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

The implementation of mass spectrometry (MS) in clinical microbiology has made significant improvement in the turnaround time from positive culture to identification, but current protein-based approaches can struggle with species level identifications because of the high degree of homology within a genus. However, other MS-based strategies for bacteria identifications that are based on lipids and small molecules have shown promise towards species-level identifications and detection of specific phenotypes, including those related to antibiotic resistance. While the concept of using multi-omics in diagnostic situations is not new, the issues of time and efficiency remain major hurdles to implementing multi-omics into situations that demand rapid and high-throughput analyses like clinical microbiology. We are addressing this gap by leveraging rapid, gas-phase ion mobility (IM) separations coupled to MS to simultaneously detect the lipids and metabolites in bacterial pathogens. Using flow-injection (FI) rather than liquid chromatography (LC), we instead rely more directly on the structural separations of the IM dimension to resolve features from different biochemical classes and aid in identifications. A head-to-head comparison demonstrates that the FI-IM-MS multi-omic strategy performs similarly to LC-IM-MS in its ability to distinguish 24 strains of the high-concern ESKAPE pathogens, while shortening overall analysis time from 17 min to 2 min per injection. We demonstrate that the IM dimension has excellent stability and reproducibility, which enables extracted IM peak areas to be used in lieu of chromatographic peak areas. Although the total number of features detected in the FI-IM-MS dataset is lower than the HILIC-IM-MS dataset, the overlap between the two datasets includes the features that are most heavily weighted in the PCA separation of the 24 strains. These results showcase the capabilities of mobility-enabled rapid multi-omics and open the possibility to detect subtle strain-level differences and resistance phenotypes in bacteria pathogens by including additional classes of biomolecules.