Analysis Of Flow Cytometric Data Research Articles

A unique human cord blood CD8+CD45RA+CD27+CD161+ T-cell subset identified by flow cytometric data analysis using Seurat.

Advances in single-cell level analytical techniques, especially cytometric approaches, have led to profound innovation in biomedical research, particularly in the field of clinical immunology. This has resulted in an expansion of high-dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis is thus becoming untenable to handle these challenges. Furthermore, most newly developed computational methods lack flexibility and interoperability, hampering their accessibility and usability. Here, we adapted Seurat, an R package originally developed for single-cell RNA sequencing (scRNA-seq) analysis, for high-dimensional flow cytometric data analysis. Based on a 20-marker antibody panel and analyses of T-cell profiles in both adult blood and cord blood (CB), we showcased the robust capacity of Seurat in flow cytometric data analysis, which was further validated by Spectre, another high-dimensional cytometric data analysis package, and conventional manual analysis. Importantly, we identified a unique CD8+ T-cell population defined as CD8+CD45RA+CD27+CD161+ T cell that was predominantly present in CB. We characterised its IFN-γ-producing and potential cytotoxic properties using flow cytometry experiments and scRNA-seq analysis from a published dataset. Collectively, we identified a unique human CB CD8+CD45RA+CD27+CD161+ T-cell subset and demonstrated that Seurat, a widely used package for scRNA-seq analysis, possesses great potential to be repurposed for cytometric data analysis. This facilitates an unbiased and thorough interpretation of complicated high-dimensional data using a single analytical pipeline and opens a novel avenue for data-driven investigation in clinical immunology.

Functional Flow Cytometry to Predict PD‐L1 Conformational Changes

AbstractThe programmed cell death protein 1/programmed cell death protein ligand 1 (PD‐1/PD‐L1) axis is one of the most widely recognized targets for cancer immunotherapy. Importantly, PD‐L1 conformational changes can hinder target binding when living cells are used. Antibody affinity, equilibrium binding, association and dissociation rates, and other affinity‐related constants are fundamental to ensure target saturation. Here, PD‐L1 changes in conformation and their potential impact on PD‐L1 function and mutation are explored. Specifically, we present detailed flow cytometry procedures to analyze PD‐L1 reactivity in myeloid‐derived suppressor cells (MDSCs). This approach can also be used to study the contribution of protein conformational changes in living cells. © 2023 Wiley Periodicals LLC.Basic Protocol 1: Sample preparation for PD‐L1+ myeloid‐derived suppressor cells detection by flow cytometryBasic Protocol 2: Protocol preparation, sample acquisition, and gating strategy for flow cytometric screening of PD‐L1+ myeloid‐derived suppressor cells in patients with lung cancerSupport Protocol 1: Bioinformatic tools for the analysis of flow cytometric data

MAIT Cell Frequencies within PBSC Grafts Are Associated with Donor CMV Serostatus and Age: An Initial Analysis from the DKMS and NMDP Graft Composition Study

Introduction We have established an international collaboration between the DKMS (Germany) and the National Marrow Donor Program (NMDP; USA) to characterize more than 2000 donor peripheral blood stem cell (PBSC) grafts. We aim to correlate graft immunophenotype with patient outcomes and perform additional analyses on biobanked aliquots of selected samples to deepen our understanding of the relationships between features of the donor graft and key patient outcomes, such as graft-versus-host disease, relapse, and overall survival. Here, we present an interim analysis, focused on the CD8 T cell compartment, describing relationships between donor characteristics and graft composition. We have developed a novel computational clustering method to integrate cytometry data from both the US and German analytical laboratories. Methods 300 donor PBSC graft samples were collected, processed, freshly stained (34-color panel for immunophenotyping of lymphocytes and hematopoietic stem cells), and analyzed using an Aurora Cytek instrument at the NMDP contract laboratory (Roswell Park, Buffalo, NY). 53 additional samples were processed in the DKMS laboratory in Dresden, Germany (equivalent antibody panel and instrument). We have developed a novel analytical pipeline for integrating these data. First, we group samples by facility, and by month run for computational convenience, to form “batches” for initial clustering. We perform Leiden clustering, and subclustering, to generate a large number of clusters per batch. To integrate data from all batches, we then cluster all subcluster centroids from all batch-specific clusterings. This gives a global clustering for each stream ( e.g. the CD8+ T cells). Cluster frequencies per sample provide features for quantifying links between immunophenotype and donor metadata ( e.g., CMV serostatus, age, and sex). Our approach offers an important complement to traditional gating and allows full exploration of the data in an unbiased fashion. Results 353 donor products were included in the analysis. The median donor age was 28 years (range 18 - 60), 59% were female and 65% were CMV positive. Initial metaclustering demonstrated that the 53 DKMS samples and the 300 NMDP samples are each well represented in all clusters, and that site-driven batch effects, while present, are mild. We initially focused on the CD8 T cell compartment (UMAP in Fig 1A) and discovered several phenotypic differences when we compared CMV seronegative and seropositive donors. CD8 T cell clusters 10 and 11 (both TCRαβ+ CD45RA+ CCR7+ HLA-DR+ CD57+; cluster 10 CCR9+; cluster 11 CCR9-) were present at higher frequency in CMV+ donors (n = 116) compared with CMV- donors (n = 220; p <0.0001; Fig 1B). Conversely, clusters 9 and 12, enriched for mucosal-associated invariant T (MAIT) cells, were significantly reduced in CMV+ donors (p <0.001). Of note, CMV serostatus was not significantly associated with age (median age in the CMV+ group was 29.4 years; vs 28.4 years in CMV- donors). We also observed an association between cluster 8 (TCRαβ+ CD45RA+ CCR7+ HLA-DR+ CD38+; consistent with an activated phenotype) and age (p = 0.0009; Fig1B). There was a trend toward decreased MAIT frequencies with age (Cluster 9; p = 0.058). When we analyzed the MAIT cell frequency as defined by traditional flow cytometry gating (CD3+ CD8+ CD161+ Vα7.2+), we observed a statistically significant relationship between younger donor age and increased MAIT cell frequency (p = 0.0002), consistent with the trend seen in cluster 9. Sex at birth was not linked with immunophenotype in our analyses thus far. Conclusions In this initial analysis of our multi-center observational study of PBSC graft products, we demonstrate the development and application of new computational tools for large-cohort flow cytometric data analysis. CMV-positive serostatus and younger donor age are both associated with an increased frequency of MAIT cells in the graft. We also found differences in CD8 T cell activation status in association with age and CMV. In the future, we will assess the relationships between these populations and patient outcomes, as well as explore cluster frequencies in association with key patient outcomes including GVHD, relapse, and overall survival. Sample collection and analysis are ongoing at both US and German sites as an ongoing collaborative effort between the NMDP and the DKMS.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Abstract 703: Transient G1 cell cycle arrest with trilaciclib enhances the generation of polyfunctional CD4+ and CD8+ T cells

Abstract When administered prior to chemotherapy, intravenous trilaciclib transiently arrests cyclin-dependent kinase 4/6-dependent cells in the G1 phase of the cell cycle, which has been shown to augment antitumor immunity. In an open-label, phase 2 trial in patients with metastatic triple-negative breast cancer (NCT02978716), administering trilaciclib prior to gemcitabine plus carboplatin improved overall survival, potentially through protection and direct activation of immune function. To determine the impact of trilaciclib on effector T-cell function, we conducted in vitro studies to assess the kinetics of cytotoxic function and generation of polyfunctional T cells. Peripheral blood mononuclear cells (PBMCs) or naïve CD4+ and CD8+ T cells were purified from 6 healthy human donors and activated with CD2/3/28 beads. On days 0, 1, and 3 post activation, 100 nM trilaciclib was added to the cells. To visualize phenotypic and functional changes, T cells that had been activated for 3, 7, or 14 days with or without trilaciclib were restimulated with phorbol myristate acetate (PMA) and ionomycin. Dimension-reduction analysis of flow cytometric data was used to identify polyfunctional subpopulations of CD4+ and CD8+ T cells. T-cell clusters with polyfunctional profiles were identified based on the coproduction of IFNγ, TNFα, and IL-2 cytokines. Subsets of polyfunctional clusters were defined according to other immune phenotypic profiles, including T-cell exhaustion (PD-1 and LAG-3), degranulation (granzyme B), proliferation (Ki-67), and transcription factors (TOX, TCF-1, and T-bet). For both CD4+ and CD8+ T cells, 3 polyfunctional subpopulations were identified based on differential expression of granzyme B, PD-1, and TOX. When CD4+ and CD8+ T cells were activated in the presence of trilaciclib and restimulated with PMA and ionomycin, there was an increase in the frequency of polyfunctional T-cell subsets producing granzyme B. This was compensated by a decrease in granzyme Blow polyfunctional cells, which was associated with increased PD-1high and TOXhigh expression. Clustering analysis of trilaciclib-treated PBMCs also revealed a reduction in proliferating CD8+ T cells with undetectable effector cytokine function based on a decrease in the frequency of CD8+ T cells expressing TOXhigh and Ki-67high. Transient G1 cell cycle arrest in T cells may limit T-cell hyperactivation, which has been shown to induce immunopathological responses. The increased generation of polyfunctional CD8+ T cells is associated with enhanced effector T-cell responses and memory T-cell differentiation. In combination with previous data showing increased differentiation of naïve T cells into memory T cells upon exposure to trilaciclib, these data support a beneficial role for trilaciclib in enhancing antitumor T-cell responses. Citation Format: Sarah Ahn, Subing Cao, Melissa A. Russo, John S. Yi. Transient G1 cell cycle arrest with trilaciclib enhances the generation of polyfunctional CD4+ and CD8+ T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 703.

T Cell Immune Profiles of Blood and Tumor in Dogs Diagnosed With Malignant Melanoma.

Investigation of canine T cell immunophenotypes in canine melanomas as prognostic biomarkers for disease progression or predictive biomarkers for targeted immunotherapeutics remains in preliminary stages. We aimed to examine T cell phenotypes and function in peripheral blood mononuclear cells (PBMC) and baseline tumor samples by flow cytometry, and to compare patient (n = 11–20) T cell phenotypes with healthy controls dogs (n = 10–20). CD3, CD4, CD8, CD25, FoxP3, Ki67, granzyme B, and interferon-γ (IFN-γ) were used to classify T cell subsets in resting and mitogen stimulated PBMCs. In a separate patient cohort (n = 11), T cells were classified using CD3, CD4, CD8, FoxP3, and granzyme B in paired PBMC and single cell suspensions of tumor samples. Analysis of flow cytometric data of individual T cell phenotypes in PBMC revealed specific T cell phenotypes including FoxP3+ and CD25+FoxP3- populations that distinguished patients from healthy controls. Frequencies of IFN-γ+ cells after ConA stimulation identified two different patient phenotypic responses, including a normal/exaggerated IFN-γ response and a lower response suggesting dysfunction. Principle component analysis of selected T cell immunophenotypes also distinguished patients and controls for T cell phenotype and revealed a clustering of patients based on metastasis detected at diagnosis. Findings supported the overall hypothesis that canine melanoma patients display a T cell immunophenotype profile that is unique from healthy pet dogs and will guide future studies designed with larger patient cohorts necessary to further characterize prognostic T cell immunophenotypes.

Integrative Study of Genotypic and Phenotypic Diversity in the Eurasian Orchid Genus Neotinea.

Knowledge of population variation across species’ ranges is a prerequisite for correctly assessing the overall variability of any group of organisms and provides an invaluable basis for unraveling evolutionary history, optimizing taxonomy and devising effective conservation strategies. Here, we examine the genus Neotinea, which represents a relatively recently delimited monophyletic genus of orchids, for which a detailed study of its overall variability was lacking. We applied a suite of biosystematic methods, consisting of flow cytometry, multivariate and geometric morphometrics, and analysis of genomic SNP data, to identify phylogenetic lineages within the genus, to delineate phenotypic variation relevant to these lineages, and to identify potential cryptic taxa within lineages. We found clear differentiation into four major lineages corresponding to the groups usually recognized within the genus: Neotinea maculata as a distinct and separate taxon, the Neotinea lactea group comprising two Mediterranean taxa N. lactea and Neotinea conica, the Neotinea ustulata group comprising two phenologically distinct varieties, and the rather complex Neotinea tridentata group comprising two major lineages and various minor lineages of unclear taxonomic value. N. conica constitutes both a monophyletic group within N. lactea and a distinct phenotype within the genus and merits its proposed subspecies-level recognition. By contrast, the spring and summer flowering forms of N. ustulata (var. ustulata and var. aestivalis) were confirmed to be distinct only morphologically, not phylogenetically. The most complex pattern emerged in the N. tridentata group, which splits into two main clades, one containing lineages from the Balkans and eastern Mediterranean and the other consisting of plants from Central Europe and the central Mediterranean. These individual lineages differ in genome size and show moderate degrees of morphological divergence. The tetraploid Neotinea commutata is closely related to the N. tridentata group, but our evidence points to an auto- rather than an allopolyploid origin. Our broad methodological approach proved effective in recognizing cryptic lineages among the orchids, and we propose the joint analysis of flow cytometric data on genome size and endopolyploidy as a useful and beneficial marker for delineating orchid species with partial endoreplication.

Development of two novel microglia-specific monoclonal antibodies to discriminate microglia from other CNS-resident macrophages by flow cytometry

Abstract Although microglia are the predominant resident macrophages of the central nervous system (CNS), other macrophages can be found in the CNS. As both populations express CD11b, microglia are generally distinguished from peripheral macrophages by lower expression of CD45. This can introduce bias in the analysis of flow cytometric data and make cell sorting challenging. Other microglia associated markers such as Aif1/Iba1 are less cell specific and therefore of limited use to dissect microglia from other brain residing macrophages. Recent single cell transcriptome studies have identified more specific signature genes of microglia, including surface molecule Tmem119 and transcription factor Sall1. To allow robust discrimination between microglia and other brain resident/infiltrating macrophages based on the expression of a single marker by flow cytometry, we have developed monoclonal rat antibodies against mouse Tmem119 and Sall1, respectively. Both clones, V3RT1GOsz (Tmem119) and NRNSTNX (Sall1), were validated by flow cytometric analysis of fresh mouse brain. Here we show that both antibodies can be used to distinguish microglia from other macrophages by flow cytometry without reliance on CD11b or CD45 expression. The availability of these specific antibodies against microglia signature molecules will contribute significantly to the ability to isolate mouse microglia and their advanced characterization.

Common flow cytometry pitfalls in diagnostic hematopathology.

Flow cytometry (FC) has proven to be an extremely versatile and useful tool in the diagnosis and monitoring of hematological diseases in addition to numerous other applications. Major advances in electronics, software, and reagents over the past years have simplified some aspects of FC, while at the same time the ability to combine 8-10 antibodies in a single tube can create both technical and interpretation issues that are more difficult to detect when using only 3-4 color combinations. Use of multiparameter panels can facilitate identification of abnormal populations; however, characteristics of the neoplastic population may create potential diagnostic pitfalls. An understanding of normal immunophenotypic patterns in states of rest, recovery, and activation is a critical first step in order to appropriately identify the abnormal populations that characterize hematopoietic neoplasms. Additionally, incorporation of newer therapeutic strategies, in particular targeted therapies, can confound standard methods for flow cytometric data analysis and knowledge of the impact of therapy on flow cytometric data is critical for accurate data interpretation. This manuscript will review preanalytical, instrument, and interpretation issues that may lead to incorrect interpretation of results.

Combined immunodeficiency in a patient with c-Rel deficiency

This study reports a homozygous mutation in REL abrogating c-Rel protein expression in a patient with combined immunodeficiency characterized by susceptibility to Mycobacterium tuberculosis, Salmonella, Cryptosporidium, and cytomegalovirus.

Progress in Automated Urinalysis.

New technological advances have paved the way for significant progress in automated urinalysis. Quantitative reading of urinary test strips using reflectometry has become possible, while complementary metal oxide semiconductor (CMOS) technology has enhanced analytical sensitivity and shown promise in microalbuminuria testing. Microscopy-based urine particle analysis has greatly progressed over the past decades, enabling high throughput in clinical laboratories. Urinary flow cytometry is an alternative for automated microscopy, and more thorough analysis of flow cytometric data has enabled rapid differentiation of urinary microorganisms. Integration of dilution parameters (e.g., creatinine, specific gravity, and conductivity) in urine test strip readers and urine particle flow cytometers enables correction for urinary dilution, which improves result interpretation. Automated urinalysis can be used for urinary tract screening and for diagnosing and monitoring a broad variety of nephrological and urological conditions; newer applications show promising results for early detection of urothelial cancer. Concomitantly, the introduction of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has enabled fast identification of urinary pathogens. Automation and workflow simplification have led to mechanical integration of test strip readers and particle analysis in urinalysis. As the information obtained by urinalysis is complex, the introduction of expert systems may further reduce analytical errors and improve the quality of sediment and test strip analysis. With the introduction of laboratory-on-a-chip approaches and the use of microfluidics, new affordable applications for quantitative urinalysis and readout on cell phones may become available. In this review, we present the main recent developments in automated urinalysis and future perspectives.

Static Magnetic Field Effect on Cell Alignment, Growth, and Differentiation in Human Cord-Derived Mesenchymal Stem Cells.

This investigation is performed to evaluate the impact of static magnetic field on the Cell growth alignment, and differentiation potential in Human Mesenchymal Stem cells derived from human newborn cords. In vitro-cultured mesenchymal stem cells derived from human newborn cords were exposed to SMF up to 24mT and compared with the control (unexposed) cultures. Viability was assessed via Trypan Blue staining and MTT assay. Cell cycle progression was studied after flow cytometry data analysis. Sox-2, Nanong, and Oct-4 Primers used for RT-PCR experiment. Morphological studies showed that the exposed cells were significantly aligned in parallel bundles in a correlation with the magnetic field lines. Viability measurements showed a significant reduction in cell viability which was noted after exposure to static magnetic field and initiated 36h after the end of exposure time. Flow cytometric data analysis confirmed a decrease in G1 phase cell population within the treated and cultured groups compared with the corresponding control samples. However, the induced changes were recovered in the cell cultures after the post-exposure culture recovery time which may be attributed to the cellular repair mechanisms. Furthermore, the proliferation rate and Oct-4 gene expression were reduced due to the 18mT static magnetic field exposure. The significant proliferation rate decrease accompanied by the Sox-2, Nanong, and Oct-4 gene expression decline, suggested the differentiation inducing effects of SMF exposure. Exposure to Static Magnetic fields up to 24mT affects mesenchymal stem cell alignment and proliferation rate as well as mRNA expression of Sox-2, Nanong, and Oct-4 genes, therefore can be considered as a new differentiation inducer in addition to the other stimulators.

Effects of recombinant human adenovirus-p53 on the regression of hepatic fibrosis.

Hepatic fibrosis is a scarring process that may progress to hepatic cirrhosis and even hepatic carcinoma if left untreated. Hepatic stellate cells (HSCs) play essential roles in the development of hepatic fibrosis. The tumor suppressor protein p53 is a transcription factor that is involved in cell proliferation, cell cycle regulation, apoptosis and DNA repair. Recombinant human adenovirus-p53 (Ad-p53) has been demonstrated to act as a promising antitumor gene therapy in various types of cancer. However, there is limited infomration regarding the therapeutic effect of Ad-p53 on the regression of hepatic fibrosis. In order to examine the underlying molecular mechanism responsible for the effects of Ad-p53 on HSCs, a rat model of hepatic fibrosis was established and HSC-T6 cells were cultured under different conditions. The expression of p53, transforming growth factor (TGF-β1) and α-smooth muscle actin (α-SMA), which is a marker of activated HSCs, was detected by immunohistochemical assays and RT-qPCR. In vitro, five different concentrations (1×106, 5×106, 1×107, 2×107 and 5×107 PFU/ml) of Ad-p53 were selected for use in the MTT assay to analyze the proliferation of HSCs at 0, 24, 48 and 72 h. Flow cytometric analysis was applied to determine the effect of three different concentrations of Ad-p53 (5×106, 1×107 and 2×107 PFU/ml) on the cell cycle and the apoptosis of HSC-T6 cells at 24 and 48 h. The results of immunohistochemical studies and RT-qPCR showed that Ad-p53 upregulated the expression of p53, and downregulated the expression of TGF-β1 and α-SMA. The MTT assay revealed that when treated with various doses of Ad-p53, the proliferation of HSCs was inhibited within a certain range of concentrations and time periods. Analysis of flow cytometric data showed that Ad-p53 arrested the cell cycle in G1 phase and significantly induced apoptosis. Taken together, these findings suggest that Ad-p53 promotes apoptosis and inhibits the proliferation of HSCs in a time- and dose-dependent manner by modulating the expression of p53, TGF-β1 and α-SMA.

Adenosine A1 receptors contribute to immune regulation after neonatal hypoxic ischemic brain injury.

Neonatal brain hypoxic ischemia (HI) often results in long-term motor and cognitive impairments. Post-ischemic inflammation greatly effects outcome and adenosine receptor signaling modulates both HI and immune cell function. Here, we investigated the influence of adenosine A1 receptor deficiency (A1R(-/-)) on key immune cell populations in a neonatal brain HI model. Ten-day-old mice were subjected to HI. Functional outcome was assessed by open locomotion and beam walking test and infarction size evaluated. Flow cytometry was performed on brain-infiltrating cells, and semi-automated analysis of flow cytometric data was applied. A1R(-/-) mice displayed larger infarctions (+33%, p < 0.05) and performed worse in beam walking tests (44% more mistakes, p < 0.05) than wild-type (WT) mice. Myeloid cell activation after injury was enhanced in A1R(-/-) versus WT brains. Activated B lymphocytes expressing IL-10 infiltrated the brain after HI in WT, but were less activated and did not increase in relative frequency in A1R(-/-). Also, A1R(-/-) B lymphocytes expressed less IL-10 than their WT counterparts, the A1R antagonist DPCPX decreased IL-10 expression whereas the A1R agonist CPA increased it. CD4(+) T lymphocytes including FoxP3(+) T regulatory cells, were unaffected by genotype, whereas CD8(+) T lymphocyte responses were smaller in A1R(-/-) mice. Using PCA to characterize the immune profile, we could discriminate the A1R(-/-) and WT genotypes as well as sham operated from HI-subjected animals. We conclude that A1R signaling modulates IL-10 expression by immune cells, influences the activation of these cells in vivo, and affects outcome after HI.

Gamma irradiation induces DNA double-strand breaks in fibroblasts: a model study for the development of biodosimetry

Double-strand breaks (DSBs) of DNAs induced by ionizing radiation can pose detrimental damages on organisms which include genetic instability and cell death. It is necessary to be able to assess health risks associated with irradiation from both accidental and therapeutic exposures in a timely manner for proper medical treatments. This present study showed the first attempt to develop a biodosimetric measure in Thailand based on the quantification of phosphorylated histone H2AX (γ-H2AX) formed at DSB sites with an aim to establish a dose response curve using a two-dimensional (2D) cell culture model. Human dermal fibroblasts were grown into monolayers before irradiated by gamma rays from a Co-60 source in a custom-made lead chamber at doses 0, 0.2, 1, 2 and 4 Gy and a dose rate of 0.21 Gy/min. After 30 min post exposure, γ-H2AX proteins were immunofluorescently labelled for evaluation by confocal microscopy and flow cytometry. The accumulation of phosphorylated γ-H2AX proteins at DSBs appeared as nuclear foci with the most prominent intensity at 4 Gy. Linear regression analysis of flow cytometric data showed a linear response (R2 = 0.9862) of foci intensity in proportion to irradiation dose. In addition, the fraction of cell viability was shown to decrease at higher doses. This technique can be further developed as a quick assessment tool to identify individuals subjected to accidental radiation in parallel to other established biodosimetric measures.

Coping with artifact in the analysis of flow cytometric data

This article highlights several sources of artifact that interfere with optimal analysis of flow cytometric data. Such problems are compounded when flow cytometry is performed on mechanically and enzymatically disaggregated solid tissues or on cultured cells, where subcellular debris, apoptotic or necrotic cells, and highly autofluorescent cells may comprise a substantial proportion of acquired events. We provide real-world examples of tissues that pose specific analytical challenges (bone marrow, breast cancer, lung cancer and adipose tissue) and suggest approaches to improve data analysis. These include the use of a sequential or hierarchical gating process, which envisions analysis as consisting of three parts: (1) removal of artifact; (2) defining classifying populations; and (3) measuring outcomes on the classifying populations. Tools for removal of artifact include use of the time parameter to detect and remove fluidic perturbations, use of doublet discrimination to avoid analysis of cell clusters, measurement of DNA content to remove subcellular debris and late apoptotic cells, Boolean gating to recognize and remove auto-fluorescent events, and the use of a dump gate (markers known to be negative on the population of interest, but expressed on interfering cells). Implementation of these strategies, as appropriate, extends the usefulness of flow cytometry to a wider range of applications.

Quality assessment of boar semen by multivariate analysis of flow cytometric data

Flow cytometry (FCM) has become very powerful over the last decades, enabling multi-parametric measurements of up to thousands of cells per second. This generates massive amounts of data on individual cell characteristics that need to be analyzed in an efficient manner from both physiological and chemical points of view. In this study, a methodology of analysis for FCM data was comprehensively studied to assess quality changes in semen extracted from boars. The proposed methodology combines new automated multi-dimensional data normalization, a density-based clustering method for identification of cell populations, and multivariate methods for post-analysis of the identified populations, enabling the exploratory evaluation and prediction/classification of subpopulations within the experimental data set. The performance of the suggested methodology was compared with the performance of an existing automated clustering method.

Automated analysis of flow cytometric data for measuring neutrophil CD64 expression using a multi-instrument compatible probability state model.

Leuko64™ (Trillium Diagnostics) is a flow cytometric assay that measures neutrophil CD64 expression and serves as an in vitro indicator of infection/sepsis or the presence of a systemic acute inflammatory response. Leuko64 assay currently utilizes QuantiCALC, a semiautomated software that employs cluster algorithms to define cell populations. The software reduces subjective gating decisions, resulting in interanalyst variability of <5%. We evaluated a completely automated approach to measuring neutrophil CD64 expression using GemStone™ (Verity Software House) and probability state modeling (PSM). Four hundred and fifty-seven human blood samples were processed using the Leuko64 assay. Samples were analyzed on four different flow cytometer models: BD FACSCanto II, BD FACScan, BC Gallios/Navios, and BC FC500. A probability state model was designed to identify calibration beads and three leukocyte subpopulations based on differences in intensity levels of several parameters. PSM automatically calculates CD64 index values for each cell population using equations programmed into the model. GemStone software uses PSM that requires no operator intervention, thus totally automating data analysis and internal quality control flagging. Expert analysis with the predicate method (QuantiCALC) was performed. Interanalyst precision was evaluated for both methods of data analysis. PSM with GemStone correlates well with the expert manual analysis, r(2) = 0.99675 for the neutrophil CD64 index values with no intermethod bias detected. The average interanalyst imprecision for the QuantiCALC method was 1.06% (range 0.00-7.94%), which was reduced to 0.00% with the GemStone PSM. The operator-to-operator agreement in GemStone was a perfect correlation, r(2) = 1.000. Automated quantification of CD64 index values produced results that strongly correlate with expert analysis using a standard gate-based data analysis method. PSM successfully evaluated flow cytometric data generated by multiple instruments across multiple lots of the Leuko64 kit in all 457 cases. The probability-based method provides greater objectivity, higher data analysis speed, and allows for greater precision for in vitro diagnostic flow cytometric assays.

Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model

Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model

Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model

Flow Cytometry is widely used for enumeration of hematopoietic stem cell (SC) levels in bone marrow, cord blood, peripheral blood, and apheresis products. The ISHAGE single-platform gating method is considered by many to be the standard for CD34+ SC enumeration. However, attempts at uniform application of this ISHAGE method have met with only partial success. We propose an automated, multivariate classification approach for SC analysis based on Probability State Modeling™ (PSM). In this study, we compare the results from automated PSM analysis with manual ISHAGE gating analysis as performed by a trained analyst. A total of 258 samples were assayed on BD FACSCanto II flow cytometers using a stain-lyse-no-wash technique. Populations were defined using CD34, CD45, 7-AAD, and light scatter. BD TruCount™ bead tubes were used for absolute SC concentrations. A PSM was designed to classify events into beads, debris, intact-dead cells, and intact-live SC; run unattended and record results. The ISHAGE and PSM methods show excellent agreement in estimating the concentration of #SC/μL: slope = 1.009, r² = 0.999. Bland-Altman Analysis for the SC concentration has an average difference (bias) of 2.018 SC/μL. The 95% confidence interval is from -59.350 to 63.380 SC/μL. The operator-to-operator agreement using PSM is perfect: r² = 1.000. Automated PSM analysis of SC listmode data produces results that agree strongly with ISHAGE gate-based results. The PSM approach provides higher reproducibility, objectivity, and speed with accuracy at least equivalent to the ISHAGE method.