Abstract: Since its inception, chromatography coupled to mass spectrometry (MS) has been the workhorse of metabolomics. Chromatography – liquid (LC) or gas (GC) – separates metabolites prior to MS detection and identification. Separation is governed by how analytes partition between the stationary and mobile phases. The general elution problem states that unless the mobile phase conditions change over the separation, most metabolites will exhibit poor separation. This is not a new problem, and analytical chemists have been using gradient separations for decades to address this issue. However, we cannot change the stationary phase throughout a separation to resolve all analytes that may have similar stationary phase selectivity.This can hinder detection and identification efforts. I have dubbed this the “general stationary phase problem”.
Comprehensive two-dimensional chromatography (GC×GC or LC×LC) provides a solution to this problem by allowing us to separate analytes along two unique stationary phases. In comprehensive separations, analytes are separated along the first column, then periodically resampled and reinjected onto a second column. The peak capacity becomes the product of the two individual columns, boosting resolution, sensitivity, and selectivity. This is powerful in metabolomics as it allows us to routinely separate 2,000 to 8,000 metabolites in a single analysis.
There are few tools available to process and analyze comprehensive two-dimensional chromatography data, presenting a major bottleneck to its adoption in metabolomics. Here, I will present my past research where I developed new data tools to processdata from GC×GC-MS based metabolomics studies. Then I will discuss my application of GC×GC-MS in plant and clinical metabolomics, including where I developed methods to assess the stability of fecal microbiota transplant formulations. Finally, I will share preliminary results from my recently founded (July 2025) independent research group at UVic where we are developing enabling data tools for LC×LC-MS based metabolomics.