Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Study on the effect of calibration standards prepared with different matrix on the accuracy of bile acid quantification using LC-MS/MS

The transition from relative to absolute quantification of metabolites is the future development trend of mass spectrometry-based metabolomics research, which could fundamentally solve the problem of comparability of data between different laboratories. However, absolute quantification of endogenous molecules is largely hampered by the lack of analyte-free matrix, leading to uncertainty and inconsistency in the preparation of calibration standards. Bile acids (BAs) are an important class of biomarkers that play a key role in disease progression. In this paper, the quantitative accuracy of calibration curves prepared in neat solvent (NSCCs), charcoal stripped matrix (SMCCs) and authentic matrix (AMCCs) were validated using quality control samples (QCs) prepared in authentic matrix. Results suggested that AMCCs could largely minimize the confidence interval (C.I.) and the deviation in accuracy compared with NSCCs and SMCCs when measured concentration is higher than 20% of the background level. In addition, experimental data demonstrated that two-step calibration strategy proposed here is a promising and reliable alternative strategy to quantify endogenous BAs in biological sample.

Wide-scope targeted analysis of bioactive lipids in human plasma by LC/MS/MS

Quantitative information on blood metabolites can be used in developing advanced medical strategies such as early detection and prevention of disease. Monitoring bioactive lipids such as steroids, bile acids, and PUFA metabolites could be a valuable indicator of health status. However, a method for simultaneously measuring these bioactive lipids has not yet been developed. Here, we report a LC/MS/MS method that can simultaneously measure 144 bioactive lipids, including steroids, bile acids, and PUFA metabolites, from human plasma, and a sample preparation method for these targets. Protein removal by methanol precipitation and purification of bioactive lipids by solid-phase extraction improved the recovery of the targeted compounds in human plasma samples, demonstrating the importance of sample preparation methods for a wide range of bioactive lipid analyses. Using the developed method, we studied the plasma from healthy human volunteers and confirmed the presence of bioactive lipid molecules associated with sex differences and circadian rhythms. The developed method of bioactive lipid analysis can be applied to health monitoring and disease biomarker discovery in precision medicine.

Development and validation of derivatization-based LC-MS/MS method for quantification of short-chain fatty acids in human, rat, and mouse plasma

Short-chain fatty acids (SCFAs), the end products of gut microbial fermentation of dietary fibers and non-digestible polysaccharides, act as a link between the microbiome, immune system, and inflammatory processes. The importance of accurately quantifying SCFAs in plasma has recently emerged to understand their biological role. In this work, a sensitive and reproducible LC-MS/MS method is reported for SCFAs quantification in three different matrices such as human, rat and mouse plasma via derivatization, using as derivatizing agent O-benzylhydroxylamine (O-BHA), coupled with liquid-liquid extraction. First, the instrumental parameters of the mass spectrometer and then the chromatographic conditions were optimized using previously SCFAs derivatives synthetized and used as standards. After that, the best conditions for derivatization and extraction from plasma were studied and a series of determinations were performed on human, rat, and mouse plasma aliquots to validate the overall method (derivatization, extraction, and LC-MS/MS determination). The method showed good performance in terms of recovery (> 80%), precision (RSD <14%), accuracy (RE < ± 10%) and sensitivity (LOQ of 0.01 µM for acetic, butyric, propionic and isobutyric acid) in all plasma samples. The method thus developed and validated was applied to the quantification of major SCFAs in adult and aged mice, germ-free mice and in germ-free recipient mice subjected to fecal transplant from adult and aged donors. Results highlighted how plasma concentrations of SCFAs are correlated with age further highlighting the importance of developing a method that is reliable for the quantification of SCFAs to study their biological role.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.