Microextraction and Microsampling Strategies in Lipidomics: Current Trends and Opportunities

Lipidomics has emerged as an important approach for biomarker discovery, elucidation of molecular mechanisms, and translational research. However, conventional lipidomics workflow which combines lab-based biospecimen collection and liquid-liquid extraction remains solvent-intensive, laborious, and poorly suited to patient-centric workflows. This review surveys emerging microextraction and microsampling strategies that address these limitations and open new opportunities for lipid analysis across clinical, preclinical and biomedical studies. First, we highlight solid-phase microextraction (SPME), including in vivo SPME, as a solvent-efficient technique that merges sampling and analyte extraction, minimizes contamination, and preserves labile lipid species while opening up new opportunities for direct sampling of flowing blood or tissue without a need to collect a defined biospecimen. Next, SPME is compared to low-volume, minimally invasive sampling using dried blood/plasma spots (DBS/DPS), volumetric absorptive microsampling (VAMS), and push-button/vacuum-assisted devices which facilitate decentralized self-collection, longitudinal designs, improved logistics, and broader population reach. SPME and microsampling have been successfully applied in both targeted and untargeted lipidomics assays spanning applications from exercise physiology and pediatric cohorts to toxicological and pharmacological monitoring, where traditional blood collection is logistically challenging or ethically constrained. Finally, key implementation challenges common to microextraction and microsampling are critically evaluated, including hematocrit effects, device-dependent recovery, analyte stability, and the development of fit-for-purpose quality-control strategies. In conclusion, novel microextraction and microsampling approaches facilitate longitudinal metabolic phenotyping with minimal invasiveness, enabling dynamic studies of lipid metabolism in real-world settings. They can make lipidomics more scalable, sustainable, and physiologically accurate, provided that strong analytical and clinical validation are in place.