Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada.
Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada.
PLoS Comput Biol. 2023 Sep 11;19(9):e1011449. doi: 10.1371/journal.pcbi.1011449. eCollection 2023 Sep.
T lymphocytes migrate into organs and interact with local cells to perform their functions. How human T lymphocytes communicate with organ-specific cells and participate in pathobiological processes remains unresolved. Brain infiltration of T lymphocytes is associated with multiple neurological disorders. Thus, to characterize the behavior of human T lymphocytes reaching the human brain, we performed time-lapse microscopy on human CD8+ T lymphocytes co-cultured with either primary human astrocytes or neurons. Using traditional manual and visual assessment of microscopy data, we identified distinct CD8+ T lymphocyte motility behaviors. However, such characterization is time and labor-intensive. In this work, we trained and validated a machine-learning model for the automated classification of behaviors of CD8+ T lymphocytes interacting with astrocytes and neurons. A balanced random forest was trained for the binary classification of established classes of cell behaviors (synapse vs. kinapse) as well as visually identified behaviors (scanning, dancing, and poking). Feature selection was performed during 3-fold cross-validation using the minimum redundancy maximum relevance algorithm. Results show promising performances when tested on a held-out dataset of CD8+ T lymphocytes interacting with astrocytes with a new experimenter and a held-out independent dataset of CD8+ T lymphocytes interacting with neurons. When tested on the independent CD8+ T cell-neuron dataset, the final model achieved a binary classification accuracy of 0.82 and a 3-class accuracy of 0.79. This novel automated classification approach could significantly reduce the time required to label cell motility behaviors while facilitating the identification of interactions of T lymphocytes with multiple cell types.
T 淋巴细胞迁移到器官中并与局部细胞相互作用以发挥其功能。人类 T 淋巴细胞如何与器官特异性细胞进行通信并参与病理生物学过程仍未得到解决。T 淋巴细胞浸润大脑与多种神经紊乱有关。因此，为了描述到达人脑的人类 T 淋巴细胞的行为，我们对与人原代星形胶质细胞或神经元共培养的人 CD8+T 淋巴细胞进行了延时显微镜观察。使用传统的显微镜数据手动和可视化评估，我们确定了不同的 CD8+T 淋巴细胞迁移行为。然而，这种特征描述既耗时又费力。在这项工作中，我们训练和验证了一个用于自动分类与星形胶质细胞和神经元相互作用的 CD8+T 淋巴细胞行为的机器学习模型。使用最小冗余最大相关性算法在 3 倍交叉验证期间进行了特征选择。结果表明，当在与星形胶质细胞相互作用的 CD8+T 淋巴细胞的保留数据集（由新实验员进行）和与神经元相互作用的独立保留数据集上进行测试时，该模型具有很好的性能。当在独立的 CD8+T 细胞-神经元数据集上进行测试时，最终模型的二进制分类准确率为 0.82，3 类分类准确率为 0.79。这种新的自动分类方法可以大大减少标记细胞迁移行为所需的时间，同时促进 T 淋巴细胞与多种细胞类型相互作用的识别。
