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Reconstruction of ensemble of single-cell time trajectories from discrete-time fluorescence data: Oscillatory MAPK dynamics

机译:从离散时间荧光数据重建单细胞时间轨迹的合奏:振荡MAPK动力学

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Abstract: Continuous time profiles of intracellular protein levels in a collection of isogenic cells is needed to achieve quantitative prediction of heterogeneity in cellular systems. However, intracellular staining based quantitative single-cell detection of protein levels, reported by emitted fluorescence, using confocal microscopy or flow cytometry can only result in discrete time series due to arresting of cell state to enable entry of reporters - antibody - into the cell. We propose a method to reconstruct the time-series of oscillatory dynamics of phosphorylated ERK (pERK), the terminal protein in the ubiquitously found MAPK cascades, based on the discrete time series data consisting of a distribution of fluorescence emitted by different ensemble of cells at different time points post-stimulation. This method employs a model autocorrelation function to predict the fluorescence from the experimental data that will correspond to a specific time point in a randomly reconstructed trajectory. We validate the method using the single-cell pERK oscillatory dynamics data consisting of 12100 data points measured in transfected cells across 121 time points by pairing reconstructed trajectories with those from original based on the constraint that the pair satisfied a certain cut-off for both mutual information score and Euclidean distance between them. Out of the 100 trajectories in the original data, our algorithm was able to reconstruct ~30 of them capturing a reasonable fraction of the amplitudes of the Fast fourier transform modes present in the original trajectory. Using the developed method, we reconstructed 2471 trajectories from pERK discrete dynamics data set consisting of distribution of fluorescence data across 16 time points obtained from single-cell flow cytometry. The dynamics of the standard deviation of the reconstructed trajectories is comparable to that of the original fluorescence data.
机译:摘要:为了获得定量预测细胞系统异质性的过程,需要连续收集等基因细胞中细胞内蛋白质水平的时间曲线。然而,使用共聚焦显微镜或流式细胞术,通过荧光发射的荧光报告的基于细胞内染色的蛋白质水平单细胞定量检测只能由于细胞状态的阻滞而导致离散的时间序列,从而使报告基因-抗体-进入细胞。我们提出了一种方法,该方法基于离散的时间序列数据,该序列由无所不在的MAPK级联细胞发出的荧光分布组成,重构了磷酸化的ERK(pERK)的振荡动力学的时间序列,该蛋白普遍存在于MAPK级联中刺激后的不同时间点。该方法采用模型自相关函数从实验数据中预测荧光,该荧光将对应于随机重建轨迹中的特定时间点。我们通过将重构的轨迹与原始轨迹配对,基于该对的相互满足一定的临界值的约束,使用单细胞pERK振荡动力学数据验证了该方法,该数据由121个时间点的转染细胞中测得的12100个数据点组成信息得分和它们之间的欧几里得距离。在原始数据中的100条轨迹中,我们的算法能够重构约30条轨迹,从而捕获原始轨迹中存在的快速傅立叶变换模式振幅的合理比例。使用开发的方法,我们从pERK离散动力学数据集重构了2471条轨迹,该数据集包括从单细胞流式细胞仪获得的16个时间点的荧光数据分布。重建轨迹的标准偏差的动力学与原始荧光数据的动力学相当。

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