Mobile Phase Contaminants Affect Neutral Lipid Analysis in LC-MS-Based Lipidomics Studies.

Lipidomics is a well-established field, enabled by modern liquid chromatography mass spectrometry (LC-MS) technology, rapidly generating large amounts of data. Lipid extracts derived from biological samples are complex, and most spectral features in LC-MS lipidomics data sets remain unidentified. In-depth analyses of commercial triacylglycerol, diacylglycerol, and cholesterol ester standards revealed the expected ammoniated and sodiated ions as well as five additional unidentified higher mass peaks with relatively high intensities.

Trimethylation Enhancement Using Diazomethane (TrEnDi) Enables Enhanced Detection of Glufosinate and 3-(Methylphosphinico)propionic Acid from Complex Canola Samples.

Over the past century, agriculture practices have transitioned from manual cultivation to the use of an array of chemical herbicides for weed control including phosphinothricin, or glufosinate (GLUF). Consequently, the potential for long-term residual GLUF exposure in the food chain has increased, highlighting the need for improved analytical strategies for its detection, as well as the detection of its main breakdown product 3-(methylphosphinico)propionic acid (MPPA). Chemical derivatization strategies have been developed to improve the detection of GLUF and MPPA via liquid chromatography tandem mass spectrometry analyses.

An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry C-Trimethylation Enhancement Using Diazomethane (C-TrEnDi)-Modified Lipid Data.

C-Trimethylation enhancement using diazomethane (C-TrEnDi) is a chemical derivatization technique that uses C-labeled diazomethane to increase mass spectrometry (MS) signal intensities for phosphatidylcholine (PC) and phosphatidylethanolamine (PE) lipid classes, both of which are of major interest in biochemistry. In silico mass spectrometry databases have become mainstays in lipidomics experiments; however, C-TrEnDi-modified PC and PE species have altered / and fragmentation patterns from their native counterparts. To build a database of C-TrEnDi-modified PC and PE species, a lipid extract from nutritional yeast was derivatized and fragmentation spectra of modified PC and PE species were mined using diagnostic fragmentation filtering by searching C-TrEnDi-modified headgroups with / 199 (PC) and 202 (PE).

Improved Chromatography and MS-Based Detection of Glyphosate and Aminomethylphosphonic Acid Using iTrEnDi.

Glyphosate (GLY), a synthetic, nonselective systemic herbicide that is particularly effective against perennial weeds, is the most used weedkiller in the world. There are growing concerns over GLY accumulation in the environment and the attendant human health-associated risks, and despite increased attention in the media, GLY and its breakdown product aminomethylphosphonic acid (AMPA) remain elusive to many analytical strategies. Chemical derivatization coupled with high-performance liquid chromatography-mass spectrometry (HPLC-MS) addresses the challenge of quantifying low levels of GLY and AMPA in complex samples.

iTrEnDi: In Situ Trimethylation Enhancement Using Diazomethane: Improved and Expanded Glycerophospholipid and Sphingolipid Analyses via a Microscale Autonomous Derivatization Platform.

Trimethylation enhancement using diazomethane (TrEnDi) is a derivatization technique that significantly enhances the signal intensity of glycerophospholipid species in mass spectrometry (MS) and tandem mass spectrometry (MS/MS) analyses. Here, we describe a novel apparatus that is able to conduct in situ TrEnDi (iTrEnDi) by generating and immediately reacting small amounts of gaseous diazoalkane with analyte molecules. iTrEnDi allows complete and rapid methylation of phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidic acid (PA), and sphingomyelin (SM) in a safe manner by removing any need for direct handling of dangerous diazoalkane solutions.

Trimethylation Enhancement Using (13)C-Diazomethane ((13)C-TrEnDi): Increased Sensitivity and Selectivity of Phosphatidylethanolamine, Phosphatidylcholine, and Phosphatidylserine Lipids Derived from Complex Biological Samples.

Significant sensitivity enhancements in the tandem mass spectrometry-based analysis of complex mixtures of several phospholipid classes has been achieved via (13)C-TrEnDi. (13)C-TrEnDi-modified phosphatidylethanolamine (PE), phosphatidylserine (PS), and phosphatidylcholine (PC) lipids extracted from HeLa cells demonstrated greater sensitivity via precursor ion scans (PISs) than their unmodified counterparts. Sphingomyelin (SM) species exhibited neither an increased nor decreased sensitivity following modification.

Trimethylation enhancement using diazomethane (TrEnDi) II: rapid in-solution concomitant quaternization of glycerophospholipid amino groups and methylation of phosphate groups via reaction with diazomethane significantly enhances sensitivity in mass spectrometry analyses via a fixed, permanent positive charge.

A novel mass spectrometry (MS)-based lipidomics strategy that exposes glycerophospholipids to an ethereal solution of diazomethane and acid, derivatizing them to contain a net fixed, permanent positive charge, is described. The sensitivity of modified lipids to MS detection is enhanced via improved ionization characteristics as well as consolidation of ion dissociation to form one or two strong, characteristic polar headgroup fragments. Our strategy has been optimized to enable a priori prediction of ion fragmentation patterns for four subclasses of modified glycerophospholipid species.

Trimethylation enhancement using diazomethane (TrEnDi): rapid on-column quaternization of peptide amino groups via reaction with diazomethane significantly enhances sensitivity in mass spectrometry analyses via a fixed, permanent positive charge.

Defining cellular processes relies heavily on elucidating the temporal dynamics of proteins. To this end, mass spectrometry (MS) is an extremely valuable tool; different MS-based quantitative proteomics strategies have emerged to map protein dynamics over the course of stimuli. Herein, we disclose our novel MS-based quantitative proteomics strategy with unique analytical characteristics.