Analysis Of Flow Cytometry Data Research Articles

Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry.

Standardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are first identified at diagnosis using a clustering algorithm. Subsequently, automated multidimensional spaces, named “Clouds”, are created around these clusters on the basis of density calculations. This step identifies the immunophenotypic pattern of the suspicious cell clusters. Thereafter, using reference samples, the “Abnormality Ratio” (AR) of each Cloud is calculated, and major malignant Clouds are retained, known as “Leukemic Clouds” (L-Clouds). In follow-up samples, MRD is identified when more cells fall into a patient’s L-Cloud compared to reference samples (AR concept). This workflow was applied on simulated data and real-life leukemia flow cytometry data. On simulated data, strong patient-dependent positive correlation (R2 = 1) was observed between the AR and spiked-in leukemia cells. On real patient data, AR kinetics was in line with the clinical evolution for five out of six patients. In conclusion, we present a convenient flow cytometry data analysis approach for the follow-up of hematological malignancies. Further evaluation and validation on more patient samples and different flow cytometry panels is required before implementation in clinical practice.

Benchmarking automated flow cytometry data analysis software using synthetic datasets

Background & Aim Recent years have seen the development of many computational tools for automated Flow Cytometry (FC) data analysis. These software tools enable automatic cell population identification, classification and novel data visualisations. Because different software will use different mathematical approaches to identify and quantify cells, outputs can potentially vary when analysing the same dataset. Often, researchers simply accept the results without understanding the underlying black box algorithms and the potential for variation of output. Comparability studies between automated FC software requires high-quality benchmarking datasets that assess the performance and robustness of the algorithms. However, currently available datasets from FC experiments lack “gold standards” due to the nature of live cell biological materials. Furthermore, the datasets that meet data complexity requirements are costly to produce and only come from a narrow range of cell or disease models. Computer simulated FC datasets offer many advantages over their real-world counterparts. Synthetic standards can potentially be created, with data properties being highly controlled to objectively and systematically compare between software. Methods, Results & Conclusion In this research a range of synthetic FC cluster data have been systematically designed and generated containing controlled separation between clusters, rare cell populations, normal and non-normal probability distributions. These reference datasets have been used to evaluate the initial performance characteristics of a number of popular flow cytometry data analysis software, with the accuracy and repeatability of software being measured as the difference between the output and a reference absolute cell number. The initial results reveal similar trends in performance between software when analysing clusters with specific degrees of separation. Certain software platforms were more suited to analysis of rare cell populations. When using non-normally distributed clusters, the presence of skewed cell populations did not affect clustering performance, but the orientation of cluster skewness did. This demonstrates that benchmarking of FC automated software platforms will be possible with a high level of testing integrity using synthetic cluster dataset design leading to enhanced confidence in the data quality of cell characterisations.

Prognostic value of autophagy markers in sepsis- a clinical case report

Abstract Sepsis is characterized by a dysregulated response to infection that is initiated by recognition of pathogen-associated molecular patterns from invasive microorganisms by pathogen recognition receptors to trigger an immune response. The goal is to establish regulatory mechanisms governing sepsis-induced cell death and correlate cell death mechanisms with clinical biochemistry pre- and post- antibiotic treatment. Towards this end, we present a case report of a 48-year-old male patient with a clinical history of adenocarcinoma, colostomy, chronic kidney disease and suspected sepsis admitted to emergency department. Patient was on an antibiotic treatment regimen (Monocef 1 g single dose and Flucloxacillin 1 g single dose for three days). Among several promising biomarkers for sepsis, the role of cell death marker p62/SQSTM1 was evaluated for its prognostic role in this particular case of sepsis. Flow cytometry and ELISA analysis of septic patient’s peripheral blood mononuclear cells (PBMCs) and serum samples were performed and correlated with serum electrolyte and serum biochemistry values pre- and post- antibiotic dose. Flow cytometry data analysis of PBMC samples from septic patient showed significantly lower p62 post- antibiotic when compared to pre- antibiotic dose [p < 0.05]. Notably, comparison of pre- and post- antibiotic treatment regimen showed significant modulation of serum ELISA levels of autophagy marker, p62/SQSTM1, that correlated with alteration in serum potassium levels [p < 0.05]. Our current study demonstrates the impact of serum potassium levels on autophagy in a patient’s case of sepsis.

Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?

This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies. In recent years, computational analysis methods have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary. Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies.

Interferon Regulatory Factor-5 in Resident Macrophage Promotes Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease is caused by genetic mutations in PKD1 or PKD2. Macrophages and their associated inflammatory cytokines promote cyst progression; however, transcription factors within macrophages that control cytokine production and cystic disease are unknown. In these studies, we used conditional Pkd1 mice to test the hypothesis that macrophage-localized interferon regulatory factor-5 (IRF5), a transcription factor associated with production of cyst-promoting cytokines (TNFα, IL-6), is required for accelerated cyst progression in a unilateral nephrectomy (1K) model. Analyses of quantitative real-time PCR (qRT-PCR) and flow-cytometry data 3 weeks post nephrectomy, a time point before the onset of severe cystogenesis, indicate an accumulation of inflammatory infiltrating and resident macrophages in 1K Pkd1 mice compared with controls. qRT-PCR data from FACS cells at this time demonstrate that macrophages from 1K Pkd1 mice have increased expression of Irf5 compared with controls. To determine the importance of macrophage-localized Irf5 in cyst progression, we injected scrambled or IRF5 antisense oligonucleotide (ASO) in 1K Pkd1 mice and analyzed the effect on macrophage numbers, cytokine production, and renal cystogenesis 6 weeks post nephrectomy. Analyses of qRT-PCR and IRF5 ASO treatment significantly reduced macrophage numbers, Irf5 expression in resident-but not infiltrating-macrophages, and the severity of cystic disease. In addition, IRF5 ASO treatment in 1K Pkd1 mice reduced Il6 expression in resident macrophages, which was correlated with reduced STAT3 phosphorylation and downstream p-STAT3 target gene expression. These data suggest that Irf5 promotes inflammatory cytokine production in resident macrophages resulting in accelerated cystogenesis.

ShinySOM: graphical SOM-based analysis of single-cell cytometry data.

SummaryShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes.Availability and implementationShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM.Supplementary information Supplementary data are available at Bioinformatics online.

Cell-Mediated Immune Responses to in vivo-Expressed and Stage-Specific Mycobacterium tuberculosis Antigens in Latent and Active Tuberculosis Across Different Age Groups.

A quarter of the global human population is estimated to be latently infected by Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). TB remains the global leading cause of death by a single pathogen and ranks among the top-10 causes of overall global mortality. Current immunodiagnostic tests cannot discriminate between latent, active and past TB, nor predict progression of latent infection to active disease. The only registered TB vaccine, Bacillus Calmette-Guérin (BCG), does not adequately prevent pulmonary TB in adolescents and adults, thus permitting continued TB-transmission. Several Mtb proteins, mostly discovered through IFN-γ centered approaches, have been proposed as targets for new TB-diagnostic tests or -vaccines. Recently, however, we identified novel Mtb antigens capable of eliciting multiple cytokines, including antigens that did not induce IFN-γ but several other cytokines. These antigens had been selected based on high Mtb gene-expression in the lung in vivo, and have been termed in vivo expressed (IVE-TB) antigens. Here, we extend and validate our previous findings in an independent Southern European cohort, consisting of adults and adolescents with either LTBI or TB. Our results confirm that responses to IVE-TB antigens, and also DosR-regulon and Rpf stage-specific Mtb antigens are marked by multiple cytokines, including strong responses, such as for TNF-α, in the absence of detectable IFN-γ production. Except for TNF-α, the magnitude of those responses were significantly higher in LTBI subjects. Additional unbiased analyses of high dimensional flow-cytometry data revealed that TNF-α+ cells responding to Mtb antigens comprised 17 highly heterogeneous cell types. Among these 17 TNF-α+ cells clusters identified, those with CD8+TEMRA or CD8+CD4+ phenotypes, defined by the expression of multiple intracellular markers, were the most prominent in adult LTBI, while CD14+ TNF-α+ myeloid-like clusters were mostly abundant in adolescent LTBI. Our findings, although limited to a small cohort, stress the importance of assessing broader immune responses than IFN-γ alone in Mtb antigen discovery as well as the importance of screening individuals of different age groups. In addition, our results provide proof of concept showing how unbiased multidimensional multiparametric cell subset analysis can identify unanticipated blood cell subsets that could play a role in the immune response against Mtb.

Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration

Abstract We present a framework to simultaneously align and smoothen data in the form of multiple point clouds sampled from unknown densities with support in a $d$-dimensional Euclidean space. This work is motivated by applications in bioinformatics where researchers aim to automatically homogenize large datasets to compare and analyze characteristics within a same cell population. Inconveniently, the information acquired is most certainly noisy due to misalignment caused by technical variations of the environment. To overcome this problem, we propose to register multiple point clouds by using the notion of regularized barycenters (or Fréchet mean) of a set of probability measures with respect to the Wasserstein metric. The first approach consists in penalizing a Wasserstein barycenter with a convex functional as recently proposed in [5]. The second strategy is to transform the Wasserstein metric itself into an entropy regularized transportation cost between probability measures as introduced in [12]. The main contribution of this work is to propose data-driven choices for the regularization parameters involved in each approach using the Goldenshluger–Lepski’s principle. Simulated data sampled from Gaussian mixtures are used to illustrate each method, and an application to the analysis of flow cytometry data is finally proposed. This way of choosing of the regularization parameter for the Sinkhorn barycenter is also analyzed through the prism of an oracle inequality that relates the error made by such data-driven estimators to the one of an ideal estimator.

Clinical and Laboratory Features Associated with Flow Cytometric CD49f Expression in Pediatric B Cell Acute Lymphoblastic Leukemia

Introduction: B cell acute lymphoblastic leukemia (B-ALL) is the most common childhood malignancy and its diagnosis depends on flow cytometry. Aberrant antigen expression is common in B-ALL and can be useful in both diagnosis and minimal residual disease (MRD) assessment. CD49f (α6 integrin) is commonly overexpressed in lymphoblasts of B-ALL relative to benign B lymphoblasts, and research suggests a role for CD49f in central nervous system (CNS) invasion in B-ALL. We aimed to characterize clinical and laboratory features associated with lymphoblast CD49f expression in pediatric B-ALL. Methods: We reviewed clinical and laboratory features, including diagnostic and day 29 (D29) MRD bone marrow aspirate flow cytometry, for patients newly diagnosed with B-ALL at our pediatric centre using current diagnostic flow cytometry panels implemented in 2013. CD49f positivity (CD49f+) was defined as greater than 20% of leukemic blasts expressing CD49f at fluorescence intensity greater than the upper limit of an internal lymphocyte population. MRD studies were considered positive with leukemic blasts ≥0.01% of mononuclear cells. Frequency of CD49f+ and CD49f median fluorescence intensity (MFI) were compared between groups defined by recurrent cytogenetic abnormalities, molecular abnormalities detected by comparative genomic hybridization, CNS involvement at diagnosis, and occurrence of relapse by Chi-square, Fisher exact, Kruskal-Wallis, and Mann-Whitney testing. Results: 158 patients were reviewed with median age at diagnosis of 56 months (range 4 to 225 months) and median 33 months follow-up (range 1 to 67 months). 82 patients had CD49f+ B-ALL (median 62.2% CD49f+ blasts, CD49f MFI 3.4) and 76 patients had CD49f negative (CD49f-) B-ALL (median 3.7% CD49f+ blasts, CD49f MFI 0.8). CD49f+ and CD49f- cases did not differ significantly in median age at diagnosis (CD49f+, 51 months; CD49f-, 64.5 months) or presenting white blood cell count (CD49f+, 8.7x109/L; CD49f-, 8.8x109/L). 24 patients had CNS involvement at diagnosis, with which CD49f expression status was not significantly associated (CD49f+, 17.1%; CD49f-, 13.4%). Median CD49f MFI (1.8 CNS negative; 1.3 CNS positive) and proportion of CD49f+ leukemic blasts (27.8% CNS negative; 15.8% CNS positive) did not significantly differ between patients presenting with or without CNS involvement. Recurrent cytogenetic abnormalities associated significantly with CD49f status (p=0.001): CD49f+ associated with higher frequency of ETV6-RUNX1 (36.6% of CD49f+ and 11.8% of CD49f-; p=0.0004) and lower frequency (without statistical significance) of KMT2A rearrangement, hypodiploidy (each 1.2% CD49f+; 6.6% CD49f-), intrachromosomal amplification of chromosome 21, and TCF3-PBX1 (neither of which occurred in patients with CD49f+). Copy number abnormalities detectable by comparative genomic hybridization in CDKN2A, CDKN2B, PAX5, IKZF1, BTG1, ERG, TP53, RB1, EBF1, and CRLF2 did not differ in frequency between patients with CD49f+ and CD49f-. D29 MRD data were available for 154 patients, 32 of whom had positive MRD results. Frequency of D29 MRD positivity was not significantly associated with CD49f expression status at diagnosis (19.7% CD49f+; 21.9% CD49f-). Relapse occurred in ten patients and its occurrence was not associated with CD49f expression status at diagnosis (4.9% CD49f+; 7.9% CD49f-). Nine patients had evaluable relapse flow cytometry data, with three demonstrating increased proportions of CD49f+ blasts (1.5- to 3.2-fold increase from diagnosis), two demonstrating similar proportions, and three demonstrating decreased proportions of CD49f+ blasts (1.9- to 25-fold decrease from diagnosis). Two had new CNS involvement at relapse (one patient with 27% CD49f+ blasts at diagnosis and 1.1% at relapse; one patient with 93.3% CD49f+ blasts at diagnosis and 99.5% at relapse) and one had CNS involvement at diagnosis without involvement at relapse (with 12.8% CD49f+ blasts at diagnosis and 12.1% at relapse). Conclusion: Using the aforementioned criteria for flow cytometry data analysis, CD49f positivity in pediatric B-ALL was not associated with increased frequency of CNS involvement at diagnosis, day 29 MRD positivity, or relapse. CD49f expression patterns changed inconsistently in patients experiencing relapse and positivity or negativity at diagnosis or relapse was not clearly associated with CNS involvement at relapse. Disclosures No relevant conflicts of interest to declare.

Algorithmic Clustering Of Single-Cell Cytometry Data-How Unsupervised Are These Analyses Really?

Algorithmic Clustering Of Single-Cell Cytometry Data-How Unsupervised Are These Analyses Really?

Understanding the contribution of operator measurement variability within Flow Cytometry data analysis for Quality Control of Cell and Gene Therapy manufacturing

Flow Cytometry is a measurement technique used in Quality Control and in-process measurements of biomanufactured Cell and Gene Therapy products. However, it contains a number of sources of measurement variation at; sample preparation, instrument setup, analysis, and post-analytical data analysis stages. The latter sees variation introduced from operator subjectivity, which is investigated here to understand what effects the interpretation of diagrammatical protocols have on inter-operator analysis.36 operators from different sites were given a series of histograms to analyse, gating a shifting peak. This was repeated with diagrammatical protocols to apply gates which reduced inter-operator variation by up to 92%. Various control limits include and exclude different results and when adjusted with a log transform differences in outlier discrimination have been found. This research supports the use of Flow Cytometry diagrammatical protocols to reduce the contribution of inter-operator variation and measurement uncertainty associated within Cell and Gene Therapy manufacturing scenarios.

Activation of the DR3-TL1A Axis in Donor Mice Leads to Regulatory T Cell Expansion and Activation With Reduction in Graft-Versus-Host Disease.

Death receptor 3 (DR3) is a tumor necrosis factor receptor superfamily member (TNFRSF25), which is minimally expressed on resting conventional T cells (though readily inducible upon cell activation), yet highly expressed on resting FoxP3+ regulatory T cells (Treg). We recently demonstrated that activation of DR3 with an agonistic antibody (4C12) leads to selective expansion and activation of Treg in healthy mice and suppression of graft-versus-host disease (GVHD) in recipient mice when donor mice are treated. However, given the long antibody half-life and concomitant safety concerns, along with the lack of a humanized agonistic antibody to DR3, both human and murine fusion proteins incorporating the natural DR3 ligand TL1A (TL1A-Ig) have been developed. Herein, we show that DR3 activation with 4C12 or with TL1A-Ig, with or without the addition of low dose IL-2 to the treatment regimen, led to a significant expansion of murine Treg in spleen, lymph nodes, and peripheral blood. Bioluminescent imaging revealed peak Treg expansion around day 7–8, with return to near baseline after 2–3 weeks. In addition to expansion, all DR3 agonist treatment regimens led to increased activation of Tregs, with significant upregulation of the activation markers ICOS, KLRG-1, PD-1, and CD103, and the proliferation marker Ki-67. The near absence of activated Treg populations in control treated spleens was also detected on tSNE analysis of flow cytometry data. Subtly different patterns of splenic Treg activation by the different DR3 agonists were noted in both tSNE analysis of flow cytometry data and RNA-sequencing analysis. However, upregulation of gene transcripts which play important roles in cell proliferation, trafficking, activation, and effector function were observed regardless of the DR3 agonist treatment regimen used. In the major MHC-mismatch model of hematopoietic cell transplantation, DR3 agonist-mediated expansion and activation of Tregs in donor mice led to a significant improvement in GVHD in recipient mice. These data provide important preclinical information regarding the outcome of DR3 activation with an agonistic antibody or natural ligand and provide insight into the therapeutic use of this approach to reduce GVHD in recipients and improve outcomes of hematopoietic cell transplantation.

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.