In the past decade, the shift from whole cell population analyses towards single cell measurement methods and techniques is based on experimental results that reveal significant levels of non-genetic heterogeneity in clonal cell populations. This heterogeneity manifests in multiple aspects of cell activity and is, in part, a result of stochastic noise in processes leading to gene expression, namely transcription and translation. The growing understanding of this occurrence has led to the development of methods to monitor and analyze heterogeneity for a more thorough description of cell populations and overall activity.

Microarray platforms have been developed that are capable of continuously monitoring each cell in a population, while simultaneously assessing multiple populations. Traditional cellular analysis methods disregard significant factors associated with heterogeneity, such as variation among individual cells and individual cell activities over time. Cellular microarray platforms, on the other hand, allow for continuous monitoring of single live cells and simultaneously generate both individual cell and average population data that are more descriptive than those of traditional methods.

Using a microarray platform, clonal populations of live yeast cells were monitored at the single cell level to characterize non-genetic heterogeneity in expression of the reporter gene lacZ and its gene product, β-galactosidase, using various concentrations of the fluorogenic substrate C₁₂FDG. The cell strain analyzed in this work contained an artificial RNA-based transcriptional activator in a system deliberately designed to simplify the intracellular events leading to gene expression, thereby minimizing the noise. Single cell data were generated in real time, and these data were used to provide a quantitative description of the noise in gene expression based on calculated values associated with noise, the population distribution of gene expression, and the identification of cells with outlier activity and discrete bursts of activity. The data show that monitoring cell populations at the single cell level generate data that are more detailed and can provide a more complete and accurate characterization of heterogeneity in gene expression. A significant amount of heterogeneity was observed in cells designed for minimal noise, which highlights the inherent nature of heterogeneity in gene expression.