Analysis Of Flow Cytometry Data Research Articles

Abstract 4989: T-distributed stochastic neighbor embedding (t-SNE) analysis of tumor infiltrating lymphocytes after treatment with a T cell activating therapy identifies a unique population of recruited CD8+ T cells and novel options for combination immunotherapy

Abstract Immune therapies for cancer seek to alter the immune profile of the tumor microenvironment (TME). A suppressive TME can counteract active immune responses by CD8+ T cells by multiple mechanisms, including induction of checkpoint inhibitor (CPI) expression. DPX™ is a lipid nanoparticle-based platform that can be formulated with cancer targets to induce potent and sustained antigen-specific T cell responses. In preclinical and clinical testing previously published, combination of DPX with low dose intermittent cyclophosphamide (ldCPA) resulted in increased tumor infiltration of antigen-specific CD8+ T cells. These T cells express PD-1, and combination treatment with PD-1 blocking antibody was shown previously to result in better tumor control in preclinical tumor models. In this study, we used tSNE analysis to understand changes to the immune profile induced by DPX/ldCPA treatment in the C3 model. We prioritized the expression of CPIs on tumor infiltrating CD8+T cells to identify potential combinations with blocking monoclonal antibodies to enhance tumor CD8+ T cell activity. Mice (C57BL/6) bearing HPV16 E7-transformed C3 tumor cells were treated with ldCPA (20 mg/kg/day PO, 7 days), followed by subcutaneous injection with HPV16 E749-57 peptide in a DPX formulation (DPX-R9F). Tumors were collected for analysis 10 days after DPX-R9F treatment and a single cell suspension prepared by digestion. Immune profiling was performed by 12-color flow cytometry, to observe differences in infiltrating immune cells and CPIs. tSNE analysis of flow cytometry data was performed to analyze differences in cellular infiltrates and phenotype with treatment. We found CD8+ T cells that infiltrated tumors in mice treated with DPX-R9F/ldCPA expressed most CPIs tested (including PD-1, Tim-3, Lag-3, ICOS), and fewer expressed CTLA-4. Treatment induced infiltration of a unique population of CD8+ T cells that was PD-1+Tim-3+CTLA-4-, whereas CD8+ T cells present in untreated tumors were primarily PD-1+Tim-3+CTLA-4+. Tumor challenge was performed to evaluate combination of DPX-R9F/ldCPA treatment with anti-CTLA-4 (Clone 9D9) to potentially impact these resident CD8+ T cells. Mice treated with the triple combination had better control of C3 tumor growth compared to mice treated with DPX-R9F/ldCPA or anti-CTLA-4 separately. With tSNE analysis we were able to have a better model to observe differences in the CD8 population with DPX/ldCPA treatment and determine the mechanism for how certain checkpoint targets are impacting tumor infiltrating cells to further sculpt the anti-tumor response to increase efficacy of treatment. Citation Format: Ava Vila-Leahey, Alecia MacKay, Genevieve Weir, Marianne Stanford. T-distributed stochastic neighbor embedding (t-SNE) analysis of tumor infiltrating lymphocytes after treatment with a T cell activating therapy identifies a unique population of recruited CD8+ T cells and novel options for combination immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4989.

Abstract 262: Anti-tumorigenic effect of ONC201 is enhanced by combination treatment with TRAIL or a DR5 agonist in endometrial cancer in vitro

Abstract ONC201 is a well-tolerated, orally active small molecule in the novel imipridone class that has anti-tumorigenic properties in a number of solid tumors, but not in non-neoplastic cells. ONC201 has demonstrated promising activity as a single agent in patients with advanced endometrial cancer in the first-in-human clinical trial with Phase II trials in endometrial cancer patients now underway. Although the mechanism of action in endometrial cell pathology has not been well studied, ONC201’s anti-tumor effect has been shown in other model systems to be p53-independent and mediated through activation of the integrated stress response (ISR) leading to DR5 activation and through induction of cell death or growth arrest. ONC201 inhibits ERK and AKT leading to upregulation of TRAIL in many systems. We hypothesized that ONC201 upregulation of DR5 could sensitize tumor cells to TRAIL and that TRAIL could convert growth arrest to cell death in ONC201-treated cells. Three endometrial cancer cell lines AN3CA, HEC1A and KLE were treated with ONC201 alone or in combination with TRAIL, or a DR5 agonist. Effects on cell viability were assessed by the Cell Titer-Glo cell viability assay, colony formation assays and cell cycle analysis by propidium iodide staining. QPCR and Western blot analysis were used to evaluate mRNA and protein expression, respectively. Assessment of TRAIL and DR5 cell surface expression was evaluated by surface staining using flow cytometry and FlowJo data analysis. ONC201 decreased the cell viability of all three endometrial cancer cell lines at clinically achievable low micro-molar concentrations. ONC201 activated the ISR and an anti-proliferative effect involving a G1 phase arrest and little cell death as indicated by increased Sub-G1 analysis and PARP cleavage. DR5 mRNA and protein expression at the cell surface were induced by ONC201. All three endometrial cancer cell lines were resistant to TRAIL alone, however, pre-treatment with ONC201 sensitized the AN3CA and KLE to TRAIL and a DR5 agonist, leading to potent cell death induction. The combination of ONC201 plus TRAIL did not cause appreciable cell death in normal human fibroblast. ONC201 decreases cell viability in endometrial cancer cells lines primarily through growth arrest while the combination of ONC201 and TRAIL or a DR5 agonist promotes cell death in AN3CA and KLE cells but not in HEC1A endometrial cancer cells. Our results suggest a novel cancer therapeutic strategy that can be exploited in the clinic. Citation Format: Jocelyn Ray, Marie Ralff, David Dicker, Wafik El-Deiry. Anti-tumorigenic effect of ONC201 is enhanced by combination treatment with TRAIL or a DR5 agonist in endometrial cancer in vitro [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 262.

Single-cell technologies in reproductive immunology.

The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.

PF246 PERSONALIZED FOLLOW‐UP OF ACUTE MYELOID LEUKEMIA THROUGH MULTIPARAMETER FLOW CYTOMETRY: A PROOF OF CONCEPT

Background:Standardization, machine learning techniques and comparison to normality are concepts that are changing the landscape of multiparameter flow cytometry data analysis in clinical hematology. We applied these concepts to the follow‐up of acute myeloid leukemia (AML), which remains challenging due to the heterogeneity of the disease.Aims:To evaluate a new flow cytometry data analysis approach for the follow‐up of measurable residual disease in AML.Methods:Based on retrospective flow cytometry data, we developed a strategy for personalized monitoring of AML. This strategy is an evolution of the “leukemia associated immunophenotype” or “LAIP”‐approach that has been used the last decades in specialized flow cytometry laboratories. It relies on new concepts, comparing simultaneously the patient's follow‐up and diagnosis results to control group samples. We named these concepts the “leukemic cloud” and the “abnormality ratio”. To evaluate this approach, flow cytometry data of 6 AML patients and 20 control patients was used. All samples were analyzed with the same 10‐parameter (FSC‐A, SSC‐A, CD45, CD34, CD117, CD13, CD33, CD19, CD3, HLA‐DR) FACS Canto II flow cytometry assay. The “abnormality ratio” results over time were compared with morphology and molecular biology results.Results:The “Abnormality Ratio” results over time were in line with the clinical evolution of the patients (see example on image below) and were overall more sensitive for detecting residual disease than morphology and molecular biology, excepted for NPM1‐mutated AML. The “Abnormality Ratio” did not consider phenotypic switches of the malignant cell population identified at diagnosis. However, the discrepancy between the “abnormality ratio” results and morphology or molecular biology results could be used to objectify these phenotypic switches.Summary/Conclusion:We developed a quasi‐fully automatable flow cytometry data analysis algorithm. It could be used as a basis for the development of “next generation” flow cytometry assays for monitoring of hematological malignancies such as AML. Prospective studies are needed to confirm the applicability and limitations of our concepts.image

Performance and immune characteristics of bronchoalveolar lavage by research bronchoscopy in pulmonary tuberculosis and other lung diseases in the Western Cape, South Africa

BackgroundTuberculosis (TB) remains a debilitating, deadly disease that warrants innovative research tools to fully understand the pathogenesis and host immune responses, particularly at the site of infection and disease. In this regard, bronchoscopies with bronchoalveolar lavage (BAL) serve as a valuable technique for site of disease sample retrieval for further clinical- and basic research. Here we investigate the feasibility of research bronchoscopies in a low/middle-income area, where TB remains rife, and assess the value of retrieved BAL cells (BALC) for downstream fluorescent-based cellular evaluations.MethodsUsing quantitative and qualitative methods, we evaluate the outcomes, safety, tolerability, participant -perception and -experience, while also providing insight into participant recruitment and screening processes of our study. Using light microscopy differential counting for BALC analysis, we evaluate the cellular composition of BAL fluid (BALF) from TB patients, healthy community controls and patients with other lung diseases. We also use flow cytometry to describe the challenges associated with fluorescence-based phenotypic analysis of autofluorescent BALC.ResultsOur findings suggest that research bronchoscopies are safe, acceptable procedures for research participants and are indeed a feasible technique for future study design. We also suggest that the majority of participants are receptive to the proposition of a second research bronchoscopy. This poses an important avenue for research entailing follow-up investigations of the same study participant. Furthermore, our results show that smoking is characterized by retrieval of BALC containing particulate matter, that interferes with fluorescence-based flow cytometry data analysis. Based on light microscopy differential cell counting, our findings suggest that there are differences in the cell yields and cellular composition of the BALF between TB patients, healthy community controls and patients with other lung diseases. We also report on subject characteristics and demographic factors, namely gender and age, that have the potential to affect cell yields and cellular data of BALF.ConclusionsThese findings will serve as a valuable reference for appropriate planning and design of studies involving clinical bronchoscopies for TB and lung disease research.

FlowDiv: a new pipeline for analyzing flow cytometric diversity

BackgroundFlow cytometry (FCM) is one of the most commonly used technologies for analysis of numerous biological systems at the cellular level, from cancer cells to microbial communities. Its high potential and wide applicability led to the development of various analytical protocols, which are often not interchangeable between fields of expertise. Environmental science in particular faces difficulty in adapting to non-specific protocols, mainly because of the highly heterogeneous nature of environmental samples. This variety, although it is intrinsic to environmental studies, makes it difficult to adjust analytical protocols to maintain both mathematical formalism and comprehensible biological interpretations, principally for questions that rely on the evaluation of differences between cytograms, an approach also termed cytometric diversity. Despite the availability of promising bioinformatic tools conceived for or adapted to cytometric diversity, most of them still cannot deal with common technical issues such as the integration of differently acquired datasets, the optimal number of bins, and the effective correlation of bins to previously known cytometric populations.ResultsTo address these and other questions, we have developed flowDiv, an R language pipeline for analysis of environmental flow cytometry data. Here, we present the rationale for flowDiv and apply the method to a real dataset from 31 freshwater lakes in Patagonia, Argentina, to reveal significant aspects of their cytometric diversities.ConclusionsflowDiv provides a rather intuitive way of proceeding with FCM analysis, as it combines formal mathematical solutions and biological rationales in an intuitive framework specifically designed to explore cytometric diversity.

The priming role of dendritic cells on the cancer cytotoxic effects of cytokine-induced killer cells

Introduction: In vitro cultivation of DCs and cytokine-induced killer cells (CIK cells) - a special phenotype of T lymphocyte populations — for cancer treatment has gained significant research interest. The goal of this study is to understand whether the priming from DCs helps CIK cells to exert their toxic function and kill the cancer cells.
 Methods: In this research, DCs were differentiated from mononuclear cells in culture medium supplemented with Granulocyte-macrophage colony-stimulating factor (GM-CSF), and Interleukin-4 (IL-4), and were induced to mature with cancer cell antigens. Umbilical cord blood mononuclear cells were induced into CIK cells by Interferon-γ (IFN-γ), anti-CD3 antibody and IL-2. After 4-day exposure (with DC:CIK = 1:10), DCs and CIK cells interacted with each other.
 Results: Indeed, DCs interacted with and secreted cytokines that stimulated CIK cells to proliferate up to 133.7%. In addition, DC-CIK co-culture also stimulated strong expression of IFN-γ. The analysis of flow cytometry data indicated that DC-CIK co-culture highly expressed Granzyme B (70.47% ± 1.53, 4 times higher than MNCs, twice higher than CIK cells) and CD3+CD56+ markers (13.27% ± 2.73, 13 times higher than MNCs, twice higher than CIK cells). Particularly, DC-CIK co-culture had the most specific lethal effects on cancer cells after 72 hours.
 Conclusion: In conclusion, co-culture of DCs and CIK cells is capable of increasing the expression of CIK-specific characteristics and CIK toxicity on cancer cells.
  
  

Predicting Natural Killer Cell Behavior with Mathematical Models

Abstract Natural killer (NK) cells are specialized lymphocytes with an innate ability to eliminate virally infected and cancerous cells, but the mechanisms that control NK cell development and cytotoxicity are incompletely understood. We hypothesize mathematical models can be used to predict how to direct NK cell development toward mature NK cells. We observed Sostdc1-knockout (KO) mice to display a partial NK cell developmental block. Building from these studies, a best fit model was simulated in which rates of transition and differentiation and cell death was determined by ordinary differential equations of deterministic compartmental models. This approach indicated that NK cell proliferation rates are not necessary to predict WT and KO population outcomes. We further hypothesize that clusters of highly cytotoxic NK cells can be identified computationally, using a combination of Ly49 receptor expression, in vitro and in vivo cytotoxicity assays using β2m−/− targets, and viSNE analysis of flow cytometry data. Sostdc1-KO NK cells also display defective cytotoxicity. We identified that KO splenic CD27+CD11b+ transitional NK cells express lower frequencies of inhibitory Ly49G2, but higher frequencies of activating Ly49H+ and D+ cells. Our viSNE results identified 3 NK cell clusters displaying a “superactive” phenotype that were virtually absent in KO mice; consistent with our observations of their hyporesponsiveness. Taken together, these data support a role for Sostdc1 in the regulation of NK cells and could provide insights into novel biological parameters to expand active NK cell numbers with high killing efficiency for immunotherapies.

Flow cytometry data analysis: Recent tools and algorithms.

Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.

Flow Plex-A tool for unbiased comprehensive flow cytometry data analysis.

IntroductionThe information content of multiparametric flow cytometry experiments is routinely underexploited given the paucity of adequate tools for unbiased comprehensive data analysis that can be applied successfully and independently by immunologists without computational training.MethodsWe aimed to develop a tool that allows straightforward access to the entire information content of any given flow cytometry panel for immunologists without special computational expertise. We used a data analysis approach which accounts for all mathematically possible combinations of markers in a given panel, coded the algorithm and applied the method to mined and self‐generated data sets.ResultsWe developed Flow Plex, a straightforward computational tool that allows unrestricted access to the information content of a given flow cytometry panel, enables classification of human samples according to distinct immune phenotypes, such as different forms of autoimmune uveitis, acute myeloid leukemia vs “healthy”, “old” vs “young”, and facilitates the identification of cell populations with potential biologic relevance to states of disease and health.ConclusionsWe provide a tool that allows immunologists and other flow cytometry users with limited bioinformatics skills to extract comprehensive, unbiased information from flow cytometry data sets.

Proteomic analysis of human mesenchymal stromal cell secretomes: a systematic comparison of the angiogenic potential

Human mesenchymal stromal cell (hMSC) secretomes have shown to influence the microenvironment upon injury, promoting cytoprotection, angiogenesis, and tissue repair. The angiogenic potential is of particular interest for the treatment of ischemic diseases. Interestingly, hMSC secretomes isolated from different tissue sources have shown dissimilarities with respect to their angiogenic profile. This study compares angiogenesis of hMSC secretomes from adipose tissue (hADSCs), bone marrow (hBMSCs), and umbilical cord Wharton’s jelly (hWJSCs). hMSC secretomes were obtained under xenofree conditions and analyzed by liquid chromatography tandem mass spectrometry (LC/MS-MS). Biological processes related to angiogenesis were found to be enriched in the proteomic profile of hMSC secretomes. hWJSC secretomes revealed a more complete angiogenic network with higher concentrations of angiogenesis related proteins, followed by hBMSC secretomes. hADSC secretomes lacked central angiogenic proteins and expressed most detected proteins to a significantly lower level. In vivo all secretomes induced vascularization of subcutaneously implanted Matrigel plugs in mice. Differences in secretome composition were functionally analyzed with monocyte and endothelial cell (EC) in vitro co-culture experiments using vi-SNE based multidimensional flow cytometry data analysis. Functional responses between hBMSC and hWJSC secretomes were comparable, with significantly higher migration of CD14++ CD16− monocytes and enhanced macrophage differentiation compared with hADSC secretomes. Both secretomes also induced a more profound pro-angiogenic phenotype of ECs. These results suggest hWJSCs secretome as the most potent hMSC source for inflammation-mediated angiogenesis induction, while the potency of hADSC secretomes was lowest. This systematic analysis may have implication on the selection of hMSCs for future clinical studies.

Multiparametric analysis of human plasma exosome phenotype by conventional flow cytometry

Purpose: Extracellular vesicles (EVs) are circulating membranous particles released from nearly all cell types. Exosomes, the smallest form of EVs (30-150 nm in diameter), contain significant amounts of protein, mRNA, miRNA, and some small noncoding RNAs (lncRNA, cirRNA). Exosomes influence various functions of both recipient and parent cells by transferring their cargo. Synovial fluid derived exosomes of patients with osteoarthritis significantly stimulated the release of inflammatory cytokine IL-1β, chemokines (CCL7, CCL15, CCL20 and CXCL1) and metalloproteinases by M1 macrophages. The goal of this study is to establish multiparametric analysis methods to characterize human plasma and synovial fluid exosome phenotypes by conventional flow cytometry, for correlation with aging and osteoarthritis phenotype and outcomes. Methods: Frozen plasma samples from 6 healthy donors (age 33.83 ± 22.08 years, Body Mass Index BMI) 23.71 ± 3.28 kg/m2, 3 male and 3 female) with EDTA as anticoagulant were purchased from a commercial vendor. The samples were completely thawed and centrifuged twice at 3000 g for 15 minutes to remove cellular debris. Exosomes in plasma were isolated by the ExoQuick ULTRA kit. Precipitated exosomes were resuspended by the ExoQuick ULTRA buffer B, and diluted in double filtered PBS (df-PBS) for staining. Fluorescence-conjugated antibodies against human CD63, CD81, CD3, CD19, CD14 and CD16 were used for exosome surface staining. Exosomes without antibody were used to determine the staining background. The Sub-micron Particle Size Reference Kit (containing 20, 100, 200, 500, 1000 and 2000 nm beads) was used to determine the size of acquired events. The ExoQuick ULTRA buffer B and df-PBF in the absence or presence of the indicated antibodies were used to determine the background noise. The percentages of the exosomes expressing each tested molecule were determined using a BD Fortessa X-20 Flow Cytometer with the BD FACSDiVa software. Flow cytometry data analysis was performed using FlowJo software. Statistical analysis of one-way ANOVA and correlation was performed using GraphPad Prism software. Results: The percentage of exosomes expressing the CD3 (T cell) marker was significantly higher (9.85% ± 6.97) than the percentage of exosomes expressing the CD63 (tetraspanin family member, 0.19% ± 0.26, p<0.01), CD16 (neutrophil, 0.23% ± 0.23, p<0.01), or CD19 (B cell, 2.96% ± 5.69, p<0.05) markers. Exosome expression was not statistically significantly different for CD3 vs. tetraspanin family member CD81 (4.37% ± 1.87) or vs. the monocyte marker CD14 (4.59% ± 4.51). Interestingly, age was significantly negatively correlated with the percentage of CD81+ exosomes (p = 0.0488, r = -0.8138), and non-significantly negatively correlated with the percentage of CD3+exosomes (p = 0.1447, r = -0.6708), suggesting the association of these exosomes with aging and aging related diseases. BMI was significantly negatively correlated with the percentage of CD16+ exosomes (p = 0.0418, r = -0.8281), and non-significantly negatively correlated with the percentage of CD14+ exosomes (p = 0.0859, r = -0.7500). There was no significant difference between male and female subjects on all tested markers. Conclusions: With optimized acquisition parameters and appropriate controls, we were able to characterize the surface phenotype of plasma exosomes by conventional flow cytometry. Our data provide evidence that T cells (producing CD3+ exosomes) are one of the major cell sources of plasma exosomes. CD81+ and CD3+ exosomes are attractive candidates as direct biomarkers of aging and aging related disease, such as osteoarthritis. Our future study will focus on segregation of exosome subpopulations for characterization of their cargo to provide insights into their role in aging and osteoarthritis as well as their biomarker and therapeutic potential. We wish to acknowledge funding support from NIH R56AG060895.

An ontology for representing hematologic malignancies: the cancer cell ontology

BackgroundWithin the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data.ResultsWe have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies.ConclusionWe have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.

The effect of acetate on population heterogeneity in different cellular characteristics of Escherichia coli in aerobic batch cultures.

Acetate as the major by-product in industrial-scale bioprocesses with Escherichia coli is found to decrease process efficiency as well as to be toxic to cells, which has several effects like a significant induction of cellular stress responses. However, the underlying phenomena are poorly explored. Therefore, we studied time-resolved population heterogeneity of the E. coli growth reporter strain MG1655/pGS20PrrnBGFPAAV expressing destabilized green fluorescent protein during batch growth on acetate and glucose as sole carbon sources. Additionally, we applied five fluorescent stains targeting different cellular properties (viability as well as metabolic and respiratory activity). Quantitative analysis of flow cytometry data verified that bacterial populations in the bioreactor are more heterogeneous in growth as well as stronger metabolically challenged during growth on acetate as sole carbon source, compared to growth on glucose or acetate after diauxic shift. Interestingly, with acetate as sole carbon source, significant subpopulations were found with some cells that seem to be more robust than the rest of the population. In conclusion, following batch cultures population heterogeneity was evident in all measured parameters. Our approach enabled a deeper study of heterogeneity during growth on the favored substrate glucose as well as on the toxic by-product acetate. Using a combination of activity fluorescent dyes proved to be an accurate and fast alternative as well as a supplement to the use of a reporter strain. However, the choice of combination of stains should be well considered depending on which population traits to aim for.

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data.

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically explore characteristics of their cellular samples. However, these technological advancements yield high-dimensional data sets that have become increasingly difficult to analyze objectively within traditional manual-based gating programs. In order to better analyze and present data, scientists partner with bioinformaticians with expertise in analyzing high-dimensional data to parse their flow cytometry data. While these methods have been shown to be highly valuable in studying flow cytometry, they have yet to be incorporated in a straightforward and easy-to-use package for scientists who lack computational or programming expertise. To address this need, we have developed ExCYT, a MATLAB-based Graphical User Interface (GUI) that streamlines the analysis of high-dimensional flow cytometry data by implementing commonly employed analytical techniques for high-dimensional data including dimensionality reduction by t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots. Additionally, ExCYT provides traditional gating options of select populations of interest for further t-SNE and clustering analysis as well as the ability to apply gates directly on t-SNE plots. The software provides the additional advantage of working with either compensated or uncompensated FCS files. In the event that post-acquisition compensation is required, the user can choose to provide the program a directory of single stains and an unstained sample. The program detects positive events in all channels and uses this select data to more objectively calculate the compensation matrix. In summary, ExCYT provides a comprehensive analysis pipeline to take flow cytometry data in the form of FCS files and allow any individual, regardless of computational training, to use the latest algorithmic approaches in understanding their data.

Using Imaging Flow Cytometry to Quantify and Optimize Giant Vesicle Production by Water-in-oil Emulsion Transfer Methods.

Many biologists, biochemists, and biophysicists study giant vesicles, which have a diameter of >1 μm, owing to their ease of characterization using standard optical methods. More recently, there has been interest in using giant vesicles as model systems for living cells and for the construction of artificial cells. In fact, there have been a number of reports about functionalizing giant vesicles using membrane-bound pore proteins and encapsulating biochemical reactions. Among the various methods for preparing giant vesicles, the water-in-oil emulsion transfer method is particularly well established. However, the giant vesicles prepared by this method have complex and heterogeneous properties, such as particle size and membrane structure. Here, we demonstrate the characterization of giant vesicles by imaging flow cytometry to provide quantitative and qualitative information about the vesicle products prepared by the water-in-oil emulsion transfer method. Through image-based analyses, several kinds of protocol byproducts, such as oil droplets and vesicles encapsulating no target molecules, were identified and successfully quantified. Further, the optimal agitation conditions for the water-in-oil emulsion transfer method were found from detailed analysis of imaging flow cytometry data. Our results indicate that a sonication-based water-in-oil emulsion transfer method exhibited a higher efficiency in producing giant vesicles, about 10 times or higher than that of vortex and rumble strip-based methods. It is anticipated that these approaches will be useful for fine-tuning giant vesicle production and subsequent applications.

Study of the physicochemical properties of exosome dispersions obtained by ultrafiltration

The article presents the results of studying the physicochemical properties of exosome preparations obtained by ultrafiltration, which indicate a high degree of the composition and properties dependence of the obtained product on the material of the filters used. Quantitative determination of proteins and nucleic acids in exosome samples using UPN-50 filters allows us to conclude that the content of the main impurity compounds in the preparation is significantly reduced compared to dispersions obtained using filters with pore sizes of 220 and 450 nm. Analysis of flow cytometry data made it possible to demonstrate that when using the UPN-50 filter, an increase in the contribution to the dispersion of all types of fractions of non-exosomal size was observed, the appearance of which can result from fraction destruction associated with pore size or filter material properties. drying of the dispersion was observed in the studied exosome samples. Fraction sizes ranged from 40 to 450 nm (an average of about 200 nm). Exosomes from the entire variety of membrane vesicles are fractions that have the most suitable characteristics that allow them to be used as a nanoscale drug delivery vehicle while ensuring the necessary quality control of the drug at the sample preparation stage.

High-Throughput Analysis of Clinical Flow Cytometry Data by Automated Gating.

Advancements in flow cytometers with capability to measure 15 or more parameters have enabled us to characterize cell populations at unprecedented levels of detail. Beyond discovery research, there is now a growing demand to dive deeper into evaluating the immune response in clinical trials for immune modulating compounds. However, for high-volume, complex flow cytometry data generated in clinical trials, conventional manual gating remains the standard of practice. Traditional manual gating is resource intense and becomes a bottleneck and an impractical method to complete high volumes of flow cytometry data analysis. Current efforts to automate “manual gating” have shown that computational algorithms can facilitate the analysis of daunting multi-parameter data; however, a greater degree of precision in comparison with traditional manual gating is needed for wide-scale adoption of automated gating methods. In an effort to more closely follow the manual gating process, our automated gating pipeline was created to include negative controls (Fluorescence Minus One [FMO]) to enhance the reliability of gate placement. We demonstrate that use of an automated pipeline, heavily relying on FMO controls for population discrimination, can analyze multi-parameter, large-scale clinical datasets with comparable precision and accuracy to traditional manual gating.

Nonspecific probe binding and automatic gating in flow cytometry and fluorescence activated cell sorting (FACS).

Flow cytometry is extensively used in cell biology to differentiate cells of interest (mutants) from control cells (wild-types). For mutant cells characterized by expression of a distinct membrane surface structure, fluorescent marker probes can be designed to bind specifically to these structures while the cells are in suspension, resulting in a sufficiently high fluorescence intensity measurement by the cytometer to identify a mutant cell. However, cell membranes may have relatively weak, nonspecific binding affinity to the probes, resulting in false positive results. Furthermore, the same effect would be present on mutant cells, allowing both specific and nonspecific binding to a single cell. We derive and analyze a kinetic model of fluorescent probe binding dynamics by tracking populations of mutant and wild-type cells with differing numbers of probes bound specifically and nonspecifically. By assuming the suspension is in chemical equilibrium prior to cytometry, we use a two-species Langmuir adsorption model to analyze the confounding effects of non-specific binding on the assay. Furthermore, we analytically derive an expectation maximization method to infer an appropriate estimate of the total number of mutant cells as an alternative to existing, heuristic methods. Lastly, using our model, we propose a new method to infer physical and experimental parameters from existing protocols. Our results provide improved ways to quantitatively analyze flow cytometry data.

Cytometree: A binary tree algorithm for automatic gating in cytometry analysis.

Flow cytometry is a powerful technology that allows the high-throughput quantification of dozens of surface and intracellular proteins at the single-cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time-consuming nature. We present a new unsupervised algorithm called "cytometree" to perform automated population identification (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distributions. Node splitting is done according to a model selection procedure based on a normalized difference of Akaike information criteria between two competing models. Post-processing of the tree structure and derived populations allows us to complete the annotation of the populations. The algorithm is shown to perform better than the state-of-the-art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods project. The algorithm is also applied to a T-cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open-source available algorithm while requiring the shortest computation time. © 2018 International Society for Advancement of Cytometry.