A Next-Generation Sensing Platform for Bacterial Metabolomics

细胞代谢物是细胞使用或产生的小分子，是细胞状态，功能和健康的直接指标。代谢组学分析是对细胞代谢产物的表征，它是我们理解细胞功能的关键。目前的代谢组学方法包括质谱和核磁共振，这些技术需要大型，昂贵的仪器和长时间的分析。在这里，另一种方法是使用集成的光学传感设备来检测细胞内以分钟为时间尺度的代谢变化的化学指纹。这些设备将对参与压力反应的细菌代谢物进行指纹识别，压力反应是细菌在动态条件下生存的基本组成部分。作为这项工作的一部分，将回答有关光学传感器的分子尺度结构与在所得数据中区分代谢物反应的能力之间关系的科学问题。该传感平台的演示和表征还将涉及快速收集大数据集，以及应用自动化大数据分析软件来解释复杂的传感器信号。这些研究的结果将有助于了解不同生境中的细菌行为，从医疗感染和工业污染到人类健康和农业中的益生菌，以及影响环境中营养循环的微生物相互作用。主要研究人员和学生将参加每年为期两周的夏季外展计划，为不同群体的高中生- ASPIRE：访问学生计划，以获取，招聘和丰富。研究生，本科生和高中生将学习如何定义，分析和解决研究问题，并重点关注代谢组学在健康和环境中的重要性以及技术创新的重要性。实用化的纳米光子传感平台将为检测动态生物功能提供一种新的透镜。控制表面增强的拉曼散射传感器架构和表面化学将被研究，以设计敏感的受体，既能够快速收集大量的数据集和复杂的生物介质中的代谢物指纹的差异。 声子-等离子体激元耦合的测量将告知功能化的传感器表面和感兴趣的代谢物之间的化学相互作用。 机器学习算法将进一步开发，最适合准确分析作为这些研究的一部分产生的大量振动光谱。将传播微生物代谢指纹库和光谱信息分析方法。纳米光子器件架构，纳米颗粒表面化学和复杂的拉曼光谱的机器学习分析的基础研究，然后将用于检测与压力反应相关的代谢指纹在多微生物细菌群落。与细菌对抗菌剂的应激反应和种间相互作用相关的代谢变化将被测量，以开发新的方法来了解如何控制，培养和减轻各种微生物-宿主相互作用-所研究的传感技术的关键应用领域。 细菌应激反应的高通量检测对于在医疗和工业环境中筛选细菌污染的抗微生物治疗是重要的。类似地，细菌应激的代谢标志物可用作环境中存在感兴趣的化学物质的指示物，例如重金属或有毒有机化合物。在这两种情况下，快速、准确地检测细菌中应激诱导的代谢变化的能力是该纳米光子传感平台独特解决的关键技术挑战。该奖项反映了NSF的法定使命，并通过使用该基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Cell metabolites are small molecules used or produced by cells and are direct indicators of the cellular state, function, and health. Metabolomic analyses is the characterization of cellular metabolites, and it is key to our understanding of cell function. Current metabolomic methods include mass spectrometry and nuclear magnetic resonance, techniques which require large, expensive instruments and long times for analysis. Here, an alternative approach is to use integrated optical sensing devices to detect the chemical fingerprints of metabolic changes within cells on the time scale of minutes. These devices will fingerprint bacterial metabolites involved in stress responses-a basic component of bacterial survival in dynamic conditions. As part of this work, scientific questions about the relationship between the molecular scale architecture of optical sensors and the ability to differentiate metabolite response in the resulting data will be answered. The demonstration and characterization of this sensing platform will also involve the rapid collection of large data sets and the application of automated big data analysis software to interpret complex sensor signals. Results from these studies will inform understanding of bacterial behavior in diverse habitats ranging from medical infections and industrial contamination to probiotics in human health and agriculture, and microbial interactions influencing nutrient cycling in the environment. The principal investigators and students will engage in an annual two-week summer outreach program for a diverse group of high school students - ASPIRE: Access Student Program to Inspire, Recruit, and Enrich. Graduate, undergraduate and high school students will learn how to define, analyze and solve research problems and communicate results that focus on the importance of metabolomics in health and the environment and the importance of technology innovation. Practical nanophotonic sensing platforms will provide a new lens for examining dynamic biological function. Control of surface-enhanced Raman scattering sensor architectures and surface chemistry will be investigated to design sensitive receptors that both enable the rapid collection of large data sets and differentiation of metabolite fingerprints in complex biological media. Measurements of phonon-plasmon coupling will inform of chemical interactions between functionalized sensor surfaces and metabolites of interest. Machine learning algorithms will be further developed that are best suited to accurately analyze the large volume of vibrational spectra generated as part of these studies. Libraries of microbial metabolic fingerprints and methods for analysis of spectral information will be disseminated. The fundamental studies of nanophotonic device architecture, nanoparticle surface chemistry, and machine learning analysis of complex Raman spectra will then be utilized for the detection of metabolic fingerprints associated with stress response in polymicrobial bacterial communities. Metabolic changes associated with bacterial stress responses to antimicrobials and inter-species interactions will be measured to develop new methods to understand how to control, nurture, and mitigate various microbe-host interactions-a key application area of the investigated sensing technologies. High throughput detection of the bacterial stress response is important for screening antimicrobial treatments for bacterial contamination in medical and industrial settings. Similarly, metabolic markers of bacterial stress can be used as indicators of the presence of chemicals of interest in the environment, such as heavy metals or toxic organic compounds. In both cases, the ability to quickly and accurately detect stress-induced metabolic changes in bacteria is a critical technological challenge uniquely addressed by this nanophotonic sensing platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.