Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples.

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.

Photo-Oxidative Protection of Chlorophyll a in C-Phycocyanin Aqueous Medium.

In this study, potential protection of chlorophyll a from illumination and oxidation-induced decomposition has been examined using C-phycocyanin (C-PC) aqueous medium. Photo-oxidation resistance of chlorophyll a was monitored in various aqueous media using ultraviolet-visible spectroscopy and direct-infusion atmospheric pressure chemical ionization mass spectrometry analysis. The spectroscopy results showed that chlorophyll a in C-PC medium experienced the lowest rate of conversion to its derivatives; thus, it was demonstrated that chlorophyll a was mostly intact in the C-PC medium. Furthermore, the C-PC treated with chlorophyll a showed the lowest concentrations of malondialdehyde, and chlorophyll a in C-PC medium did not cause serious damage to human liver cells in vitro after intensive illumination. Therefore, we propose a new method of protecting chlorophyll a from photodegradation and oxidation using C-PC aqueous medium.