Import Type: TED

Our laboratory requires the capacity for gas chromatography coupled to high-resolution quadrupole time-of-flight mass spectrometry (GC-QTOF) to support advanced untargeted metabolomics of microbial systems, with a central objective of building a curated, high-confidence database of microbial strain-specific metabolic and volatile profiles that can be systematically mined as new biological questions arise. Achieving this goal requires an analytical platform capable of comprehensive, reproducible, and informationrich data acquisition that extends beyond the limitations of conventional GC-MS approaches.

Microbial metabolomes are chemically diverse and highly strain dependent, encompassing volatile, semi-volatile, and derivatized metabolites produced across a wide dynamic range. Analyses of microbial extracts frequently yield complex chromatograms with extensive co-elution, while SPME headspace sampling of cultures captures transient and low-abundance microbial volatile organic compounds (mVOCs) that are central to microbial communication, competition, and environmental adaptation. High-resolution MS coupled to GC separation is uniquely suited to resolve this complexity.

A GC-QTOF provides accurate-mass full-spectrum acquisition across the entire GC-elutable mass range, enabling elemental composition assignment and confident differentiation of isobaric compounds via fragmentation. This level of mass accuracy and resolving power is essential for untargeted metabolomics and is a prerequisite for generating strain profiles that remain interoperable over time, even as databases expand and analytical questions evolve.

In order to combine our existing datasets of secondary metabolites and strain extracts, we require a system that can produce high abundances of molecular ions in the source. This will allow us to investigate the fragmentation of specific ions in very complex samples without the need for baseline separation. This is needed for higher throughput analysis of strains, and integrating our current fragmentation libraries into the new system. Unlike typical single-quadrupole GC-MS systems, which rely primarily on nominal-mass spectra, a QTOF enables precursor-specific, accurate-mass fragmentation experiments. Incorporating MS/MS information directly into strain profiles significantly increases their long-term value for downstream data mining, comparative analyses, and cross-study validation.

The system’s ability to acquire complete, untargeted datasets with stable mass accuracy and wide dynamic range is critical for database-driven research. Full-spectrum acquisition ensures that all detectable metabolites are recorded in each analysis, allowing retrospective interrogation of historical data as new microbial metabolites are discovered or as additional strains are introduced into the database. This capability eliminates the need for re-analysis of archived samples and maximizes the scientific return from each experiment.

Equally important is the platform’s compatibility with advanced spectral deconvolution, retention index alignment, and metabolomics software workflows, which enables consistent feature extraction and annotation across large sample sets. These capabilities are essential for constructing a robust, searchable strain metabolomics database that supports longitudinal studies, comparative strain analysis, and hypothesis-driven data mining.

In summary, acquisition of a high-resolution GC-QTOF with accurate-mass MS/MS functionality is essential for establishing a scalable, reusable microbial metabolomics infrastructure. This instrument will enable our laboratory to gener-ate durable, high-confidence strain profiles that support discovery-driven research today while serving as a foundational resource for future data mining and systems-level microbial analysis.

Capabilities Required for Building a Searchable Microbial Strain Database and incorporation of current data:
• Accurate-mass, full-spectrum acquisition of GC-eluting metabolites, ensuring long-term interpretability of un-targeted datasets
• High-resolution MS/MS fragmentation, enabling confident structural annotation and validation of strain-specific metabolites
• Fast acquisition rates that are independent of resolution, compatible with SPME headspace and narrow GC peaks, preserving reproducibility across strains
• Wide dynamic range and high sensitivity, capturing both dominant metabolic features and low-abundance strain markers
• Retrospective data mining of archived datasets, allowing new metabolites or patterns to be identified without re-analysis
• A robust data acquisition technique creating data the is interoperable with public and in-house datasets
• High abundance molecular ion spectra from an EI source to allow CID fragmentation for in-house spectral library comparisons