Bacteria and pathogen characterizations using outer membrane vesicles

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PROJECT SUMMARY There is growing recognition that extracellular vesicles (EVs)–micrometer- or nanometer-sized lipid particles containing protein and nucleic acid cargoes–are shed by all domains of life. The ubiquity of prokaryotic EVs suggests that the presence of bacterial EVs in biofluids could be exploited for new diagnostics/prognostics and even therapies for diverse pathogenic bacteria. There is, however, an unmet need for methods able to solve a fundamental problem confounding the exploitation of EV information in biology and medicine--EVs are naturally highly heterogeneous particles and thus those of interest may be present only at very low abundance in a mixture of other EVs. In this Phase I project, our overall experimental goal is to address the need for analyzing EV heterogeneity (subpopulations) by focusing on an important class of bacterial nanoparticles called outer membrane vesicles (OMVs). Specifically, our novel technology platform is designed to address the problem of resolving OMV subpopulations at the single OMV level by directly correlating surface protein and nucleic acid cargoes (here small RNAs or sRNAs) of dispersed OMVs, through highly multiplexed fluorescence imaging analysis coupled with amplified readouts of both cargoes. Our ultimate goal is to develop a unique imaging platform for the high-content, high-throughput analysis of the protein and nucleic acid cargoes of single OMVs (and other EVs) obtained from any biological sample. Our platform could enable novel diagnostic/prognostic “liquid biopsy” tests for bacterial infections by providing a minimally invasive and more informative alternative (or supplement) to conventional methods, which often rely on lengthy culturing of target organisms from clinical samples or specimens. Our innovative platform simultaneously analyzes, in one pass, up to 10 potential OMV biomarkers in as many as 106 dispersed OMVs obtained from a biofluid such as blood, saliva, or stool. Unlike conventional methods, our platform can rapidly: 1) analyze diverse EVs; 2) simultaneously read multiple OMV surface markers and thus detect subpopulations; 3) read single OMV cargoes, greatly raising information yield com- pared to typical methods producing pooled cargo data; and 4) identify OMV subpopulations based on unique combinations of biomarkers from points 1-3. We therefore propose three stepwise objectives. First, to optimize the combined protein/nucleic acid analyses of OMVs shed by representative examples of Gram-negative and Gram-positive bacteria (E. coli and S.aureus, respectively). Second, to test the capability of our platform for the analysis in situ of sRNA cargo using dispersed single OMVs representing these major group classifications. Third, combine our surface molecule and sRNA analysis methods for the correlated direct detection in situ of both protein and nucleic acid cargoes in dispersed single OMVs representing the two major groups. We anticipate that our platform could become a new research/diagnostic/prognostic tool for managing pathologies in which OMV analysis is clinically informative, and for monitoring normal or aberrant microbiome status.