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

生物膜 原位 多细胞生物 纳米技术 表面增强拉曼光谱 拉曼光谱 材料科学 等离子体子 生物系统 拉曼散射 生物 化学 光学 光电子学 细菌 物理 细胞 有机化学 遗传学
作者
Aditya Garg,Wonil Nam,Wei Wang,Peter J. Vikesland,Wei Zhou
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:8 (3): 1132-1142 被引量:13
标识
DOI:10.1021/acssensors.2c02412
摘要

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.
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