Accurate quantification of endogenous N-acylethanolamides by chemical isotope labeling coupled with liquid chromatography-tandem mass spectrometry

An integrated strategy for in-depth profiling of N-acylethanolamines in biological samples by UHPLC-HRMS

BACKGROUND

N-acylethanolamines (NAEs) are a class of naturally occurring bioactive lipids that play crucial roles in various physiological processes, particularly exhibiting neuroprotective and anti-inflammatory properties. However, the comprehensive profiling of endogenous NAEs in complex biological matrices is challenging due to their low abundance, structural similarity and the limited availability of commercial standards. Here, we propose an integrated strategy for comprehensive profiling of NAEs that combines chemical derivatization and a three-dimensional (3D) prediction model based on quantitative structure-retention time relationship (QSRR) using liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS).

RESULTS

After acetyl chloride (ACC) derivatization, the detection sensitivity of NAEs was significantly improved. We developed a QSRR prediction model to construct an in-house database for 141 NAEs, encompassing information on RT, MS1 (m/z), and MS/MS spectra. Propargylamine-labeled fatty acids were synthesized as RT calibrants across various analytical conditions to enhance the robustness of the RT prediction model. NAEs in biological samples were then in-depth profiled using parallel reaction monitoring (PRM) acquisition. This integrated strategy identified and annotated a total of 50 NAEs across serum, hippocampus and cortex tissues from a 5xFAD mouse model of Alzheimer's disease (AD). Notably, the levels of polyunsaturated NAEs, particularly NAE 20:5 and NAE 22:6, were significantly decreased in 5xFAD mice compared to WT mice, as confirmed by accurate quantitation using ACC-d0/d3 derivatization.

SIGNIFICANCE

Our integrated strategy exhibits great potential for the in-depth profiling of NAEs in complex biological samples, facilitating the elucidation of NAE functions in diverse physiological and pathological processes.

Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model

Mekong schistosomiasis is a parasitic disease caused by blood flukes in the Lao People’s Democratic Republic and in Cambodia. The standard method for diagnosis of schistosomiasis is detection of parasite eggs from patient samples. However, this method is not sufficient to detect asymptomatic patients, low egg numbers, or early infection. Therefore, diagnostic methods with higher sensitivity at the early stage of the disease are needed to fill this gap. The aim of this study was to identify potential biomarkers of early schistosomiasis using an untargeted metabolomics approach. Serum of uninfected and S. mekongi-infected mice was collected at 2, 4, and 8 weeks post-infection. Samples were extracted for metabolites and analyzed with a liquid chromatography-tandem mass spectrometer. Metabolites were annotated with the MS-DIAL platform and analyzed with Metaboanalyst bioinformatic tools. Multivariate analysis distinguished between metabolites from the different experimental conditions. Biomarker screening was performed using three methods: correlation coefficient analysis; feature important detection with a random forest algorithm; and receiver operating characteristic (ROC) curve analysis. Three compounds were identified as potential biomarkers at the early stage of the disease: heptadecanoyl ethanolamide; picrotin; and theophylline. The levels of these three compounds changed significantly during early-stage infection, and therefore these molecules may be promising schistosomiasis markers. These findings may help to improve early diagnosis of schistosomiasis, thus reducing the burden on patients and limiting spread of the disease in endemic areas.