Flow cytometry is a powerful tool for quantitative biology because it can perform single-cell analysis of large cell populations using multiple parameters. Results are often visualized as a two-dimensional scatter or contour plot. Because these plots can be relatively diffuse, it is not always straightforward to discern a relationship between measured parameters. We have demonstrated that quantitative trends can be fit to the single-cell data generated from a heterogeneous population. We engineered Abelson virus-transformed pre-B cells to express a broad range of oncogenic Ras levels. Instead of individual cultures with individual expression levels, a continuous range of levels was expressed by different cells in one heterogeneous culture. We then stained cells for downstream Erk phosphorylation to monitor MAPK signaling or employed an E2F-responsive genetic reporter to monitor cell-cycle activity. Subsequent analysis by flow cytometry and locally weighted scatterplot smoothing (LOWESS) revealed that increasing Ras oncogene expression led to increasing MAPK signaling. In contrast, E2F activity peaked at an optimal, intermediate level of Ras. To make this analytical method widely available to others, we have provided a software application that performs LOWESS on any two-parameter population data collected by flow cytometry.

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