In Situ Spatiotemporal SERS Measurements and Multivariate Analysis of Virally Infected Bacterial Biofilms Using Nanolaminated Plasmonic Crystals,ACS Sensors

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In Situ Spatiotemporal SERS Measurements and Multivariate Analysis of Virally Infected Bacterial Biofilms Using Nanolaminated Plasmonic Crystals
ACS Sensors ( IF 8.2 ) Pub Date : 2023-03-09 , DOI: 10.1021/acssensors.2c02412
Aditya Garg ₁ , Wonil Nam _{1,

2} , Wei Wang ₃ , Peter Vikesland ₃ , Wei Zhou ₁

Affiliation

In situ spatiotemporal biochemical characterization of the activity of living multicellular biofilms under external stimuli remains a significant challenge. Surface-enhanced Raman spectroscopy (SERS), combining the molecular fingerprint specificity of vibrational spectroscopy with the hotspot sensitivity of plasmonic nanostructures, has emerged as a promising noninvasive bioanalysis technique for living systems. However, most SERS devices do not allow reliable long-term spatiotemporal SERS measurements of multicellular systems because of challenges in producing spatially uniform and mechanically stable SERS hotspot arrays to interface with large cellular networks. Furthermore, very few studies have been conducted for multivariable analysis of spatiotemporal SERS datasets to extract spatially and temporally correlated biological information from multicellular systems. Here, we demonstrate in situ label-free spatiotemporal SERS measurements and multivariate analysis of Pseudomonas syringae biofilms during development and upon infection by bacteriophage virus Phi6 by employing nanolaminate plasmonic crystal SERS devices to interface mechanically stable, uniform, and spatially dense hotspot arrays with the P. syringae biofilms. We exploited unsupervised multivariate machine learning methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), to resolve the spatiotemporal evolution and Phi6 dose-dependent changes of major Raman peaks originating from biochemical components in P. syringae biofilms, including cellular components, extracellular polymeric substances (EPS), metabolite molecules, and cell lysate-enriched extracellular media. We then employed supervised multivariate analysis using linear discriminant analysis (LDA) for the multiclass classification of Phi6 dose-dependent biofilm responses, demonstrating the potential for viral infection diagnosis. We envision extending the in situ spatiotemporal SERS method to monitor dynamic, heterogeneous interactions between viruses and bacterial networks for applications such as phage-based anti-biofilm therapy development and continuous pathogenic virus detection.

中文翻译：

使用纳米层压等离子体晶体对病毒感染的细菌生物膜进行原位时空 SERS 测量和多变量分析

活的多细胞生物膜在外部刺激下的活性的原位时空生化表征仍然是一个重大挑战。表面增强拉曼光谱 (SERS) 将振动光谱的分子指纹特异性与等离子体纳米结构的热点敏感性相结合，已成为一种很有前途的生命系统非侵入性生物分析技术。然而，大多数 SERS 设备不允许对多细胞系统进行可靠的长期时空 SERS 测量，因为在生产空间均匀且机械稳定的 SERS 热点阵列以与大型蜂窝网络接口方面存在挑战。此外，很少有研究对时空 SERS 数据集进行多变量分析，以从多细胞系统中提取空间和时间相关的生物信息。在这里，我们展示了原位无标记时空 SERS 测量和多变量分析丁香假单胞菌生物膜在发育过程中和被噬菌体病毒 Phi6 感染后，通过使用纳米层压等离子体晶体 SERS 装置将机械稳定、均匀和空间密集的热点阵列与丁香假单胞菌生物膜连接起来。我们利用无监督多元机器学习方法，包括主成分分析 (PCA) 和层次聚类分析 (HCA)，来解决源自丁香假单胞菌生化成分的主要拉曼峰的时空演变和 Phi6 剂量依赖性变化生物膜，包括细胞成分、细胞外聚合物质 (EPS)、代谢物分子和富含细胞裂解物的细胞外介质。然后，我们使用线性判别分析 (LDA) 对 Phi6 剂量依赖性生物膜反应的多类分类进行了监督多变量分析，证明了病毒感染诊断的潜力。我们设想扩展原位时空 SERS 方法以监测病毒和细菌网络之间的动态、异质相互作用，用于基于噬菌体的抗生物膜疗法开发和连续致病病毒检测等应用。

更新日期：2023-03-09

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