A numerical analysis model for interpretation of flow cytometric studies of ex vivo phagocytosis.

Abstract

The study of ex vivo phagocytosis via flow cytometry requires that one distinguish experimentally between uptake and adsorption of fluorescently labeled targets by phagocytes. Removal of the latter quantity from the analysis is the most common means of analyzing such data. Because the probability of phagocytosis is a function of the probability of adsorption, and because partially quenched fluorescence after uptake often overlaps with that of negative controls, this approach is suboptimal at best. Here, we describe a numerical analysis model which overcomes these limitations. We posit that the random adsorption of targets to macrophages, and subsequent phagocytosis, is a function of three parameters: the ratio of targets to macrophages (m), the mean fluorescence intensity imparted to the phagocyte by the internalized target (alpha), and the probability of phagocytosis per adsorbed target (p). The potential values of these parameters define a parameter space and their values at any point in parameter space can be used to predict the fraction of adsorption(+) and [adsorption(-), phagocytosis(+)] cells that might be observed experimentally. By systematically evaluating the points in parameter space for the latter two values and comparing them to experimental data, the model arrives at sets of parameter values that optimally predict such data. Using activated THP-1 cells as macrophages and platelets as targets, we validate the model by demonstrating that it can distinguish between the effects of experimental changes in m, alpha, and p. Finally, we use the model to demonstrate that platelets from a congenitally thrombocytopenic WAS patient show an increased probability of ex vivo phagocytosis. This finding correlates with other evidence that rapid in vivo platelet consumption contributes significantly to the thrombocytopenia of WAS. Our numerical analysis method represents a useful and innovative approach to multivariate analysis.