Statistical agglomeration: peak summarization for direct infusion lipidomics

Comprehensive and High-Coverage Lipidomic Analysis of Oilseeds Based on Ultrahigh-Performance Liquid Chromatography Coupled with Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry

Oilseeds are an important source of dietary lipids, and a comprehensive analysis of oilseed lipids is of great significance to human health, while information about the global lipidomes in oilseeds was limited. Herein, an ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry method for comprehensive lipidomic profiling of oilseeds was established and applied. First, the lipid extraction efficiency and lipid coverage of four different lipid extraction methods were compared. The optimized methyl tert-butyl ether extraction method was superior to isopropanol, Bligh-Dyer, and Folch extraction methods, in terms of the operation simplicity, lipid coverage, and number of identified lipids. Then, global lipidomic analysis of soybean, sesame, peanut, and rapeseed was conducted. A total of 764 lipid molecules, including 260 triacylglycerols, 54 diacylglycerols, 313 glycerophospholipids, 36 saccharolipids, 35 ceramides, 30 free fatty acids, 21 fatty esters, and 15 sphingomyelins were identified and quantified. The compositions and contents of lipids significantly varied among different oilseeds. Our results provided a theoretical basis for the selection and breeding of varieties of oilseed as well as deep processing of oilseed for the edible oil industry.

One- vs two-phase extraction: re-evaluation of sample preparation procedures for untargeted lipidomics in plasma samples

Lipidomics is a rapidly developing field in modern biomedical research. While LC-MS systems are able to detect most of the known lipid classes in a biological matrix, there is no single technique able to extract all of them simultaneously. In comparison with two-phase extractions, one-phase extraction systems are of particular interest, since they decrease the complexity of the experimental procedure. By using an untargeted lipidomics approach, we explored the differences/similarities between the most commonly used two-phase extraction systems (Folch, Bligh and Dyer, and MTBE) and one of the more recently introduced one-phase extraction systems for lipid analysis based on the MMC solvent mixture (MeOH/MTBE/CHCl₃). The four extraction methods were evaluated and thoroughly compared against a pooled extract that qualitatively and quantitatively represents the average of the combined extractions. Our results show that the lipid profile obtained with the MMC system displayed the highest similarity to the pooled extract, indicating that it was most representative of the lipidome in the original sample. Furthermore, it showed better extraction efficiencies for moderate and highly apolar lipid species in comparison with the Folch, Bligh and Dyer, and MTBE extraction systems. Finally, the technical simplicity of the MMC procedure makes this solvent system highly suitable for automated, untargeted lipidomics analysis.

Proteomics, lipidomics, metabolomics: a mass spectrometry tutorial from a computer scientist's point of view

Background

For decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development. Progress towards solving any of these disparate problems depends upon overcoming the common challenge of interpreting the large data sets generated. Despite interim successes, many data interpretation problems in mass spectrometry are still challenging. Further, though these challenges are inherently interdisciplinary in nature, the significant domain-specific knowledge gap between disciplines makes interdisciplinary contributions difficult.

Results

This paper provides an introduction to the burgeoning field of computational mass spectrometry. We illustrate key concepts, vocabulary, and open problems in MS-omics, as well as provide invaluable resources such as open data sets and key search terms and references.

Conclusions

This paper will facilitate contributions from mathematicians, computer scientists, and statisticians to MS-omics that will fundamentally improve results over existing approaches and inform novel algorithmic solutions to open problems.