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Cellular binding of annexin V and membrane permeability to 7-aminoactinomycin D (7AAD) are important tools for studying apoptosis and cell death by flow cytometry. Combining viability markers with cell surface marker expression is routinely used to study various cell lineages. Current classification methods using strict thresholds, or ''gates,'' on the fluorescent intensity of these markers are subjective in nature and may not fully describe the phenotypes of interest. We have developed objective criteria for phenotypic boundary recognition through the application of statistical pattern recognition. This task was achieved using artificial neural networks (ANNs) that were trained to recognize subsets of cells with known phenotypes, and then used to determine decision boundaries based on statistical measures of similarity. This approach was then used to test the hypothesis that erythropoietin (EPO) inhibits apoptosis and cell death in erythroid precursor cells in murine bone marrow. Our method was developed for classification of viability using an in vitro cell system and then applied to an ex vivo analysis of murine late-stage erythroid progenitors. To induce apoptosis and cell death in vitro, an EPO-dependent human leukemic cell line, UT-7 EPO cells were incubated without recombinant human erythropoietin (rhEPO) for 72 h. Five different ANNs were trained to recognize live, apoptotic, and dead cells using a ''known'' subset of the data for training, and a K-fold cross validation procedure for error estimation. The ANNs developed with the in vitro system were then applied to classify cells from an ex vivo study of rhEPO treated mice. Tg197 (human tumor necrosis-a transgenic mice, a model of anemia of chronic disease) received a single s.c. dose of 10,000 U/kg rhEPO and femoral bone marrow was collected 1, 2, 4, and 8 days after dosing. Femoral bone marrow cells were stained with TER-119 PE, CD71 APC enable identification of erythroid precursors, and annexin V FITC and 7AAD to identify the apoptotic and dead cells. During classification forward and side angle light scatter were also input to all pattern recognition systems. Similar decision boundaries between live, apoptotic, and dead cells were consistently identified by the neural networks. The best performing network was a radial basis function multi-perceptron that produced an estimated average error rate of 4.5% AE 0.9%. Using these boundaries, the following results were reached: depriving UT-7 EPO cells of rhEPO induced apoptosis and cell death while the addition of rhEPO rescued the cells in a dose-dependent manner. In vivo, treatment with rhEPO resulted in an increase of live erythroid cells in the bone marrow to 119.8% AE 9.8% of control at the 8 day time point. However, a statistically significant transient increase in TER-119 1 CD71 1 7AAD 1 dead erythroid precursors was observed at the 1 and 2 day time points with a corresponding decrease in TER-119 1 CD71 1 7AAD 2 Annexin V 2 live erythroid precursors, and no change in the number of TER-119 1 C...

Analysis of multicolor flow cytometric data is traditionally based on the judgment of an expert, generally time consuming, sometimes incomplete and often subjective in nature. In this article, we investigate another statistical method using a Sequential Univariate Gating (SUG) algorithm to identify regions of interest between two groups of multivariate flow cytometric data. The metric used to differentiate between the groups of univariate distributions in SUG is the Kolmogorov-Smirnov distance (D) statistic. The performance of the algorithm is evaluated by applying it to a known three-color data set looking at activation of CD41 and CD81 lymphocytes with anti-CD3 antibody treatment and comparing the results to the expert analysis. The algorithm is then applied to a four-color data set used to study the effects of recombinant human erythropoietin (rHuEPO) on several murine bone marrow populations. SUG was used to identify regions of interest in the data and results compared to expert analysis and the current state-of-the-art statistical method, Frequency Difference Gating (FDG). Cluster analysis was then performed to identify subpopulations responding differently to rHuEPO. Expert analysis, SUG and FDG identified regions in the data that showed activation of CD41 and CD81 lymphocytes with anti-CD3 treatment. In the rHuEPO treated data sets, the expert and SUG identified a dose responsive expansion of only the erythroid precursor population. In contrast, FDG resulted in identification of regions of interest both in the erythroid precursors as well as in other bone marrow populations. Clustering within the regions of interest defined by SUG resulted in identification of four subpopulations of erythroid precursors that are morphologically distinct and show a differential response to rHuEPO treatment. Greatest expansion is seen in the basophilic and poly/orthochromic erythroblast populations with treatment. Identification of populations of interest can be performed using SUG in less subjective, time efficient, biologically interpretable manner that corroborates with the expert analysis. The results suggest that basophilic erythroblasts cells or their immediate precursors are an important target for the effects of rHuEPO in murine bone marrow. The MATLAB implementation of the method described in the article, both experimental data and other supplemental materials are freely available at http://web.mac.com/ acidrap18. ' 2008 International Society for Advancement of Cytometry Key terms flow cytometry; multivariate data analysis; Kolmogorov-Smirnov statistic; FDG; erythropoietin; cluster analysis ERYTHROPOEISIS is the process by which new red blood cells are formed in the bone marrow and erythropoietin (EPO) is the primary driver of this process (1-3). EPO acts by stimulating a cell surface receptor known as erythropoietin receptor (EPO-R) (1,2). Advances in recombinant technology have helped in the development of human recombinant EPO (rHuEPO) (4,5). The earliest morphologically identifiable erythroid progenitor...

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