Topsham, ME, United States
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Wong L.,Trillium Diagnostics LLC | Hunsberger B.C.,Verity Software House | Bruce Bagwell C.,Verity Software House | Davis B.H.,Trillium Diagnostics LLC
International Journal of Laboratory Hematology | Year: 2013

Summary: Background: Flow cytometric methods (FCMs) are the contemporary standard for fetal red blood cell (RBC) quantitation and fetomaternal hemorrhage (FMH) detection. FCM provides greater sensitivity and repeatability relative to manual microscopic Kleihauer-Betke methods. FCM assays are not totally objective, employing subjective manual gating of fetal RBCs with measureable interobserver imprecision. We investigated Probability State Modeling to automate analysis of fetal RBCs using an assay for hemoglobin F (HbF)-containing RBCs. Methods: Two hundred human bloods were processed using the FMH QuikQuant™ assay (Trillium Diagnostics, Brewer, ME, USA). A Probability State Model (PSM) was designed to enumerate fetal RBCs by selecting the three RBCs subpopulation based on differences in intensity levels of several parameters. The GemStone™ program uses a PSM that requires no operator intervention. Routine manual analysis by experienced users was performed, along with replicate analyses for both methods. Results: The PSM by GemStone™ correlates strongly with the expert manual analysis, r2 = 0.9986. The mean absolute difference of the FMH results between GemStone™ and manual 'expert' analysis was 0.04% with no intermethod bias detected. Manual gating demonstrated coefficient of variations (CVs) of 10.6% for intra-analyst replicates and 22.6% for interanalyst imprecision. The interanalyst agreement in GemStone™ is a perfect correlation, r2 = 1.00, and no imprecision with a 0.00% CV. Conclusion: Automated PSM analysis of fetal RBCs strongly correlates with expert traditional manual analysis. PSM enumerates fetal RBCs accurately with significantly greater objectivity and lower imprecision than the traditional manual gating method. Thus, PSM provides a means to markedly improve interlaboratory variance with FMH assays based upon subjective gating strategies. © 2013 John Wiley & Sons Ltd.

Tanqri S.,Biogen Idec | Vall H.,LabCorp | Kaplan D.,University of Western States | Hoffman B.,Livermore | And 4 more authors.
Cytometry Part B - Clinical Cytometry | Year: 2013

Clinical diagnostic assays, may be classified as quantitative, quasi-quantitative or qualitative. The assay's description should state what the assay needs to accomplish (intended use or purpose) and what it is not intended to achieve. The type(s) of samples (whole blood, peripheral blood mononuclear cells (PBMC), bone marrow, bone marrow mononuclear cells (BMMC), tissue, fine needle aspirate, fluid, etc.), instrument platform for use and anticoagulant restrictions should be fully validated for stability requirements and specified. When applicable, assay sensitivity and specificity should be fully validated and reported; these performance criteria will dictate the number and complexity of specimen samples required for validation. Assay processing and staining conditions (lyse/wash/fix/perm, stain pre or post, time and temperature, sample stability, etc.) should be described in detail and fully validated. © 2013 International Clinical Cytometry Society.

Bray C.,Verity Software House | Spidlen J.,BC Cancer Agency | Brinkman R.R.,BC Cancer Agency
Cytometry Part A | Year: 2012

The Flow Cytometry Standard (FCS) format was developed back in 1984. Since then, FCS became the standard file format supported by all flow cytometry software and hardware vendors. Over the years, updates were incorporated to adapt to technological advancements in both flow cytometry and computing technologies. However, flexibility in how data may be stored in FCS has led to implementation difficulties for instrument vendors and third party software developers. In this technical note, we are providing implementation guidance and examples related to FCS 3.1, the latest version of the standard. By publishing this text, we intend to prevent potential compatibility issues that could be faced when implementing the FCS spillover and preferred display keywords that have arisen during discussions among some implementers. © 2012 International Society for Advancement of Cytometry.

Biancotto A.,U.S. National Institutes of Health | Dagur P.K.,U.S. National Institutes of Health | Fuchs J.C.,U.S. National Institutes of Health | Wiestner A.,U.S. National Institutes of Health | And 2 more authors.
Modern Pathology | Year: 2012

Increased numbers of T regulatory (T reg) cells are found in B-chronic lymphocytic leukemia, but the nature and function of these T regs remains unclear. Detailed characterization of the T regs in chronic lymphocytic leukemia has not been performed and the degree of heterogeneity of among these cells has not been studied to date. Using 15-color flow cytometry we show that T reg cells, defined using CD4, CD25, and forkhead box P3 (FOXP3), can be divided into multiple complex subsets based on markers used for nave, memory, and effector delineation as well as markers of T reg activation. Furthermore FOXP3 + cells can be identified among CD4 + CD25 - as well as CD8 + CD4 + populations in increased proportions in patients with chronic lymphocytic leukemia compared with healthy donors. Significantly different frequencies of nave and effector T regs populations are found in healthy donor controls compared with donors with chronic lymphocytic leukemia. A population of CCR7 + CD39 + T regs was significantly associated with chronic lymphocytic leukemia. This population demonstrated slightly reduced suppressive activity compared with total T regs or T regs of healthy donors. These data suggest that FOXP3-expressing cells, particularly in patients with chronic lymphocytic leukemia are much more complex for T reg sub-populations and transitions than previously reported. These findings demonstrate the complexity of regulation of T-cell responses in chronic lymphocytic leukemia and illustrate the use of high-dimensional analysis of cellular phenotypes in facilitating understanding of the intricacies of cellular immune responses and their dysregulation in cancer. © 2012 USCAP, Inc. All rights reserved.

Inokuma M.S.,BD Biosciences | Maino V.C.,BD Biosciences | Bagwell C.B.,Verity Software House
Journal of Immunological Methods | Year: 2013

Flow cytometric analysis enables the simultaneous single-cell interrogation of multiple biomarkers for phenotypic and functional identification of heterogeneous populations. Analysis of polychromatic data has become increasingly complex with more measured parameters. Furthermore, manual gating of multiple populations using standard analysis techniques can lead to errors in data interpretation and difficulties in the standardization of analyses. To characterize high-dimensional cytometric data, we demonstrate the use of probability state modeling (PSM) to visualize the differentiation of effector/memory CD8+ T cells. With this model, four major CD8+ T-cell subsets can be easily identified using the combination of three markers, CD45RA, CCR7 (CD197), and CD28, with the selection markers CD3, CD4, CD8, and side scatter (SSC). PSM enables the translation of complex multicolor flow cytometric data to pathway-specific cell subtypes, the capability of developing averaged models of healthy donor populations, and the analysis of phenotypic heterogeneity. In this report, we also illustrate the heterogeneity in memory T-cell subpopulations as branched differentiation markers that include CD127, CD62L, CD27, and CD57. © 2013 The Authors.

Bagwell C.B.,Verity Software House | Hunsberger B.C.,Verity Software House | Herbert D.J.,Verity Software House | Munson M.E.,Verity Software House | And 3 more authors.
Cytometry Part A | Year: 2015

As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches. © 2015 International Society for Advancement of Cytometry.

Herbert D.J.,Verity Software House | Miller D.T.,Friendship | Bruce Bagwell C.,Verity Software House
Cytometry Part B - Clinical Cytometry | Year: 2012

Background: 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. Methods: 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. 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. Conclusions: 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. Copyright © 2012 International Clinical Cytometry Society.

Bagwell C.B.,Verity Software House
Methods in molecular biology (Clifton, N.J.) | Year: 2011

Recent advances in biotechnology have resulted in cytometers capable of performing numerous correlated measurements of cells, often exceeding ten. In the near future, it is likely that this number will increase by fivefold and perhaps even higher. Traditional analysis strategies based on examining one measurement versus another are not suitable for high-dimensional data analysis because the number of measurement combinations expands geometrically with dimension, forming a kind of complexity barrier. This dimensionality barrier limits cytometry and other technologies from reaching their maximum potential in visualizing and analyzing important information embedded in high-dimensional data.This chapter describes efforts to break through this barrier and allow the visualization and analysis of any number of measurements with a new paradigm called Probability State Modeling (PSM). This new system creates a virtual progression variable based on probability that relates all measurements. PSM can produce a single graph that conveys more information about a sample than hundreds of traditional histograms. These PSM overlays reveal the rich interplay of phenotypic changes in cells as they differentiate. The end result is a deeper appreciation of the molecular genetic underpinnings of ontological processes in complex populations such as found in bone marrow and peripheral blood.Eventually these models will help investigators better understand normal and abnormal cellular progressions and will be a valuable general tool for the analysis and visualization of high-dimensional data.

Munson M.E.,Verity Software House
Cytometry Part A | Year: 2010

Investigating the response of cells to specific agonists may involve the use of cell tracking dyes to assess the extent of stimulated proliferation, frequently reported as the proliferation index (PI). Calculation of PI uses a model for cell division that expects the cell number to double as cells proliferate through each successive generation. It is often useful to compare the PI of a stimulated control population with that of a population in the presence of some agent, whether chemical, pharmacologic, or cellular. For such comparison studies, the nature of the metric being used must be taken into account to accurately assess the extent of inhibition. Specifically, the metric used in ModFit LT (Verity Software House, Topsham, ME) and in FCS Express (De Novo Software, Los Angeles, CA) uses a metric with a lower limit of unity, whereas the metric used in FlowJo (Treestar, Ashland, OR) has a lower limit of zero. For studies involving cell proliferation comparisons using tracking dye dilution, a new equation is proposed as the appropriate calculation to use when determining the percent of relative response based on proliferation index values for a metric whose lower limit is unity. © 2010 International Society for Advancement of Cytometry.

Hunsberger B.C.,Verity Software House | Bagwell C.B.,Verity Software House
Cytometry Part B - Clinical Cytometry | Year: 2012

Background: Flow Cytometry is the standard for the detection of glycosylphosphatidylinositol (GPI)-deficient clones in paroxysmal nocturnal hemoglobinuria (PNH) and related disorders. Although the International Clinical Cytometry Society (ICCS) and the International PNH Interest Group (IPIG) have published guidelines for PNH assays, data analysis has not been standardized. Current analyses use manual gating to enumerate PNH cells. We evaluate an automated approach to identify GPI-deficient leukocytes using a GemStone™ (Verity Software House) probability state model (PSM). Methods: Five hundred and thirty patient samples were assayed on BD Canto II flow cytometers using a stain-lyse-wash technique. Populations were defined using CD15, CD45, CD64 and side scatter. GPI-deficient myeloid cells were recognized as FLAER-, CD24-, and dim or absent CD16. GPI-deficient monocytic cells were identified as FLAER- and CD14-. The data were not censored in any way. A PSM was designed to enumerate monocytic and myeloid cells by adjusting the peaks and line spreads of the data, and recording the normal and GPI-deficient counts. No operator adjustments were made to the automated analysis. Results: By human analysis, 53 of 530 samples showed GPI-deficient clones. Automated analysis identified the same 53 clones; there were 0 false positives and 0 false negatives. Sensitivity was 100% and specificity 100%. Gating and automated results (percent GPI-deficient cells) were highly correlated: r2 = 0.997 for monocytic cells, and r 2 = 0.999 for myeloids. Mean absolute differences were 0.94% for monocytes and 0.78% for myeloid cells. Conclusions: Automated analysis of GPI-deficient leukocytes produces results that agree strongly with gate-based results. The probability-based approach provides higher speed, objectivity, and reproducibility. Copyright © 2012 International Clinical Cytometry Society.

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