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.

中文翻译：

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转染后单细胞翻译动力学的多实验非线性混合效应建模。

单细胞延时研究促进了对细胞通路及其固有的细胞间变异性的定量理解。然而，从各个实验中检索到的参数取决于模型，并且如果仅基于一种实验，则它们的估计是有限的。因此，整合在不同条件下收集的数据的方法有望改善模型验证和信息内容。在这里，我们提出了一种用于机械途径模型的多实验非线性混合效应建模方法，该方法允许集成多个单细胞扰动实验。我们利用微图案单细胞阵列的大规模并行读出，将这种方法应用于转染后绿色荧光蛋白的翻译。我们证明，来自扰动实验的数据的整合允许细胞间变异性（即参数密度）的稳健重建，而每个单独的实验提供的信息不足。事实上，我们表明，在群体水平上整合数据集还可以通过打破对称性来提高对单个细胞的估计，尽管每个细胞仅在一个实验中进行测量。此外，我们确认所提出的方法对于实验重复中的批次效应而言是稳健的，并且可以提供对批次效应性质的机械见解。我们预计所提出的多实验非线性混合效应建模方法将作为单细胞动力学中细胞异质性分析的基础。