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Background: Multiple myeloma remains an incurable disease with multiple relapses due to residual myeloma cells in the bone marrow of patients after therapy. Presence of small number of cancer cells in the body after cancer treatment, called minimal residual disease, has been shown to be prognostic for progression-free and overall survival. However, for multiple myeloma, it is unclear whether patients attaining minimal residual disease negativity may be candidates for treatment discontinuation. We investigated, if longitudinal flow cytometry-based monitoring of minimal residual disease (flow-MRD) may predict disease progression earlier and with higher sensitivity compared to biochemical assessments., Methods: Patients from the Nordic countries with newly diagnosed multiple myeloma enrolled in the European-Myeloma-Network-02/Hovon-95 (EMN02/HO95) trial and undergoing bone marrow aspiration confirmation of complete response, were eligible for this Nordic Myeloma Study Group (NMSG) substudy. Longitdudinal flow-MRD assessment of bone marrow samples was performed to identify and enumerate residual malignant plasma cells until observed clinical progression., Results: Minimal residual disease dynamics were compared to biochemically assessed changes in serum free light chain and M-component. Among 20 patients, reaching complete response or stringent complete response during the observation period, and with ≥3 sequential flow-MRD assessments analysed over time, increasing levels of minimal residual disease in the bone marrow were observed in six cases, preceding biochemically assessed disease and clinical progression by 5.5 months and 12.6 months (mean values), respectively. Mean malignant plasma cells doubling time for the six patients was 1.8 months (95% CI, 1.4-2.3 months). Minimal malignant plasma cells detection limit was 4 × 10-5., Conclusions: Flow-MRD is a sensitive method for longitudinal monitoring of minimal residual disease dynamics in multiple myeloma patients in complete response. Increasing minimal residual disease levels precedes biochemically assessed changes and is an early indicator of subsequent clinical progression., Trial Registration: NCT01208766., (© 2022. The Author(s).)

Background: It is important to confirm CD30 expression in T-cell lymphoma cases, but immunohistochemical staining for CD30 is not commonly performed and no comparison has been done between the results of flow cytometry (FCM) and immunohistochemical staining for CD30. Therefore, we devised a notation that we termed proportion of immunoreactivity/expression for FCM (PRIME-F notation), based on the cellular proportion showing different antigen-antibody reactivity., Methods: We retrospectively compiled 211 cases of T-cell lymphoma, assessed via FCM, from major hospitals in Miyagi Prefecture from January 2012 to January 2019, and compared 52 of these cases with the immunohistochemical immunoreactive (IR) pattern of CD30 (PRIME-I notation). The PRIME-F notation was divided into five levels: notations starting with "-" followed by 3, 2, and 1 ">" correspond to level-I, level-II, or level-III; notations starting with "(dim)+" correspond to level-IV; and those starting with "+" or "(bright)+" correspond to level-V., Results: The 52 cases of PRIME-F notation with "+" included 16 cases of peripheral T-cell lymphoma (PTCL/NOS), 3 of follicular T-cell lymphoma (FTL), 3 of angioimmunoblastic T-cell lymphoma (AITL), 6 of extranodal NK/T-cell lymphoma/nasal type (ENKL), 18 of adult T-cell lymphoma (ATL), and 6 cases of anaplastic large cell lymphoma (ALCL). Eight of the 52 cases were immunohistochemically CD30-negative. In the PRIME-F level-I to III group (excluding false-positive cases), 21.7% (5 out of 23 cases) were < 10% positive for CD30 upon immunohistochemistry (IHC). Contrarily, in the level-IV & -V group, no CD30 positivity rate of < 10% upon IHC was found (0%) (p = 0.0497). In level-IV, 42.9% of cases presented a CD30 negative rate > 1/3 upon IHC, while in level-V, only 7.1% (one out of 14 cases) did. The CD30 negative rate tended to be low (p = 0.0877) in level-V., Conclusions: To our knowledge, this is the first report describing the correspondence between FCM and immunohistochemistry findings for CD30 through newly proposed notations. The PRIME-F and PRIME-I notations for CD30 showed a minor positive correlation. The PRIME notation is considered universally applicable to antibodies, and notations of both FCM and IHC show great potential for big data., (© 2021. The Author(s).)

Background & Aims: Current antiviral therapies control but rarely eliminate HBV, leaving chronic HBV carriers at risk of developing hepatocellular carcinoma (HCC). Lacking or dysfunctional virus-specific adaptive immunity prevents control of HBV and allows the virus to persist. Restoring antiviral T-cell immunity could lead to HBV elimination and cure of chronically infected patients., Methods: We constructed bispecific T-cell engager antibodies that are designed to induce antiviral immunity through simultaneous binding of HBV envelope proteins (HBVenv) on infected hepatocytes and CD3 or CD28 on T cells. T-cell engager antibodies were employed in co-cultures with healthy donor lymphocytes and HBV-infected target cells. Activation of the T-cell response was determined by detection of pro-inflammatory cytokines, effector function (by cytotoxicity) and antiviral effects. To study in vivo efficacy, immune-deficient mice were transplanted with HBVenv-positive and -negative hepatoma cells., Results: The 2 T-cell engager antibodies synergistically activated T cells to become polyfunctional effectors that in turn elicited potent antiviral effects by killing infected cells and in addition controlled HBV via non-cytolytic, cytokine-mediated antiviral mechanisms. In vivo in mice, the antibodies attracted T cells specifically to the tumors expressing HBVenv resulting in T-cell activation, tumor infiltration and reduction of tumor burden., Conclusion: This study demonstrates that the administration of HBVenv-targeting T-cell engager antibodies facilitates a robust T-cell redirection towards HBV-positive target cells and provides a feasible and promising approach for the treatment of chronic viral hepatitis and HBV-associated HCC., Lay Summary: T-cell engager antibodies are an interesting, novel therapeutic tool to restore immunity in patients with chronic hepatitis B. As bispecific antibodies, they bind envelope proteins on the surface of the hepatitis B virus (HBV) and CD3 or CD28 on T cells. This way, they induce a potent antiviral and cytotoxic T-cell response that leads to the elimination of HBV-positive cells. These bispecific T-cell engager antibodies are exciting therapeutic candidates for chronic hepatitis B and HBV-associated hepatocellular carcinoma., Competing Interests: Conflict of interest U.P., F.M.,F.B., G.M. and O.Q. are named as inventors on patents WO 2015/036606 held by HMGU and DKFZ and WO 2016/146702 held by HMGU, DKFZ and TUM. U.P. serves as ad hoc advisor for Roche, Gilead, GSK, Merck, Arbutus and Vir Biotech. U.P. is co-founder and share-holder of SCG Cell Therapy who licensed patents WO 2015/036606 and WO 2016/146702. The remaining authors do not disclose a conflict of interest. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2021 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Tacrolimus (FK506) and rapamycin (RAPA) are widely used to maintain long-term immunosuppression after organ transplantation. However, the impact of accumulative drug administration on the recipients' immune systems remains unclear. We investigated the impact of 3-year FK506 or RAPA treatment after renal transplantation on the human immune systems. A discovery cohort of 30 patients was first recruited, and we discovered two distinctive T lineage suppressive regulatory patterns induced by chronic treatment of FK506 and RAPA. The increased percentage of senescent CD8 + CD57 + T lineages and less responsive T cell receptor (TCR) pathway in the FK506 group indicate better graft acceptance. Meanwhile, percentages of regulatory T cells (Tregs) and expression of CTLA-4 were both up to two-fold higher in the RAPA group, suggesting the inconsistent reactivation potential of the FK506 and RAPA groups when an anti-tumour or anti-infection immune response is concerned. Additionally, up-regulation of phosphorylated signaling proteins in T lineages after in vitro CD3/CD28 stimulation suggested more sensitive TCR-signaling pathways reserved in the RAPA group. An independent validation cohort of 100 renal transplantation patients was further investigated for the hypothesis that long-term RAPA administration mitigates the development of tumours and infections during long-term intake of immunosuppressants. Our results indicate that RAPA administration indeed results in less clinical oncogenesis and infection. The deep phenotyping of T-cell lineages, as educated by the long-term treatment of different immunosuppressants, provides new evidence for personalized precision medicine after renal transplantations., (© 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

Background: Diabetes mellitus (DM) is a chronic metabolic disorder associated with higher risk of having cardiovascular disease. Platelets play a promising role in the pathogenesis of cardiovascular complications in diabetes. Since last several decades, garlic and its bioactive components are extensively studied in diabetes and its complications. Our aim was to explore the antiplatelet property of allyl methyl sulfide (AMS) focusing on ameliorating platelet activation in diabetes., Method: We used streptozotocin- (STZ-) induced diabetic rats as model for type 1 diabetes. We have evaluated the effect of allyl methyl sulfide on platelet activation by administrating AMS to diabetic rats for 10 weeks. Flow cytometry-based analysis was used to evaluate the platelet activation, platelet aggregation, platelet macrophage interaction, and endogenous ROS generation in the platelets obtained from control, diabetes, and AMS- and aspirin-treated diabetic rats., Results: AMS treatment for 10 weeks effectively reduced the blood glucose levels in diabetic rats. Three weeks of AMS (50 mg/kg/day) treatment did not reduce the activation of platelets but a significant ( p < 0.05) decrease was observed after 10 weeks of treatment. Oral administration of AMS significantly ( p < 0.05) reduced the baseline and also reduced ADP-induced aggregation of platelets after 3 and 10 weeks of treatment. Furthermore, 10 weeks of AMS treatment in diabetic rats attenuated the endogenous ROS content ( p < 0.05) of platelets and platelet macrophage interactions. The inhibition of platelet activation in diabetic rats after AMS treatment was comparable with aspirin treatment (30 mg/kg/day)., Conclusion: We observed an inhibitory effect of allyl methyl sulfide on platelet aggregation, platelet activation, platelet macrophage interaction, and increased ROS levels in type 1 diabetes. Our data suggests that AMS can be useful to control cardiovascular complication in diabetes via inhibition of platelet activation., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Navya Malladi et al.)

A correct identification of seropositive individuals for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is of paramount relevance to assess the degree of protection of a human population to present and future outbreaks of the COVID-19 pandemic. We describe here a sensitive and quantitative flow cytometry method using the cytometer-friendly non-adherent Jurkat T-cell line that stably expresses the full-length native spike "S" protein of SARS-CoV-2 and a truncated form of the human EGFR that serves a normalizing role. S protein and huEGFRt coding sequences are separated by a T2A self-cleaving sequence, allowing to accurately quantify the presence of anti-S immunoglobulins by calculating a score based on the ratio of fluorescence intensities obtained by double-staining with the test sera and anti-EGFR. The method allows to detect immune individuals regardless of the result of other serological tests or even repeated PCR monitoring. As examples of its use, we show that as much as 28% of the personnel working at the CBMSO in Madrid is already immune. Additionally, we show that anti-S antibodies with protective neutralizing activity are long-lasting and can be detected in sera 8 months after infection., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

Background: Detection of micrometastases in the regional lymph nodes is one of the most important prognostic factors for melanoma patients. Our aim was to evaluate the suitability of flow cytometry for rapid quantification of disseminated melanoma cells in sentinel lymph nodes (SLN)., Methods: 132 SLNs from 104 patients diagnosed with melanoma were analyzed by flow cytometry, utilizing the extracellular marker melanoma-associated chondroitin sulfate proteoglycan, in addition to quantitative immunocytology and conventional histopathology, including immunohistochemistry. For quantification, the number of melanoma-positive cells per million lymph node cells (disseminated cancer cell density, DCCD) detected by flow cytometry was compared to the DCCD obtained by immunocytology., Results: Compared to histopathology and immunocytology, flow cytometry exhibited a sensitivity of 50% and a specificity of 85%. DCCDs of immunocytology and flow cytometry of the 37 immunocytologically positive SLNs showed a positive correlation (Spearman's ρ = 0.7, P < 0.0001). In 10 SLNs from 9 patients with high tumor load, the flow cytometric DCCD was 8-fold higher on average than the immunocytologic DCCD., Conclusions: Although flow cytometry is not yet suitable for early detection of metastatic melanoma, it promises to become a valuable tool for rapidly quantifying tumor load in high-risk patients., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Macrophage-inducible C-type lectin [Mincle] signalling plays a proinflammatory role in different organs such as the brain and liver, but its role in intestinal inflammation, including Crohn's disease [CD], remains unknown., Methods: The characteristics of Mincle signalling expression in CD patients and experimental colitis were examined. The functional role of Mincle signalling in the intestine was addressed in experimental colitis models in vivo by using Mincle knock-out [Mincle-/-] mice. In addition, neutralising anti-Mincle antibody, downstream spleen tyrosine kinase [Syk] inhibitor, and Mincle pharmacological agonist were used to study the Mincle signalling in intestine. Bone marrow-derived macrophages were collected from mice and used to further verify the effect of Mincle signalling in macrophages., Results: This study has shown that Mincle signalling was significantly elevated in active human CD and experimental colitis, and macrophages were the principal leukocyte subset that upregulate Mincle signalling. Mincle deficiency and Syk pharmacological inhibition ameliorated the colitis by reducing induced macrophage pyroptosis, and activation of Mincle with the agonist aggravated the intestinal inflammation. The ex vivo studies demonstrated that activation of Mincle signalling promoted the release of proinflammatory cytokines, whereas its absence restricted release of proinflammatory cytokines from pyroptosis of macrophages. In addition, Mincle/Syk signalling in macrophages could promote the production of chemokines to recruit neutrophils by activating mitogen-activated protein kinase [MAPK] during intestinal inflammation., Conclusions: Mincle signalling promotes intestinal mucosal inflammation by inducing macrophage pyroptosis. Modulation of the Mincle/Syk axis emerges as a potential therapeutic strategy to target inflammation and treat CD., (© The Author(s) 2020. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background and Aims: Ulcerative colitis [UC] is associated with excessive neutrophil infiltration and collateral tissue damage, but the link is not yet completely understood. Since c-MET receptor tyrosine kinase [MET] is required for neutrophil chemoattraction and cytotoxicity in response to its ligand hepatocyte growth factor [HGF], we aimed to identify the function of HGF-MET signalling in neutrophils in UC patients and in mice during intestinal inflammation., Methods: Serum and colonic biopsies from healthy controls and UC patients with active [Mayo endoscopic subscore 2-3] and inactive [Mayo endoscopic subscore 0-1] disease were collected to assess the level of serum and colonic HGF. Disease progression and immune cell infiltration were assessed during dextran sodium sulphate [DSS] colitis in wild-type and MRP8-Cre MET-LoxP mice., Results: Increased mucosal HGF expression was detected in patients with active UC, and in mice during the inflammatory phase of DSS colitis. Similarly, serum HGF was significantly increased in active UC patients and positively correlated with C-reactive protein and blood neutrophil counts. Flow cytometric analysis also demonstrated an upregulation of colonic MET+ neutrophils during DSS colitis. Genetic ablation of MET in neutrophils reduced the severity of DSS-induced colitis. Concomitantly, there was a decreased number of TH17 cells, which could be due to a decreased production of IL-1β by MET-deficient neutrophils., Conclusions: These data highlight the central role of neutrophilic HGF-MET signalling in exacerbating damage during intestinal inflammation. Hence, selective blockade of this pathway in neutrophils could be considered as a novel therapeutic approach in UC., (© The Author(s) 2020. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: It is estimated that 19.2% of kidneys exported for candidates with >98% calculated panel reactive antibodies are transplanted into unintended recipients, most commonly due to positive physical crossmatch (PXM). We describe the application of a virtual crossmatch (VXM) that has resulted in a very low rate of transplantation into unintended recipients., Methods: We performed a retrospective review of kidneys imported to our center to assess the reasons driving late reallocation based on the type of pretransplant crossmatch used for the intended recipient., Results: From December 2014 to October 2017, 254 kidneys were imported based on our assessment of a VXM. Of these, 215 (84.6%) were transplanted without a pretransplant PXM. The remaining 39 (15.4%) recipients required a PXM on admission using a new sample because they did not have an HLA antibody test within the preceding 3 months or because they had a recent blood transfusion. A total of 93% of the imported kidneys were transplanted into intended recipients. There were 18 late reallocations: 9 (3.5%) due to identification of a new recipient medical problem upon admission, 5 (2%) due to suboptimal organ quality on arrival, and only 4 (1.6%) due to a positive PXM or HLA antibody concern. A total of 42% of the recipients of imported kidneys had a 100% calculated panel reactive antibodies. There were no hyperacute rejections and very infrequent acute rejection in the first year suggesting no evidence for immunologic memory response., Conclusions: Seamless sharing is within reach, even when kidneys are shipped long distances for highly sensitized recipients. Late reallocations can be almost entirely avoided with a strategy that relies heavily on VXM.

Biological differences of interest in large, high-dimensional flow cytometry datasets are often obscured by undesired variations caused by differences in cytometers, reagents, or operators. Each variation type requires a different correction strategy, and their unknown contributions to overall variability hinder automated correction. We now describe swiftReg, an automated method that reduces undesired sources of variability between samples and particularly between batches. A high-resolution cluster map representing the multidimensional data is generated using the SWIFT algorithm, and shifts in cluster positions between samples are measured. Subpopulations are aligned between samples by displacing cell parameter values according to registration vectors derived from independent or locally-averaged cluster shifts. Batch variation is addressed by registering batch control or consensus samples, and applying the resulting shifts to individual samples. swiftReg selectively reduces batch variation, enhancing detection of biological differences. swiftReg outputs registered datasets as standard .FCS files to facilitate further analysis by other tools.

Human autoantibodies targeting myelin oligodendrocyte glycoprotein (MOG Ab) have become a useful clinical biomarker for the diagnosis of a spectrum of inflammatory demyelinating disorders. Live cell-based assays that detect MOG Ab against conformational MOG are currently the gold standard. Flow cytometry, in which serum binding to MOG-expressing cells and control cells are quantitively evaluated, is a widely used observer-independent, precise, and reliable detection method. However, there is currently no consensus on data analysis; for example, seropositive thresholds have been reported using varying standard deviations above a control cohort. Herein, we used a large cohort of 482 sera including samples from patients with monophasic or relapsing demyelination phenotypes consistent with MOG antibody-associated demyelination and other neurological diseases, as well as healthy controls, and applied a series of published analyses involving a background subtraction (delta) or a division (ratio). Loss of seropositivity and reduced detection sensitivity were observed when MOG ratio analyses or when 10 standard deviation (SD) or an arbitrary number was used to establish the threshold. Background binding and MOG ratio value were negatively correlated, in which patients seronegative by MOG ratio had high non-specific binding, a characteristic of serum that must be acknowledged. Most MOG Ab serostatuses were similar across analyses when optimal thresholds obtained by ROC analyses were used, demonstrating the robust nature and high discriminatory power of flow cytometry cell-based assays. With increased demand to identify MOG Ab-positive patients, a consensus on analysis is vital to improve patient diagnosis and for cross-study comparisons to ultimately define MOG Ab-associated disorders., (Copyright © 2020 Tea, Pilli, Ramanathan, Lopez, Merheb, Lee, Zou, Liyanage, Bassett, Thomsen, Reddel, Barnett, Brown, Dale, Brilot and the Australasian New Zealand MOG Study Group.)

The goal of many single-cell studies on eukaryotic cells is to gain insight into the biochemical reactions that control cell fate and state. In this paper we introduce the concept of Effective Stoichiometric Spaces (ESS) to guide the reconstruction of biochemical networks from multiplexed, fixed time-point, single-cell data. In contrast to methods based solely on statistical models of data, the ESS method leverages the power of the geometric theory of toric varieties to begin unraveling the structure of chemical reaction networks (CRN). This application of toric theory enables a data-driven mapping of covariance relationships in single-cell measurements into stoichiometric information, one in which each cell subpopulation has its associated ESS interpreted in terms of CRN theory. In the development of ESS we reframe certain aspects of the theory of CRN to better match data analysis. As an application of our approach we process cytomery- and image-based single-cell datasets and identify differences in cells treated with kinase inhibitors. Our approach is directly applicable to data acquired using readily accessible experimental methods such as Fluorescence Activated Cell Sorting (FACS) and multiplex immunofluorescence., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: PKS is a member of the SAB or Board of Directors of Merrimack Pharmaceutical, Glencoe Software, Applied Biomath, and RareCyte and has received research funding from Novartis and Merck. PKS declares that none of these relationships are directly or indirectly related to the content of this manuscript.

Purpose: Chronic granulomatous disease (CGD) is the most common phagocyte defect disease. Here, we describe 114 CGD patients in our center and report a rare female infant with XL-CGD to provide a better understanding of diagnosis, treatment, and prenatal diagnosis of CGD., Method: Patients were diagnosed by DHR-1,2,3 flow cytometry assays and gene analysis. X chromosome inactivation analysis and gp91 phox protein test were used for a female infant with XL-CGD., Results: XL-CGD accounts for the majority of cases in China and results in higher susceptibility to some infections than AR-CGD. The DHR assay can help diagnose CGD quickly, and atypical results should be combined with clinical manifestations, genetic analysis, and regular follow-up. For prenatal diagnosis, both gDNA and cDNA genotypes of amniotic fluid cells should be identified, and cord blood DHR assays should be performed to identify female XL-CGD patients.

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

High-dimensional flow and mass cytometry allow cell types and states to be characterized in great detail by measuring expression levels of more than 40 targeted protein markers per cell at the single-cell level. However, data analysis can be difficult, due to the large size and dimensionality of datasets as well as limitations of existing computational methods. Here, we present diffcyt , a new computational framework for differential discovery analyses in high-dimensional cytometry data, based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Our approach provides improved statistical performance, including for rare cell populations, along with flexible experimental designs and fast runtimes in an open-source framework., Competing Interests: The authors declare no competing interests.

During the past decade, the number of novel technologies to interrogate biological systems at the single-cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single-cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high-dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single-cell data in an automated and unbiased way., (© 2018 Federation of European Biochemical Societies.)

Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make such highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better than the currently available best performing clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. DEPECHE is implemented in the open source R package DepecheR currently available at github.com/Theorell/DepecheR., Competing Interests: The authors have declared that no competing interests exist.

New cytometric techniques continue to push the boundaries of multi-parameter quantitative data acquisition at the single-cell level particularly in immunology and medicine. Sophisticated analysis methods for such ever higher dimensional datasets are rapidly emerging, with advanced data representations and dimensional reduction approaches. However, these are not yet standardized and clinical scientists and cell biologists are not yet experienced in their interpretation. More fundamentally their range of statistical validity is not yet fully established. We therefore propose a new method for the automated and unbiased analysis of high-dimensional single cell datasets that is simple and robust, with the goal of reducing this complex information into a familiar 2D scatter plot representation that is of immediate utility to a range of biomedical and clinical settings. Using publicly available flow cytometry and mass cytometry datasets we demonstrate that this method (termed CytoBinning), recapitulates the results of traditional manual cytometric analyses and leads to new and testable hypotheses., Competing Interests: The authors have declared that no competing interests exist.

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry., (© 2018 International Society for Advancement of Cytometry.)

Part of the flow/mass cytometry data analysis process is aligning (matching) cell subsets between relevant samples. Current methods address this cluster-matching problem in ways that are either computationally expensive, affected by the curse of dimensionality, or fail when population patterns significantly vary between samples. Here, we introduce a quadratic form (QF)-based cluster matching algorithm (QFMatch) that is computationally efficient and accommodates cases where population locations differ significantly (or even disappear or appear) from sample to sample. We demonstrate the effectiveness of QFMatch by evaluating sample datasets from immunology studies. The algorithm is based on a novel multivariate extension of the quadratic form distance for the comparison of flow cytometry data sets. We show that this QF distance has attractive computational and statistical properties that make it well suited for analysis tasks that involve the comparison of flow/mass cytometry samples.

Morphological microscopic examinations of nucleated cells in body fluid (BF) samples are performed to screen malignancy. However, the morphological differentiation is time-consuming and labor-intensive. This study aimed to develop a new flowcytometry-based gating analysis mode "XN-BF gating algorithm" to detect malignant cells using an automated hematology analyzer, Sysmex XN-1000. XN-BF mode was equipped with WDF white blood cell (WBC) differential channel. We added two algorithms to the WDF channel: Rule 1 detects larger and clumped cell signals compared to the leukocytes, targeting the clustered malignant cells; Rule 2 detects middle sized mononuclear cells containing less granules than neutrophils with similar fluorescence signal to monocytes, targeting hematological malignant cells and solid tumor cells. BF samples that meet, at least, one rule were detected as malignant. To evaluate this novel gating algorithm, 92 various BF samples were collected. Manual microscopic differentiation with the May-Grunwald Giemsa stain and WBC count with hemocytometer were also performed. The performance of these three methods were evaluated by comparing with the cytological diagnosis. The XN-BF gating algorithm achieved sensitivity of 63.0% and specificity of 87.8% with 68.0% for positive predictive value and 85.1% for negative predictive value in detecting malignant-cell positive samples. Manual microscopic WBC differentiation and WBC count demonstrated 70.4% and 66.7% of sensitivities, and 96.9% and 92.3% of specificities, respectively. The XN-BF gating algorithm can be a feasible tool in hematology laboratories for prompt screening of malignant cells in various BF samples.

The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC)., (Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.)

Background: Iron deficiency and iron deficiency anemia are prevalent among pregnant women particularly in developing countries. This study aimed to evaluate the iron status among Egyptian pregnant women and its impact on their neutrophil's count and antimicrobial functions., Method: Ninety pregnant females underwent complete blood count, iron profile, flow cytometric studies for neutrophil myeloperoxidase expression & oxidative burst using dihydrorhodamine 123 (DHR) after phorbol-12-myristate-13-acetate (PMA) stimulation as well as neutrophil phagocytic and lytic indices., Results: According to percent saturation 54/90 women (60%) were iron deficient (<15% saturation) (cases) and 36/90 (40%) were iron sufficient (controls). A higher proportion of iron deficient pregnant women were in their third trimester compared to controls. No significant difference was found between the iron deficient & sufficient groups as regards anemia despite a positive correlation between haemoglobin level and percent saturation (P=.02). Both the phagocytic and lytic indices were significantly lower among the cases compared to controls (P=.014 & .002 respectively). Cases and controls were comparable as regards flow cytometric studies of neutrophils' myeloperoxidase and oxidative burst (P>.05). No significant correlation was found between any of the iron profile parameters and the oxidative burst by flow cytometry., Conclusion: Functional microphage assay (phagocytic and lytic indices) may be more relevant and cost effective than flow cytometry assays of myeloperoxidase and oxidative burst in reflecting either iron status or cellular immunity in pregnancy., (© 2017 Wiley Periodicals, Inc.)

Mass cytometry enables highly multiplexed profiling of cellular immune responses in limited-volume samples, advancing prospects of a new era of systems immunology. The capabilities of mass cytometry offer expanded potential for deciphering immune responses to infectious diseases and to vaccines. Several studies have used mass cytometry to profile protective immune responses, both postinfection and postvaccination, although no vaccine-development program has yet systematically employed the technology from the outset to inform both candidate design and clinical evaluation. In this article, we review published mass cytometry studies relevant to vaccine development, briefly compare immune profiling by mass cytometry to other systems-level technologies, and discuss some general considerations for deploying mass cytometry in the context of vaccine development.-Reeves, P. M., Sluder, A. E., Raju Paul, S., Scholzen, A., Kashiwagi, S., Poznansky, M. C. Application and utility of mass cytometry in vaccine development., (© FASEB.)

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

Background: Thorough review of current workload, staffing, and testing practices in clinical laboratories allows for optimization of laboratory efficiency and quality. This information is largely missing with regard to clinical flow cytometry laboratories. The purpose of this survey is to provide comprehensive, current, and accurate data on testing practices and laboratory staffing in clinical laboratories performing flow cytometric studies., Methods: Survey data was collected from flow cytometry laboratories through the ASCP website. Data was collected on the workload during a 1-year time period of full-time and part-time technical and professional (M.D./D.O./Ph.D. or equivalent) flow cytometry employees. Workload was examined as number of specimens and tubes per full time equivalent (FTE) technical and professional staff. Test complexity, test result interpretation, and reporting practices were also evaluated., Results: There were 205 respondent laboratories affiliated predominantly with academic and health system institutions. Overall, 1,132 FTE employees were reported with 29% professional FTE employees and 71% technical. Fifty-one percent of the testing performed was considered high complexity and 49% was low complexity. The average number of tubes per FTE technologist was 1,194 per year and the average number of specimens per FTE professional was 1,659 per year. The flow cytometry reports were predominantly written by pathologists (57%) and were typically written as a separate report (58%)., Conclusions: This survey evaluates the overall status of the current practice of clinical flow cytometry and provides a comprehensive dataset as a framework to help laboratory departments, directors, and managers make appropriate, cost-effective staffing decisions. © 2016 International Clinical Cytometry Society., (© 2016 International Clinical Cytometry Society.)

Many automated gating algorithms for flow cytometry data are based on the concept of unimodal cell populations. However, in this article, we show that criteria previously used to make decisions on unimodality cannot adequately distinguish unimodal from bimodal densities. We show that dip and bandwidth tests for unimodality, taken from the statistics literature, can do this with consistent and low error rates. These tests also have the possibility to adjust the significance level to handle the trade-off between failing to detect a second mode and seeing a second mode when there is none. The differences between the dip and bandwidth tests are elucidated using real data from the FlowCAP I challenge, also guidelines for flow cytometry data preprocessing are given. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

Objective: Tonsil surgical biopsy or excision is a very common procedure. However, there exist no consensus guidelines for the pathologic handling of tonsil specimens; gross and/or microscopic evaluation may be used. Diagnosis of tonsillar hematologic malignancy requires histology, immunohistochemistry and/or flow cytometry. Data regarding the utility of flow cytometry in tonsillar tissues are limited. We assessed our experience with flow cytometry for tonsil diagnosis with regard to accuracy and use patterns at a tertiary academic medical center., Methods: We retrospectively analyzed all surgically biopsied or excised tonsil specimens that underwent flow cytometry evaluation from August 2011 to March 2014. Patient clinical information, intraoperative frozen section, histology, immunohistochemistry, and flow cytometry diagnoses were recorded., Results: The study included 154 tonsil specimens from 89 females and 65 males. Patients averaged 27.4 years old (range 2-87 years); 73 were pediatric. Both histology and flow cytometry were benign for 148 patients (96.1%). Hematolymphoid malignancy was diagnosed in 6 adults by histology/immunohistochemistry: diffuse large B-cell lymphoma (2), small B-cell lymphoma (2), concomitant follicular lymphoma and histiocytic sarcoma (1), and extraosseous plasmacytoma (1). Flow cytometry identified abnormal populations in 5 of 6 cases, and detected clonal populations in 2 reactive follicular hyperplasia cases., Conclusion: Tonsillar hematolymphoid malignancy is uncommon, and flow cytometry was less accurate than histology/immunohistochemistry for its diagnosis. Despite the rarity of tonsillar lymphoma in children, nearly half of study patients were pediatric. Intraoperative frozen section diagnosis showed excellent sensitivity for malignancy, and could be used to effectively triage cases for flow cytometry evaluation.

SPADE stands for spanning-tree progression analysis for density-normalized events. It combines downsampling, clustering and a minimum-spanning tree to provide an intuitive visualization of high-dimensional single-cell data, which assists with the interpretation of the cellular heterogeneity underlying the data. SPADE has been widely used for analysis of high-content flow cytometry data and CyTOF data. The downsampling and clustering components of SPADE are both stochastic, which lead to stochasticity in the tree visualization it generates. Running SPADE twice on the same data may generate two different tree structures. Although they typically lead to the same biological interpretation of subpopulations present in the data, robustness of the algorithm can be improved. Another avenue of improvement is the interpretation of the SPADE tree, which involves visual inspection of multiple colored versions of the tree based on expression of measured markers. This is essentially manual gating on the SPADE tree and can benefit from automated algorithms. This article presents improvements of SPADE in both aspects above, leading to a deterministic SPADE algorithm and a software implementation for semiautomated interpretation. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

In the context of large-scale human system immunology studies, controlling for technical and biological variability is crucial to ensure that experimental data support research conclusions. In this study, we report on a universal workflow to evaluate both technical and biological variation in multiparameter flow cytometry, applied to the development of a 10-color panel to identify all major cell populations and T cell subsets in cryopreserved PBMC. Replicate runs from a control donation and comparison of different gating strategies assessed the technical variability associated with each cell population and permitted the calculation of a quality control score. Applying our panel to a large collection of PBMC samples, we found that most cell populations showed low intraindividual variability over time. In contrast, certain subpopulations such as CD56 T cells and Temra CD4 T cells were associated with high interindividual variability. Age but not gender had a significant effect on the frequency of several populations, with a drastic decrease in naive T cells observed in older donors. Ethnicity also influenced a significant proportion of immune cell population frequencies, emphasizing the need to account for these covariates in immune profiling studies. We also exemplify the usefulness of our workflow by identifying a novel cell-subset signature of latent tuberculosis infection. Thus, our study provides a universal workflow to establish and evaluate any flow cytometry panel in systems immunology studies., (Copyright © 2017 by The American Association of Immunologists, Inc.)

Pooled sample analysis by mass cytometry barcoding carries many advantages: reduced antibody consumption, increased sample throughput, removal of cell doublets, reduction of cross-contamination by sample carryover, and the elimination of tube-to-tube-variability in antibody staining. A single-cell debarcoding algorithm was previously developed to improve the accuracy and yield of sample deconvolution, but this method was limited to using fixed parameters for debarcoding stringency filtering, which could introduce cell-specific or sample-specific bias to cell yield in scenarios where barcode staining intensity and variance are not uniform across the pooled samples. To address this issue, we have updated the algorithm to output debarcoding parameters for every cell in the sample-assigned FCS files, which allows for visualization and analysis of these parameters via flow cytometry analysis software. This strategy can be used to detect cell type-specific and sample-specific effects on the underlying cell data that arise during the debarcoding process. An additional benefit to this strategy is the decoupling of barcode stringency filtering from the debarcoding and sample assignment process. This is accomplished by removing the stringency filters during sample assignment, and then filtering after the fact with 1- and 2-dimensional gating on the debarcoding parameters which are output with the FCS files. These data exploration strategies serve as an important quality check for barcoded mass cytometry datasets, and allow cell type and sample-specific stringency adjustment that can remove bias in cell yield introduced during the debarcoding process.

Cytometry aims to analyze cells, of any type, using dedicated instruments. The quantitative aspect makes flow cytometry (FCM) a good complementary tool for morphology. Most of the identification tools are based on immunostaining of cell structure details and more and more tools are available in terms of specificities and labels. FCM is under exponential development thanks to technical, immunological and data analysis progresses. Actual generations are now routinely using 6 to 10 simultaneous immuno-labeling on 20 to 100,000 cells, at high speed and short sample preparation and can easily detect rare events at frequency below 10 -4 cells. Data interpretation is complex and requires expertise. Mathematical tools are available to support analysis and classification of cells based. Cells from tissues can also be analyzed by FCM after mechanical and or enzymatic separation, but in situ cells can also be analyzed with the help of cytometry. Very new instruments bring spectral analysis, image in flow and mass spectrometry. Medical applications are very broad, notably in hemopathies, immunology, solid tumors, but also microbiology, toxicology, drug discovery, food and environmental industry. But, the limit of FCM is its dependence on operator from sample preparation, instrument settings up to data analysis and a strong effort is now under progress for standardization and constitution of international data bank for references and education., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)

Extracellular vesicles (EV) are small membrane vesicles produced by cells upon activation and apoptosis. EVs are heterogeneous according to their origin, mode of release, membrane composition, organelle and biochemical content, and other factors. Whereas it is apparent that EVs are implicated in intercellular communication, they can also be used as biomarkers. Continuous improvements in pre-analytical parameters and flow cytometry permit more efficient assessment of EVs; however, methods to more objectively distinguish EVs from cells and background, and to interpret multiple single-EV parameters are lacking. We used spanning-tree progression analysis of density-normalized events (SPADE) as a computational approach for the organization of EV subpopulations released by platelets and erythrocytes. SPADE distinguished EVs, and logically organized EVs detected by high-sensitivity flow cytofluorometry based on size estimation, granularity, mitochondrial content, and phosphatidylserine and protein receptor surface expression. Plasma EVs were organized by hierarchy, permitting appreciation of their heterogeneity. Furthermore, SPADE was used to analyze EVs present in the synovial fluid of patients with inflammatory arthritis. Its algorithm efficiently revealed subtypes of arthritic patients based on EV heterogeneity patterns. Our study reveals that computational algorithms are useful for the analysis of high-dimensional single EV data, thereby facilitating comprehension of EV functions and biomarker development.

Statistical methods currently recommended for analysis of in vitro micronucleus data are based on small sample sizes. The tests are designed to evaluate linear trends and differences between treated and control samples. When using flow cytometric analysis, >5 times the number of cells are easily evaluated, and the variance estimates from these large samples are small. Application of these recommended tests to large samples resulted in statistically significant outcomes which were not considered to be biologically meaningful. Alternative statistical methods for testing trends and differences among treatments that were either widely used, or sample-size independent, were investigated. Using data from 95 experiments (from 2011-2013) where 19% of the experiments were considered positive, results for the various statistical methods were compared. When using either the recommended or alternate methods, 42-68% of the experiments resulted in statistically significant results (p < 0.05). A new concept was then tested using the same data sets: the "z' factor", designed to identify 'hits' during high throughput screening. Using this simple-to-compute statistic the number of significant calls was reduced to 27%. Then, when combined with a biological criterion based on historical vehicle control data, there was restoration of the original positive frequency (19%). Given the larger sample sizes evaluated using flow cytometry, we have demonstrated that traditional statistical tests may be overly sensitive to small changes in micronucleus induction, and that a simple-to-compute index of separation (z') may be a better tool for analysis, provided that the response is first determined to be biologically meaningful. Environ. Mol. Mutagen. 57:589-604, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Flow chambers are common tools used for studying thrombus formation in vitro. However, the use of such devices is not standardised and there is a large diversity among the flow chamber systems currently used, and also in the methods used for quantifying the thrombus development. It was the study objective to evaluate a new method for analysis and quantification of platelet thrombus formation that can facilitate comparison of results between research groups. Whole blood was drawn over a collagen patch in commercial Ibid or in-house constructed PDMS flow chambers. Five percent of the platelets were fluorescently labelled and z-stack time-lapse images were captured during thrombus formation. Images were processed in a Python script in which the number of platelets and their respective x-, y- and z-positions were obtained. For comparison with existing methods the platelets were also labelled and quantified using fluorescence intensity and thrombus volume estimations by confocal microscopy. The presented method was found less sensitive to microscope and image adjustments and provides more details on thrombus development dynamics than the methods for measuring fluorescence intensity and thrombus volume estimation. The platelet count method produced comparable results with commercial and PDMS flow chambers, and could also obtain information regarding the stability of each detected platelet in the thrombus. In conclusion, quantification of thrombus formation by platelet count is a sensitive and robust method that enables measurement of platelet accumulation and platelet stability in an absolute scale that could be used for comparisons between research groups.

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

The prognosis of myelodysplastic syndromes (MDS) is currently estimated by using the revised International Prognostic Scoring System (IPSS-R). Several studies have shown that further refinement of prognostication for MDS can be achieved by adding flow cytometric parameters. However, widespread implementation of flow cytometry for the prognosis of MDS is hampered by complexity of the analysis. Therefore, the aim of this study was to construct a robust and practical flow cytometric score that could be implemented as a routine procedure. To achieve this, bone marrow aspirates of 109 MDS patients were analyzed by flow cytometry. A second cohort consisting of 103 MDS patients was used to validate the MDS flow cytometric score (MFS). The parameters forming the MFS were sideward light scatter and CD117 expression of myeloid progenitor cells and CD13 expression on monocytes. Three MFS risk categories were formed. Patients with MDS and intermediate MFS scores had significantly better overall survival (OS) compared with the patients with high MFS scores. The MFS further refined prognostication within the IPSS-R low-risk category, by identifying patients with worse OS in case of high MFS. In conclusion, a practical three parameter flow cytometric prognostic score was constructed enabling further refinement of prognostication of MDS.

Adaptive immune responses are complex dynamic processes whereby B and T cells undergo division and differentiation triggered by pathogenic stimuli. Deregulation of the response can lead to severe consequences for the host organism ranging from immune deficiencies to autoimmunity. Tracking cell division and differentiation by flow cytometry using fluorescent probes is a major method for measuring progression of lymphocyte responses, both in vitro and in vivo. In turn, mathematical modeling of cell numbers derived from such measurements has led to significant biological discoveries, and plays an increasingly important role in lymphocyte research. Fitting an appropriate parameterized model to such data is the goal of these studies but significant challenges are presented by the variability in measurements. This variation results from the sum of experimental noise and intrinsic probabilistic differences in cells and is difficult to characterize analytically. Current model fitting methods adopt different simplifying assumptions to describe the distribution of such measurements and these assumptions have not been tested directly. To help inform the choice and application of appropriate methods of model fitting to such data we studied the errors associated with flow cytometry measurements from a wide variety of experiments. We found that the mean and variance of the noise were related by a power law with an exponent between 1.3 and 1.8 for different datasets. This violated the assumptions inherent to commonly used least squares, linear variance scaling and log-transformation based methods. As a result of these findings we propose a new measurement model that we justify both theoretically, from the maximum entropy standpoint, and empirically using collected data. Our evaluation suggests that the new model can be reliably used for model fitting across a variety of conditions. Our work provides a foundation for modeling measurements in flow cytometry experiments thus facilitating progress in quantitative studies of lymphocyte responses.

Background: The hereditary spherocytosis (HS) is a common cause of inherited hemolytic anemia that is difficult to identify in many cases. Currently, there are new economic parameters for red cell disorder evaluation, such as hyperdense cell percentage (%Hyper) obtained from automated analyzers., Objective: In this study we determined the %Hyper efficacy and compared its accuracy with a flow cytometry method in HS, hemoglobinopathy and healthy individuals, in order to allow the use of %Hyper in HS screening., Methods: Patients treated at the outpatient clinic at the Hospital de Clínicas de Porto Alegre (HCPA) were allocated into three groups according to their clinical condition: HS, hemoglobinopathy or healthy groups. Flow-cytometric osmotic fragility test (FCM OF) method was used as reference to compare with %Hyper, both performed from K2EDTA samples., Results: Fifty-eight individuals were included in this study. We found that the %Hyper cut-off point of 6.4% showed an excellent sensitivity (92.3%) and specificity (90.7%) to detect HS. Besides, %Hyper presented a significant negative correlation with FCM OF in identifying HS (Rs=-0.525; P<0.001)., Conclusions: %Hyper could be a tool for screening HS, before requesting additional tests. It is a fast and cost-effective test, which is easily obtained in complete blood count, favoring its use in clinical laboratories. However, this test does not replace flow cytometric methods for confirmation of atypical cases., (Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Applying different technologies to monitor disease activity and treatment efficacy are essential in a complex disease such as multiple sclerosis. Combining current assays with flow cytometry could create a powerful tool for such analyses. The cell surface expression level of CD74, the MHC class II invariant chain, is a potential disease biomarker that could be monitored by FACS analysis in order to assess disease progression and the clinical efficacy of partial MHC class II constructs in treating MS. These constructs, which can bind to and down-regulate CD74 cell-surface expression on monocytes and inhibit macrophage migration inhibitory factor (MIF) effects, can reverse clinical and histological signs of EAE. These properties of partial class II constructs are highly compatible with a flow cytometry approach for monitoring CD74 expression as a possible biomarker for disease activity/progression and as a treatment response marker.

Sorting performance can be evaluated with regard to Purity, Yield and/or Recovery of the sorted fraction. Purity is a check on the quality of the sample and the sort decisions made by the instrument. Recovery and Yield definitions vary with some authors regarding both as how efficient the instrument is at sorting the target particles from the original sample, others distinguishing Recovery from Yield, where the former is used to describe the accuracy of the instrument's sort count. Yield and Recovery are often neglected, mostly due to difficulties in their measurement. Purity of the sort product is often cited alone but is not sufficient to evaluate sorting performance. All of these three performance metrics require re-sampling of the sorted fraction. But, unlike Purity, calculating Yield and/or Recovery calls for the absolute counting of particles in the sorted fraction, which may not be feasible, particularly when dealing with rare populations and precious samples. In addition, the counting process itself involves large errors. Here we describe a new metric for evaluating instrument sort Recovery, defined as the number of particles sorted relative to the number of original particles to be sorted. This calculation requires only measuring the ratios of target and non-target populations in the original pre-sort sample and in the waste stream or center stream catch (CSC), avoiding re-sampling the sorted fraction and absolute counting. We called this new metric Rmax, since it corresponds to the maximum expected Recovery for a particular set of instrument parameters. Rmax is ideal to evaluate and troubleshoot the optimum drop-charge delay of the sorter, or any instrument related failures that will affect sort performance. It can be used as a daily quality control check but can be particularly useful to assess instrument performance before single-cell sorting experiments. Because we do not perturb the sort fraction we can calculate Rmax during the sort process, being especially valuable to check instrument performance during rare population sorts., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Myelodysplastic syndromes (MDS) are classified by the WHO as myeloid neoplasms, and are characterized by cytopenia and dysplasia in one or more myeloid cell lines. Recently, a flow cytometric score (FCM-score) was published capable of discriminating low-grade MDS from non-clonal cytopenias (Della Porta et al., 2012). We tested the applicability of the FCM-score in a patient population from a large peripheral teaching hospital in The Netherlands. The evaluation of the proposed FCM score in low-grade MDS showed a high sensitivity and specificity, and clinically significant positive and negative likelihood ratios. The use of CD10 and CD19 positivity to identify progenitor B-cell blasts provided a specific and precize method to separate progenitor B-cell blasts from myeloid blasts within the CD34+/low CD45+ population and may be more convenient compared to the published method using low SSC and CD45 expression. This study confirms the value of utilizing the FCM-score in our patient population., (© 2014 International Clinical Cytometry Society.)

Multiparameter flow cytometry is an indispensable method for assessing antigen-specific T cells in basic research and cancer immunotherapy. Proficiency panels have shown that cell sample processing, test protocols and data analysis may all contribute to the variability of the results obtained by laboratories performing ex vivo T cell immune monitoring. In particular, analysis currently relies on a manual, step-by-step strategy employing serial gating decisions based on visual inspection of one- or two-dimensional plots. It is therefore operator dependent and subjective. In the context of continuing efforts to support inter-laboratory T cell assay harmonization, the CIMT Immunoguiding Program organized its third proficiency panel dedicated to the detection of antigen-specific CD8(+) T cells by HLA-peptide multimer staining. We first assessed the contribution of manual data analysis to the variability of reported T cell frequencies within a group of laboratories staining and analyzing the same cell samples with their own reagents and protocols. The results show that data analysis is a source of variation in the multimer assay outcome. To evaluate whether an automated analysis approach can reduce variability of proficiency panel data, we used a hierarchical statistical mixture model to identify cell clusters. Challenges for automated analysis were the need to process non-standardized data sets from multiple centers, and the fact that the antigen-specific cell frequencies were very low in most samples. We show that this automated method can circumvent difficulties inherent to manual gating strategies and is broadly applicable for experiments performed with heterogeneous protocols and reagents.

Measuring changes of the T cell receptor (TCR) repertoire is important to many fields of medicine. Flow cytometry is a popular technique to study the TCR repertoire, as it quickly provides insight into the TCR-Vβ usage among well-defined populations of T cells. However, the interpretation of the flow cytometric data remains difficult, and subtle TCR repertoire changes may go undetected. Here, we introduce a novel means for analyzing the flow cytometric data on TCR-Vβ usage. By applying economic statistics, we calculated the Gini-TCR skewing index from the flow cytometric TCR-Vβ analysis. The Gini-TCR skewing index, which is a direct measure of TCR-Vβ distribution among T cells, allowed us to track subtle changes of the TCR repertoire among distinct populations of T cells. Application of the Gini-TCR skewing index to the flow cytometric TCR-Vβ analysis will greatly help to gain better understanding of the TCR repertoire in health and disease.