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High-dimensional cell phenotyping is a powerful tool to study molecular and cellular changes in health and diseases. CyTOF enables high-dimensional cell phenotyping using tens of surface and intra-cellular markers. To utilize the full potential of CyTOF, we need advanced clustering and machine learning methodologies to enable automated gating of the complex data. Here we show that critical modifications to a machine learning based FlowSOM package and precise parameter optimization can enable us to reliably analyze the complex CyTOF data. We show the impact of key parameters on clustering outcomes while addressing bugs within the publicly available package. We modified the FlowSOM pipeline to fix the bugs, enable scalability to handle large datasets and perform parameter optimization. We further validated this modified pipeline on a substantial external immunological dataset demonstrating the need of data-specific tailored parameter optimization to ensure reliable definition and interrogation of immune cell populations associated with immune disorders. Optimized FlowSOM pipeline for reliable clustering of high-dimensional cytometry data. [ABSTRACT FROM AUTHOR]

High-dimensional cell phenotyping is a powerful tool to study molecular and cellular changes in health and diseases. CyTOF enables high-dimensional cell phenotyping using tens of surface and intra-cellular markers. To utilize the full potential of CyTOF, we need advanced clustering and machine learning methodologies to enable automated gating of the complex data. Here we show that critical modifications to a machine learning based FlowSOM package and precise parameter optimization can enable us to reliably analyze the complex CyTOF data. We show the impact of key parameters on clustering outcomes while addressing bugs within the publicly available package. We modified the FlowSOM pipeline to fix the bugs, enable scalability to handle large datasets and perform parameter optimization. We further validated this modified pipeline on a substantial external immunological dataset demonstrating the need of data-specific tailored parameter optimization to ensure reliable definition and interrogation of immune cell populations associated with immune disorders. [ABSTRACT FROM AUTHOR]

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

Minimal residual disease (MRD) is of major importance in onco-hematology, particularly in acute myeloid leukemia (AML). MRD measures the amount of leukemia cells remaining in a patient after treatment, and is an essential tool for disease monitoring, relapse prognosis, and guiding treatment decisions. Patients with a negative MRD tend to have superior disease-free and overall survival rates. Considerable effort has been made to standardize MRD practices. A variety of techniques, including flow cytometry and molecular methods, are used to assess MRD, each with distinct strengths and weaknesses. MRD is recognized not only as a predictive biomarker, but also as a prognostic tool and marker of treatment efficacy. Expected advances in MRD assessment encompass molecular techniques such as NGS and digital PCR, as well as optimization strategies such as unsupervised flow cytometry analysis and leukemic stem cell monitoring. At present, there is no perfect method for measuring MRD, and significant advances are expected in the future to fully integrate MRD assessment into the management of AML patients. [ABSTRACT FROM AUTHOR]

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery [ABSTRACT FROM AUTHOR]

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

As the size and complexity of high‐dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end‐to‐end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre‐processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single‐cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre). [ABSTRACT FROM AUTHOR]

The number of patients with liver diseases has increased significantly with the progress of global industrialization. Hepatic fibrosis, one of the most common liver diseases diagnosed in many developed countries, occurs in response to chronic liver injury and is primarily driven by the development of inflammation. Earlier immunological studies have been focused on the importance of the innate immune response in the pathophysiology of steatohepatitis and fibrosis, but recently, it has also been reported that adaptive immunity, particularly B cells, plays an essential role in hepatic inflammation and fibrosis. However, despite recent data showing the importance of adaptive immunity, relatively little is known about the role of B cells in the pathogenesis of steatohepatitis fibrosis. In this study, a single-cell-based, high-dimensional mass cytometric investigation of the peripheral blood mononuclear cells collected from mice belonging to three groups [normal chow (NC), thioacetamide (TAA), and 11beta-HSD inhibitor drug] was conducted to further understand the pathogenesis of liver fibrosis through reliable noninvasive biomarkers. Firstly, major immune cell types and their population changes were qualitatively analyzed using UMAP dimensionality reduction and two-dimensional visualization technique combined with a conventional manual gating strategy. The population of B cells displayed a twofold increase in the TAA group compared to that in the NC group, which was recovered slightly after treatment with the 11beta-HSD inhibitor drug. In contrast, the populations of NK cells, effector CD4+ T cells, and memory CD8+ T cells were significantly reduced in the TAA group compared with those in the NC group. Further identification and quantification of the major immune cell types and their subsets were conducted based on automated clustering approaches [PhenoGraph (PG) and FlowSOM]. The B-cell subset corresponding to PhenoGraph cluster PG#2 (CD62LhighCD44highLy6chigh B cells) and PG#3 (CD62LhighCD44highLy6clow B cell) appears to play a major role in both the development of hepatic fibrosis and recovery via treatment, whereas PG#1 (CD62LlowCD44highLy6clow B cell) seems to play a dominant role in the development of hepatic fibrosis. These findings provide insights into the roles of cellular subsets of B cells during the progression of, and recovery from, hepatic fibrosis.

γδ T cells are a highly versatile immune lineage involved in host defense and homeostasis, but questions remain around their heterogeneity, precise function and role during health and disease. We used multi−parametric flow cytometry, dimensionality reduction, unsupervised clustering, and self-organizing maps (SOM) to identify novel γδ T cell naïve/memory subsets chiefly defined by CD161 expression levels, a surface membrane receptor that can be activating or suppressive. We used middle-to-old age individuals given immune blockade is commonly used in this population. Whilst most Vδ1+subset cells exhibited a terminal differentiation phenotype, Vδ1− subset cells showed an early memory phenotype. Dimensionality reduction revealed eight γδ T cell clusters chiefly diverging through CD161 expression with CD4 and CD8 expression limited to specific subpopulations. Comparison of matched healthy elderly individuals to bronchiectasis patients revealed elevated Vδ1+ terminally differentiated effector memory cells in patients potentially linking this population with chronic proinflammatory disease. [ABSTRACT FROM AUTHOR]

The number of patients with liver diseases has increased significantly with the progress of global industrialization. Hepatic fibrosis, one of the most common liver diseases diagnosed in many developed countries, occurs in response to chronic liver injury and is primarily driven by the development of inflammation. Earlier immunological studies have been focused on the importance of the innate immune response in the pathophysiology of steatohepatitis and fibrosis, but recently, it has also been reported that adaptive immunity, particularly B cells, plays an essential role in hepatic inflammation and fibrosis. However, despite recent data showing the importance of adaptive immunity, relatively little is known about the role of B cells in the pathogenesis of steatohepatitis fibrosis. In this study, a single-cell-based, high-dimensional mass cytometric investigation of the peripheral blood mononuclear cells collected from mice belonging to three groups [normal chow (NC), thioacetamide (TAA), and 11beta-HSD inhibitor drug] was conducted to further understand the pathogenesis of liver fibrosis through reliable noninvasive biomarkers. Firstly, major immune cell types and their population changes were qualitatively analyzed using UMAP dimensionality reduction and two-dimensional visualization technique combined with a conventional manual gating strategy. The population of B cells displayed a twofold increase in the TAA group compared to that in the NC group, which was recovered slightly after treatment with the 11beta-HSD inhibitor drug. In contrast, the populations of NK cells, effector CD4+ T cells, and memory CD8+ T cells were significantly reduced in the TAA group compared with those in the NC group. Further identification and quantification of the major immune cell types and their subsets were conducted based on automated clustering approaches [PhenoGraph (PG) and FlowSOM]. The B-cell subset corresponding to PhenoGraph cluster PG#2 (CD62LhighCD44highLy6chigh B cells) and PG#3 (CD62LhighCD44highLy6clow B cell) appears to play a major role in both the development of hepatic fibrosis and recovery via treatment, whereas PG#1 (CD62LlowCD44highLy6clow B cell) seems to play a dominant role in the development of hepatic fibrosis. These findings provide insights into the roles of cellular subsets of B cells during the progression of, and recovery from, hepatic fibrosis. [ABSTRACT FROM AUTHOR]

Diabetic neuropathic pain is a common and devastating complication of type 1 diabetes, but the mechanism by which it develops and persists is yet to be fully elucidated. This study utilised high-dimensional suspension mass cytometry in a pilot cohort to investigate differences in peripheral blood immunophenotypes between type 1 diabetes patients with (n ​= ​9) and without (n ​= ​9) peripheral neuropathic pain. The abundance and activation of several leukocyte subsets were investigated with unsupervised clustering approaches FlowSOM and SPADE, as well as by manual gating. Major findings included a proportional increase in CD4+ central memory T cells and an absolute increase in classical monocytes, non-classical monocytes, and mature natural killer cells in type 1 diabetes patients with pain compared to those without pain. The expression of CD27, CD127, and CD39 was upregulated on select T cell populations, and the phosphorylated form of pro-inflammatory transcription factor MK2 was upregulated across most populations. These results provide evidence that distinct immunological signatures are associated with painful neuropathy in type 1 diabetes patients. Further research may link these changes to mechanisms by which pain in type 1 diabetes is initiated and maintained, paving the way for much needed targeted treatments.

Acute cellular rejection (ACR) after liver transplantation (LT) goes along with allograft dysfunction, which is diagnosed by liver biopsy and concomitant histological analysis, representing the gold standard in clinical practice. Yet, liver biopsies are invasive, costly, time-intensive and require expert knowledge. Herein we present substantial evidence that blood plasma residing peripheral liver-derived extracellular particles (EP) could be employed to diagnose ACR non-invasively. In vitro experiments showed organ-specific EP release from primary human hepatocytes under immunological stress. Secondly, analysis of consecutive LT patients (n=11) revealed significant heightened EP concentrations days before ACR. By conducting a diagnostic accuracy study (n = 69, DRKS00011631), we explored the viability of using EP as a liquid biopsy for diagnosing ACR following LT. Consequently, novel EP populations in samples were identified using visualization of t-distributed stochastic neighbor embedding (viSNE) and self-organizing maps (FlowSOM) algorithms. As a result, the ASGR1+CD130+Annexin V+ EP subpopulation exhibited the highest accuracy for predicting ACR (area under the curve: 0.80, 95% confidence interval [CI], 0.70–0.90), with diagnostic sensitivity and specificity of 100% (95% CI, 81.67–100.0%) and 68.5% (95% CI, 55.3–79.3%), respectively. In summary, this new EP subpopulation presented the highest diagnostic accuracy for detecting ACR in LT patients.

Acute cellular rejection (ACR) after liver transplantation (LT) goes along with allograft dysfunction, which is diagnosed by liver biopsy and concomitant histological analysis, representing the gold standard in clinical practice. Yet, liver biopsies are invasive, costly, time-intensive and require expert knowledge. Herein we present substantial evidence that blood plasma residing peripheral liver-derived extracellular particles (EP) could be employed to diagnose ACR non-invasively. In vitro experiments showed organ-specific EP release from primary human hepatocytes under immunological stress. Secondly, analysis of consecutive LT patients (n=11) revealed significant heightened EP concentrations days before ACR. By conducting a diagnostic accuracy study (n = 69, DRKS00011631), we explored the viability of using EP as a liquid biopsy for diagnosing ACR following LT. Consequently, novel EP populations in samples were identified using visualization of t-distributed stochastic neighbor embedding (viSNE) and self-organizing maps (FlowSOM) algorithms. As a result, the ASGR1+CD130+Annexin V+ EP subpopulation exhibited the highest accuracy for predicting ACR (area under the curve: 0.80, 95% confidence interval [CI], 0.70–0.90), with diagnostic sensitivity and specificity of 100% (95% CI, 81.67–100.0%) and 68.5% (95% CI, 55.3–79.3%), respectively. In summary, this new EP subpopulation presented the highest diagnostic accuracy for detecting ACR in LT patients. [ABSTRACT FROM AUTHOR]

The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single‐cell level. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize, and interpret these high‐dimensional data sets. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high‐dimensional data sets using expert gating, t‐distributed stochastic neighbor embedding (t‐SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high‐dimensional clustering or dimensionality reduction algorithms such as t‐SNE, UMAP, or FlowSOM. However, large scale studies combined with an in‐depth technical analysis will be needed to assess differences between these technologies in more detail. © 2020 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Stochastic multicolor transgenic labeling systems, such as the Brainbow reporters, have emerged as powerful tools in lineage tracing experiments. Originally designed for large‐scale mapping of neuronal projections in densely populated tissues, they have been repurposed for diverse uses. The Brainbow 2.1‐derived Confetti reporter was used, for example, to define stem cell clonality and dynamics in crypts of the intestinal mucosa, T‐cell clonality, microglial heterogeneity, and B‐cell clonal evolution in germinal centers. Traditionally, read‐outs have relied on imaging in situ, providing information about cellular localization within tissue stroma. However, recent applications of the technique have moved into hematopoietically derived motile cell types, for example, T and B lymphocytes and their progeny, creating an unmet need to survey larger populations of cells ex vivo to determine labeling densities or skews in color representation over time to read‐out clonal expansion and selection effects. Originally designed for imaging methods, these reporters encode information in the spectral properties of fluorophores and their subcellular localization, making them poorly suited to traditional flow cytometry analyses. The advent of high‐content imaging and imaging flow cytometry have recently closed the gap between flow cytometry and imaging. We analyzed a 10‐color biallelic Confetti reporter using flow and imaging flow cytometry. Beyond its use as a high‐throughput method for measuring reporter labeling densities and color distributions over time, it also opens the door to new avenues of research relying on similar read‐outs, for example, tumor heterogeneity and clonal dynamics. © 2020 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Cell cycle analysis by mass cytometry (MC) is hampered by the poor resolution of the Iridium‐labeled DNA intercalator compared to DNA‐specific fluorescent dyes. We report here a minimum cell cycle panel for MC consisting of Ir‐intercalator (DNA content), IdU (S phase), anti‐pS28HistoneH3 (mitosis), anti‐CDT1 (G1 phase) and anti‐Geminin (non‐G1 phases). Cell cycle distributions obtained by MC were not significantly different from fluorescence flow cytometry results (r2 = 0.98, P < 0.001). Further subdivision of the G1 and G2 phases could be done with anti‐pS780RB1 (late G1) and anti‐PLK1 (late G2), respectively. A disadvantage of MC is that aggregates of cells cannot easily be removed while retaining all single cells. We have developed an analysis pipeline including unsupervised clustering by FlowSOM and subsequent single‐cell gating. When performed on cells stained with the cell cycle panel, this analysis pipeline successfully identified debris, dead/apoptotic cells, nonsingle‐cell populations and the major cell cycle phases. The presented cell cycle panel and analysis pipeline thus achieves true single‐cell analysis at the same time as any additional channels in the panel are open for phenotyping and cell cycle‐resolved expression or modification analysis. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multiple Myeloma (MM) is preceded by the clinically stable condition monoclonal gammopathy of undetermined significance (MGUS). Critical immune events that discriminate MGUS from newly diagnosed MM (ND)MM patients remain unknown, but may involve changes in the regulatory T cell (Treg) compartment that favor myeloma growth. To address this possibility, we used mass cytometry and the unsupervised clustering algorithm Flow self-organizing map (FlowSOM) to interrogate the distribution of multiple subsets within CD25+CD127low/negTreg in matched bone marrow (BM) and peripheral blood (PB) of MGUS and NDMM patients. Both mass cytometry and flow cytometry confirmed a trend toward prevalence of CD39−Treg within the Treg compartment in BM and PB of NDMM patients compared to CD39−Treg in MGUS patients. FlowSOM clustering displayed a phenotypic organization of Treg into 25 metaclusters that confirmed Treg heterogeneity. It identified two subsets which emerged within CD39−Treg of NDMM patients that were negligible or absent in CD39−Treg of MGUS patients. One subset was found in both BM and PB which phenotypically resembled activated Treg based on CD45RO, CD49d, and CD62L expression; another subset resembled BM-resident Treg based on its tissue-resident CD69+CD62L−CD49d− phenotype and restricted location within the BM. Both subsets co-expressed PD-1 and TIGIT, but PD-1 was expressed at higher levels on BM-resident Treg than on activated Treg. Within BM, both subsets had limited Perforin and Granzyme B production, whilst activated Treg in PB acquired high Perforin and Granzyme B production. In conclusion, the use of mass cytometry and FlowSOM clustering discovered two discrete subsets of CD39−Treg which are discordant in MGUS and NDMM patients and may be permissive of myeloma growth which warrants further study. Understanding the regulatory properties of these subsets may also advance MGUS and MM diagnosis, prognosis, and therapeutic implications for MM 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.

Multiparameter flow cytometry (MFC) is a powerful and versatile tool to accurately analyze cell subsets, notably to explore normal and pathological hematopoiesis. Yet, mostly supervised subjective strategies are used to identify cell subsets in this complex tissue. In the past few years, the implementation of mass cytometry and the big data generated have led to a blossoming of new software solutions. Their application to classical MFC in hematology is however still seldom reported. Here, we show how one of these new tools, the FlowSOM R solution, can be applied, together with the Kaluza® software, to a new delineation of hematopoietic subsets in normal human bone marrow (BM). We thus combined the unsupervised discrimination of cell subsets provided by FlowSOM and their expert‐driven node‐by‐node assignment to known or new hematopoietic subsets. We also show how this new tool could modify the MFC exploration of hematological malignancies both at diagnosis (Dg) and follow‐up (FU). This can be achieved by direct comparison of merged listmodes of reference normal BM, Dg, and FU samples of a representative acute myeloblastic case tested with the same immunophenotyping panel. This provides an immediate unsupervised evaluation of minimal residual disease. © 2019 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multiple Myeloma (MM) is preceded by the clinically stable condition monoclonal gammopathy of undetermined significance (MGUS). Critical immune events that discriminate MGUS from newly diagnosed MM (ND)MM patients remain unknown, but may involve changes in the regulatory T cell (Treg) compartment that favor myeloma growth. To address this possibility, we used mass cytometry and the unsupervised clustering algorithm Flow self-organizing map (FlowSOM) to interrogate the distribution of multiple subsets within CD25+CD127low/negTreg in matched bone marrow (BM) and peripheral blood (PB) of MGUS and NDMM patients. Both mass cytometry and flow cytometry confirmed a trend toward prevalence of CD39−Treg within the Treg compartment in BM and PB of NDMM patients compared to CD39−Treg in MGUS patients. FlowSOM clustering displayed a phenotypic organization of Treg into 25 metaclusters that confirmed Treg heterogeneity. It identified two subsets which emerged within CD39−Treg of NDMM patients that were negligible or absent in CD39−Treg of MGUS patients. One subset was found in both BM and PB which phenotypically resembled activated Treg based on CD45RO, CD49d, and CD62L expression; another subset resembled BM-resident Treg based on its tissue-resident CD69+CD62L−CD49d− phenotype and restricted location within the BM. Both subsets co-expressed PD-1 and TIGIT, but PD-1 was expressed at higher levels on BM-resident Treg than on activated Treg. Within BM, both subsets had limited Perforin and Granzyme B production, whilst activated Treg in PB acquired high Perforin and Granzyme B production. In conclusion, the use of mass cytometry and FlowSOM clustering discovered two discrete subsets of CD39−Treg which are discordant in MGUS and NDMM patients and may be permissive of myeloma growth which warrants further study. Understanding the regulatory properties of these subsets may also advance MGUS and MM diagnosis, prognosis, and therapeutic implications for MM patients. [ABSTRACT FROM AUTHOR]

Background: The presence of >94% classical monocytes (MO1, CD14++/CD16-) in peripheral blood (PB) has an excellent performance for the diagnosis of chronic myelomonocytic leukemia (CMML). However, the monocyte gating strategy is not well defined. The objective of the study was to compare monocyte gating strategies and propose an optimal one., Methods: This is a prospective, single center study assessing monocyte subsets in PB. First, we compared monocyte subsets using 13 monocyte gating strategies in 10 samples. Then we developed our own 10 color tube and tested it on 124 patients (normal white blood cell counts, reactive monocytosis, CMML and a spectrum of other myeloid malignancies). Both conventional and computational (FlowSOM) analyses were used., Results: Comparing different monocyte gating strategies, small but significant differences in %MO1 and percentually large differences in %MO3 (nonclassical monocytes) were found, suggesting that the monocyte gating strategy can impact monocyte subset quantification. Then, we designed a 10-color tube for this purpose (CD45/CD33/CD14/CD16/CD64/CD86/CD300/CD2/CD66c/CD56) and applied it to 124 patients. This tube allowed proper monocyte gating even in highly abnormal PB. Computational analysis found a higher %MO1 and lower %MO3 compared to conventional analysis. However, differences between conventional and computational analysis in both MO1 and MO3 were globally consistent and only minimal differences were observed when comparing the ranking of patients according to %MO1 or %MO3 obtained with the conventional versus the computational approach., Conclusions: The choice of monocyte gating strategy appears relevant for the monocyte subset distribution test. Our 10-color proposal allowed satisfactory monocyte gating even in highly abnormal PB. Computational analysis seems promising to increase reproducibility in monocyte subset quantification., (© 2022 International Clinical Cytometry Society.)

Background: High-quality data on bone marrow involvement (BMI) assessed by flow cytometry (FC) in follicular lymphoma (FL) is lacking., Aims: We set up a prospective protocol with a 10-color tube and acquisition of 500.000 leukocytes on a Nav flow cytometer for evaluation of BMI in FL by FC., Materials and Methods: FC was compared with a combination of histopathology and IGH gene rearrangement, which were considered the gold standard. We also compared BMI by FC with PET., Results: Fifty-two patients were included (median 67 years, 54% female). BMI by FC was seen in 35 (67%), with a median involvement of 1.2% (interquartile range: 0.3%-7%) of leukocytes. Comparison with the gold standard revealed two false negatives and two false positives (potentially true involvement undetected by the gold standard). BMI by PET was seen in 14/46 (30%). Immunophenotype of FL in the bone marrow was highly heterogeneous. The most common phenotypic abnormality was dim expression of CD19 (>0.5 log loss in 30% of patients). CD10 was negative in 13 (37%) and incompletely positive (overlap with the negative population) in a further 8 (28%) while entirely positive only in 14 (48%). Other abnormalities (loss of CD20, gain or loss of CD79b, expression of CD43, and substantial loss of CD45) were rare. Computational analysis by means of FlowSOM confirmed the heterogeneous phenotype, with FL from different patients clustering in unrelated metaclusters., Conclusion: BMI by FL was frequent and immunophenotype was heterogeneous. However, this protocol enabled detection of FL in bone marrow in the vast majority of patients with bone marrow involvement by the gold standard., (© 2022 International Clinical Cytometry Society.)

Vaccines induce a highly complex immune reaction in secondary lymphoid organs to generate immunological memory against an antigen or antigens of interest. Measurement of post immunization immune responses generated by specialized lymphocyte subsets requires time-dependent sampling, usually of the blood. Several T and B cell subsets are involved in the reaction, including CD4 and CD8 T cells, T follicular helper cells (Tfh), and germinal center B cells alongside their circulating (c) counterparts; cTfh and antibody secreting cells. Multicolor flow cytometry of peripheral blood mononuclear cells (PBMC) coupled with high-dimensional analysis offers an opportunity to study these cells in detail. Here we demonstrate a method by which such data can be generated and analysed using software that renders multidimensional data on a two dimensional map to identify rare vaccine-induced T and B cell subsets., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Simple Summary: In acute myeloid leukemia (AML), minimal/measurable residual disease (MRD) can be assessed based on molecular markers or immunophenotypic features evaluated at diagnosis, through multiparameter flow cytometry (MFC) for the latter. New artificial intelligence tools allow to perform unsupervised analysis of MFC data. The Flow-Self-Organizing-Maps (FlowSOM) tool was used here to concomitantly compare MFC features of normal bone marrow together with diagnosis and follow-up bone marrow samples from 40 AML patients for the evaluation of MRD. MFC results were compared to molecular MRD, showing high concordance. This opens the road for a new easy and objective way of assessing MRD even in AML patients without molecular markers. The assessment of minimal residual disease (MRD) is increasingly considered to monitor response to therapy in hematological malignancies. In acute myeloblastic leukemia (AML), molecular MRD (mMRD) is possible for about half the patients while multiparameter flow cytometry (MFC) is more broadly available. However, MFC analysis strategies are highly operator-dependent. Recently, new tools have been designed for unsupervised MFC analysis, segregating cell-clusters with the same immunophenotypic characteristics. Here, the Flow-Self-Organizing-Maps (FlowSOM) tool was applied to assess MFC-MRD in 96 bone marrow (BM) follow-up (FU) time-points from 40 AML patients with available mMRD. A reference FlowSOM display was built from 19 healthy/normal BM samples (NBM), then simultaneously compared to the patient's diagnosis and FU samples at each time-point. MRD clusters were characterized individually in terms of cell numbers and immunophenotype. This strategy disclosed subclones with varying immunophenotype within single diagnosis and FU samples including populations absent from NBM. Detectable MRD was as low as 0.09% in MFC and 0.051% for mMRD. The concordance between mMRD and MFC-MRD was 80.2%. MFC yielded 85% specificity and 69% sensitivity compared to mMRD. Unsupervised MFC is shown here to allow for an easy and robust assessment of MRD, applicable also to AML patients without molecular markers. [ABSTRACT FROM AUTHOR]