MSACL 2023 Abstract

Introduction
There is growing interest and appreciation in the use of mass spectrometry-based multi-omics approaches to study biological processes and diseases from a systems-level perspective. The performance of discovery-level multi-omics typically involves the partitioning of a complex sample into its individual components, followed by a thorough analysis of each “ome” under optimized LC and MS conditions. However, these multi-omics experiments must be streamlined before their findings can be implemented into diagnostic or prognostic applications. The rapid gas-phase structural separations afforded by IM-MS provide an opportunity for high-throughput measurements of biological samples containing mixtures of lipids, metabolites, peptides, and other biochemicals.

Objectives
We are developing methods for high-throughput multi-omics based on flow injection analysis (FI) and ion mobility-mass spectrometry (IM-MS). The feasibility and advantages of FI-IM-MS will be demonstrated for the identification of microorganisms to the species and strain levels using integrated lipidomic and metabolomic features.

Methods
Pseudomonas aeruginosa (n=3), Acinetobacter baumannii (n=3), and Staphylococcus aureus (n=7) strains were cultured overnight in tryptic soy broth, with six replicates per strain. Bacteria suspensions in sterile saline solution were adjusted to achieve a consistent cell density and an equal volume was used to obtain pelleted sample via centrifugation. One set of replicates (n=3) was extracted using the Bligh & Dyer two-layer liquid-liquid extraction method, from which the aqueous and organic layers were collected. The second set of replicates (n=3) was extracted using simplified, single-phase extraction based on acetonitrile, methanol and water. The three sets of extracts (i.e., lipids from Bligh & Dyer, metabolites from Bligh & Dyer, and lipids+metabolites from single-phase) were analyzed first by hydrophilic interaction liquid chromatography (HILIC) coupled to a traveling wave ion mobility-mass spectrometry (TWIM-MS), and then by flow-injection (FI) analysis couple to TWIM-MS. Data was collected in positive and negative ionization modes. Data were analyzed using Progenesis QI.

Results
In comparing the HILIC and FI-IM-MS methods, we observed a strong positive correlation between peaks areas for the same analytes in both methods. The FI-IM-MS method yielded RSDs below 15% for 95% of the features detected in replicate injections of a pooled mixture. In all extractions and analyses, the lipid and/or metabolite profiles were sufficient to separate the microorganisms based on their Gram stain status (positive or negative) and to the species level in principal components analyses (PCA). Although fewer total features were detected from the FI-IM-MS using the same significance threshold, the same individual metabolites and lipids were identified as major contributors to the group differences in the PCA. The combination of both lipids and metabolites from the single-phase extraction method improved the separation of strain-level differences within all three organisms. Lipids and metabolites that contributed to the PCA separation from single-ome experiments were among the features contributing to the PCA clustering in the single-phase, multi-ome dataset.

Conclusion
Our preliminary evaluation of the FI-IM-MS approach to multi-omics has demonstrated that combining lipid and metabolite experiments into a single method improves the resolution of microorganism identifications to the strain level without sacrificing throughput or precision.