition, with the imaging power of fluorescence microscopy. Here, all bacteria are labeled with Tag-it Violet™ and bound bacteria are differentially stained with a DyLight™ 650-conjugated antibody. Images are analyzed using spot count and other algorithms. Outputs include the percent of neutrophils associated with bacteria, the percent of neutrophils with internalized bacteria, and the percent of internalized bacteria. This basic protocol can be adapted to a variety of particle types and can be used for multiplex analysis in combination with staining for different neutrophil surface and intracellular markers.

A miRNA signature in endothelial cell-derived extracellular vesicles in tumor-bearing mice.

Extracellular vesicles (EVs) play important roles in tumor progression by altering immune surveillance, promoting vascular dysfunction, and priming distant sites for organotropic metastases. The miRNA expression patterns in circulating EVs are important diagnostic tools in cancer. However, multiple cell types within the tumor microenvironment (TME) including cancer cells and stromal cells (e.g. immune cells, fibroblasts, and endothelial cells, ECs) contribute to the pool of circulating EVs. Because EVs of different cellular origins have different functional properties, auditing the cargo derived from cell type-specific EVs in the TME is essential. Here, we demonstrate that a murine EC lineage-tracing model (Cdh5-CreERT2:ZSGreenl/s/l mice) can be used to isolate EC-derived extracellular vesicles (EC-EVs). We further show that purified ZSGreen+ EVs express expected EV markers, they are transferable to multiple recipient cells, and circulating EC-EVs from tumor-bearing mice harbor elevated levels of specific miRNAs (e.g. miR-30c, miR-126, miR-146a, and miR-125b) compared to non tumor-bearing counterparts. These results suggest that, in the tumor setting, ECs may systemically direct the function of heterotypic cell types either in the circulation or in different organ micro-environments via the cargo contained within their EVs.

A Modified Injector and Sample Acquisition Protocol Can Improve Data Quality and Reduce Inter-Instrument Variability of the Helios Mass Cytometer.

Mass cytometry is a powerful tool for high-dimensional single cell characterization. Since the introduction of the first commercial CyTOF mass cytometer by DVS Sciences in 2009, mass cytometry technology has matured and become more widely utilized, with sequential platform upgrades designed to address specific limitations and to expand the capabilities of the platform. Fluidigm's third-generation Helios mass cytometer introduced a number of upgrades over the previous CyTOF2. One of these new features is a modified narrow bore sample injector that generates smaller ion clouds, which is expected to improve sensitivity and throughput. However, following rigorous testing, we find that the narrow-bore sample injector may have unintended negative consequences on data quality and result in lower median and higher coefficients of variation in many antibody-associated signal intensities. We describe an alternative Helios acquisition protocol using a wider bore injector, which largely mitigates these data quality issues. We directly compare these two protocols in a multisite study of 10 Helios instruments across 7 institutions and show that the modified protocol improves data quality and reduces interinstrument variability. These findings highlight and address an important source of technical variability in mass cytometry experiments that is of particular relevance in the setting of multicenter studies. © 2019 International Society for Advancement of Cytometry.

Imaging Flow Cytometry Quantifies Neural Genome Dynamics.

Somatic mosaicism is a common consequence of normal development. DNA repair is simply not perfect, and each cell's genome incurs continuous DNA damage as a consequence of transcription, replication, and other cell biological stressors. Brain somatic mosaicism is particularly noteworthy because the vast majority of an individual's neurons are with that individual for life and neural circuits give rise directly to behavioral phenotypes. Brain somatic mosaicism, now revealed and tractable due to advances in single cell 'omic approaches, has emerged as an intriguing and unexplored aspect of neuronal diversity. Furthermore, the study of DNA damage during early neurodevelopment, when the rate of mutagenesis is high, is the perfect starting point to understand the origins of brain mosaicism. Flow cytometry is a highly efficient technique to study cell cycle and intracellular proteins of interest, particularly those related to DNA damage, but it lacks the high resolution of microscopy to examine the localization of these proteins. In this study, we outline a novel single-cell approach to quantify DNA double-strand break (DNA DSB) dynamics during early human neurodevelopment by applying imaging flow cytometry (IFC) to human-induced pluripotent stem cell-derived neural progenitor cells (NPCs) undergoing neurogenesis. We establish an increase of DNA DSBs by quantifying γH2AX foci in mildly stressed NPCs using various single-cell approaches in addition to IFC including fluorescent microscopy, conventional flow cytometry, and measuring DNA DSBs with the comet assay. We demonstrate the dose-dependent sensitive detection of γH2AX foci through IFC and reveal the dynamics of DNA DSBs in proliferating and differentiating neural cells in early neurogenesis. © 2019 International Society for Advancement of Cytometry.