Analysis Of Flow Cytometry Data Research Articles

Liver-Resident Bystander CD8+ T Cells Contribute to Liver Disease Pathogenesis in Chronic Hepatitis D Virus Infection

The hepatitis D virus (HDV) causes the most severe form of chronic hepatitis, often progressing to cirrhosis within 5 to 10 years. There is no curative treatment, and the mechanisms underlying the accelerated liver disease progression are unknown. Innate and adaptive immune responses were studied in blood and liver of 24 patients infected with HDV and 30 uninfected controls by multiparameter flow cytometry in correlation with disease severity and stage. The 2 main intrahepatic innate immune-cell populations, mucosal-associated invariant T cells and natural killer (NK) cells, were reduced in the livers of patients infected with HDV compared with those of uninfected controls but were more frequently activated in the liver compared with the blood. Most intrahepatic cluster of differentiation (CD) 8-positive (CD8+) T cells were memory cells or terminal effector memory cells, and most of the activated and degranulating (CD107a+) HDV-specific and total CD8+ T cells were liver-resident (CD69+C-X-C motif chemokine receptor 6+). Unsupervised analysis of flow cytometry data identified an activated, memory-like, tissue-resident HDV-specific CD8+ T-cell cluster with expression of innate-like NK protein 30 (NKp30) and NK group 2D (NKG2D) receptors. The size of this population correlated with liver enzyme activity (r= 1.0). NKp30 and NKG2D expression extended beyond the HDV-specific to the total intrahepatic CD8+ T-cell population, suggesting global bystander activation. This was supported by the correlations between (i) NKG2D expression with degranulation of intrahepatic CD8+ T cells, (ii) frequency of degranulating CD8+ T cells with liver enzyme activity and the aspartate aminotransferase-to-platelet ratio index score, and by the invitro demonstration of cytokine-induced NKG2D-dependent cytotoxicity. Antigen-nonspecific activation of liver-resident CD8+ T cells may contribute to inflammation and disease stage in HDV infection.

FlowGrid enables fast clustering of very large single-cell RNA-seq data.

Scalable clustering algorithms are needed to analyze millions of cells in single cell RNA-seq (scRNA-seq) data. Here, we present an open source python package called FlowGrid that can integrate into the Scanpy workflow to perform clustering on very large scRNA-seq datasets. FlowGrid implements a fast density-based clustering algorithm originally designed for flow cytometry data analysis. We introduce a new automated parameter tuning procedure, and show that FlowGrid can achieve comparable clustering accuracy as state-of-the-art clustering algorithms but at a substantially reduced run time for very large single cell RNA-seq datasets. For example, FlowGrid can complete a one-hour clustering task for one million cells in about five min. https://github.com/holab-hku/FlowGrid. Supplementary data are available at Bioinformatics online.

Abstract 164: Development of DeepCytoFlow: A novel deep learning pipeline enabling automated gating of flow cytometry data

Abstract Introduction Following the advent of cellular therapies, flow cytometry has become a central tool for simultaneous assessment of drug levels and disease burden due to its ability for high dimensional cellular analysis. However, identification and monitoring of myriad immune- and leukemic cell phenotypes requires a manual gating process by subject matter experts. Although this process is the current gold standard for flow data analysis, it is slow, tedious, and prone to subjectivity. Herein, we describe our approach to automate flow data analysis using novel strategies that reduces the reliance on clustering methods that are unable to filter challenging noisy data sets inherent in flow data. Methods We developed DeepCytoFlow, a novel deep learning framework adapted for flow cytometry data analysis. DeepCytoFlow utilizes a convoluted neural network structure to analyze flow cytometry data files to generate appropriately gated cell populations in a matter of minutes significantly reducing the time required for manual analysis. The neural networks were first trained using a unique data set generated manually by multiple operators and tested using an independent data set to establish concordance in assessments. In addition, we evaluated the performance of our method using both accuracy and F1-score. Results Our integrated DeepCytoFlow pipeline employing neural network design shows promise on clustering-based analysis methods in terms of accuracy, precision and recall. More specifically, DeepCytoFlow is capable of correctly classifying various immune cell lineages (e.g., CD4 and CD8 T-cells). However, a limitation was observed on its ability to classify rare cell populations with a very limited prevalence. In an effort to improve performance, we are currently designing a new network architecture, and increasing our training and test dataset's size and quality. Conclusions DeepCytoFlow provides proof of concept of the applicability of deep learning for automation of gating in flow cytometry. The high adaptability of deep learning to a large set of problems suggests that this technique can be broadly applied to a larger set of biomarkers for the purpose for automated gating. With additional improvements, we believe DeepCytoFlow can enable a fast, accurate, and robust platform for analysts to perform consistent and, repeatable flow analysis for biomarker monitoring in clinical trials investigating novel immunotherapies. Citation Format: Arjun Chandran, Jennifer Tsau, Dalia Gaddis, Shyam Sarikonda, Naveen Dakappagari, Christian Laing, Swetha Ann Thomas. Development of DeepCytoFlow: A novel deep learning pipeline enabling automated gating of flow cytometry data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 164.

Circulating immune cell populations related to primary breast cancer, surgical removal, and radiotherapy revealed by flow cytometry analysis

BackgroundAdvanced breast cancer (BC) impact immune cells in the blood but whether such effects may reflect the presence of early BC and its therapeutic management remains elusive.MethodsTo address this question, we used multiparametric flow cytometry to analyze circulating leukocytes in patients with early BC (n = 13) at the time of diagnosis, after surgery, and after adjuvant radiotherapy, compared to healthy individuals. Data were analyzed using a minimally supervised approach based on FlowSOM algorithm and validated manually.ResultsAt the time of diagnosis, BC patients have an increased frequency of CD117+CD11b+ granulocytes, which was significantly reduced after tumor removal. Adjuvant radiotherapy increased the frequency of CD45RO+ memory CD4+ T cells and CD4+ regulatory T cells. FlowSOM algorithm analysis revealed several unanticipated populations, including cells negative for all markers tested, CD11b+CD15low, CD3+CD4−CD8−, CD3+CD4+CD8+, and CD3+CD8+CD127+CD45RO+ cells, associated with BC or radiotherapy.ConclusionsThis study revealed changes in blood leukocytes associated with primary BC, surgical removal, and adjuvant radiotherapy. Specifically, it identified increased levels of CD117+ granulocytes, memory, and regulatory CD4+ T cells as potential biomarkers of BC and radiotherapy, respectively. Importantly, the study demonstrates the value of unsupervised analysis of complex flow cytometry data to unravel new cell populations of potential clinical relevance.

AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data

Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry.

Natural Killer Cell Subpopulations and Inhibitory Receptor Dynamics in Myelodysplastic Syndromes and Acute Myeloid Leukemia.

Natural killer (NK) cells are key innate immunity effectors that play a major role in malignant cell destruction. Based on expression patterns of CD16, CD56, CD57, and CD94, three distinct NK cell maturation stages have been described, which differ in terms of cytokine secretion, tissue migration, and the ability to kill target cells. Our study addressed NK cell maturation in bone marrow under three conditions: a normal developmental environment, during pre-leukemic state (myelodysplastic syndrome, MDS), and during leukemic transformation (acute myeloblastic leukemia, AML). In this study, we used a new tool to perform multicolor flow cytometry data analysis, based on principal component analysis, which allowed the unsupervised, accurate discrimination of immature, mature, and hypermature NK subpopulations. An impaired NK/T cell distribution was observed in the MDS bone marrow microenvironment compared with the normal and AML settings, and a phenotypic shift from the mature to the immature state was observed in NK cells under both the MDS and AML conditions. Furthermore, an impaired NK cell antitumor response, resulting in changes in NK cell receptor expression (CD159a, CD158a, CD158b, and CD158e1), was observed under MDS and AML conditions compared with the normal condition. The results of this study provide evidence for the failure of this arm of the immune response during the pathogenesis of myeloid malignancies. NK cell subpopulations display a heterogeneous and discordant dynamic on the spectrum between normal and pathological conditions. MDS does not appear to be a simple, intermediate stage but rather serves as a decisive step for the mounting of an efficient or ineffective immune response, leading to either the removal of the tumor cells or to malignancy.

Assessment of Protocol Impact on Subjectivity Uncertainty When Analyzing Peripheral Blood Mononuclear Cell Flow Cytometry Data Files.

Measured variability of product within Cell and Gene Therapy (CGT) manufacturing arises from numerous sources across pre-analytical to post-analytical phases of testing. Operators are a function of the manufacturing process and are an important source of variability as a result of personal differences impacted by numerous factors. This research uses measurement uncertainty in comparison to Coefficient of Variation to quantify variation of participants when they complete Flow Cytometry data analysis through a 5-step gating sequence. Two study stages captured participants applying gates using their own judgement, and then following a diagrammatical protocol, respectively. Measurement uncertainty was quantified for each participant (and analysis phase) by following Guide to the Expression of Uncertainty in Measurement protocols, combining their standard deviations in quadrature from each gating step in the respective protocols. When participants followed a diagrammatical protocol, variation between participants reduced by 57%, increasing confidence in a more uniform reported cell count percentage. Measurement uncertainty provided greater resolution to the analysis processes, identifying that most variability contributed in the Flow Cytometry gating process is from the very first gate, where isolating target cells from dead or dying cells is required. This work has demonstrated the potential for greater usage of measurement uncertainty within CGT manufacturing scenarios, due to the resolution it provides for root cause analysis and continuous improvement.

K-means quantization for a web-based open-source flow cytometry analysis platform

Flow cytometry (FCM) is an analytic technique that is capable of detecting and recording the emission of fluorescence and light scattering of cells or particles (that are collectively called “events”) in a population1. A typical FCM experiment can produce a large array of data making the analysis computationally intensive2. Current FCM data analysis platforms (FlowJo3, etc.), while very useful, do not allow interactive data processing online due to the data size limitations. Here we report a more effective way to analyze FCM data on the web. Freecyto is a free and intuitive Python-flask-based web application that uses a weighted k-means clustering algorithm to facilitate the interactive analysis of flow cytometry data. A key limitation of web browsers is their inability to interactively display large amounts of data. Freecyto addresses this bottleneck through the use of the k-means algorithm to quantize the data, allowing the user to access a representative set of data points for interactive visualization of complex datasets. Moreover, Freecyto enables the interactive analyses of large complex datasets while preserving the standard FCM visualization features, such as the generation of scatterplots (dotplots), histograms, heatmaps, boxplots, as well as a SQL-based sub-population gating feature2. We also show that Freecyto can be applied to the analysis of various experimental setups that frequently require the use of FCM. Finally, we demonstrate that the data accuracy is preserved when Freecyto is compared to conventional FCM software.

CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data

BackgroundThe rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge.ResultsHere, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner.ConclusionsCytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.

A multiple acetal chalcone-BODIPY-based fluorescence: synthesis, physical property, and biological studies.

Fluorescent probes with outstanding physical and biological properties are superior for functional fluorescent dyes design. However, few studies pay attention to the stability of specific groups in fluorescent probes. The aldehyde group in the fluorescent probe is highly active but unstable under certain conditions. Therefore, we introduced ethoxy groups to realize the conversion to aldehyde groups under acidic conditions and avoid the instability of straightforward aldehyde groups. In this work, two fluorophores based on the multi acetal difluoroboraindacene (BODIPY) units with combination of the pharmaceutical intermediate chalcone have been firstly developed. In the design part, chalcone was introduced as a medium for fluorophore and multiple acetal. The mild synthesis strategy is based on the ligand ((Z)-2-chloro-1-(difluoroboranyl)-5-((4-ethyl-3,5-dimethyl-2H-pyrrol-2-ylidene)(phenyl)methyl)-1H-pyrrole) and connects with chalcone in (2E,2'E)-3,3'-(1,3-phenylene)bis(1-(2,4-bis(2,2-diethoxyethoxy)phenyl)prop-2-en-1-one). The emission wavelengths of the products are around 530nm with high fluorescence intensity. To highlight the biological characteristics of these novel BODIPY fluorescents, we further demonstrated biological analysis studies on MTT and flow cytometry assays. The IC50 values of BODIPY 5 ranged from 79 ± 6.11 to 63 ± 5.67μM and BODIPY 6 were found to be 86 ± 4.07 to 58 ± 10.51μM in tested cell lines. Flow cytometry data analysis shows that the representative agent 6 and reference have similar rational apoptosis rates in first quadrant. Last but not least, 6 shows outstanding biological compatibility and cell imaging potential in live cell imaging and in vivo assay, not only is the fluorescence prominent enough, but also rapidly distributes. Thus, our study reports a mild synthesis strategy and full biological analysis on BODIPY fluorescents, and the subtle modulation of the physical and biological properties by pharmaceutical substituents makes these designed chalcone-BODIPY-based dyes hopeful to realize drug functional fluorescent dyes. Two new highly sensitive BODIPY fluorophores are synthesized based on the ligand ((Z)-2-chloro-1-(difluoroboranyl)-5-((4-ethyl-3,5-dimethyl-2H-pyrrol-2-ylidene)(phenyl)methyl)-1H-pyrrole), which connects with chalcone in (2E,2'E)-3,3'-(1,3/4-phenylene)bis(1-(2,4-bis(2,2-diethoxyethoxy)phenyl)prop-2-en-1-one). Multiple acetals were introduced and the physical and biological properties of BODIPYs are described with MTT assay and in vitro and in vivo imaging.

Current trends in flow cytometry automated data analysis software.

Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.

PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure.

Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

Tailor: Targeting heavy tails in flow cytometry data with fast, interpretable mixture modeling.

Automated clustering workflows are increasingly used for the analysis of high parameter flow cytometry data. This trend calls for algorithms which are able to quickly process tens of millions of data points, to compare results across subjects or time points, and to provide easily actionable interpretations of the results. To this end, we created Tailor, a model-based clustering algorithm specialized for flow cytometry data. Our approach leverages a phenotype-aware binning scheme to provide a coarse model of the data, which is then refined using a multivariate Gaussian mixture model. We benchmark Tailor using a simulation study and two flow cytometry data sets, and show that the results are robust to moderate departures from normality and inter-sample variation. Moreover, Tailor provides automated, non-overlapping annotations of its clusters, which facilitates interpretation of results and downstream analysis. Tailor is released as an R package, and the source code is publicly available at www.github.com/matei-ionita/Tailor.

High-Dimensional Immune Monitoring for Chimeric Antigen Receptor T Cell Therapies.

High-dimensional flow cytometry experiments have become a method of choice for high-throughput integration and characterization of cell populations. Here, we present a summary of state-of-the-art R-based pipelines used for differential analyses of cytometry data, largely based on chimeric antigen receptor (CAR) T cell therapies. These pipelines are based on publicly available R libraries, put together in a systematic and functional fashion, therefore free of cost. In recent years, existing tools tailored to analyze complex high-dimensional data such as single-cell RNA sequencing (scRNAseq) have been successfully ported to cytometry studies due to the similar nature of flow cytometry and scRNAseq platforms. Existing environments like Cytobank (Kotecha et al., 2010), FlowJo (FlowJo™ Software) and FCS Express (https://denovosoftware.com) already offer a variety of these ported tools, but they either come at a premium or are fairly complicated to manage by an inexperienced user. To mitigate these limitations, experienced cytometrists and bioinformaticians usually incorporate these functions into an RShiny (https://shiny.rstudio.com) application that ultimately offers a user-friendly, intuitive environment that can be used to analyze flow cytometry data. Computational tools and Shiny-based tools are the perfect answer to the ever-growing dimensionality and complexity of flow cytometry data, by offering a dynamic, yet user-friendly exploratory space, tailored to bridge the space between the lab experimental world and the computational, machine learning space.

Definition of Erythroid Differentiation Subsets in Normal Human Bone Marrow Using FlowSOM Unsupervised Cluster Analysis of Flow Cytometry Data.

Definition of Erythroid Differentiation Subsets in Normal Human Bone Marrow Using FlowSOM Unsupervised Cluster Analysis of Flow Cytometry Data.

Implementing flowDensity for Automated Analysis of Bone Marrow Lymphocyte Population

Introduction: Manual gating of flow cytometry (FCM) data for marrow cell analysis is a standard approach in current practice, although it is time- and labor-consuming. Recent advances in cytometry technology have led to significant efforts in developing partially or fully automated analysis methods. Although multiple supervised and unsupervised FCM data analysis algorithms have been developed, they have not been widely adopted by the clinical and research laboratories. In this study, we evaluated flowDensity, an open source freely available algorithm. as an automated analysis tool for classification of lymphocyte subsets in the bone marrow biopsy specimens. Materials and Methods: FlowDensity-based gating was applied to 102 normal bone marrow samples and compared with the manual analysis. Independent expression of each cell marker was assessed for comprehensive expression analysis and visualization. Results: Our findings showed a correlation between the manual and flowDensity-based gating in the lymphocyte subsets. However, flowDensity-based gating in the populations with a small number of cells in each cluster showed a low degree of correlation. Comprehensive expression analysis successfully identified and visualized the lymphocyte subsets. Discussion: Our study found that although flowDensity might be a promising method for FCM data analysis, more optimization is required before implementing this algorithm into day-to-day workflow.

High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia.

Simple SummaryB-cell Acute Lymphoblastic Leukaemia is one of the most common cancers in childhood, with 20% of patients eventually relapsing. Flow cytometry is routinely used for diagnosis and follow-up, but it currently does not provide prognostic value at diagnosis. The volume and the high-dimensional character of this data makes it ideal for its exploitation by means of Artificial Intelligence methods. We collected flow cytometry data from 56 patients from two hospitals. We analysed differences in intensity of marker expression in order to predict relapse at the moment of diagnosis. We finally correlated this data with biomolecular information, constructing a classifier based on CD38 expression.Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher’s Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse.

Harnessing lipid signaling pathways to target specialized pro-angiogenic neutrophil subsets for regenerative immunotherapy.

To gain insights into neutrophil heterogeneity dynamics in the context of sterile inflammation and wound healing, we performed a pseudotime analysis of single-cell flow cytometry data using the spanning-tree progression analysis of density-normalized events algorithm. This enables us to view neutrophil transitional subsets along a pseudotime trajectory and identify distinct VEGFR1, VEGFR2, and CXCR4 high-expressing pro-angiogenic neutrophils. While the proresolving lipid mediator aspirin-triggered resolvin D1 (AT-RvD1) has a known ability to limit neutrophil infiltration, our analysis uncovers a mode of action in which AT-RvD1 leads to inflammation resolution through the selective reprogramming toward a therapeutic neutrophil subset. This accumulation leads to enhanced vascular remodeling in the skinfold window chamber and a proregenerative shift in macrophage and dendritic cell phenotype, resulting in improved wound closure after skin transplantation. As the targeting of functional immune subsets becomes the key to regenerative immunotherapies, single-cell pseudotime analysis tools will be vital in this field.

Quantifying Operator Subjectivity within Flow Cytometry Data Analysis as a Source of Measurement Uncertainty and the Impact of Experience on Results.

Flow cytometry is a complex measurement characterization technique, utilized within the manufacture, measurement, and release of cell and gene therapy products for rapid, high-content, and multiplexed discriminatory cell analysis. A number of factors influence the variability in the measurement reported including, but not limited to, biological variation, reagent variation, laser and optical configurations, and data analysis methods. This research focused on understanding the contribution of manual operator variability within the data analysis phase. Thirty-eight participants completed a questionnaire, providing information about experience and motivational factors, before completing a simple gating study. The results were analyzed using gauge repeatability and reproducibility techniques to quantify participant uncertainty. The various stages of the gating sequence were combined through summation in quadrature and expanded to give each participant a representative uncertainty value. Of the participants surveyed, 85% preferred manual gating to automated data analysis, with the primary reasons being legacy ("it's always been done that way") and accuracy, not in the metrological sense but in the clear definition of the correct target population. The median expanded uncertainty was calculated as 3.6% for the population studied, with no significant difference among more or less experienced users. Operator subjectivity can be quantified to include within measurement uncertainty budgets, required for various standards and qualifications. An emphasis on biomanufacturing measurement terminology is needed to help understand future and potential solutions, possibly looking at translational clinical models to engage and enhance better training and protocols within industrial and research settings.

Comparing the effects of two different strains of mycobacteria, Mycobacterium vaccae NCTC 11659 and M. vaccae ATCC 15483, on stress-resilient behaviors and lipid-immune signaling in rats

Stress-related disorders, such as posttraumatic stress disorder (PTSD), are highly prevalent and often difficult to treat. In rodents, stress-related, anxiety-like defensive behavioral responses may be characterized by social avoidance, exacerbated inflammation, and altered metabolic states. We have previously shown that, in rodents, subcutaneous injections of a heat-killed preparation of the soil-derived bacterium Mycobacterium vaccae NCTC 11659 promotes stress resilience effects that are associated with immunoregulatory signaling in the periphery and the brain. In the current study, we sought to determine whether treatment with a heat-killed preparation of the closely related M. vaccae type strain, M. vaccae ATCC 15483, would also promote stress-resilience in adult male rats, likely due to biologically similar characteristics of the two strains. Here we show that immunization with either M. vaccae NCTC 11659 or M. vaccae ATCC 15483 prevents stress-induced increases in hippocampal interleukin 6 mRNA expression, consistent with previous studies showing that M. vaccae NCTC 11659 prevents stress-induced increases in peripheral IL-6 secretion, and prevents exaggeration of anxiety-like defensive behavioral responses assessed 24 h after exposure to inescapable tail shock stress (IS) in adult male rats. Analysis of mRNA expression, protein abundance, and flow cytometry data demonstrate overlapping but also unique effects of treatment with the two M. vaccae strains on immunological and metabolic signaling in the host. These data support the hypothesis that treatment with different M. vaccae strains may immunize the host against stress-induced dysregulation of physiology and behavior.