PDF(Pubmed)
Abstract：
Tuberculosis (TB) is the world\'s deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen\'s antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.
摘要：
结核病是世界上最致命的传染病,超过150万人死亡和1000万新病例报告。致病菌结核分枝杆菌(Mtb)培养需近40d,确定病原体的抗生素敏感性所需的步骤。Mtb的快速鉴定和快速抗生素敏感性测试对于有效的患者治疗和对抗抗生素耐药性至关重要。这里,我们展示了一个快速的，无文化,以及使用拉曼光谱和机器学习的结核病无抗生素孵育药敏试验。我们从超过25,000个Mtb复合菌株BacillusCalmette-Guérin（BCG）对四种主要抗结核药物之一具有抗性的细胞中收集了很少到单细胞的拉曼光谱，异烟肼,利福平,莫西沙星,和阿米卡星,以及泛敏感的野生型菌株。通过在这些数据上训练神经网络，我们对每个菌株的抗生素耐药性进行分类，干样本和患者痰样本。在干燥的样品上，我们在所有五种BCG菌株中实现了>98%的耐药与易感分类准确率。在患者痰样本中，我们达到~79%的平均分类精度。我们开发了一种特征识别算法，以验证我们的机器学习模型正在使用生物学相关的光谱特征来评估分枝杆菌菌株的耐药性。最后,我们演示了如何通过开发低成本，在资源有限的环境中部署这种方法，便携式拉曼显微镜，成本<5,000美元。我们展示了该仪器和我们的机器学习模型如何使显微镜和光谱学相结合，以实现对BCG的精确少到单细胞药物敏感性测试。
