State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
Wuqing District Center for Disease Control and Prevention, Tianjin 301700, China.
Anal Chem. 2024 Mar 19;96(11):4419-4429. doi: 10.1021/acs.analchem.3c04421. Epub 2024 Mar 6.
Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (, , and ) and >95% for two species of ( and ), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.
阻抗流式细胞术（IFC）已被证明是一种用于无标记细菌研究的有效工具，可实时获得电特性。然而，由于数据差异不显著，IFC 技术对不同种类细菌的准确区分仍然是一个挑战。在这里，我们开发了一种卷积神经网络（ConvNet）深度学习方法，以提高 IFC 区分各种细菌种类的准确性和效率。首先，通过 ConvNet 模型对超过 100 万组的各种细菌的阻抗数据（每组包含 42 个特征）进行了训练。为了提高数据分析的效率，引入了 Spearman 相关系数和随机森林算法的平均减少精度，以消除特征相互作用，并提取与细菌壁和膜结构相关的阻抗不透明度作为细菌分化的主要特征。此外，选择了 25 个优化特征，对于三组细菌（、和）的区分准确率>96%，对于两种（和）的区分准确率>95%，而机器学习算法（复杂树、线性判别和 K-最近邻算法）的最大准确率为 76.4%。此外，还对不同混合比例的不同种类的掺杂物样本进行了细菌分化。基于 IFC 的 ConvNet 深度学习辅助数据分析方法在分析大量数据集方面具有优势，并且能够从多组分信息中提取主要特征，这将推动生物传感和数据分析领域的发展和进步。
