Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
Adv Sci (Weinh). 2024 Nov;11(41):e2403547. doi: 10.1002/advs.202403547. Epub 2024 Sep 6.
Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised machine learning, requiring the extraction of features that not only faithfully preserve this heterogeneity but also effectively discriminate between established biological states, all while remaining interpretable. To tackle these challenges, a self-training deep learning framework designed for fine-grained and interpretable phenotyping is presented. This framework incorporates an unsupervised teacher model with interpretable features to facilitate feature learning in a student deep neural network (DNN). Significantly, an autoencoder-based regularizer is designed to encourage the student DNN to maximize the heterogeneity associated with molecular perturbations. This method enables the acquisition of features with enhanced discriminatory power, while simultaneously preserving the heterogeneity associated with molecular perturbations. This study successfully delineated fine-grained phenotypes within the heterogeneous protrusion dynamics of migrating epithelial cells, revealing specific responses to pharmacological perturbations. Remarkably, this framework adeptly captured a concise set of highly interpretable features uniquely linked to these fine-grained phenotypes, each corresponding to specific temporal intervals crucial for their manifestation. This unique capability establishes it as a valuable tool for investigating diverse cellular dynamics and their heterogeneity.
揭示活细胞动力学的细粒度表型对于全面理解健康和患病生物过程中的异质性至关重要。然而，对于无监督机器学习来说，这一努力带来了重大的技术挑战，需要提取不仅能忠实保留这种异质性，而且能有效区分既定生物学状态的特征，同时仍然具有可解释性。为了解决这些挑战，提出了一种用于细粒度和可解释表型的自我训练深度学习框架。该框架包含一个具有可解释特征的无监督教师模型，以促进学生深度神经网络 (DNN) 中的特征学习。重要的是，设计了基于自动编码器的正则化器，以鼓励学生 DNN 最大化与分子扰动相关的异质性。该方法能够获得具有增强辨别力的特征，同时保留与分子扰动相关的异质性。本研究成功描绘了迁移上皮细胞中不均匀突起动力学的细粒度表型，揭示了对药理学扰动的特定反应。值得注意的是，该框架巧妙地捕捉到了一组简洁的高度可解释的特征，这些特征与这些细粒度表型独特相关，每个特征对应于对其表现至关重要的特定时间间隔。这种独特的能力使其成为研究多种细胞动力学及其异质性的有价值工具。
