Spatial profiling and data analysis in microbial metabolome analysis

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Yu, Jian
mass spectrometry , spatial profiling , data analysis , microbial metabolome
Spatial profiling, enhanced by various mass spectrometry (MS) techniques, is rapidly evolving as an innovative method to visualize the distribution of diverse metabolites. While much of the current research focuses on achieving micrometer-level spatial resolution, our work explores the liquid micro-junction surface sampling probe (LMJ-SSP) technique at the millimeter level. Our aim is to develop a rapid and robust protocol covering both sampling and data analysis. In this thesis, we utilize the LMJ-SSP for rapid screening and spatial profiling of microbial natural products. LMJ-SSP demonstrates superior sampling throughput as no sample preparation is required. To enhance the throughput of analyzing MS spectra, we developed various algorithms. In Chapter 2, we introduce principal component analysis (PCA)-based software for rapid analysis of ambient ionization spectra acquired from different microbial samples. In Chapter 3, a hyperspectral visualization algorithm is developed to convert molecular features of microbial colonies into different colors for easier interpretation. Given the primary objective of spatial profiling to discover metabolites with distinct properties, establishing connections between spatial profiling and meaningful metabolites is paramount. We present a workflow in Chapter 4, covering rapid spatial profiling, unsupervised MS spectra analysis, and molecular networking. This approach leads to the discovery of potential biosynthetic pathways of novel tambjamine compounds. Further analysis of the potential biosynthetic mechanism of tambjamine compounds is demonstrated in Chapter 5 through collaboration with the Ross lab. Leveraging rapid spatial profiling by MS, conventional chromatography-based MS proves powerful in isolating undiscovered natural products with diverse conformations. This journey from rapid spatial profiling to conventional metabolome analysis exemplifies how spatial profiling can swiftly provide guidance to potential natural products and how these insights can be effectively utilized.
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