CAREER: Integrated molecular and computational sensing (IMACS) for label-free bacteriomics

PI提出了新的压缩传感和并行采集成像方法，以监测许多细胞（约数千个），并通过表面增强和正常拉曼成像（SERI NRI）获得独特的分子条形码，与现有的拉曼技术相比，通量优势约为1000倍。本文提出了一种多焦点压缩感知快拍拉曼成像方法，取代了现有共焦拉曼显微镜的点扫描成像方法。为了实现超过mm 2的大面积SERI，PI设计了一类具有分层纳米结构的新型高度可靠的等离子表面，以选择性地对细胞表面和内部细胞器（用于识别和计数）、细胞外环境中的细胞分泌蛋白质、酶和外毒素进行成像（用于毒力和生物膜形成评估），以及生物膜生长和异质性。多元计算算法将被开发并应用于分子检测和识别、生物荧光背景的分离、空间-光谱-时间特征提取、模式识别和库匹配。IMACS可以使无标记的调查细菌孢子形成和萌发在人口水平和网络行为，如群体感应及其作用的分泌或募集的细胞外因子。IMACS可以提供生物膜成熟和异质性的关键信息。

The PI proposes novel compressed sensing and parallel acquisition imaging approaches to monitor many cells (~thousands) and obtain the unique molecular barcodes via surface enhanced and normal Raman imaging (SERI & NRI) with a throughput advantage ~1000X over existing Raman techniques. Instead of point-scan as used in current confocal Raman microscopy, multi-foci and compressed-sensing snapshot Raman imaging are proposed. To realize large area SERI over mm2, the PI engineers a new class of highly reliable plasmonic surface with hierarchical nanoarchitecture to selectively image cell surfaces and internal organelles (for identification and counting), cell-secreted proteins, enzymes and exotoxins in the extracellular environment (for virulence and biofilm formation assessment), as well as biofilm growth and heterogeneity. Multivariate computational algorithms will be developed and employed in molecular detection and identification, separation of biological fluorescence background, and spatio-spectro-temporal feature extraction, pattern recognition and library matching. IMACS could enable label-free investigation of bacterial sporulation and germination at the population level and network behavior such as quorum sensing and its role in the secretion or recruitment of extracellular factors. IMACS could provide critical information for biolfilm maturation and heterogeneity.