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.

Non-viral gene delivery is constrained by the dwell time that most synthetic nucleic acid nanocarriers spend inside endosomal compartments. In order to overcome this endosomal-release bottleneck, methods are required that measure nanocarrier uptake kinetics and transfection efficiency simultaneously. Here, we employ live-cell imaging on single-cell arrays (LISCA) to study the delivery-time distribution of lipid-based mRNA complexes under varied serum conditions. By fitting a translation-maturation model to hundreds of individual eGFP reporter fluorescence time courses, the protein expression onset times and the expression rates after transfection are determined. Using this approach, we find that delivery timing and protein expression rates are not intrinsically correlated at the single-cell level, even though population-averaged values of both parameters conjointly change as a function of increasing external serum protein fraction. Lipofectamine-mediated delivery showed decreased transfection efficiency and longer delivery times with increasing serum protein concentration. This is in contrast to ionizable lipid nanoparticle (i-LNP)-mediated transfer, which showed increased efficiency and faster uptake in the presence of serum. In conclusion, the interdependences of single-cell expression rates and onset timing provide additional clues on uptake and release mechanisms, which are useful for improving nucleic acid delivery.

The original version of this Article had an incorrect Article number of 1, an incorrect Volume of 5 and an incorrect Publication year of 2019. These errors have now been corrected in the PDF and HTML versions of the Article.