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多激发拉曼光谱法用于人工痰介质中细菌病原体的无标记、应变水平表征。

Multi-Excitation Raman Spectroscopy for Label-Free, Strain-Level Characterization of Bacterial Pathogens in Artificial Sputum Media.

机构信息

School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, SO17 1BJ Southampton, United Kingdom.

National Biofilms Innovation Centre (NBIC) and Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.

出版信息

Anal Chem. 2022 Jan 18;94(2):669-677. doi: 10.1021/acs.analchem.1c02501. Epub 2022 Jan 3.

Abstract

The current methods for diagnosis of acute and chronic infections are complex and skill-intensive. For complex clinical biofilm infections, it can take days from collecting and processing a patient's sample to achieving a result. These aspects place a significant burden on healthcare providers, delay treatment, and can lead to adverse patient outcomes. We report the development and application of a novel multi-excitation Raman spectroscopy-based methodology for the label-free and non-invasive detection of microbial pathogens that can be used with unprocessed clinical samples directly and provide rapid data to inform diagnosis by a medical professional. The method relies on the differential excitation of non-resonant and resonant molecular components in bacterial cells to enhance the molecular finger-printing capability to obtain strain-level distinction in bacterial species. Here, we use this strategy to detect and characterize the respiratory pathogens and as typical infectious agents associated with cystic fibrosis. Planktonic specimens were analyzed both in isolation and in artificial sputum media. The resonance Raman components, excited at different wavelengths, were characterized as carotenoids and porphyrins. By combining the more informative multi-excitation Raman spectra with multivariate analysis (support vector machine) the accuracy was found to be 99.75% for both species (across all strains), including 100% accuracy for drug-sensitive and drug-resistant . The results demonstrate that our methodology based on multi-excitation Raman spectroscopy can underpin the development of a powerful platform for the rapid and reagentless detection of clinical pathogens to support diagnosis by a medical expert, in this case relevant to cystic fibrosis. Such a platform could provide translatable diagnostic solutions in a variety of disease areas and also be utilized for the rapid detection of anti-microbial resistance.

摘要

当前用于诊断急性和慢性感染的方法既复杂又需要专业技能。对于复杂的临床生物膜感染,从采集和处理患者样本到得出结果可能需要数天时间。这些方面给医疗保健提供者带来了巨大的负担,延误了治疗,并可能导致患者病情恶化。我们报告了一种新型的基于多激发拉曼光谱的方法的开发和应用,该方法用于非标记和非侵入式检测微生物病原体,可以直接用于未经处理的临床样本,并提供快速数据,为医疗专业人员提供诊断依据。该方法依赖于细菌细胞中非共振和共振分子成分的差分激发,以增强分子指纹识别能力,从而在细菌物种中获得菌株水平的区分。在这里,我们使用该策略来检测和表征呼吸道病原体和作为与囊性纤维化相关的典型传染病原体。浮游生物标本分别在分离和人工痰介质中进行分析。用不同波长激发的共振拉曼成分被表征为类胡萝卜素和卟啉。通过将更具信息量的多激发拉曼光谱与多元分析(支持向量机)相结合,发现对于两种物种(所有菌株)的准确率均为 99.75%,包括对药敏和耐药的准确率均为 100%。结果表明,我们基于多激发拉曼光谱的方法可以为快速、无试剂的临床病原体检测提供支持,以支持医疗专家的诊断,在这种情况下与囊性纤维化相关。这样的平台可以为各种疾病领域提供可转化的诊断解决方案,也可以用于快速检测抗微生物耐药性。

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