| dc.description.abstract | Natural product discovery and synthetic biology workflows use natural constructs such as bacteria for the production and discovery of chemical commodities. Natural product discovery leverages the millions of years of microbial evolution to identify and isolate novel chemical structures that possess potent bioactivity (i.e. anticancer and antibiotic properties). Alternatively, synthetic biology strategies aim to engineer microbial cell factories that produce chemicals ranging from fuels to therapeutics using renewable feedstocks and biomass. Fully realized, these fields can address global challenges across medical, environmental, energy, and economic sectors. The current rate-limiting step in synthetic biology and natural product discovery workflows are analytical methods that characterize the metabolic phenotypes resulting from specific editing events or conditions inducing secondary metabolite biosynthesis, respectively. To accelerate these workflows and their ultimate commercial utility, rapid analytical methods comprehensively characterizing metabolic phenotypes are required. For these reasons, we have developed desorption electrospray ionization – imaging mass spectrometry (DESI-IMS) workflows to characterize microbial biosynthesis. DESI is an ambient ionization technique that reduces sample preparation by directly sampling biological tissues without sample pretreatment and under ambient conditions. We demonstrate the utility of DESI-IMS to accelerate natural product discovery workflows by using spatial location as a means for prioritization of bioactive secondary metabolites in microbial co-cultures. Additionally, we demonstrate the value of our sampling method for investigating microbial co-cultures by measuring unique metabolites not detected using traditional workflows characterizing liquid fermentation. Furthermore, after evaluating other DESI-IMS methods profiling solid-phase fermentation, we demonstrate that the developed microporous membrane scaffold method yields higher sensitivity and repeatability as well as more robust analyte localizations. Adapting this method to synthetic biology workflows, we performed multiplexed and untargeted analyses of microorganisms engineered for chemical production. In this manner, we provide multiple avenues for future engineering strategies across multiple strains in a single acquisition which include: (i) identifying the highest production of specific target molecules, (ii) determining off-target products, and (iii) characterizing the metabolomic profile of engineered microorganisms. In sum, we demonstrate that multidimensional metabolic phenotyping using DESI-IMS accelerates natural product discovery and synthetic biology workflows. | |
