Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection
| Autor: | Anita Reiser, Jan Hasenauer, Fabian J. Theis, Laura Fink, Fabian Froehlich, Joachim O. Rädler, Thomas Ligon, Daniel Woschée |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Population level Computer science Kinetics Perturbation (astronomy) General Biochemistry Genetics and Molecular Biology Article Model validation 03 medical and health sciences 0302 clinical medicine Drug Discovery Dynamical systems Massively parallel lcsh:QH301-705.5 Applied Mathematics Translation (biology) Regulatory networks Computer Science Applications ddc Nonlinear system 030104 developmental biology lcsh:Biology (General) Modeling and Simulation Mixed effects Biological system 030217 neurology & neurosurgery |
| Zdroj: | npj Systems Biology and Applications, Vol 4, Iss 1, Pp 1-12 (2018) NPJ Syst. Biol. Appl. 5 (2018) NPJ Systems Biology and Applications |
| DOI: | 10.1101/285478 |
| Popis: | 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. Statistical inference: mixed effect modeling enables integration of single-cell data Single-cell time-lapse data provide a rich source of information on cellular pathways and cell-to-cell variability, yet, we lack statistical methods to extract this information. A team led by Joachim Rädler at the Ludwig-Maximilians-Universität München and Jan Hasenauer at the Helmholtz Zentrum München addressed this problem and demonstrated that mixed effect modeling enables a rigorous integration of single-cell data collected in different experiments. The experimental and computational study of mRNA transfection revealed that the use of mixed-effect models improves parameter identifiability and resolves symmetries, which are limitations of existing approaches. The proposed approach is widely applicable to single- cell time-lapse data and improves data exploitation. The in-depth assessment of single-cell dynamics will provide novel insights in disease progression and mRNA-based treatment options. |
| Databáze: | OpenAIRE |
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