Wei Jian, Gao Wenbing, Yang Xinlong, Yu Zhuotong, Su Fei, Han Chengwu, Xing Xiaoxing
College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China.
Department of Integrative Oncology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China.
Biomicrofluidics. 2024 Jan 23;18(1):014103. doi: 10.1063/5.0181287. eCollection 2024 Jan.
Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.
有丝分裂是一个关键的生物学过程，在此过程中，亲代细胞经历精确控制的功能阶段并分裂为两个子细胞。一些药物可以抑制细胞有丝分裂，例如，抗癌药物与肿瘤细胞增殖相互作用，最终导致有丝分裂在特定阶段停滞或细胞死亡。将机器学习与微流控阻抗流式细胞术（IFC）相结合，为在单细胞水平上对药物处理的细胞进行无标记和高通量分类提供了一种简洁的方法。基于IFC的单细胞分析产生了大量与细胞电生理参数相关的数据，而机器学习有助于建立这些数据与特定细胞状态之间的相关性。这项工作展示了机器学习在细胞状态分类中的应用，包括G1/S期与凋亡状态之间以及G2/M期与凋亡状态之间的二元区分，以及在由G2/M期停滞和凋亡这两种药物诱导状态之外，对包含一个对药物不敏感亚组的三个亚群进行分类。用作模型训练输入特征的阻抗幅度和相位是从药物处理的肿瘤细胞的IFC测量数据集中提取的。这里利用了深度神经网络（DNN）模型，其结构（例如，隐藏层数和每层中的神经元数）针对每种给定的细胞类型和药物进行了优化。对于H1650细胞，我们在G1/S期与凋亡状态之间的分类准确率为78.51%，在G2/M期与凋亡状态之间的分类准确率为82.55%。对于HeLa细胞，我们在紫杉醇处理诱导的G2/M期与凋亡状态之间的分类准确率达到了96.94%。对于长春碱处理的HeLa细胞，在活细胞与非活细胞状态之间以及G2/M期与凋亡状态之间的区分中，甚至达到了接近100%的更高准确率。我们还展示了DNN模型对经紫杉醇或长春碱处理的完整细胞样本中的三个亚群进行高精度分类的能力。
