High-dimensional Flow Cytometry Data Research Articles

Potential immunomodulatory effects of CAS+IMD monoclonal antibody cocktail in hospitalized patients with COVID-19

Passive administration of SARS-CoV-2 neutralizing monoclonal antibodies (mAbs), such as CAS+IMD (Casirivimab+Imdevimab) antibody cocktail demonstrated beneficial effects on clinical outcomes in hospitalized patients with COVID-19 who were seronegative at baseline and outpatients. However, little is known about their impact on the host immunophenotypes. We conducted an immunoprofiling study in 46 patients from a single site of a multi-site trial of CAS+IMD in hospitalized patients. We collected longitudinal samples during October 2020∼April 2021, prior to the emergence of the Delta and Omicron variants and the use of COVID-19 vaccines. All collected samples were analyzed without exclusion and post-hoc statistical analysis was performed. We examined the dynamic interplay of CAS+IMD with host immunity applying dimensional reduction approach on plasma proteomics and high dimensional flow cytometry data. Using an unbiased clustering method, we identified unique immunophenotypes associated with acute inflammation and disease resolution. Compared to placebo group, administration of CAS+IMD accelerated the transition from an acute inflammatory immunophenotype, to a less inflammatory or "resolving" immunophenotype, as characterized by reduced tissue injury, proinflammatory markers and restored lymphocyte/monocyte imbalance independent of baseline serostatus. Moreover, CAS+IMD did not impair the magnitude or the quality of host T cell immunity against SARS-CoV-2 spike protein. Our results identified immunophenotypic changes indicative of a possible SARS-CoV-2 neutralizing antibodies-induced anti-inflammatory effect, without an evident impairment of cellular antiviral immunity, suggesting that further studies of Mabs effects on SAS-CoV-2 or other viral mediated inflammation are warranted. Regeneron Pharmaceuticals Inc and federal funds from the Department of Health and Human Services; Administration for Strategic Preparedness and Response; Biomedical Advanced Research and Development Authority, under OT number: HHSO100201700020C.

Development of a Machine Learning Decision Support System for Clinical Flow Cytometry

Abstract High parameter flow cytometry is a highly utilized tool in both research and clinical settings for accurate immunophenotyping in immunology, oncology, and many other disciplines. With increasing dimensionality, manual analysis of data becomes more complex and time consuming. Over the past decade, several automated methods have been developed for cell population clustering and dimensionality reduction of high parameter flow cytometry data which have sped up and simplified the discovery of cell populations not observed by manual gating strategies. However, the input and output of such tools are stochastic in nature, thus making their results difficult to reuse with novel samples such as diagnostic patient samples. Several groups have recently attempted to utilize machine learning algorithms to analyze clinical flow cytometry data. These studies focused on training machine learning models with a goal of predicting a specific diagnosis for each sample. This approach can be limiting when the diagnostic features are not perfectly met to predict a specific diagnosis as well as the absence of the patient's clinical data. To our knowledge, no studies have utilized machine learning to identify and classify individual cells based on their raw fluorescence signature to various cellular populations, whether normal or abnormal, to aid a diagnostician to interpret their significance in a clinical context. In this study, we utilized a data set of ~240 million individual cells from ~9,500 flow cytometry files from 2,300 patients to train a machine learning model to predict cell population classifications as annotated by expert hematopathologists. These annotations were implemented into an end-to-end machine learning based decision support system allowing for rapid, automated analysis of high dimensional flow cytometry data with an output aiding the diagnostician in the generation of a diagnosis. A clinical validation highlighted the robustness of this methodology with 100% sensitivity and 94% specificity in the identification of abnormal B-cell populations on a cohort of 1,500 patients. This decision support system also identifies abnormal T-cell and myeloid cell populations with 100% sensitivity. Overall, the decision support system saves significant time as it removes the need for manual gating and analysis, and aids the diagnostician in identifying discrete cell populations and offering descriptive information about any abnormal populations identified.

CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data

Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/.

PyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells.

While conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python. pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs. pyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts, along with the raw flow cytometry input data.

AliGater: a framework for the development of bioinformatic pipelines for large-scale, high-dimensional cytometry data.

AliGater is an open-source framework to accelerate the development of bioinformatic pipelines for the analysis of large-scale, high-dimensional flow cytometry data. AliGater provides a Python package for automatic feature extraction workflows, as well as building blocks to construct analysis pipelines. We illustrate the use of AliGater in a high-resolution flow cytometry-based genome-wide association study on 46 immune cell populations in 14288 individuals. Source code and documentation at https://github.com/LudvigEk/aligater and https://aligater.readthedocs.io.

Expanding the use of clustering and dimensionality reduction in high parameter flow cytometry data through machine learning for novel samples.

Abstract High parameter flow cytometry is a highly utilized tool for accurate immunophenotyping and diagnostics in immunology, oncology, and many other disciplines. Over the past decade, several methods have been developed for automated clustering and dimensionality reduction of high parameter flow cytometry data which has sped up and simplified the discovery of cell populations not observed by manual gating strategies. However, the input and output of such tools are stochastic in nature, thus making their results difficult to reuse with novel samples. To address this challenge, we present a method utilizing machine learning to predict cluster labels and dimensionality reduction coordinates on novel samples. For proof of principle, we utilized high parameter (22 marker) flow cytometry data which examines myeloid hematopoiesis on bone marrow aspirate samples. Training data for machine learning consisted of pooled, normal, bone marrow. Phenograph was used for initial population clustering and UMAP for dimensionality reduction. A random forest model was found to be most accurate in predicting Phenograph clusters (98%) on novel data while a k-nearest neighbors (knn) model was found most accurate for UMAP coordinate prediction. The utility of this model was observed by examining acute myeloid leukemia with aberrant immunophenotypes, which were not included in the training data set. For these samples, predicted clusters and UMAP coordinates correlated with early progenitor populations from the training set. This approach allows for a de novo sample clustering and dimensionality reduction assignment onto an already established and characterized model, allowing for rapid, automated interpretation of high dimensional flow cytometry data. Supported by Cedars-Sinai Pathology and Laboratory Medicine Minigrant

Analyzing immune response to engineered hydrogels by hierarchical clustering of inflammatory cell subsets.

Understanding the immune response to hydrogel implantation is critical for the design of immunomodulatory biomaterials. To study the progression of inflammation around poly(ethylene glycol) hydrogels presenting Arg-Gly-Asp (RGD) peptides and vascular endothelial growth factor, we used temporal analysis of high-dimensional flow cytometry data paired with intravital imaging, immunohistochemistry, and multiplexed proteomic profiling. RGD-presenting hydrogels created a reparative microenvironment promoting CD206+ cellular infiltration and revascularization in wounded dorsal skin tissue. Unbiased clustering algorithms (SPADE) revealed significant phenotypic transition shifts as a function of the cell-adhesion hydrogel properties. SPADE identified an intermediate macrophage subset functionally regulating in vivo cytokine secretion that was preferentially recruited for RGD-presenting hydrogels, whereas dendritic cell subsets were preferentially recruited to RDG-presenting hydrogels. Last, RGD-presenting hydrogels controlled macrophage functional cytokine secretion to direct polarization and vascularization. Our studies show that unbiased clustering of single-cell data provides unbiased insights into the underlying immune response to engineered materials.

Distinct tumor-infiltrating lymphocyte landscapes are associated with clinical outcomes in localized non-small-cell lung cancer

Despite the importance of tumor-infiltrating T lymphocytes (TILs) in cancer biology, the relationship between TIL phenotypes and their prognostic relevance for localized non-small-cell lung cancer (NSCLC) has not been well established. Fresh tumor and normal adjacent tissue was prospectively collected from 150 patients with localized NSCLC. Tissue was comprehensively characterized by high-dimensional flow cytometry of TILs integrated with immunogenomic data from multiplex immunofluorescence, T-cell receptor sequencing, exome sequencing, RNA sequencing, targeted proteomics, and clinicopathologic features. While neither the magnitude of TIL infiltration nor specific TIL subsets were significantly prognostic alone, the integration of high-dimensional flow cytometry data identified two major immunotypes (IM1 and IM2) that were predictive of recurrence-free survival independent of clinical characteristics. IM2 was associated with poor prognosis and characterized by the presence of proliferating TILs expressing cluster of differentiation 103, programmed cell death protein 1, T-cell immunoglobulin and mucin-domain containing protein 3, and inducible T-cell costimulator. Conversely, IM1 was associated with good prognosis and differentiated by an abundance of CD8+ T cells expressing cytolytic enzymes, CD4+ T cells lacking the expression of inhibitory receptors, and increased levels of B-cell infiltrates and tertiary lymphoid structures. While increased B-cell infiltration was associated with good prognosis, the best prognosis was observed in patients with tumors exhibiting high levels of both B cells and T cells. These findings were validated in patient tumors from The Cancer Genome Atlas. Our study suggests that although the number of infiltrating T cells is not associated with patient survival, the nature of the infiltrating T cells, resolved in distinct TIL immunotypes, is prognostically relevant in NSCLC and may inform therapeutic approaches to clinical care.

Cover story: Eur. J. Immunol. 8'21

European Journal of ImmunologyVolume 51, Issue 8 Cover PictureFree Access Cover story: Eur. J. Immunol. 8'21 First published: 04 August 2021 https://doi.org/10.1002/eji.202170080AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Our front cover features a high-dimensional BH-SNE representation of the CD4 T cell phenotype in patients affected by COVID-19. The image is taken from Manfredi and Abbati et al., and developed through cytoChain, an innovative application for the analysis of high-dimensional flow cytometry data. CytoChain refined the COVID-19 dataset analysis published by Neumann et al. (DOI 10.1002/cti2.1204), underlining previously missed differences among COVID-19 patients. A novel set of surface markers was thus uncovered, indicating how a T cell subset expressing multiple inhibitory receptors is more abundant in severe COVID-19 cases. Volume51, Issue8August 2021 RelatedInformation

Baby’s First Macrophage: characterizing Hofbauer Cell phenotype through gestation using unsupervised dimensionality reduction

Abstract Fetal placental macrophages (Hofbauer cells (HCs)) appear at 18 days post-conception and are maintained throughout pregnancy. Despite their identification more than a century ago, there are few studies characterizing the evolution of human HC phenotype through gestation. Using high-dimensional flow cytometry, we measured expression of CD68, CD80, CD86, HLA-DR, CD163, CD206, and CD209 on human HCs ex vivo. This panel includes markers previously validated on term human HCs for immune activation (CD80, CD86) and modulation (DC-SIGN, HLA-DR). To best capture the spectrum of HC diversity across gestation, we employed an unbiased approach to analyze processed datasets from HCs isolated from placentae 12–17 weeks gestational age (n=5), 17–24 weeks gestational age (n=7) and at term (n=5) using t-distributed stochastic neighbor embedding analysis (tSNE). HC populations were more diverse early in pregnancy and at term, compared to the second trimester; this is anticipated given the changing microenvironment following initial placental anchoring to eventual parturition. Marker expression heatmaps were generated to assess gestation-dependent changes in HC phenotype; HCs expressing activation markers were most frequent in early gestation, and reduced by term. HCs bearing markers of immune modulation were most frequent at mid-gestation, and least in early gestation. These results demonstrate the power of utilizing computational methods to analyze high-dimensional flow cytometry data collected from an understudied cell type and demonstrate how HCs phenotypically evolve during human pregnancy.

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.

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.

Varicella zoster virus productively infects human natural killer cells and manipulates phenotype.

Varicella zoster virus (VZV) is a ubiquitous human alphaherpesvirus, responsible for varicella upon primary infection and herpes zoster following reactivation from latency. To establish lifelong infection, VZV employs strategies to evade and manipulate the immune system to its advantage in disseminating virus. As innate lymphocytes, natural killer (NK) cells are part of the early immune response to infection, and have been implicated in controlling VZV infection in patients. Understanding of how VZV directly interacts with NK cells, however, has not been investigated in detail. In this study, we provide the first evidence that VZV is capable of infecting human NK cells from peripheral blood in vitro. VZV infection of NK cells is productive, supporting the full kinetic cascade of viral gene expression and producing new infectious virus which was transmitted to epithelial cells in culture. We determined by flow cytometry that NK cell infection with VZV was not only preferential for the mature CD56dim NK cell subset, but also drove acquisition of the terminally-differentiated maturity marker CD57. Interpretation of high dimensional flow cytometry data with tSNE analysis revealed that culture of NK cells with VZV also induced a potent loss of expression of the low-affinity IgG Fc receptor CD16 on the cell surface. Notably, VZV infection of NK cells upregulated surface expression of chemokine receptors associated with trafficking to the skin –a crucial site in VZV disease where highly infectious lesions develop. We demonstrate that VZV actively manipulates the NK cell phenotype through productive infection, and propose a potential role for NK cells in VZV pathogenesis.

Abstract B002: Automated target antigen characterization on residual leukemic cells by flow cytometry

Abstract Introduction: In acute myeloid leukemia (AML), the prognostic relevance of minimal residual disease (MRD) detection has increased within the past decade and currently, clinical trials are evaluating the value of MRD-guided therapy. Immunotherapy represents an exciting prospect to eliminate residual chemorefractory leukemic cells. Development and implementation of these therapies rely on effective and highly sensitive methods to detect and characterize target antigen expression on residual cells. Methods: To test the feasibility of the recently proposed viSNE (visualization of t-distributed stochastic neighbor embedding) algorithm to detect and characterize rare, aberrant populations we analyzed patient samples acquired within 3 months prior to hematological relapse, which were MRD-negative by traditional, manual gating. 400.000 events were acquired using an 8 color, 10 parameter panel on a Navios® flow cytometer (Beckman Coulter, Brea, CA, USA). For the bioinformatic analysis, the patient's sample and a healthy donor sample measured on the same machine were combined digitally and viSNE clustering was applied to the resulting dataset. As previously suggested, MRD positivity was defined as the presence of a distinct cluster of >100 cells which consisted of >90% patient cells. Then, expression intensity of the target antigens CD33 and CD123 was compared to primary diagnosis and relapse. Results: From our database, we identified 12 patients with comprehensive MRD flow assessment available within 3 months prior to hematological relapse who were in CR by cytomorphology at the time of MRD assessment. 9 of these patients were MRD-positive by traditional LAIP-gating (>0.1%). This first analysis therefor focused on the remaining 3 patients with MRD-levels at 0.01%, 0.03% and 0.07% at 21, 42 and 37 days prior to hematological relapse. Using viSNE, distinct clusters of 5058, 225 and 1043 events consisting of 95.5%, 92.6% and 93.8% patient cells were identified, consistent with MRD positivity as defined above. In all three cases, the cells identified by the viSNE algorithm showed the immunophenotype seen at relapse, while there were immunophenotypical shifts compared to primary diagnosis, possibly explaining false-negative MRD-assessment by traditional gating. Specifically, there was a decrease in CD34-expression in one case, and an increase in CD33-expression in 2 cases. Concerning the target antigens CD33 and CD123, both antigens were present on residual cells, showing stable (CD123) expression or an increased (CD33) expression compared to primary diagnosis. Target antigen expression was identical compared to the immunophenotype seen at relapse. Conclusion: viSNE clustering detected minor, aberrant populations in 3 patients with subsequent hematological relapse, which were not detected by traditional LAIP-gating strategy. viSNE might therefor aid in the analysis of high-dimensional flow cytometry data to detect small, aberrant populations. Target antigen expression could then be determined on these residual cells indicating feasibility of this approach to characterize these rare events for the development of personalized immunotherapeutic strategies as well as response assessment during such therapies. Further validation is ongoing. Citation Format: Thomas Köhnke, Sandra Rechkemmer, Veit Leonhard Bücklein, Wolfgang Hiddemann, Marion Subklewe. Automated target antigen characterization on residual leukemic cells by flow cytometry. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr B002.

GEM/GANN: A multivariate computational strategy for auto-characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high-dimensional flow cytometry data.

The dramatic increase in the complexity of flow cytometric datasets requires new computational approaches that can maximize the amount of information derived and overcome the limitations of traditional gating strategies. Herein, we present a multivariate computational analysis of the HIV-infected flow cytometry datasets that were provided as part of the FlowCAP-IV Challenge using unsupervised and supervised learning techniques. Out of 383 samples (stimulated and unstimulated), 191 samples were used as a training set (34 individuals whose disease did not progress, and 157 individuals whose disease did progress). Using the results from the training set, the participants in the Challenge were then asked to predict the condition and progression time of the remaining individuals (45 "nonprogressors" and 147 "progressors"). To achieve this, we first scaled down data resolution and then excluded doublet cells from the analysis using Expectation Maximization approaches. We then standardized all samples into histograms and used Genetic Algorithm-Neural Network to extract feature sets from the datasets, the reliability of which were examined using WEKA-implemented classifiers. The selected feature set resulted in a high sensitivity and specificity for the discrimination of progressors and nonprogressors in the training set (average True Positive Rate = 1.00 and average False Positive Rate = 0.033). The capacity of the feature set to predict real-time survival time was better when using data from the "unstimulated" training set (r = 0.825). The P-values and 95% confidence interval log-rank ratios between actual and predicted survival time in the test set were 0.682 and 0.9542 ± 0.24 for the unstimulated dataset, and 0.4451 and 0.9173 ± 0.23 for the stimulated dataset. Our analytic strategy has demonstrated a promising capacity to extract useful information from complex flow cytometry datasets, despite a significance imbalance and variation between the training and test sets.

Elucidation of seventeen human peripheral blood B‐cell subsets and quantification of the tetanus response using a density‐based method for the automated identification of cell populations in multidimensional flow cytometry data

Advances in multiparameter flow cytometry (FCM) now allow for the independent detection of larger numbers of fluorochromes on individual cells, generating data with increasingly higher dimensionality. The increased complexity of these data has made it difficult to identify cell populations from high-dimensional FCM data using traditional manual gating strategies based on single-color or two-color displays. To address this challenge, we developed a novel program, FLOCK (FLOw Clustering without K), that uses a density-based clustering approach to algorithmically identify biologically relevant cell populations from multiple samples in an unbiased fashion, thereby eliminating operator-dependent variability. FLOCK was used to objectively identify seventeen distinct B-cell subsets in a human peripheral blood sample and to identify and quantify novel plasmablast subsets responding transiently to tetanus and other vaccinations in peripheral blood. FLOCK has been implemented in the publically available Immunology Database and Analysis Portal-ImmPort (http://www.immport.org)-for open use by the immunology research community. FLOCK is able to identify cell subsets in experiments that use multiparameter FCM through an objective, automated computational approach. The use of algorithms like FLOCK for FCM data analysis obviates the need for subjective and labor-intensive manual gating to identify and quantify cell subsets. Novel populations identified by these computational approaches can serve as hypotheses for further experimental study.

ImmPort FLOCK: Automated cell population identification in high dimensional flow cytometry data (42.17)

Abstract Historically, investigators have analyzed flow cytometry (FCM) data using manual gating strategies and two-dimensional dot plot visualization to identify and compare specific cell populations between samples. However, this approach is subjective and does not scale well as the number of parameters detectable with different fluorochrome reagents increases. We have developed a software system for automated population discovery in multidimensional FCM data - FLOw Clustering without K (FLOCK), which specifically takes into account the unique features of FCM data, including non-spherical parameter distributions and sparse cell populations. FLOCK includes the following steps: data cleansing, data shrinking, data normalization, informative dimension selection, dense hyper-region identification, histogram partitioning, centroid calculation, clustering, visualization, population statistics calculation, cell ontology mapping. As a result, FLOCK automatically and objectively determines the number of cell populations detectable in a sample and assigns population membership to each cell event, from the multi-parameter flow cytometry data, in the absence of manual gating. Our study of normal human peripheral blood has identified 17 unique B lymphocyte populations based on the expression of IgD, IgG, CD24, CD27, CD38 & B220. Supported by NIH N01AI40076.

An 11-color flow cytometric assay for identifying, phenotyping, and assessing endocytic ability of peripheral blood dendritic cell subsets in a single platform

Human peripheral blood dendritic cells (PBDC) are a rare population comprised of several distinctive subsets. Analysis of these cells has been hindered by their low frequency. In this study, we report a novel direct ex vivo 11-color flow cytometric assay that combines subset identification with analysis of activation status and endocytic ability of three major PBDC subsets (CD1c +CD11c + “MDC1,” CD141 +CD11c + “MDC2,” and CD303 +CD11c − “PDC”) within a single platform. This method eliminates the need for DC enrichment, isolation, or prolonged culture. Human peripheral blood mononuclear cells (PBMC) from healthy donors are incubated with FITC-dextran directly ex vivo, prior to cell surface staining with various markers. As expected, PBDC identified by this assay express low levels of CD40 and CD86 directly ex vivo, and significantly upregulate expression of these molecules upon stimulation with toll-like receptor ligands LPS and CpG oligonucleotides. In addition, PDC internalize FITC-labeled dextran poorly in comparison to MDC1 and MDC2 subsets. Specificity of FITC-dextran endocytosis is further verified by imaging flow cytometry. Furthermore, the combination of surface markers used in this assay reveals a previously unreported CD4 +CD11c +CD303 −CD1c −CD141 − cell population. Taken together, this assay is a rapid and cost-effective method that avoids manipulation of PBDC while providing direct ex vivo high-dimensional flow cytometry data for PBDC studies.