A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

Abstract

Graphical AbstractHighlights d Dynamic single-cell data pose opportunities and challenges for mechanistic models d Non-linear mixed-effects models disentangle sources of cellular heterogeneity d Methods for flexible, scalable parameter inference and for sub-process identification d Several sub-processes contribute dynamically to heterogeneity in yeast endocytosis SUMMARY Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intracellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and populationaverage parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of subprocesses helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

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