title: A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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-alpha 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.
  
  
  RESULTS
  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% +/- 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% +/- 9.8% of control at the 8 day time point. However, a statistically significant transient increase in TER-119(+) CD71(+) 7AAD(+) dead erythroid precursors was observed at the 1 and 2 day time points with a corresponding decrease in TER-119(+) CD71(+) 7AAD(-) Annexin V(-) live erythroid precursors, and no change in the number of TER-119(+) CD71(+) annexin V(+) 7AAD(-) apoptotic erythroid precursors in the bone marrow.
  
  
  CONCLUSIONS
  A statistical pattern recognition approach to viability classification provides an objective rationale for setting decision boundaries between "positive" and "negative" intensity measures in cytometric data. Using this approach we have confirmed that rhEPO inhibits apoptosis and cell death in an EPO dependent cell line in vitro, but failed to do so in vivo, suggesting EPO may not act as a simple antiapoptotic agent in the bone marrow. Rather, homeostatic mechanisms may regulate the pharmacodynamic response to rhEPO.
authors.affiliation: Department of Biomedical Engineering, School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania 19104, USA.
authors.forename: John
authors.initials: J
authors.lastname: Quinn
authors.forename: Paul W
authors.initials: PW
authors.lastname: Fisher
authors.forename: Renold J
authors.initials: RJ
authors.lastname: Capocasale
authors.forename: Ram
authors.initials: R
authors.lastname: Achuthanandam
authors.forename: Moshe
authors.initials: M
authors.lastname: Kam
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Bugelski
authors.forename: Leonid
authors.initials: L
authors.lastname: Hrebien
mesh_terms: D000818:Animals; D017209:Apoptosis; D001854:Bone Marrow Cells / Q000166:cytology*; D019070:Cell Lineage; D002470:Cell Survival; D002478:Cells, Cultured; D004921:Erythropoietin; D005260:Female; D006801:Humans; D051379:Mice; D008822:Mice, Transgenic; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010641:Phenotype; D012372:ROC Curve; D011994:Recombinant Proteins; D013997:Time Factors
chemical_list: D011994:Recombinant Proteins; D004921:Erythropoietin