Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

Lipidomics coverage improvement is essential for functional lipid and pathway construction.A powerful approach to discovering organism lipidome is to combine various data acquisitions,such as full scan mass spectrometry(full MS),data-dependent acquisition(DDA),and data-independent acquisition(DIA).Caenorhabditis elegans(C.elegans)is a useful model for discovering toxic-induced metabolism,highthroughput drug screening,and a variety of human disease pathways.To determine the lipidome of C.elegans and investigate lipid disruption from the molecular level to the system biology level,we used integrative data acquisition.The methyl-tert-butyl ether method was used to extract L4 stage C.elegans after exposure to triclosan(TCS),perfluorooctanoic acid,and nanopolystyrene(nPS).Full MS,DDA,and DIA integrations were performed to comprehensively profile the C.elegans lipidome by Q-Exactive Plus MS.All annotated lipids were then analyzed using lipid ontology and pathway analysis.We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways.The biological investigations revealed that when C.elegans were exposed to nPS,lipid droplets were disrupted,whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group.The nPS treatment caused a significant disruption in lipid storage.Triacylglycerol,glycerophospholipid,and ether class lipids were those primarily hindered by toxicants.Finally,toxicant exposure frequently involved numerous lipid-related pathways,including the phosphoinositide 3-kinase/protein kinase B pathway.In conclusion,an integrative data acquisition strategy was used to characterize the C.elegans lipidome,providing valuable biological insights into hypothesis generation and validation.

Advances in Liquid Chromatography-Mass Spectrometry-Based Lipidomics: A Look Ahead

Lipidomics is a subfield of metabolic phenotyping that focuses on high-throughput profiling and quantification of lipids.Essential roles of lipidomics in translational and clinical research have emerged,especially over the past decade.Most lipidomic pipelines have been developed using mass spectrometry(MS)-based methods.Because of the complexity of the data,generally,computational demands are much higher in untargeted lipidomic studies.In the current paper,we primarily discussed the recent advances in untargeted liquid chromatography-mass spectrometry-based lipidomics,covering various facets from analytical strategies to functional interpretations.The current practice of tandem MS-based lipid annotation in untargeted lipidomics studies was demonstrated.Notably,we highlighted the essential characteristics of machine learning models,together with a data partitioning strategy,to facilitate appropriate modeling and validation in metabolic phenotyping studies.Critical aspects of data sharing were briefly mentioned.Finally,certain recommendations were suggested toward more standardized and sustainable lipidomics analysis strategies as independent platforms,and as members of the omics family.