Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing
Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing
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DOI：
10.1021/acsnano.0c05693
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发表时间：
2020-11-24
期刊：
影响因子：
17.1
通讯作者：
Ragan, Regina
中科院分区：
文献类型：
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作者：
Thrift, William John;Ronaghi, Sasha;Ragan, Regina
快速抗菌药物敏感性测试 (AST) 是一种不可或缺的工具，可减少不必要地使用强效广谱抗生素导致多重耐药细菌增殖的情况。使用由表面增强拉曼散射（SERS）传感器组成的传感器平台，控制纳米间隙化学和机器学习算法来分析复杂的光谱数据，抗生素暴露后的细菌代谢谱与敏感性相关。深度神经网络模型能够在抗生素暴露后 10 分钟内从 SERS 数据中区分大肠杆菌和铜绿假单胞菌对未处理细胞的抗生素反应，准确率超过 99%。深度学习分析还能够区分未处理细胞的反应，抗生素剂量比传统生长测定中观察到的最低抑制浓度低 10 倍。此外，使用生成模型（变分自动编码器）对 SERS 数据进行分析，可识别铜绿假单胞菌裂解物数据中与抗生素功效相关的光谱特征。根据这一见解，选择代谢物的组合数据集来扩展变分自动编码器的潜在空间。该无培养数据集极大地提高了分类准确性，可在 30 分钟内选择有效的抗生素治疗。使用扩展的潜在空间，无监督贝叶斯高斯混合分析在区分 SERS 中对抗生素培养物敏感和耐药的准确率达到 99.3%。判别模型和生成模型可以通过少量标记数据快速提供高分类精度，这极大地减少了使用传统生长测定验证表型 AST 所需的时间。因此，这项工作概述了一种实现实用快速 AST 的有前途的方法。
Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.