Design of experiments-driven optimization of dual ultra-high-performance liquid chromatography high resolution mass spectrometry for integrated fecal metabolomics and lipidomics

Recently, feces has gained increased attention in metabolomics and lipidomics research due to its ability to reflect complex diet-host-microbiome interactions. Traditionally, these analyses rely on separate workflows, resulting in longer analysis times and increased instrument load. To address these limitations, we present a dual ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (dual UHPLC-HRMS) approach. As a first step, two previously validated single UHPLC-HRMS methods for metabolomics and lipidomics, each demonstrating robust chromatographic separation of compounds, covering a broad physicochemical range (LogP -5.30 to 21.90), were selected. To integrate both workflows into the dual platform, thirteen critical LC-MS parameters were systematically optimized using a Design of Experiments (DoE). The parameters encompassed ion generation, transmission and detection, injection-related factors, unified column oven conditions, and source geometry. This approach enabled a robust dual workflow, achieving a 21% reduction in analysis time. Targeted evaluation demonstrated consistent detection of 287 metabolites (260 with CV<20%) and 162 lipids (144 with CV<20%), thereby outperforming the single methods, which detected 272 metabolites (232 with CV<20%) and 145 lipids (116 with CV<20%), respectively. Untargeted analysis further demonstrated increased coverage, with an additional 1652 metabolite and 3966 lipid features detected with the dual method without compromising repeatability of the feature signal intensities (79.4% vs. 81.2% for metabolomics and 87.4% vs. 82.7% for lipidomics, with CV<30%). Our novel dual UHPLC-HRMS workflow enhances analytical throughput, while also improving fecal metabolome and lipidome coverage and repeatability, offering a robust and cost-effective solution for large-scale studies.