CONVERSION AND INTEGRATION OF OMICS DATA FROM A PROTOTYPE, BENCHTOP MULTI-REFLECTING TIME-OF-FLIGHT (MRT) PLATFORM WITH THIRD-PARTY INFORMATIC WORKFLOWS

Importance of Topic

Ongoing improvements in mass spectrometry such as multi-reflecting ToF enable high resolution, high accuracy data needed to capture complex omics profiles. A generic conversion pipeline supports data sharing and interoperability with third party informatic tools, ensuring efficient metabolomics and lipidomics workflows in research and QAQC laboratories.

Objectives and Study Overview

This study presents a data conversion and integration pipeline for DIA and DDA acquisitions on a prototype benchtop multi-reflecting ToF instrument. It aims to automatically generate mzML files during acquisition and route them to popular informatic platforms including Skyline, MS-Dial and XCMS for metabolomic and lipidomic analysis.

Methodology and Instrumentation

• The conversion pipeline is embedded within a connectivity framework that leverages an acquisition method editor, sample submission interface or a direct API link to trigger automated generation of mzML during LC MS runs
• Users can tailor mzML outputs with centroiding, binary compression, spectrum indexing and bit precision options for compatibility with third party tools
• Data were acquired using a UPLC front end coupled to a Xevo MRT benchtop multi-reflecting ToF mass spectrometer
• Lipidomic samples comprised NIST plasma spiked with a labeled internal standard mixture and processed via Skyline and MS-Dial. Metabolomic samples included serial levels of NIST urine extracts processed via XCMS

Main Results and Discussion

• Skyline processing of spiked plasma provided calibration curves with R squared values above 0.99, sub 0.5 ppm mass error in extracts of labeled lipids and excellent dynamic range
• MS-Dial workflows enabled discovery of endogenous lipids such as PC(36 1) with mass errors below 1 ppm at precursor and fragment levels
• XCMS analysis of pooled urine QC and level samples achieved consistent retention time alignment, statistical differentiation across exposure groups and identification of smoking related metabolites with mass errors below 0.5 ppm
• The flexible pipeline maintained high mass resolution under fast LC conditions and ensured robust data throughput and quality

Benefits and Practical Applications

• Automated conversion streamlines data export during acquisition reducing manual intervention and error
• Tailored mzML ensures compatibility with diverse informatic platforms accelerating metabolomic and lipidomic workflows
• High mass accuracy and resolution of the multi-reflecting ToF system enhance confidence in compound identification for research and quality control applications

Future Trends and Possibilities

Anticipated developments include tighter integration with cloud based informatic services, support for emerging data independent acquisition strategies and expanded compatibility with machine learning driven annotation tools. Ongoing improvements in data formats and orchestration will further accelerate high throughput omics studies.

Conclusion

The presented conversion and integration pipeline demonstrates a seamless workflow from acquisition to third party informatics yielding high fidelity omics data. Integration within the acquisition environment ensures automated, flexible mzML generation and compatibility with leading analysis platforms. This approach enhances reproducibility and throughput in metabolomics and lipidomics research.

References

1. Tsugawa H et al MS DIAL data independent MS MS deconvolution for comprehensive metabolome analysis Nat Methods 12 523 526 2015
2. Adams KJ et al Skyline for Small Molecules a unifying software package for quantitative metabolomics J Proteome Res 19 4 1447 1458 2020
3. Tautenhahn R et al XCMS Online a web based platform to process untargeted metabolomics data Anal Chem 84 11 5035 5039 2012
4. Barupal et al Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach Environ Health Perspect 127 9 EHP4713 2019