Rapid and reproducible LC-HRMS for combined lipidomics and metabolomics from minimal serum volumes: application to age-related macular degeneration

The increasing clinical relevance of lipidomics and metabolomics demands analytical methods that are not only sensitive and comprehensive but also efficient, reproducible, operationally simple, and compatible with microscale workflows to minimize sample consumption and enhance accessibility. We present an integrated LC-HRMS-based workflow for the untargeted profiling of lipids and (semi)polar metabolites from minimal serum volumes, optimized for high-throughput clinical applications. The method requires only 10 μL of serum and employs a simplified methanol/ methyl tert-butyl ether (1:1, v /v) extraction protocol, reducing operator variability and enabling simultaneous lipid-metabolite coverage. Ready-to-use internal standards and a semi-automated data processing script streamline the workflow, eliminating the need for programming expertise. The approach enabled confident identification of over 440 lipid species across 23 classes and broad metabolite coverage (<1000 metabolite features) in both ionization modes. Internal standard normalization improved analytical precision, achieving relative standard deviations of 6 % (positive mode) and 5 % (negative mode), while enhancing quality control clustering and reducing inter-sample variability. Applied to a clinical cohort of patients with age-related macular degeneration (AMD), the method detected consistent lipid and metabolite alterations, including a 34-fold increase in a highly unsaturated triglyceride (TG 22:6_22:6_22:6), and significant reductions in fatty acids, complex glycans, and sphingomyelins (fold changes 0.6–0.8). This workflow offers a clinically relevant and scalable platform for serum profiling and can be easily adapted to other biomatrices and diseases with broad applicability in biomarker discovery, nutritional studies, and precision medicine. • LC-HRMS workflow enables integrated lipidomics and metabolomics from serum. • Uses only 10 μL of serum and a simplified methanol:MTBE extraction. • Internal standard normalization improves reproducibility (5–6 %) and QC clustering. • Identifies over 440 lipid species across 23 classes with broad metabolite coverage. • Reveals lipid remodeling and metabolic dysregulation in serum of AMD patients.