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Chlamydia trachomatis (CT) is the most common bacterial sexually transmitted infection globally. Understanding natural immunity to CT will inform vaccine design. This study aimed to profile immune cells and associated functional features in CT-infected women, and determine immune profiles associated with reduced risk of ascended endometrial CT infection and CT reinfection. PBMCs from CT-exposed women were profiled by mass cytometry and random forest models identified key features that distinguish outcomes. CT+ participants exhibited higher frequencies of CD4+ Th2, Th17, and Th17 DN CD4 T effector memory (TEM) cells than uninfected participants with decreased expression of T cell activation and differentiation markers. No significant differences were detected between women with or without endometrial CT infection. Participants who remained follow-up negative (FU-) showed higher frequencies of CD4 T central memory (TCM) Th1, Th17, Th1/17, and Th17 DN but reduced CD4 TEM Th2 cells than FU+ participants. Expression of markers associated with central memory and Th17 lineage were increased on T cell subsets among FU- participants. These data indicate that peripheral T cells exhibit distinct features associated with resistance to CT reinfection. The highly plastic Th17 lineage appears to contribute to protection. Addressing these immune nuances could promote efficacy of CT vaccines.

This chapter was originally written in 2011. The idea was to give some history of cell cycle analysis before and after flow cytometry became widely accessible; provide references to educational material for single parameter DNA content analysis, introduce and discuss multiparameter cell cycle analysis in a methodological style, and in a casual style, discuss aspects of the work over the last 40years that we have given thought, performing some experiments, but didn't publish. It feels like there is a linear progression that moves from counting cells for growth curves, to counting labeled mitotic cells by autoradiography, to DNA content analysis, to cell cycle states defined by immunofluorescence plus DNA content analysis, to extraction of cell cycle expression profiles, and finally to probability state modeling, which should be the "right" way to analyze cytometric cell cycle data. This is the sense of this chapter. In 2023, we have updated it, but the exciting, expansive aspects brought about by spectral and mass cytometry are still young and developing, and thus have not been vetted, reviewed, and presented in mature form., (Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

High-dimensional mass cytometry data potentially enable a comprehensive characterization of immune cells. In order to positively affect clinical trials and translational clinical research, this advanced technology needs to demonstrate a high reproducibility of results across multiple sites for both peripheral blood mononuclear cells (PBMC) and whole blood preparations. A dry 30-marker broad immunophenotyping panel and customized automated analysis software were recently engineered and are commercially available as the Fluidigm® Maxpar® Direct™ Immune Profiling Assay™. In this study, seven sites received whole blood and six sites received PBMC samples from single donors over a 2-week interval. Each site labeled replicate samples and acquired data on Helios™ instruments using an assay-specific acquisition template. All acquired sample files were then automatically analyzed by Maxpar Pathsetter™ software. A cleanup step eliminated debris, dead cells, aggregates, and normalization beads. The second step automatically enumerated 37 immune cell populations and performed label intensity assessments on all 30 markers. The inter-site reproducibility of the 37 quantified cell populations had consistent population frequencies, with an average %CV of 14.4% for whole blood and 17.7% for PBMC. The dry reagent coupled with automated data analysis is not only convenient but also provides a high degree of reproducibility within and among multiple test sites resulting in a comprehensive yet practical solution for deep immune phenotyping., (© 2019 International Clinical Cytometry Society.)

Mass cytometry is an emerging technology capable of 40 or more correlated measurements on a single cell. The complexity and volume of data generated by this platform have accelerated the creation of novel methods for high-dimensional data analysis and visualization. A key step in any high-level data analysis is the removal of unwanted events, a process often referred to as data cleanup. Data cleanup as applied to mass cytometry typically focuses on elimination of dead cells, debris, normalization beads, true aggregates, and coincident ion clouds from raw data. We describe a probability state modeling (PSM) method that automatically identifies and removes these elements, resulting in FCS files that contain mostly live and intact events. This approach not only leverages QC measurements such as DNA, live/dead, and event length but also four additional pulse-processing parameters that are available on Fluidigm Helios™ and CyTOF® (Fluidigm, Markham, Canada) 2 instruments with software versions of 6.3 or higher. These extra Gaussian-derived parameters are valuable for detecting well-formed pulses and eliminating coincident positive ion clouds. The automated nature of this new routine avoids the subjectivity of other gating methods and results in unbiased elimination of unwanted events. © 2019 International Society for Advancement of Cytometry., (© 2019 International Society for Advancement of Cytometry.)

The fundamental purpose of log and log-like transforms for cytometry is to make measured population variabilities as uniform as possible. The long-standing success of the log transform was its ability to stabilize linearly increasing gain-dependent uncertainties and the success of the log-like transforms is that they extend this notion to include zero and negative measurement values. This study derives and examines a transform called VLog that stabilizes the three general sources of variability: (1) gain-dependent variability, (2) photo-electron counting error, and (3) signal-independent sources of error. Somewhat surprisingly, this transform has a closed-form solution and therefore is relatively simple to implement. By including some quantitation elements in its formulation, the shape-dependent arguments, α and β, usually do not require optimization for different datasets. The simplicity and generality of the transform may make it a useful tool for cytometry and possibly other technologies. © 2016 International Society for Advancement of Cytometry., (© 2016 International Society for Advancement of Cytometry.)

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.)

Background: 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)., Methods: 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., Results: 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., Conclusion: 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., (© 2015 International Clinical Cytometry Society.)

Background: Human progenitor and B-cell development is a highly regulated process characterized by the ordered differential expression of numerous cell-surface and intracytoplasmic antigens. This study investigates the underlying coordination of these modulations by examining a series of normal bone marrow samples with the method of probability state modeling or PSM., Results: The study is divided into two sections. The first section examines B-cell stages subsequent to CD19 up-regulation. The second section assesses an earlier differentiation stage before and including CD19 up-regulation. POST-CD19 ANTIGENIC UP-REGULATION: Statistical analyses of cytometry data derived from sixteen normal bone marrow specimens revealed that B cells have at least three distinct coordinated changes, forming four stages labeled as B1, B2, B3, and B4. At the end of B1; CD34 antigen expression down-regulates with TdT while CD45, CD81, and CD20 slightly up-regulate. At the end of B2, CD45 and CD20 up-regulate. At the end of B3 and beginning of B4; CD10, CD38, and CD81 down-regulate while CD22 and CD44 up-regulate. PRE-CD19 ANTIGENIC UP-REGULATION: Statistical analysis of ten normal bone marrows revealed that there are at least two measurable coordinated changes with progenitors, forming three stages labeled as P1, P2, and P3. At the end of P1, CD38 up-regulates. At the end of P2; CD19, CD10, CD81, CD22, and CD9 up-regulate while CD44 down-regulates slightly., Conclusions: These objective results yield a clearer immunophenotypic picture of the underlying cellular mechanisms that are operating in these important developmental processes. Also, unambiguously determined stages define what is meant by "normal" B-cell development and may serve as a preliminary step for the development of highly sensitive minimum residual disease detection systems. A companion article is simultaneously being published in Cytometry Part A that will explain in further detail the theory behind PSM. Three short relevant videos are available in the online supporting information for both of these papers., (© 2015 International Clinical Cytometry Society.)

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. Published by Elsevier B.V. All rights reserved.)

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 r² = 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.)

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 naïve, 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 naïve 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.

Flow cytometry is the most widely used technology for analyzing apoptosis. The multiparametric nature of flow cytometry allows several apoptotic characteristics to be combined in a single sample, making it a powerful tool for analyzing the complex progression of apoptotic death. This chapter provides guidelines for combining caspase detection, annexin V binding, DNA dye exclusion, and other single apoptotic assays into multiparametric assays.This approach to analyzing apoptosis provides far more information than single parameter assays that provide only an ambiguous "percent apoptotic" result, given that multiple early, intermediate and late apoptotic stages can be visualized simultaneously. This multiparametric approach is also amenable to a variety of flow cytometric instrumentation, both old and new.

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.

Background: The remarkable success of cytometry over the past 30 years is largely due to its uncanny ability to display populations that vastly differ in numbers and fluorescence intensities on one scale. The log transform implemented in hardware as a log amplifier or in software normalizes signals or channels so that these populations appear as clearly discernible peaks. With the advent of multiple fluorescence cytometry, spectral crossover compensation of these signals has been necessary to properly interpret the data. Unfortunately, because compensation is a subtractive process, it can produce negative and zero valued data. The log transform is undefined for these values and, as a result, forces computer algorithms to truncate these values, creating a few problems for cytometrists. Data truncation biases displays making properly compensated data appear undercompensated; thus, enticing many operators to overcompensate their data. Also, events truncated into the first histogram channel are not normally visible with typical two-dimensional graphic displays, thus hiding a large number of events and obscuring the true proportionality of negative distributions. In addition, the log transform creates unequal binning that can dramatically distort negative population distributions., Methods and Results: The HyperLog transform is a log-like transform that admits negative, zero, and positive values. The transform is a hybrid type of transform specifically designed for compensated data. One of its parameters allows it to smoothly transition from a logarithmic to linear type of transform that is ideal for compensated data., Conclusions: The HyperLog transform is easily implemented in computer systems and results in display systems that present compensated data in an unbiased manner., (Copyright (c) 2005 Wiley-Liss, Inc.)

Background: There is often a need in flow cytometry to display and analyze histograms at resolutions lower than those native to the data. It is common, for example, to analyze DNA histograms at 256-channel resolution, even though the data were acquired at 1,024 channels or more. The most common method for reducing resolution, referred to as the consecutive summation (CS) method, can introduce distortions into the shape of histograms. Peaks that were symmetric in the original data can become skewed in the reduced-resolution histogram. Data analysis can be negatively affected by the distortions produced by reducing the histogram resolution. An alternative technique for reducing histogram resolution, the unbiased summation (US) method, minimizes shape distortion. This paper describes the US method and examines the benefits it provides in the analysis of DNA histograms., Methods: Reduced chi-square (RCS) was used to measure the response to three experimental variables in the least-squares analysis of simulated DNA histograms. For each variable (the percentage of coefficient of variation [%CV], number of events, and mean position of the G1 distribution), a test data set of 1,000 histograms was generated at 1,024-channel resolution. Histogram resolutions were reduced with each method and then analyzed with ModFit LT cell-cycle analysis software (Verity Software House, Topsham, ME). S-phase error and processor computation time of each method also were evaluated. A Monte Carlo experiment was performed to compare CS and US methods to theoretically correct reductions., Results: CS method analysis results were negatively affected by changes in %CV, number of events, and G1 peak position. The US method produced consistently lower RCS values (more accurate results) within the tested ranges. The US method eliminated bias in S-phase error and had negligible impact on analysis processing speed. It improved RCS values 44.50% on average (P < 0.0002) with actual DNA histograms. Whereas the CS method became less accurate (chi-square test) as the amount of reduction increased, the US method was unaffected, producing consistently better results., Conclusions: The US method is recommended for reducing histogram resolution in modeling applications such as DNA cell-cycle analysis. It may have implications in other areas of flow cytometric data analysis., (Copyright 2003 Wiley-Liss, Inc.)

Both DNA ploidy and S-phase ploidy are promising prognostic factors for node-negative breast cancer patients. Based largely on the analysis of one large study, much of the reported problems with these factors have been caused by some unappreciated complexities in categorizing DNA ploidy into low- and high-risk groups and the lack of some necessary adjustments to eliminate unwanted correlations between DNA S-phase and ploidy. When both DNA ploidy and S-phase are compensated properly, they become independent prognostic markers, forming a powerful prognostic model.

Developing a reliable and quantitative assessment of the potential virulence of a malignancy has been a long-standing goal in clinical cytometry. DNA histogram analysis provides valuable information on the cycling activity of a tumor population through S-phase estimates; it also identifies nondiploid populations, a possible indicator of genetic instability and subsequent predisposition to metastasis. Because of conflicting studies in the literature, the clinical relevance of both of these potential prognostic markers has been questioned for the management of breast cancer patients. The purposes of this study are to present a set of 10 adjustments derived from a single large study that optimizes the prognostic strength of both DNA ploidy and S-phase and to test the validity of this approach on two other large multicenter studies. Ten adjustments to both DNA ploidy and S-phase were developed from a single node-negative breast cancer database from Baylor College (n = 961 cases). Seven of the adjustments were used to reclassify histograms into low-risk and high-risk ploidy patterns based on aneuploid fraction and DNA index optimum thresholds resulting in prognostic P values changing from little (P < 0.02) or no significance to P < 0.000005. Other databases from Sweden (n = 210 cases) and France (n = 220 cases) demonstrated similar improvement of DNA ploidy prognostic significance, P < 0.02 to P < 0.0009 and P < 0.12 to P < 0.002, respectively. Three other adjustments were applied to diploid and aneuploid S-phases. These adjustments eliminated a spurious correlation between DNA ploidy and S-phase and enabled them to combine independently into a powerful prognostic model capable of stratifying patients into low, intermediate, and high-risk groups (P < 0.000005). When the Baylor prognostic model was applied to the Sweden and French databases, similar significant patient stratifications were observed (P < 0.0003 and P < 0.00001, respectively). The successful transference of the Baylor prognostic model to other studies suggests that the proposed adjustments may play an important role in standardizing this test and provide valuable prognostic information to those involved in the management of breast cancer patients.

Unlabelled: Flow cytometers collect data in an attempt to understand some aspect of a biological system. Mathematical models are developed to extract relevant population features from the data, to help users attain their goals. This unit examines what models are and how they are routinely used., Keywords: flow cytometry; listmode data; data analysis; modeling Flow cytometers collect data in an attempt to understand some aspect of a biological system.

In 1984, the first flow cytometry data file format was proposed as Flow Cytometry Standard 1.0 (FCS1.0). FCS 1.0 provided a uniform file format allowing data acquired on one computer to be correctly read and interpreted on other computers running a variety of operating systems. That standard was modified in 1990 and adopted by the Society of Analytical Cytology as FCS 2.0. Here, we report on an update of the FCS 2.0 standard which we propose to designate FCS 3.0. We have retained the basic four segment structure of earlier versions (HEADER, TEXT, DATA and ANALYSIS) in order to maintain analysis software compatibility, where possible. The changes described in this proposal include a method to collect files larger than 100 megabytes (not possible in earlier versions of the standard), the inclusion of international characters in the TEXT portions of the file, a method of verifying data integrity using a 16-bit cyclic redundancy check, and increased keyword support for cluster analysis and time acquisition. This report summarizes the work of the ISAC Data File Standards Committee. The complete and detailed FCS 3.0 standard is available through the ISAC office [Sherwood Group, 60 Revere Drive, Ste 500, Northbrook, IL 60062, phone: (847) 480-9080 ext. 231, fax: (847) 480-9282, E-mail: isac@sherwood-group.com] or through the internet at the ISAC WWW site, http://nucleus.immunol.washington.edu/ISAC.ht ml.

This article reports the use of flow cytometry to determine tumor nuclear DNA content and its correlations with clinical outcome in a series of patients with parathyroid carcinoma. Information concerning nine patients with parathyroid cancer (aged 25 to 88 years) was reviewed. Paraffin-embedded, formalin-fixed archival tissue was used to determine tumor DNA content flow cytometrically. Twenty-five operative procedures were performed in nine patients, including 11 parathyroidectomies, two wide local excisions, six central neck dissections, and four median sternotomies for resection of metastases. With flow cytometry used to determine a tumor DNA index, five patients had evidence of tumor aneuploidy; in two patients two aneuploid peaks were evident. The DNA index ranged from 0.7 (hypodiploid) to 1.92 (mean, 1.31). Follow-up ranged from 1 to 18 years. Four patients died. Five were alive 1 to 13 years after diagnosis of parathyroid disease. Four of the five patients with evidence of tumor aneuploidy had metastatic disease and died, and the fifth has had three local recurrences. The four patients with diploid tumors were alive and free of disease 1, 3, 4, and 8 years after the initial operation. It is concluded that in patients with clinically or pathologically demonstrated parathyroid cancer, flow cytometry may help differentiate those whose cancers are likely to behave indolently (diploid tumors) from those with tumors (aneuploid) more likely to behave aggressively by recurring locally or metastasizing.

Twenty-four hyperplastic parathyroid glands from 11 patients with multiple endocrine neoplasia type I (MEN-I), and 36 hyperplastic parathyroid glands in 15 patients with sporadic primary hyperparathyroidism, ie, not associated with MEN, were analyzed for DNA by flow cytometry. Sixteen of 24 hyperplastic parathyroid glands from patients with MEN-I were DNA diploid, and eight were DNA aneuploid. Thirty-three of 36 hyperplastic parathyroid glands from patients without MEN were DNA diploid, and only three were DNA aneuploid. The mean percentage of 4c level (a measure of the G2M phase of the cell cycle) of DNA diploid hyperplastic parathyroid glands taken from patients with MEN-I was 8.1% +/- 4.5%, which is significantly higher than the 3.5% +/- 3.4% for those taken from patients without MEN. Our results show that there is a difference in nuclear DNA content between hyperplastic parathyroid glands in patients with MEN-I and those in patients without MEN.

Two-hundred seventy-four patients with primary hyperparathyroidism had selective removal of enlarged parathyroid glands. Biopsies were taken from all parathyroid glands. Normal-size glands were not resected irrespective of their histological appearance. After a mean follow-up of 13.5 years the rates of persistent and recurrent hyperparathyroidism were, respectively, 3.6% and 0.7%. Transient and permanent hypoparathyroidism occurred in 24% and 2.5% of the patients. The microscopic appearance of enlarged glands and of biopsies taken from normal-size glands were reviewed by two pathologists. Normal parathyroid glands were distinguished from abnormal glands fairly accurately (sensitivity 93%, specificity 80%). Microscopic classification of abnormal parathyroid glands as adenomas or hyperplastic glands correlated poorly with the gross classification as single or multigland disease. Flow cytometric DNA analysis of paraffin embedded parathyroid tissue showed significant differences for DNA index, % S-phase and % G2M (p less than 0.001). Differentiating single from multigland disease by means of DNA analysis was not possible. In conclusion, removal of only enlarged parathyroid glands results in acceptable rates of persistent and recurrent hyperparathyroidism. Biopsies should only be taken sparingly to prevent transient and permanent hypoparathyroidism. Microscopic examination and flow cytometric DNA analysis can differentiate normal from abnormal parathyroid glands but are unable to differentiate abnormal glands into single or multigland disease.

Detection of DNA tetraploid populations requires a high index of suspicion at the time of data acquisition and frequently requires subsequent off-line analysis for confirmation, including evaluation of the hypertetraploid region. To analyze these specimens, the flow cytometer operator must run all specimens with the G0G1 peak in low channels or rerun specimens in which tetraploidy is suspected with a lower photomultiplier tube (PMT) voltage or lower amplifier gain setting. Re-analysis may not be possible in specimens with few cells. A simple modification to the cytometer allows PMT signal splitting with simultaneous processing of the signal by two different amplifiers. This allows simultaneous acquisition of histograms optimized for both the hypotetraploid and hypertetraploid regions.

In this paper we describe a theory of DNA histogram debris generation and compensation that can be applied to paraffin-embedded frozen tissue preparations. The theory predicts the distribution of fragments generated from single and multiple random sectioning of three-dimensional ellipsoids representing nuclei. The fragment distribution is assumed to be a major component of the underlying debris in DNA histograms. A comparison of S-phase fractions (SPF) from matched tissue prepared by frozen and formalin-fixed paraffin-embedded DNA methods demonstrates the usefulness of the theory.

The Data File Standards Committee of the Society for Analytical Cytology presents a Standard to be used for the storage of data associated with flow cytometric measurements. The Standard specifies a format that provides for the inclusion of all information necessary to fully describe: 1) the instrument used for the measurement; 2) the sample measured; 3) the data obtained; and 4) the results of analysis of the data. The Committee and the Society for Analytical Cytology point out that the use of this Standard by all those individuals and companies that generate or use data taken with flow cytometers or generate methods of analysis for the data will encourage the sharing of such data and methods of analysis.

Five commonly used fixatives (AZF, B-5, Bouin's, formalin, and Zenker's) were evaluated for their effect on the flow cytometric analysis of DNA and total nuclear protein (TNP) in solid tumors. Data were obtained with the use of colonic adenocarcinoma, squamous carcinoma of the lung, mammary adenocarcinoma, and spleen with a plasma cell leukemic infiltrate. The parameters examined were G0-G1 DNA staining intensity, %G0-G1, percent coefficient of variation (%CV), percent debris, and TNP staining intensity. The results showed that variations in the fixation of solid tumor significantly affected flow cytometric-derived parameters. In this study, paraffin-embedded tissue (PET) fixed in 10% (v/v) neutral buffered formalin (NBF) produced the best results, with a %CV below 4.7, whereas fixatives such as Zenker's and B-5 produced poor %CVs (above 6.0) or uninterpretable TNP and light scatter data. These data suggest that a portion of all tissue samples be fixed in NBF to allow for subsequent analysis by fixative-sensitive assays such as DNA in situ hybridization and flow cytometry.

We have recently described the presence of a guanylate cyclase [GTP pyrophosphate-lyase (cyclizing), EC 4.6.1.2] inhibitor (GCI) in an aqueous extract of the balsam pear (Momordica charantia abbreviata). Because the guanylate cyclase-cyclic GMP system is though to be involved in cell growth, DNA and RNA synthesis, and possible malignant transformation, we examined the effect of the aqueous extract containing GCI on an undifferentiated adenocarcinoma of the rat prostate and concanavalin-A-stimulated [3H]thymidine incorporation into cultured splenic lymphocytes, a process thought to be mediated by cyclic GMP. The results demonstrate that the extract of the balsam pear blocks both the growth of the rat prostatic adencarcinoma in vitro and [3H]thymidine incorporation into DNA. DNA histograms from flow cytometry indicated that the extract containing GCI inhibited in the G2 + M phase of the cell cycle, a presumed locus of cyclic GMP effects. In addition, guanylate cyclase activity was significantly greater in the tumor than normal prostate tissue and was decreased by the extract containing GCI. Cyclic GMP levels in the tumor in culture wer also decreased by addition of the extract. It remains to be determined whether or not the anti-tumor agent and GCI are the same substance.

Flow cytometry (FCM) was used to monitor the radiation effects promoted by incorporated tritiated thymidine (3H-TdR) on phytohemagglutinin (PHA)-stimulated human peripheral blood lymphocytes stained with propidium iodide (PI). Lymphocyte microcultures were continuously labeled or pulse-labeled for various periods of time with different 3H-TdR concentrations. Two types of DNA histogram analyses were performed on unperturbed and 3H]TdR perturbed lymphocytes. The data analyses consisted of statistical analyses between averaged groups of histograms (nonparametric analysis) and cell cycle analyses (parametric analysis) to determine the percentages of cells in G0 + G1, S and G2 + M. The results showed that (a) 3H-TdR when added to proliferating lymphocytes under certain conditions (both short-term continuous and pulse-labeling) caused a highly significant increase in the proportion of tetraploid (4C) cells by FCM, (b) the increase in the proportion of 4C cells represented a block in G2 and (c) the relative increase in the percentage of 4C cells was proportional to 3H-TdR incorporation which was proportional to labeling time and concentration. Therefore, it was concluded that short labeling times be used to minimize adverse radiation effects when 3H-TdR is used to assay substances affecting lymphocyte proliferation or in the estimation of cell cycle time.

An examination is made of how the observed DNA distributions spread from their original biological distributions. Using distributions from testicular and hepatic tissue we find that the current assumptions for DNA distribution analysis need reexamination and suggest how this might be done.

The amounts of DNA in individual cells of synchronized or log phase cultures can be determined from flow cytometry measurements of DNA:propidium fluorescence. We have used a model based on cell transit times through the cell cycle to obtain rates of DNA synthesis corresponding to different parts of the S phase of log phase cells. The five rates predicted by this method for log phase HeLa cells correlate well with those obtained by following the progression of highly synchronized HeLa cells through S phase. The absolute values of the rates can be expressed in channel number (proportional to fluorescent intensity), picograms of DNA, number of nucleotides, or molecular weight of DNA versus time, when the total cell cycle time is known, or in relative units, when it is not. The fastest rates for HeLa and 2RA cells are at the beginning and end of S phase, whereas WI-38 cells exhibited a gradual increase in rates from beginning to end of S phase. The determination of rates of DNA synthesis in log phase cultures, rather than synchronized ones, represents a substantial reduction in time, eliminates the possibility of induced metabolic artifacts as a result of the synchronization procedure, and permits the screening of many cell types that may not be amenable to precise synchronization.

Enzyme kinetic studies from this laboratory (M. Dobersen and S. Greer, Biochemistry 17:920-928, 1978) suggested that deoxycytidine could antagonize the toxicity of 5-halogenated analogs of deoxycytidine without interfering with their antiviral activity. Antagonism by deoxycytidine of the toxicity of 5-chlorodeoxycytidine without impairing its anti-herpes simplex virus type 2 activity is demonstrated in the present studies. Tetrahydrouridine, an inhibitor of cytidine deaminase, was utilized. The high Km for deoxycytidine (0.6 mM) with respect to the herpes pyrimidine nucleoside kinase as compared with the low Km for 5-chlorodeoxycytidine (1.1 microM) accounts for the absence of antagonism of the antiviral activity. The high Km for 5-chlorodeoxycytidine (56 microM) as compared with the low Km of deoxycytidine (2 microM) with respect to mammalian deoxycytidine kinase accounts, in great part, for the antagonism of toxicity. In addition, antagonism of toxicity by deoxycytidine is the result of factors other than the kinetic parameters of nucleoside kinases, as indicated by its antagonism of the cytotoxicity of 5-chlorodeoxyuridine. This may be attributed to replenishment of low dCTP pools, diminished because of effector inhibition of ribonucleoside diphosphate reductase by Cl-dUTP. Resistance of the herpes-encoded enzymes to effector control may also play a role in the selective antagonism. Cell culture studies with high concentrations of tetrahydrouridine and 2'-deoxytetrahydrouridine suggest that competition by deoxycytidine for deaminases may not play a major role. The fact that deoxycytidine antagonizes the toxicity of chlorodeoxyuridine also argues against competition for the deaminases as a major reason for its effect. Limited studies with a topical herpes simplex virus type 2 infection system indicate heightened efficacy of 5-chlorodeoxycytidine (and tetrahydrouridine) when deoxycytidine is coadministered. The concepts of selective antagonism of a chemotherapeutic agent derived from these studies may be applied to other approaches that extent beyond viral chemotherapy.

The in vitro immunopharmacological effects of phenytoin (PHT) and its major metabolite 5-(4-hydroxyphenyl)-5-phenylhydantoin (HPPH) were investigated by flow cytometric analysis of the DNA content of mitogen stimulated mouse spleen cells. The qualitative effects of PHT and HPPH were similar in concanavalin A stimulated mouse spleen cells with both compounds causing an increase in the percentage of S phase cells. The data suggests that this effect is due to an augmentation of cell cycling as demonstrated by the significant increase in 4N cells in PHT treated cultures relative to control cultures following colcemid treatment. A PHT time course study revealed an increase in S phase cells and a subsequent increase in 4N cells. PHT had no significant effect on congenitally athymic nude mouse spleen cells stimulated with lipopolysaccharide (LPS) except at the highest concentration tested (80 micrograms/ml) where a depression of cell cycling was observed. HPPH caused a colcemid-like accumulation of 4N cells in the LPS stimulated nude mouse spleen cell cultures. PHT and HPPH were found to be effective in enhancing cell cycling in cultures containing a significant population of T-cells stimulated with a T-cell mitogen whereas an inhibitory effect was observed in cultures without T-cells stimulated with a B-cell mitogen. The capacity of PHT to enhance the mitogenic action of concanavalin A may relate to its capacity to induce immunologic abnormalities and lymphadenopathy in humans.

The R 3327 G tumor responds to estrogen in early stages, but relapses when estrogen therapy is continued beyond 50 days postimplantation. Measurement of DNA content per cell by flow cytometric analysis revealed two populations of cells in the tumors with ploidies of 2 c and 3.2 c. The proportion of aneuploid cells (3.2 c), determined from the flow cytometric DNA distributions, correlated well with tumor weight and age in control and estrogen treated animals. The simple parameter of per cent aneuploid cells thus adequately reflected the responsive and unresponsive states of tumors under hormonal therapy.

Tritiated thymidine was found to affect the cell cycle progression of phytohemagglutinin-stimulated human lymphocytes. By means of flow cytometry a statistically significant increase in the G2 and M phases of the cell cycle was observed in cultures with low concentrations of tritiated thymidine added 18 hours before the cultures were harvested.

The technique of flow cytometric DNA histogram analysis (FCM) shows there to be two distinct cell populations (diploid vs aneuploid) in the poorly differentiated R3327-G rat prostatic adenocarcinoma. The following study compares tumor weight measurements with several FCM computer-based methods designed to determine rapidly the proliferative status of tumors. Hypophysectomy, bilateral adrenalectomy, orchiectomy, sham operations, or diethylstilbestrol treatments were initiated when the tumors were palpable (day 21) and continued until the tumors were excised (day 52). Hypophysectomy, orchiectomy, adrenalectomy, and diethylstilbestrol treatments all resulted in significant inhibition by tumor weight. Quantitation of the percentage of mid-S phase aneuploid cells by summation gave the best correlation with tumor weight. Tumors grown in hypophysectomized, orchiectomized, adrenalectomized, or diethylstilbestrol-treated animals showed a significant reduction in the proportion of mid-S phase cells as compared with controls. The calculation of the percentage of all aneuploid cells was significantly reduced in hypophysectomy, orchiectomy, and diethylstilbestrol-treated animals. However, tumors grown in adrenalectomized animals were not significantly different from controls by this method. Adrenalectomy was found to be the least effective form of therapy, and this was reflected in all of the parameters measured. These data show that FCM analysis may be useful in the quantitation of prostatic carcinoma response to therapy.

A nonparametric statistical test for the analysis of flow cytometry derived histograms is presented. The method involves smoothing and translocation of data, area normalization, channel by channel determination of the mean and S.D., and use of Bayes' theorem for unknown histogram classification. With this statistical method, different sets of histograms from numerous biological systems can be compared.

DNA distributions of HeLa S-3 cells in spinner culture exhibit significant time-dependent changes. The major differences appear to occur in the S-phase region. Significant changes in the rates of DNA synthesis in several S-phase subcompartments correlated well with the changes in the DNA distributions. It is proposed that fluctuations in these rates of DNA synthesis are a reflection of the inherent instability of these abnormal, heteroploid cells.

A calf thymocyte crude aqueous extract was tested for DNA synthesis inhibitory activity using phytohemagglutinin-stimulated human peripheral blood lymphocytes. Inhibition of DNA synthesis was assayed using tritiated thymidine and flow cytometry. Although the calf thymocyte crude extract inhibited tritiated thymidine incorporation by over 50%, only very slight changes in the flow cytometric analysis were observed. When dibutyryl-cyclic adenosine monophosphate was used as an inhibitor, a correlation in terms of the inhibition of tritiated thymidine to the inhibition by flow cytometry was observed.