Simultaneous 3-Nitrophenylhydrazine Derivatization Strategy of Carbonyl, Carboxyl and Phosphoryl Submetabolome for LC-MS/MS-Based Targeted Metabolomics with Improved Sensitivity and Coverage

Abstract

Metabolomics is a powerful and essential technology for profiling metabolic phenotypes and exploring metabolic reprogramming, which enables the identification of biomarkers and provides mechanistic insights into physiology and disease. However, its applications are still limited by the technical challenges particularly in its detection sensitivity for the analysis of biological samples with limited amount, necessitating the development of highly sensitive approaches. Here, we developed a h ighly s ensitive liquid chromatography tandem mass spectrometry method based on a 3-nitrophenylhydrazine (3-NPH) d erivatization strategy that simultaneously targets c arbonyl, c arboxyl, and p hosphoryl groups for t argeted m etabolomic analysis (HSDccp-TM) in biological samples. By testing 130 endogenous metabolites including organic acids, amino acids, carbohydrates, nucleotides, carnitines, and vitamins, we showed that the derivatization strategy resulted in significantly improved detection sensitivity and chromatographic separation capability. Metabolic profiling of merely 60 oocytes and 5000 hematopoietic stem cells primarily isolated from mice demonstrated that this method enabled routine metabolomic analysis in trace amounts of biospecimens. Moreover, the derivatization strategy bypassed the tediousness of inferring the MS fragmentation patterns and simplified the complexity of monitoring ion pairs of metabolites, which greatly facilitated the metabolic flux analysis (MFA) for glycolysis, the tricarboxylic acid (TCA) cycle, and pentose phosphate pathway (PPP) in cell cultures. In summary, the novel 3-NPH derivatization-based method with high sensitivity, good chromatographic separation, and broad coverage showed great potential in promoting metabolomics and MFA, especially in trace amounts of biospecimens.

Precise Metabolomics Reveals a Diversity of Aging‐Associated Metabolic Features

“…Chemical derivatization approaches are frequently used to improve the ionization efficiency and sensitivity of MS detection, in addition to increasing the coverage of metabolites. [13,34,35] Metabolic reaction network (MRN)-based recursive algorithm (MetDNA) substantially expands metabolite annotation and facilitates high-coverage metabolomics. [24,36] In this study, a cocktail of 64 ISs were introduced into routine metabolomics protocols, which not only achieves accurate quantitation but also substantially assists in increasing metabolite coverage and improving analytical precision based on our primary rules such as minimal COVs in IS-metabolite pair matching.…”

Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer

“…Its coupling with chromatographic separation, such as gas chromatography and liquid chromatography (LC), has been widely used for metabolomic analysis by providing both molecular weight and structural information ( Liu et al, 2013 ; Alonso et al, 2015 ; Nash and Dunn, 2019 ; Hou et al, 2020 ; Harrieder et al, 2022 ). Efforts have been done in method development for sample preparation, chromatographic separation and derivatization-based detection to improve the metabolome coverage ( Yuan et al, 2018 ; An et al, 2021 ; Meng et al, 2021 ). Besides, computational approaches are also dramatically developed to assist data interpretation and metabolite global annotation ( Bonini et al, 2020 ; Chen et al, 2021 ; Duehrkop et al, 2021 ).…”

Quantification of carbohydrates in human serum using gas chromatography–mass spectrometry with the stable isotope-labeled internal standard method

“…Furthermore, the carbohydrate studies on biological samples have mostly focused on the semi-quantitative analysis of untargeted metabolomics. [9][10][11][12][13] Carbohydrates are commonly used as biomarkers to screen early-stage patients, which has led to current research on single carbohydrates that can be used to test for metabolic illnesses. [10][11][12][13] The comprehensive and systematic quantitative analysis of carbohydrate profiles in serum has been given less attention.…”