Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry

Abstract

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

Biomarker enrichment medium: A defined medium for metabolomic analysis of microbial pathogens

“…Untargeted analysis of different batches of MH medium was performed with the peak picking function in El-MAVEN v0.12.0 with a 10 ppm m/z window and a minimum peak intensity set to 50,000. Targeted peak assignments were based on the Mass Spectrometry Metabolite Library of Standards (MSMLS, IROA Technologies) as previously described (Groves et al, 2022). Statistical analyses were conducted using the R statistical software platform (R Core Team, 2022) using in-house software tools previously published [https://zenodo.org/record/6403220#.YkYXoi9730o; (Rydzak et al, 2022)].…”

“…Furthermore, while most production biomarkers (i.e., arabitol, succinate, xanthine, mevalonate, citrulline, and nicotinate) were still suitable for species differentiation on RPMI, levels of succinate, xanthine, citrulline, and nicotinate were on average 7.5-fold lower when the respective species that produced these markers were grown on RPMI (Figure 3, Source Data File 3). These decreased levels in key biomarkers can have significant impacts on diagnostic accuracy when performing MS/MS fragmentation using less sensitive clinical triple quadrupole mass spectrometers (Groves et al, 2022). Moreover, production of N 1 ,N 12diacetylspermine-used for identification of EF-was so low on RPMI that it did not meet diagnostic thresholds.…”

Moving beyond descriptive studies: harnessing metabolomics to elucidate the molecular mechanisms underpinning host-microbiome phenotypes

“…With this objective in mind, we have increasingly favored extractions in 4 °C 50% methanol:H 2 O (at a 1:50 or 1:20 volume:volume dilution or 50 mg tissue/mL) for general studies involving central carbon metabolism [131][132][133] , extracellular metabolome analyses [134][135][136] , and other projects involving the analysis of polar metabolites [137][138][139] (see Section S1 in the SI file for detailed extraction protocols). We have shown that this simple extraction, when coupled with hydrophilic interaction liquid chromatography (HILIC) mass spectrometry (MS), can reproducibly capture metabolites over thousands of samples with minimal technical error (coefficient of variation <0.15 over 1000+ injections) 140 . We find that this method is a good first choice for analyzing polar compounds when the molecular targets are poorly defined; however, other methods can be better optimized for specific compound classes such as SCFAs.…”

“…These metabolites are more commonly associated with systemic effects (i.e., are found in the bloodstream and tissues such as the brain and liver) 12,20,21,111,124,159 , but can also serve as important immune regulators in the GI tract 30,122,161,162 . These hydrophilic compounds are most easily analyzed by LC-MS using HILIC 140,[163][164][165] but can also be derivatized and analyzed by GC-MS 14,109,112 . Our preferred strategy for aqueous analyzes of the GI tract and feces uses a zwitterionic HILIC (Thermo Fisher Syncronis™) stationary phase combined with a short linear ammonium formate (aqueous phase)/ acetonitrile with formic acid (organic phase) gradient to capture amino acids, carbohydrates, nucleotide derivatives, and other common compounds that are found outside the cell 140 .…”

“…These hydrophilic compounds are most easily analyzed by LC-MS using HILIC 140,[163][164][165] but can also be derivatized and analyzed by GC-MS 14,109,112 . Our preferred strategy for aqueous analyzes of the GI tract and feces uses a zwitterionic HILIC (Thermo Fisher Syncronis™) stationary phase combined with a short linear ammonium formate (aqueous phase)/ acetonitrile with formic acid (organic phase) gradient to capture amino acids, carbohydrates, nucleotide derivatives, and other common compounds that are found outside the cell 140 . We have recently shown that the uptake and secretion of these compounds is a sensitive predictor of microbial taxa 56 .…”