Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise

Despite continued technological improvements, measurement errors will always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem becomes even more serious in the context of cell signaling studies that are specifically designed to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. It is not clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. To address these fundamental single-cell analysis and experiment design challenges, we propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations and Fisher Information Matrix-based criteria to decide between experiments. Using simulations and single-cell experiments for a reporter gene controlled by an HIV promoter construct, we demonstrate how our approach can analyze and redesign experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.