Thiol radical-based chemical isotope labelling for sterols quantitation through high performance liquid chromatography-tandem mass spectrometry analysis

Instrument-type effects on chemical isotope labeling LC-MS metabolome analysis: Quadrupole time-of-flight MS vs. Orbitrap MS

Chemical isotope labeling (CIL) LC-MS is a powerful tool for metabolome analysis with markedly improved metabolomic coverage and quantification accuracy over the conventional LC-MS technique. In addition, with differential isotope labeling, each labeled metabolite is detected as a peak pair in the mass spectra, offering the possibility of differentiating true metabolite peaks from the singlet noise or background peaks. In this study, we examined the effects of instrument type on the detectability of true metabolites with a focus on the comparison of quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers. Using the same ultra-high-performance liquid chromatography setup and optimized running conditions for QTOF and Orbitrap, we compared the total number of peak pairs detected and identified from the two instruments using human urine and serum as the test samples. Many common peak pairs were detected from the two instruments; however, there were a significant number of unique peak pairs detected in each type of instrument. By combining the datasets obtained using QTOF and Orbitrap, the total number of peak pairs detected could be significantly increased. We also examined the effect of mass resolving power on peak pair detection in Orbitrap (60,000 vs. 120,000 resolution). The observed differences in peak pair detectability were much less than those of QTOF vs. Orbitrap. However, the type of peak pairs detected using different resolutions could be somewhat different, offering the possibility of increasing the overall number of peak pairs by combining the two datasets obtained at two different resolutions. The results from this study clearly indicate that instrument type can have a profound effect on metabolite detection in CIL LC-MS. Therefore, comparison of metabolome data generated using different instruments needs to be carefully done. Moreover, future research (e.g., hardware modifications) is warranted to minimize the differences in order to generate more reproducible metabolome data from different types of instruments.

Click Isotope Mass Probe for Highly Selective Determination of Trace Steroid Hormones in Food Samples

Matrix effects are a great challenge for the quantitative analysis of complex food samples by liquid chromatography-tandem mass spectrometry analysis (LC-MS/MS). Stable isotope labeling (SIL) has been widely used as an effective strategy to eliminate matrix effects. Herein, a copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) click-reaction-based SIL method was proposed for a highly sensitive and selective determination of six synthetic steroid hormones in three different food samples (milk, yogurt, and eggs) by high-performance liquid chromatography (HPLC)-MS/MS. A pair of novel SIL agents, N-(2-azidyl ethyl) aniline (d₀-AEA) and d₅-N-(2-azidyl ethyl) aniline (d₅-AEA) were synthesized to label steroid hormones in the samples and standard solution, respectively. The reaction accomplishes in 30 min at 60 °C. The heavy labeled standards were used as internal standards (ISs), which experience the identical ionization processes with light labeled samples to minimize matrix effects. After derivatization, the ionization efficiencies of steroid hormones were greatly improved by 2-54-folds, and the matrix effects ranged from 88.6 to 99.8%. The established method achieved satisfactory detection limits (0.1-2.5 μg L^-1) and high recoveries (85-102%). These results demonstrated that the proposed method holds unique advantages for trace steroid hormones analysis in foodstuffs.

Production, Characterization, Delivery, and Cholesterol-Lowering Mechanism of Phytosterols: A Review

Phytosterols are natural plant-based bioactive compounds that can lower blood cholesterol levels and help prevent cardiovascular diseases. Consequently, they are being utilized in functional foods, supplements, and pharmaceutical products designed to improve human health. This paper summarizes different approaches to isolate, purify, and characterize phytosterols. It also discusses the hypolipidemic mechanisms of phytosterols and their impact on cholesterol transportation. Phytosterols have a low water-solubility, poor chemical stability, and limited bioavailability, which limits their utilization and efficacy in functional foods. Strategies are therefore being developed to overcome these shortcomings. Colloidal delivery systems, such as emulsions, oleogels, liposomes, and nanoparticles, have been shown to be effective at improving the water-dispersibility, stability, and bioavailability of phytosterols. These delivery systems can be used to incorporate phytosterols into a broader range of cholesterol-lowering functional foods and beverages. We also discuses several issues that need to be addressed before these phytosterol delivery systems can find widespread commercial utilization.

High tolerance to instrument drifts by differential chemical isotope labeling LC-MS: A case study of the effect of LC leak in long-term sample runs on quantitative metabolome analysis

Metabolomics study of a biological system often involves the analysis of many comparative samples over a period of several days or weeks. This process of long-term sample runs can encounter unexpected instrument drifts such as small leaks in liquid chromatography-mass spectrometry (LC-MS), degradation of column performance, and MS signal intensity change. A robust analytical method should ideally tolerate these instrumental drifts as much as possible. In this work, we report a case study to demonstrate the high tolerance of differential chemical isotope labeling (CIL) LC-MS method for quantitative metabolome analysis. In a study of using a rat model to examine the metabolome changes during rheumatoid arthritis (RA) disease development and treatment, over 468 samples were analyzed over a period of 15 days in three batches. During the sample runs, a small leak in LC was discovered after a batch of analyses was completed. Reanalysis of these samples was not an option as sample amounts were limited. To overcome the problem caused by the small leak, we applied a method of retention time correction to the LC-MS data to align peak pairs from different runs with different degrees of leak, followed by peak ratio calculation and analysis. Herein, we illustrate that using 12 C-/13 C-peak pair intensity values in CIL LC-MS as a measurement of concentration changes in different samples could tolerate the signal drifts, while using the absolute intensity values (ie, 12 C-peak as in conventional LC-MS) was not as reliable. We hope that the case study illustrated and the method of overcoming the small-leak-caused signal drifts can be helpful to others who may encounter this kind of situation in long-term sample runs.