Single-cell kinetics of siRNA-mediated mRNA degradation.

RNA interference (RNAi) enables the therapeutic use of small interfering RNAs (siRNAs) to silence disease-related genes. The efficiency of silencing is commonly assessed by measuring expression levels of the target protein at a given time point post-transfection. Here, we determine the siRNA-induced fold change in mRNA degradation kinetics from single-cell fluorescence time-courses obtained using live-cell imaging on single-cell arrays (LISCA). After simultaneous transfection of mRNAs encoding eGFP (target) and CayRFP (reference), the eGFP expression is silenced by siRNA. The single-cell time-courses are fitted using a mathematical model of gene expression. Analysis yields best estimates of related kinetic rate constants, including mRNA degradation constants. We determine the siRNA-induced changes in kinetic rates and their correlations between target and reference protein expression. Assessment of mRNA degradation constants using single-cell time-lapse imaging is fast (<30 h) and returns an accurate, time-independent measure of siRNA-induced silencing, thus allowing the exact evaluation of siRNA therapeutics.

Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection.

Single-cell time-lapse studies have advanced the quantitative understanding of cellular pathways and their inherent cell-to-cell variability. However, parameters retrieved from individual experiments are model dependent and their estimation is limited, if based on solely one kind of experiment. Hence, methods to integrate data collected under different conditions are expected to improve model validation and information content. Here we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.