Dynamic Expression Profiles from Static Cytometry Data: Component Fitting and Conversion to Relative, “Same Scale” Values

BACKGROUND: Cytometry of asynchronous proliferating cell populations produces data with an extractable time-based feature embedded in the frequency of clustered, correlated events. Here, we present a specific case of general methodology for calculating dynamic expression profiles of epitopes that oscillate during the cell cycle and conversion of these values to the same scale. METHODS: Samples of K562 cells from one population were labeled by direct and indirect antibody methods for cyclins A2 and B1 and phospho-S10-histone H3. The same indirect antibody was used for both cyclins. Directly stained samples were counter-stained with 4′6-diamidino-2-phenylindole and indirectly stained samples with propidium to label DNA. The S phase cyclin expressions from indirect assays were used to scale the expression of the cyclins of the multi-variate direct assay. Boolean gating and two dimensional, sequential regions set on bivariate displays of the directly conjugated sample data were used to untangle and isolate unique, unambiguous expression values of the cyclins along the four-dimensional data path through the cell cycle. The median values of cyclins A2 and B1 from each region were correlated with the frequency of events within each region. RESULTS: The sequential runs of data were plotted as continuous multi-line linear equations of the form y = [(y(i+1)−y(i))/(x(i+1)−x(i))]x + y(i)−[(y(i+1)−y(i))/(x(i+1)−x(i))]x(i) (line between points (x(i),y(i)) and (x(i+1), y(i+1))) to capture the dynamic expression profile of the two cyclins. CONCLUSIONS: This specific approach demonstrates the general methodology and provides a rule set from which the cell cycle expression of any other epitopes could be measured and calculated. These expression profiles are the “state variable” outputs, useful for calibrating mathematical cell cycle models.