Real-Time Library Searching Enhances Structural Characterization of Glycerophospholipids and Sphingomyelins, Reveals Study in Analytical Chemistry

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A new study reveals that real-time library searching significantly improves the structural characterization of glycerophospholipids and sphingomyelins, offering enhanced selectivity and comprehensive analysis. By utilizing real-time identifications to trigger targeted MSn analyses, researchers gain deeper insights into these crucial lipid species.

Advancements in mass spectrometry (MS)-based lipidomics have revolutionized the identification and characterization of complex lipid mixtures. However, the level of structural detail obtained from these analyses greatly depends on the analytical techniques employed. In a recent study published in Analytical Chemistry, researchers from The Morgridge Institute for Research and the University of Wisconsin-Madison introduced a novel approach called real-time library searching (RTLS) to enhance the structural characterization of glycerophospholipids and sphingomyelins (1). By utilizing the real-time identifications of lipid classes, the RTLS method triggers targeted MSn analyses, enabling improved selectivity and a more comprehensive understanding of these important lipid species.

The RTLS method is used to enhance targeted MSn analyses in lipidomics research. The process involves the identification of lipid classes in real time, which then triggers class-specific MSn analyses for a more detailed structural elucidation. This approach offers improved selectivity compared to traditional methods that rely on characteristic ions or neutral losses. With the RTLS method, researchers can achieve comprehensive analysis and gain deeper insights into the structural composition of complex lipid mixtures. This advancement in lipidomics research enables a more accurate and detailed understanding of lipid species and their roles in biological processes.

Traditionally, collisionally activated data-dependent acquisition experiments provided lipid identifications at the species or molecular species level. While valuable, the structural resolution of these identifications could be further enhanced by integrating both positive and negative mode analyses, which required separate runs or polarity switching. The introduction of real-time identifications on orbital trap mass spectrometry platforms opened new possibilities. Leveraging this functionality, the RTLS approach allows for on-the-fly identification of eluting molecular species, which then triggers class-targeted MSn analyses. This targeted approach significantly improves the structural characterization of phosphotidylcholines, phosphotidylethanolamines, phosphotidylinositols, phosphotidylglycerols, phosphotidylserine, and sphingomyelins in the positive ion mode.


The class-based RTLS method demonstrates superior selectivity compared to the conventional methodology of triggering MSn based on characteristic ions or neutral losses. By incorporating real-time identifications and class-specific MSn, researchers achieved enhanced structural granularity of the analyzed lipid species. This advancement has the potential to unravel previously hidden details of glycerophospholipids and sphingomyelins, shedding light on their functional roles in various biological processes.

The implications of this research are significant, as it paves the way for more accurate and comprehensive lipidomics studies. By combining the power of real-time library searching with targeted MSn analyses, scientists can delve deeper into the structural characteristics of these important lipid classes. The improved selectivity offered by the RTLS method enables researchers to uncover novel insights into lipid function and their involvement in diseases, ultimately advancing our understanding of complex biological systems.


(1) Brademan, D. R.; Overmyer, K. A.; He, Y.; Barshop, W. D.; Canterbury, J. D.; Bills, B. J.; Anderson, B. J.; Hutchins, P. D.; Sharma, S.; Zabrouskov, V.; McAlister, G. C.; Coon, J. J. Improved Structural Characterization of Glycerophospholipids and Sphingomyelins with Real-Time Library Searching. Anal. Chem. 2023, 95 (20), 7813–7821. DOI: