Institute of Cell Biology, University of Bern, Bern, Switzerland.
ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Mol Syst Biol. 2021 Apr;17(4):e10026. doi: 10.15252/msb.202010026.
Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional trajectories. Typically, the extraction of relevant information from time series data relies on predefined, human-interpretable features. Without a priori knowledge of the system, the predefined features may fail to cover the entire spectrum of dynamics. Here we present CODEX, a data-driven approach based on convolutional neural networks (CNNs) that identifies patterns in time series. It does not require a priori information about the biological system and the insights into the data are built through explanations of the CNNs' predictions. CODEX provides several views of the data: visualization of all the single-cell trajectories in a low-dimensional space, identification of prototypic trajectories, and extraction of distinctive motifs. We demonstrate how CODEX can provide new insights into ERK and Akt signaling in response to various growth factors, and we recapitulate findings in p53 and TGFβ-SMAD2 signaling.
目前使用活细胞荧光生物传感器研究细胞信号转导动态的研究通常会产生数千个单细胞、异质、多维轨迹。通常，从时间序列数据中提取相关信息依赖于预定义的、可由人类解释的特征。如果没有系统的先验知识，预定义的特征可能无法涵盖整个动态范围。在这里，我们提出了 CODEX，这是一种基于卷积神经网络 (CNN) 的数据驱动方法，它可以识别时间序列中的模式。它不需要关于生物系统的先验信息，并且对数据的深入了解是通过解释 CNN 的预测来建立的。CODEX 提供了数据的多种视图：在低维空间中可视化所有单细胞轨迹，识别原型轨迹，并提取独特的图案。我们展示了 CODEX 如何为 ERK 和 Akt 信号对各种生长因子的反应提供新的见解，并且我们重述了 p53 和 TGFβ-SMAD2 信号中的发现。
