单细胞拉曼技术在微生物群落动态监测中的方法学研究

Metagenomic approaches are commonly used for dynamic monitoring of microbiota structure and functions, however, they are usually not able to analyze live cells non-invasively, perform real-time analysis or provide information on the physiological states of cells. Recently published results from our research group have showed that single-cell Raman spectra (SCRS) provides information on bacterial phylogeny as well as their physiological status, thus suggested a new approach for monitoring microbiota. Employing the human oral microbiota that underlie the development of gingivitis as a model, this proposal will investigate the key scientific question of how the genetic diversity and physiological complexity of microbiota affect the individual and collective SCRS acquired from the microbiota, and thus establish a link between the SCRS and the structure/function of microbiota. On the other hand, analysis of 16S amplicons and total metagenomic DNA will be employed to test and validate the feasibility of using SCRS as a proxy for the structure and function of microbiota. Based on these efforts, the potential of using microbial SCRS for characterization and monitoring of gingivitis development will be probed. This novel, SCRS-based approach for microbiota analysis may overcome the several key drawbacks of metagenomic approaches and thus find exciting and wide applications in microbial ecology.

目前，以宏基因组手段为代表的菌群结构与功能监测方法面临细胞的侵害性、分析的离线性、无法检测细胞生理状态等关键缺陷。本团队前期发现，微生物的单细胞拉曼图谱（SCRS）能够反映口腔微生物种系发生和生理状态。基于此，申请人将以口腔微生物为模式菌群，以牙龈炎发生发展为模式生物过程，通过考察菌群遗传多样性和生理状态复杂性如何影响群落中单细胞拉曼表型等关键科学问题，尝试建立细胞群体中单细胞表征的集合与群落结构和功能的相互关联。同时，将基于单细胞和群落两个水平的16S和基因组分析，验证群体水平SCRS作为一种菌群结构与功能的标识手段（proxy）的可行性。在此基础上，将探索基于群落SCRS来定量监测牙龈炎发展过程的策略。这些努力将克服现有菌群检测方法的一些核心缺陷，因此有可能为诸多应用领域的微生态研究提供一种共性的新思路和新方法。

本研究综合利用单细胞拉曼技术和元基因组分析方法，解析菌群结构与功能，开拓了一系列临床应用。首先，研究人员充分利用和发展单细胞拉曼技术优势，克服现有菌群检测方法的一些核心缺陷，为临床微生物耐药性检测提供一种全新的思路和方法。其次，本项目还利用口腔菌群的时空动态变化规律，实现了口腔疾病（包括龋病和牙龈炎）的诊断、预警和预后，初步展示了口腔微生物组在口腔疾病发生发展及治疗中的巨大作用。因此，本项目充分验证了单细胞拉曼光谱技术可以作为微生物结构和功能的检测手段， 同时也充分展示了微生物组学在个体化精准医疗中的巨大应用机会。

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