Streamlined Arsenolipid Identification via Direct Arsenic Detection Using RPLC-ESI-QTOF-MS with Collision-Induced Dissociation.
Xiao-Lei LiuPublished in: Journal of the American Society for Mass Spectrometry (2023)
Arsenolipids are organoarsenicals with a long aliphatic chain that have been identified in a wide array of marine organisms. Precise analysis of arsenolipids is crucial for evaluating their toxicity, ensuring food safety, monitoring the environment, and gaining insights into the evolution of arsenic biogeochemistry. However, the discovery of new arsenolipids is often impeded by existing analytical challenges, notably the need for multiple instruments, such as the combination of liquid chromatography electrospray ionization mass spectrometry (LC-ESI-MS) and inductively coupled plasma mass spectrometry (LC-ICP-MS). This study introduces a high-throughput untargeted analytical method on the basis of an unsophisticated instrumental configuration, LC-ESI-MS with collision-induced dissociation (CID) at 200 eV. This approach provides efficient dissociation of arsenic atoms from their precursor lipids and direct detection of the organic-bound arsenic as monatomic cations, As + . Application of this method has shown promise in rapidly characterizing arsenolipids in diverse samples, which has led to the discovery of a wide range of novel arsenolipids, including seven previously unidentified thioxoarsenolipids in ancient marine sediments.
Keyphrases
- mass spectrometry
- liquid chromatography
- ms ms
- high throughput
- high resolution mass spectrometry
- heavy metals
- drinking water
- tandem mass spectrometry
- simultaneous determination
- high performance liquid chromatography
- capillary electrophoresis
- high resolution
- gas chromatography
- solid phase extraction
- high glucose
- small molecule
- diabetic rats
- loop mediated isothermal amplification
- label free
- single cell
- oxidative stress
- real time pcr
- multiple sclerosis
- electron transfer
- drug induced
- big data
- human health
- gram negative
- polycyclic aromatic hydrocarbons
- machine learning
- transition metal
- ionic liquid