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Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection

View ORCID ProfileF. Fröhlich, A. Reiser, L. Fink, D. Woschée, View ORCID ProfileT. Ligon, View ORCID ProfileF. J. Theis, View ORCID ProfileJ. O. Rädler, View ORCID ProfileJ. Hasenauer
doi: https://doi.org/10.1101/285478
F. Fröhlich
1Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
2Center for Mathematics, Technische Universität München, Garching 85748, Germany
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A. Reiser
3Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München 80539, Germany
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L. Fink
3Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München 80539, Germany
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D. Woschée
3Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München 80539, Germany
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T. Ligon
3Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München 80539, Germany
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F. J. Theis
1Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
2Center for Mathematics, Technische Universität München, Garching 85748, Germany
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J. O. Rädler
3Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, München 80539, Germany
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  • For correspondence: jan.hasenauer@helmholtz-muenchen.de
J. Hasenauer
1Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
2Center for Mathematics, Technische Universität München, Garching 85748, Germany
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  • For correspondence: jan.hasenauer@helmholtz-muenchen.de
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Summary

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 20, 2018.
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Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection
F. Fröhlich, A. Reiser, L. Fink, D. Woschée, T. Ligon, F. J. Theis, J. O. Rädler, J. Hasenauer
bioRxiv 285478; doi: https://doi.org/10.1101/285478
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Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection
F. Fröhlich, A. Reiser, L. Fink, D. Woschée, T. Ligon, F. J. Theis, J. O. Rädler, J. Hasenauer
bioRxiv 285478; doi: https://doi.org/10.1101/285478

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