A Carbon Nanotube Sensor Array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning
A Carbon Nanotube Sensor Array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning
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DOI：
10.1021/acs.analchem.1c04661
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发表时间：
2022-02-15
影响因子：
7.4
通讯作者：
Star, Alexander
中科院分区：
文献类型：
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作者：
Liu, Zhengru;V. Shurin, Galina;Star, Alexander
开发强大的细胞识别策略在生物化学研究中很重要，但缺乏明确的靶分子在某些应用中造成了瓶颈。本文构建了一种碳纳米管传感器阵列，用于活体和死亡哺乳动物细胞的无标记识别。制作了三种类型的碳纳米管场效应晶体管，并利用线性判别分析（LDA）和支持向量机（SVM）从传输特性曲线中提取不同的特征进行模型训练。使用LDA作为算法，每个传感器组中超过90%的样品中的活细胞和死细胞被准确分类。采用递归特征消除交叉验证（RFECV）方法处理过拟合问题，优化模型，优化后的模型仅用4个特征就能成功分类细胞，验证准确率高达97.9%。RFECV方法还揭示了分类中的关键特征，表明不同的传感机制参与了分类。最后，将优化后的LDA模型应用于未知样本的预测，准确率为87.5- 93.8%，表明所构建的模型能够很好地识别活细胞和死细胞样本。
Developing robust cell recognition strategies is important in biochemical research, but the lack of well-defined target molecules creates a bottleneck in some applications. In this paper, a carbon nanotube sensor array was constructed for the label-free discrimination of live and dead mammalian cells. Three types of carbon nanotube field-effect transistors were fabricated, and different features were extracted from the transfer characteristic curves for model training with linear discriminant analysis (LDA) and support-vector machines (SVM). Live and dead cells were accurately classified in more than 90% of samples in each sensor group using LDA as the algorithm. The recursive feature elimination with cross-validation (RFECV) method was applied to handle the overfitting and optimize the model, and cells could be successfully classified with as few as four features and a higher validation accuracy (up to 97.9%) after model optimization. The RFECV method also revealed the crucial features in the classification, indicating the participation of different sensing mechanisms in the classification. Finally, the optimized LDA model was applied for the prediction of unknown samples with an accuracy of 87.5-93.8%, indicating that live and dead cell samples could be well-recognized with the constructed model.