(590b) Quantitative molecular genetics through analysis of single-cell, population data generated by flow cytometry.

Flow cytometry is a valuable method for analysis of cell populations. The capability to individually analyze thousands to millions of cells provides a rich source of experimental data. Traditionally, this has allowed researchers to identify and census populations of cells. In this study we utilize flow cytometry to quantitatively assess a relationship between ectopic gene expression and cell phenotype. We used a synthetic promoter library to generate a broad range of gene expression levels within a single heterogeneous cell population and then analyzed the cells by flow cytometry. Because even clonal populations of cells exhibit variability, it was important to develop a reliable and systematic method to generate mean trends. First, in our cultures we added a reference population that allows one to normalize results and minimize culture-to-culture variability. Second, we developed a LOWESS (locally weighted scatter plot smoothing) data-fitting method for analysis of population data generated by flow cytometry. We have also developed a standalone software application for convenient analysis using our approach.