Manual Gating Research Articles

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies.

Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.

Single cell immune profiling of dengue virus patients reveals intact immune responses to Zika virus with enrichment of innate immune signatures.

The genus Flavivirus contains many mosquito-borne human pathogens of global epidemiological importance such as dengue virus, West Nile virus, and Zika virus, which has recently emerged at epidemic levels. Infections with these viruses result in divergent clinical outcomes ranging from asymptomatic to fatal. Myriad factors influence infection severity including exposure, immune status and pathogen/host genetics. Furthermore, pre-existing infection may skew immune pathways or divert immune resources. We profiled immune cells from dengue virus-infected individuals by multiparameter mass cytometry (CyTOF) to define functional status. Elevations in IFNβ were noted in acute patients across the majority of cell types and were statistically elevated in 31 of 36 cell subsets. We quantified response to in vitro (re)infection with dengue or Zika viruses and detected a striking pattern of upregulation of responses to Zika infection by innate cell types which was not noted in response to dengue virus. Significance was discovered by statistical analysis as well as a neural network-based clustering approach which identified unusual cell subsets overlooked by conventional manual gating. Of public health importance, patient cells showed significant enrichment of innate cell responses to Zika virus indicating an intact and robust anti-Zika response despite the concurrent dengue infection.

CDC2 and plasmacytoid dendritic cells diminish from tissues of patients with non-Hodgkin orbital lymphoma and idiopathic orbital inflammation.

Non‐Hodgkin orbital lymphoma (NHOL) and idiopathic orbital inflammation (IOI) are common orbital conditions with largely unknown pathophysiology. To investigate the immune cell composition of these diseases, we performed standardized 29 parameter flow cytometry phenotyping in peripheral blood mononuclear cells of 18 NHOL patients, 21 IOI patients, and 41 unaffected controls. Automatic gating by FlowSOM revealed decreased abundance of meta‐clusters containing dendritic cells in patients, which we confirmed by manual gating. A decreased percentage of (HLA‐DR+CD303+CD123+) plasmacytoid dendritic cells (pDC) in the circulation of IOI patients and decreased (HLA‐DR+CD11c+CD1c+) conventional dendritic cells (cDC) type‐2 for IOI patients were replicated in an independent cohort of patients and controls. Meta‐analysis of both cohorts demonstrated that pDCs are also decreased in blood of NHOL patients and highlighted that the decrease in blood cDC type‐2 was specific for IOI patients compared to NHOL or controls. Deconvolution‐based estimation of immune cells in transcriptomic data of 48 orbital biopsies revealed a decrease in the abundance of pDC and cDC populations within the orbital microenvironment of IOI patients. Collectively, these data suggest a previously underappreciated role for dendritic cells in orbital disorders.

Scada for Prototype of Double Track Railroad Gate Arm Automation Based on PLC M221

Most of the gate arm at railroad crossings in Indonesia still using manual systems. Operator error and failure of a manual gate arm operate are factors in the occurrence of accidents on the railroad crossing. SCADA for railroad gate arm is an automation control system to open and close a railroad gate arm that has 2 lanes (double track). The system uses a PLC Schneider M221 as a controller and an HMI as an interface. The HMI allows operators to monitor system conditions in real-time. Four pairs of photosensors on the right and left side of the gate arm are used to detect trains in each lane. Information from the photosensor is processed by the PLC to control the gate arm motor, buzzer, indicator lights, and information display to the HMI. There are 2 operation modes, automatic and manual. In automatic mode, when the photosensor detects a train is about to pass, the gate arm closes, the buzzer goes on, the red light flashes. After the photosensor detects that the train has passed, the gate arm will open, the buzzer will off, and the green light will off. if the photosensor are damaged then manual operation mode is using by pressing the open or close button.

Altered Dynamics of Monocyte Subpopulations and Pro-Inflammatory Signaling Pathways in Polycythemia Vera Revealed By Mass Cytometry

Introduction: Polycythemia vera (PV) is a clonal hematologic malignancy characterized by overproduction of mature erythrocytes that exhibits a propensity for transformation to myelofibrosis (MF) and/or secondary acute myeloid leukemia (sAML). Like the majority of myeloproliferative neoplasms (MPN), the defining genetic feature of PV is the JAK2 V617F mutation, occurring in >95% of patients, resulting in overactive JAK-STAT signaling which drives myeloproliferation and can contribute to inflammation. We have demonstrated that aberrant NFkB pathway signaling plays an integral role in maintaining monocyte-derived inflammatory cytokine production in MF (Fisher et al, Leukemia 2017 & 2019). We have recently shown that inflammation is a hallmark of PV as well, but the signaling pathways and cell populations responsible have not been elucidated (Fowles et al, Leukemia 2019). Mass cytometry (CyTOF) enables the simultaneous measurement of 40+ markers with single cell resolution, thereby allowing comprehensive analysis of intracellular signaling networks in defined hematopoietic cell populations. We utilized mass cytometry to investigate cell populations and inflammatory signaling pathways in a cohort of PV patients including those who transformed to MF or secondary acute myeloid leukemia (sAML). Methods: Plasma cytokine levels were measured with the U-PLEX Human Cytokine 10plex assay (MSD) from 20 normal donors, 22 PV, 92 MF and 6 sAML patients, as well as serial samples from PV patients transformed to MF/sAML. Cryopreserved cells bone marrow (BM) and/or peripheral blood (PB) from 15 PV patients and 12 normal donors from were incubated with or without TPO or TNF and then stained with surface and intracellular antibodies prior to being subjected to mass cytometric analysis. Cell population frequency and intracellular signaling were identified and calculated through viSNE analysis and manual gating in Cytobank. Results: Plasma levels of inflammatory cytokines IL-6, IL-8, MIP-1a, and TNF increased with disease severity from PV to MF and sAML. For serial samples, an increased number of elevated cytokines was observed in post-transformation samples, suggesting a role for inflammation in MPN disease progression. Mass cytometry analysis revealed expansion of CD14+CD16+ intermediate monocytes and CD16+CD14-CD33+ non-classical monocytes in PV PB compared to both normal BM and PB. A reduction in CD14+CD16- classical monocytes was observed in PV compared to normal in both BM and PB, suggesting a shift in pro-inflammatory subsets in PV. Heightened NFKB activation in response to TNF stimulation was observed in lineage negative CD34+ cells in the majority of PV PB and BM samples tested compared to normal. Similarly, abnormally elevated pSTAT3/5 levels were frequently observed in response to TPO stimulation. In monocyte populations, basal elevations in several signaling markers including pNFkB, pSTAT1, and pMAPKAP2 were identified. Notably, in serial samples from patients transformed to MF or sAML, evidence of progressive NFkB hyperactivation associated with disease evolution was observed. Conclusions: Inflammatory cytokines are known to be elevated in MPN in part through constitutive JAK-STAT signaling, and recently we identified NFkB signaling as an important contributor in MF. To our knowledge NFkB has not previously been investigated in PV. This study suggests that NFkB signaling is similarly important to PV and might play a role in disease transformation. Our plasma cytokine data identified a correlation with higher cytokine levels associated with disease severity from PV to MF and sAML. This is consistent with prior studies associating IL-6 and IL-8 with leukemic transformation of MF. The observed expansion of CD14+CD16+ monocytes in PV is notable because studies have associated this population with increased endothelial cell adhesion and atherosclerosis. These associations have also been reported in JAK2 V617F-mutant red blood cells and neutrophils, raising the question of how the JAK2 mutation affects the function of pro-inflammatory monocyte subsets. Integration of this study with molecular and gene expression data to further investigate the role of inflammation in MPN progression are ongoing. Disclosures Oh: Novartis: Consultancy; Incyte: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees.

Comparison of Minimal Residual Disease Detection between the Manual and Automated Gating Strategies of Euroflow Next-Generation Flow in Patients with Multiple Myeloma

Comparison of Minimal Residual Disease Detection between the Manual and Automated Gating Strategies of Euroflow Next-Generation Flow in Patients with Multiple Myeloma

IMMU-37. SINGLE-CELL SYSTEMS NEUROIMMUNOLOGY REVEALS IMMUNOSUPPRESSIVE CORRELATES WITH VENTRICULAR STEM CELL NICHE CONTACT IN HUMAN GLIOBLASTOMA

Abstract Glioblastomas make up more than 60% of adult primary brain tumors and carry a median survival of less than 15 months despite aggressive standard therapy. Immunotherapy, which is now standard of care for many solid tumors, offers an appealing therapeutic approach that may improve outcomes for glioblastoma patients. Predictive features in glioblastomas that may inform responsiveness to different immunotherapeutic modalities, however, are still lacking. Recent studies have demonstrated that patients whose tumors show radiographic contact with the lateral ventricles, and thus the stem cell niche of the ventricular-subventricular zone (V-SVZ), have reduced survival outcomes compared to patients whose tumors do not contact the V-SVZ. We therefore hypothesized that tumor contact with the V-SVZ engenders a unique, immunosuppressive microenvironment that promotes tumor growth by suppressing anti-tumor immunity. Glioblastoma tumors, obtained in accordance with the Declaration of Helsinki and with institutional IRB approval (#131870, #030372, #181970), were disaggregated into single-cell suspensions and multi-dimensional single-cell mass cytometry was performed to interrogate >30 immune parameters in thirteen immune populations infiltrating human glioblastomas. Using advanced computational dimensionality-reduction tools (Citrus, t-SNE, FlowSOM, and MEM), we identified distinctions among the abundance and phenotypes of tumor-infiltrating immune cells. Firstly, on the basis of tumor contact with the V-SVZ, Citrus identified differential abundance of five T and myeloid cell subsets among glioblastomas. Secondly, differential expression of five functional immune markers was observed in seven distinct immune cell subsets infiltrating glioblastoma tumors. Further, both immune abundance and marker expression correlated with patient outcome. Manual gating analysis and parallel computational pipelines confirmed that comparable cell subsets could be identified with traditional approaches and unsupervised algorithmic analysis. These results provide key insights into the immune microenvironment of glioblastomas. In addition, several clinically actionable immunotherapeutic targets were uncovered that may be used to optimize treatment strategies for glioblastomas based on V-SVZ contact status.

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?

Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry.

Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information-rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks, hence it would be advantageous to replace manual gating with an automated process. Ideally, this automated process would be based on stain-free measurements, as the currently used staining techniques are expensive and potentially confounding. These stain-free measurements originate from the brightfield and darkfield image channels, which capture transmitted and scattered light, respectively. To realize this automated, stain-free approach, advanced machine learning (ML) methods are required. Previous works have successfully tested this approach on cell cycle phase classification with both a classical ML approach based on manually engineered features, and a deep learning (DL) approach. In this work, we compare both approaches extensively on the problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream-X MK II imaging flow cytometer. Two samples were stained for the identification of eight white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both data sets, four ML classifiers were evaluated on stain-free imagery with stratified 5-fold cross-validation. On the white blood cell data set, the best obtained results were 0.778 and 0.703 balanced accuracy for classical ML and DL, respectively. On the eosinophil data set, this was 0.871 and 0.856 balanced accuracy. We conclude that classifying cell types based on only stain-free images is possible with all four classifiers. Noteworthy, we also find that the DL approaches tested in this work do not outperform the approaches based on manually engineered features. © 2019 International Society for Advancement of Cytometry.

Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.

High‐throughput single‐cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally‐optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Whole blood preservation methods alter chemokine receptor detection in mass cytometry experiments

A potential hurdle when applying mass cytometry to the field study setting is the streamlining of sample collection while at the same time protecting the integrity of important cell epitopes. Whole blood preservation kits applying fixation and/or permeabilization agents are increasingly used in clinical trials to preserve leukocytes needed for downstream analysis. We here present a structured overview of leukocyte surface marker detectability in samples processed with four commercially available whole blood preservation kits; 1) Proteomic Stabilizer, 2) Stable-Lyse V2 and Stable-Store V2, 3) Cytodelics and 4) Lyse/Fix Buffer, as well as in samples treated with buffers included in Mass-tag Cellular Barcoding kits. Isolated leukocytes were stained with a 28-marker panel (including 7 chemokine receptors) of metal-conjugated antibodies and analysed on a mass cytometer. Exploration of the data by manual gating and viSNE analysis showed that although many markers were similarly detected across all sample conditions, most of the chemokine receptors in our panel, particularly CXCR3, CCR4, CCR6 and CXCR5, were incorrectly detected in the preserved samples and thus incompatible with the fixation and permeabilization agents found in whole blood preservation kits and in buffers used prior to barcoding.

Minimizing Batch Effects in Mass Cytometry Data.

Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-barcode technical variability. However, with only 20 samples per barcode, multiple barcode sets (batches) are required to address questions in robustly powered study designs. A batch adjustment procedure is required to reduce variability across batches and to facilitate direct comparison of runs performed across multiple barcodes run over weeks, months, or years. We describe a method using technical replicates that are included in each run to determine and apply an appropriate adjustment per batch without manual intervention. The use of technical replicate samples (i.e., anchors or reference samples) avoids assumptions of sample homogeneity among batches, and allows direct estimation of batch effects and appropriate adjustment parameters applicable to all samples within a batch. Quantification of cell subpopulations and mean signal intensity pre- and post-adjustment using both manual gating and unsupervised clustering demonstrate substantial mitigation of batch effects in the anchor samples used for this adjustment calculation, and in a second validation set of technical replicates.

Abstract 021: Single Cell Multiplex Mass Cytometry Reveals Differential Abundance of Specific Memory and Regulatory T Cell Populations in Human Hypertension

Hypertension is the leading risk factor for morbidity and mortality worldwide. Emerging evidence in animal models demonstrates the importance of a variety of innate and adaptive immune cells in hypertension. We hypothesized that the abundance and phenotype of specific circulating immune cell subsets is altered in human hypertension reflecting disease pathophysiology. We performed unbiased high dimensional, single cell profiling of peripheral blood mononuclear cells in humans with a panel of 31 cell surface markers using mass cytometry. Unsupervised computational analysis from 11 control and 10 hypertensive individuals matched for age, gender, race, and body mass index revealed consistent increases in a novel memory helper T cell subset in hypertension. Manual two dimensional gating revealed that this CD4 + CD45RO + CD62L + CCR7 - CD161 lo memory cell population is nearly 2-fold increased in hypertensive subjects (1.0 vs 1.9%). As an alternative to starting with unsupervised analysis, manual gating for major known immune cell populations revealed a 40% decrease in memory cytotoxic T cells and a 29% decrease in regulatory T cells in hypertensive individuals (3.2 vs 1.9% and 1.3 vs 0.93%, respectively). Unsupervised analysis of the cellular subsets of these populations using Phenograph revealed a selective 63% decrease in PD-1 + memory cytotoxic T cells and 49% decrease in CCR10 + regulatory T cells in hypertension (0.67 vs 0.25% and 0.37 vs 0.19%, respectively). Taken together, these results demonstrate that in human hypertension there are increases in a novel memory helper T cell subset and decreases in PD-1 + memory cytotoxic and CCR10 + regulatory T cell populations. These findings may provide new insights into hypertension pathogenesis and potential therapeutic targets.

A Computational Pipeline for the Diagnosis of CVID Patients.

Common variable immunodeficiency (CVID) is one of the most frequently diagnosed primary antibody deficiencies (PADs), a group of disorders characterized by a decrease in one or more immunoglobulin (sub)classes and/or impaired antibody responses caused by inborn defects in B cells in the absence of other major immune defects. CVID patients suffer from recurrent infections and disease-related, non-infectious, complications such as autoimmune manifestations, lymphoproliferation, and malignancies. A timely diagnosis is essential for optimal follow-up and treatment. However, CVID is by definition a diagnosis of exclusion, thereby covering a heterogeneous patient population and making it difficult to establish a definite diagnosis. To aid the diagnosis of CVID patients, and distinguish them from other PADs, we developed an automated machine learning pipeline which performs automated diagnosis based on flow cytometric immunophenotyping. Using this pipeline, we analyzed the immunophenotypic profile in a pediatric and adult cohort of 28 patients with CVID, 23 patients with idiopathic primary hypogammaglobulinemia, 21 patients with IgG subclass deficiency, six patients with isolated IgA deficiency, one patient with isolated IgM deficiency, and 100 unrelated healthy controls. Flow cytometry analysis is traditionally done by manual identification of the cell populations of interest. Yet, this approach has severe limitations including subjectivity of the manual gating and bias toward known populations. To overcome these limitations, we here propose an automated computational flow cytometry pipeline that successfully distinguishes CVID phenotypes from other PADs and healthy controls. Compared to the traditional, manual analysis, our pipeline is fully automated, performing automated quality control and data pre-processing, automated population identification (gating) and deriving features from these populations to build a machine learning classifier to distinguish CVID from other PADs and healthy controls. This results in a more reproducible flow cytometry analysis, and improves the diagnosis compared to manual analysis: our pipelines achieve on average a balanced accuracy score of 0.93 (±0.07), whereas using the manually extracted populations, an averaged balanced accuracy score of 0.72 (±0.23) is achieved.

Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology.

Recent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.

Ultrafast clustering of single-cell flow cytometry data using FlowGrid

BackgroundFlow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells.ResultsHere, we present a new clustering algorithm that combines the advantages of density-based clustering algorithm DBSCAN with the scalability of grid-based clustering. This new clustering algorithm is implemented in python as an open source package, FlowGrid. FlowGrid is memory efficient and scales linearly with respect to the number of cells. We have evaluated the performance of FlowGrid against other state-of-the-art clustering programs and found that FlowGrid produces similar clustering results but with substantially less time. For example, FlowGrid is able to complete a clustering task on a data set of 23.6 million cells in less than 12 seconds, while other algorithms take more than 500 seconds or get into error.ConclusionsFlowGrid is an ultrafast clustering algorithm for large single-cell flow cytometry data. The source code is available at https://github.com/VCCRI/FlowGrid.

Expansion and activation of distinct central memory T lymphocyte subsets in complex regional pain syndrome

BackgroundComplex regional pain syndrome (CRPS) is a debilitating condition where trauma to a limb results in devastating persistent pain that is disproportionate to the initial injury. The pathophysiology of CRPS remains unknown; however, accumulating evidence suggests it is an immunoneurological disorder, especially in light of evidence of auto-antibodies in ~ 30% of patients. Despite this, a systematic assessment of all circulating leukocyte populations in CRPS has never been performed.MethodsWe characterised 14 participants as meeting the Budapest clinical criteria for CRPS and assessed their pain ratings and psychological state using a series of questionnaires. Next, we performed immunophenotyping on blood samples from the 14 CRPS participants as well as 14 healthy pain-free controls using mass cytometry. Using a panel of 38 phenotypic and activation markers, we characterised the numbers and intracellular activation status of all major leukocyte populations using manual gating strategies and unsupervised cluster analysis.ResultsWe have shown expansion and activation of several distinct populations of central memory T lymphocytes in CRPS. The number of central memory CD8+ T cells was increased 2.15-fold; furthermore, this cell group had increased phosphorylation of NFkB and STAT1 compared to controls. Regarding central memory CD4+ T lymphocytes, the number of Th1 and Treg cells was increased 4.98-fold and 2.18-fold respectively, with increased phosphorylation of NFkB in both populations. We also found decreased numbers of CD1c+ myeloid dendritic cells, although with increased p38 phosphorylation. These changes could indicate dendritic cell tissue trafficking, as well as their involvement in lymphocyte activation.ConclusionsThese findings represent the first mass cytometry immunophenotyping study in any chronic pain state and provide preliminary evidence of an antigen-mediated T lymphocyte response in CRPS. In particular, the presence of increased numbers of long-lived central memory CD4+ and CD8+ T lymphocytes with increased activation of pro-inflammatory signalling pathways may indicate ongoing inflammation and cellular damage in CRPS.

P152 ANALYSIS OF HUMAN COLONIC TISSUE AND PERIPHERAL BLOOD BY MASS CYTOMETRY REVEALS IMMUNE SIGNATURES DIFFERENTIATING INFLAMMATORY STATES IN UC, CD, AND CONTROLS

Intro: Inflammatory bowel disease (IBD), consisting of ulcerative colitis (UC) and Crohn’s disease (CD), leads to chronic intestinal inflammation associated with significant morbidity. The underlying mucosal dysregulation is not entirely understood, and many patients are refractory to available medical therapy. Defining inflammatory and disease-specific immune signatures would improve our understanding of IBD and help identify novel biomarkers and targets for treatment. We used mass cytometry (CYTOF) to profile colonic lamina propria (LP) and peripheral blood (PB) mononuclear (MN) cells of patients with IBD as well as non-IBD controls. LPMCs (n=87) and PBMCs (n=88) were obtained from non-IBD controls (LP n=18, PB n=27), uninflamed UC (UC-; LP n=13, PB n=6), inflamed UC (UC+; LP n=19, PB n=21), uninflamed CD (CD-; LP n=24, PB n=9), and inflamed CD (CD+; LP n=13, PB n=25) patients, cryopreserved and thawed in batches. Cells were stimulated with PMA/ionomycin, stained with a panel of CYTOF antibodies, and data analyzed with both automated clustering and manual gating. LP differences distinguish inflamed IBD (IBD+) from uninflamed IBD (IBD-) and from non-IBD: IBD LP is enriched with HLADR+CD38+ (“activated”) T cells (26.4% IBD+ > 13.9% IBD- > 5.9% non-IBD, out of total CD45+ cells [p=5.6e-07]). Tregs (1.10% IBD+ > 0.6% IBD- > 0.26% non-IBD [p=0.00067]) and IL-1b+ macrophages (0.31% IBD+, 0.07% IBD-, 0.23% non-IBD [p=2.1e-07]) were increased in IBD+. Additionally, IBD- had fewer DCs than IBD+ and non-IBD (4.6% IBD+, 2.4% IBD-, 5.8% non-IBD [p=0.00024]). LP differences distinguish UC+ from CD+: UC+ LP has fewer T cells compared to CD+ (UC+ 59% vs. CD+ 69% [p=0.013]) with a trend towards more B cells. Among T cells, CD4+ are more abundant in UC+ than in CD+ and non-IBD (UC+ 19% vs. CD+ 14% [p=0.026] vs. 14% non-IBD [p=0.024]). There are more “activated” CD4+ T cells in UC+ that express IL-17 over IFNg with the opposite being true in CD+ (ratio 1.55 IL-17:IFNg in UC+ vs. 0.27 IL17:IFNg in CD+ [p=0.0017]). CD+ LP has greater IL-1b+ CD123+DCs compared to UC+ and non-IBD (0.026% UC+, 0.082% CD+, 0.034% non-IBD [p=0.011]). The unique PB difference distinguishing CD+ from UC+ and non-IBD was an increase in IL-1b+ monocytes in CD+ (0.24% UC+, 0.69% CD+, 0.29% non-IBD [p=0.009]). CYTOF analysis of LP and PB reveals differences associated with colonic inflammation as well as immune characteristics that differentiate UC and CD. Whereas differences in the LP involve populations of activated T cells, Tregs, and innate immune cells, significant differences in the PB are restricted to innate cells. Certain signatures, such as greater abundance of IL-1b+ monocytes in the PB of CD+ patients, could potentially be used to distinguish inflammatory disease states in the periphery and identify new avenues of diagnostic and treatment approaches.

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.

Analysis of Mass Cytometry Data.

The CyTOF system produces single cell protein expression data similar to that from flow cytometry, but with an increased number of features measured. Traditionally, analysis of these data is carried out using manual gating, but with the increased dimensionality, manual gating becomes a suboptimal analysis strategy in some cases. To address this, a number of data analysis tools for tasks such as clustering, differential abundance analysis, and visualization have been developed and made freely available. We here introduce some of the more popular tools for CyTOF analysis and exemplify their utility in a common analysis workflow.