| Background. Heterogeneous cell populations have previously been described as noisy. However, recent studies have demonstrated that heterogeneity can be biologically significant. We present here an approach for rapid and complete identification of heterogeneous cell populations from high-throughput flow cytometry data. We have developed a novel measure Slope Differentiation Identification (SDI) using flow cytometry-based protein expression, quantifying the rate of change in protein expression between two conditions (exponential and stationary phase) of yeast cells, as a function of cell size or cell granularity.;Results. SDI had superior Gene Ontology enrichment when compared with other approaches such as k-means clustering and an approach based on the bi-modality of the fluorescence intensity distribution. Cell populations were also validated using gradient-separation followed by microscopy, where proteins with high SDI measure showed significant levels of differentiation between high and low density cells.;Conclusion. Overall, our approach has identified novel protein expression patterns that differentiate quiescent and non-quiescent cell populations. |
