RT Journal Article
SR Electronic
T1 Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 285478
DO 10.1101/285478
A1 F. Fröhlich
A1 A. Reiser
A1 L. Fink
A1 D. Woschée
A1 T. Ligon
A1 F. J. Theis
A1 J. O. Rädler
A1 J. Hasenauer
YR 2018
UL http://biorxiv.org/content/early/2018/03/20/285478.abstract
AB Single-cell time-lapse studies have advanced the quantitative understanding of cell-to-cell variability. However, as the information content of individual experiments is limited, methods to integrate data collected under different conditions are required.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.