Rapid Multi-Omics for Bacteria Identifications using Flow Injection-Ion Mobility-Mass Spectrometry

Antibiotic resistance is one of the most serious public health concerns of our time. With limited development of new antimicrobials, attention has shifted towards ensuring that existing therapeutics maintain their efficacy against bacterial pathogens. In the case of bacterial infections, the ability to rapidly determine the organism and corresponding antibiotic susceptibility is vital to developing an effective treatment plan and preventing misuse of antibiotics. While there is currently no single, universal technology capable of obtaining both identifications and susceptibilities, the implementation of mass spectrometry in clinical microbiology has made significant improvement in the turnaround time from positive culture to identification. The current mass spectrometry approach exploits the unique protein fingerprints found across different genera of bacteria but struggles with identifications to the species level or lower because of the high degree of homology within a genus. However, other areas of development relying on the detection of bacterial lipids and small molecules with mass spectrometry have shown promise towards species-level identifications and detection of specific phenotypes, including those related to antibiotic resistance. While the concept of using multiple omics (or multi-omics) in diagnostic situations is not new, the issues of time and efficiency remain major hurdles to implementing multi-omic mass spectrometry into routine practice. To simultaneously obtain information provided by lipids and small molecules, we have developed a multi-omics strategy to bacterial identifications that relies on rapid gas separation separations by structure and mass using ion mobility-mass spectrometry (IM-MS). Proof of concept is demonstrated using strains of the leading causes of bacterial infections – the ESKAPE pathogens (Enterococci sp., Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter sp.). These results showcase the speed and capability of an IM-MS multi-omics workflow and show promise for expansion into more detailed identification methods in the future.