A High-Throughput Integrated Nontargeted Metabolomics and Lipidomics Workflow Using Microelution Enhanced Matrix...

Sample pretreatment for mass spectrometry (MS)-based metabolomics and lipidomics is normally conducted independently with two sample aliquots and separate matrix cleanup procedures, making the two-step process sample-intensive and time-consuming. Herein, we introduce a high-throughput pretreatment workflow for integrated nontargeted metabolomics and lipidomics leveraging the enhanced matrix removal (EMR)-lipid microelution 96-well plates. The EMR-lipid technique was innovatively employed to effectively separate and isolate non-lipid small metabolites and lipids in sequence using significantly reduced sample amounts and organic solvents. Our proposed methodology enables parallel profiling of metabolome and lipidome within a single sample aliquot using ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Following method development and optimization with representative metabolites at levels comparable to those detected in human blood, the optimized workflow was applied to prepare metabolome-lipidome from maternal and umbilical cord-blood sera prior to comprehensive profiling using three different UHPLC columns. Results indicate that, compared with conventional two-step metabolomics-lipidomics sample pretreatment workflow, this new approach substantially reduces sample amount and processing time, while still preserving metabolite profiles and revealing additional MS features. Over 2500 metabolites were annotated in human sera with >1000 shared across maternal and cord blood. The shared metabolites are closely linked to various physiological functions, including nutrient transfer, hormonal regulation, waste product clearance, and metabolic programming, underscoring the significant impact of maternal metabolic activities on neonatal metabolic health. In summary, the proposed workflow enables efficient sample pretreatment for nontargeted metabolomics-lipidomics using one single sample while achieving broad metabolite coverage, highlighting its remarkable applicability in clinical and preclinical research.