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来监测许多细胞（~数千），并通过表面增强和正常拉曼成像（SERI &amp； NRI）获得独特的分子条形码，其吞吐量比现有的拉曼技术优势约1000倍。采用多焦点和压缩感知快照拉曼成像技术，取代了当前共聚焦拉曼显微镜使用的点扫描成像技术。为了实现超过mm2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体表面，以选择性地成像细胞表面和内部细胞器（用于鉴定和计数），细胞分泌的蛋白质，酶和细胞外环境中的外毒素（用于毒性和生物膜形成评估），以及生物膜的生长和异质性。多元计算算法将在分子检测和鉴定、生物荧光背景分离、时空特征提取、模式识别和文库匹配等方面得到发展和应用。IMACS可以在群体水平上无标记地研究细菌的孢子形成和萌发，以及群体感应等网络行为及其在细胞外因子的分泌或募集中的作用。IMACS可以为生物膜成熟度和异质性提供重要信息。

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.