Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software.

Michael J Wilde,Paul S Monks,Chris E Brightling,Rebecca L Cordell,Neil J Greening,Bo Zhao,Salman Siddiqui,Robert C Free,Wadah Ibrahim,Amisha Singapuri

doi:10.1021/acs.analchem.0c02844

Michael J Wilde, Paul S Monks + Show 8 more

Open Access

https://doi.org/10.1021/acs.analchem.0c02844

Copy DOI

Abstract

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both nontargeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant high-complexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a first-pass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment to automatically correct the variation in retention times captured by a routine reference standard. Variation in 1tR and 2tR was reduced on average from 8 and 16% CV prealignment to less than 1 and 2% post alignment, respectively. The interface enables automation and creation of new functions and increases the interconnectivity between chemometric tools, providing a window for integrating data-processing software with larger informatics-based data management platforms.

Highlights

Advanced analytical technologies are revealing new chemical complexities in our environment, our bodies, and our food and commodities
Comprehensive two-dimensional gas chromatography (GC×GC) is a technique that affords unparalleled separation of volatile and semivolatile matrices, providing a powerful analytical tool for both nontargeted and targeted analyses.[1−3] The increased peak capacity and separation of individual compounds within complex mixtures can benefit studies focused on biomarker discovery and signature profiling, such as global metabolomics.[4−7] Targeted methods for screening, such as biomonitoring persistent organic pollutants, benefit from less-extensive sample preparations and increased confidence in chemical assignment.[8,9]
A plethora of software tools exist for chemometric processing of GC×GC data (Table S1)

Summary

■ INTRODUCTION

Advanced analytical technologies are revealing new chemical complexities in our environment (e.g., wastewaters and air quality), our bodies (e.g., metabolome and microbiome), and our food and commodities (e.g., packaging, materials, and petrochemicals). 677 files comprising breath and corresponding environmental air samples (representing a large dataset), analyzed at different times throughout the recruitment period, were transferred to the input folder and a new command and batch file was produced This command and batch file (as described in example 3) instructed the software to find the date and tray number at the beginning of each sample (e.g., a new function added by the user) and align the chromatogram (e.g., an existing function within the commercial software) using the algorithm developed by Gros et al.[38] (e.g., integrating published open-source code) based on the corresponding match file automatically generated from the reference chromatogram with the same date and tray number. Compliance with guidelines and the use of informatics-based platforms are becoming increasingly pertinent, and soon, only the software which can demonstrate interconnectivity, capable of interfacing with other chemometric tools, will be practical for large-scale discovery studies

■ CONCLUSIONS

■ ACKNOWLEDGMENTS

■ REFERENCES