Department of Physics, Cornell University, Ithaca, NY, United States of America.
College of Veterinary Medicine, Cornell University, Ithaca, NY, United States of America.
PLoS One. 2024 May 20;19(5):e0297947. doi: 10.1371/journal.pone.0297947. eCollection 2024.
In various biological systems, analyzing how cell behaviors are coordinated over time would enable a deeper understanding of tissue-scale response to physiologic or superphysiologic stimuli. Such data is necessary for establishing both normal tissue function and the sequence of events after injury that lead to chronic disease. However, collecting and analyzing these large datasets presents a challenge-such systems are time-consuming to process, and the overwhelming scale of data makes it difficult to parse overall behaviors. This problem calls for an analysis technique that can quickly provide an overview of the groups present in the entire system and also produce meaningful categorization of cell behaviors. Here, we demonstrate the application of an unsupervised method-the Variational Autoencoder (VAE)-to learn the features of cells in cartilage tissue after impact-induced injury and identify meaningful clusters of chondrocyte behavior. This technique quickly generated new insights into the spatial distribution of specific cell behavior phenotypes and connected specific peracute calcium signaling timeseries with long term cellular outcomes, demonstrating the value of the VAE technique.
在各种生物系统中，分析细胞行为如何随时间协调，将有助于更深入地了解组织对生理或超生理刺激的反应。此类数据对于建立正常组织功能以及导致慢性疾病的损伤后事件序列是必要的。然而，收集和分析这些大型数据集提出了一个挑战——此类系统处理起来很耗时，并且数据的压倒性规模使得难以解析整体行为。这个问题需要一种能够快速提供整个系统中存在的组的概述的分析技术，并且还能够对细胞行为进行有意义的分类。在这里，我们展示了一种无监督方法——变分自动编码器（VAE）——在冲击诱导损伤后软骨组织中学习细胞特征并识别软骨细胞行为有意义的聚类的应用。该技术快速提供了对特定细胞行为表型的空间分布的新见解，并将特定的超急性钙信号时间序列与长期细胞结果联系起来，证明了 VAE 技术的价值。
