Enhancing coverage in LC–MS-based untargeted metabolomics by a new sample preparation procedure using mixed-mode solid-phase extraction and two derivatizations

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

Analysis of endogenous metabolites using multifunctional derivatization and capillary RPLC-MS

Heterogeneity in metabolite structure and charge state complicates their analysis in electrospray mass spectrometry (ESI-MS). Complications such as diminished signal response and quantitation can be reduced by sequential dual-stage derivatization and capillary RP LC-ESI-MS analysis. Our sequential dual-stage chemical derivatization reacts analyte primary amine and hydroxyl groups with a linear acyl chloride head containing a tertiary amine moiety. Analyte carboxylate groups are then coupled to a linear amine tag with a tertiary amine moiety. This increase in the number of tags on analytes increases analyte proton affinity and hydrophobicity. We derivatized 250 metabolite standards which on average improved signal to noise by >44-fold, with an average limit of detection of 66 nM and R² of 0.98. This system detected 107 metabolites from 18 BAECs, 111 metabolites from human urine, and 153 from human serum based on retention time, exact mass, and MS/MS matches from a derivatized standard library. As a proof of concept, aortic endothelial cells were treated with epinephrine and analyzed by the dual-stage derivatization. We observed changes in 32 metabolites with many increases related to energy metabolism, specifically in the TCA cycle. A decrease in lactate levels and corresponding increase in pyruvate levels suggest that epinephrine causes a movement away from glycolytic reliance on energy and a shift towards the more efficient TCA respiration for increasing energy.

Liquid Chromatography-Mass Spectrometry (LC-MS) Derivatization-Based Methods for the Determination of Fatty Acids in Biological Samples

Fatty acids (FAs) play pleiotropic roles in living organisms, acting as signaling molecules and gene regulators. They are present in plants and foods and may affect human health by food ingestion. As a consequence, analytical methods for their determination in biological fluids, plants and foods have attracted high interest. Undoubtedly, mass spectrometry (MS) has become an indispensable technique for the analysis of FAs. Due to the inherent poor ionization efficiency of FAs, their chemical derivatization prior to analysis is often employed. Usually, the derivatization of the FA carboxyl group aims to charge reversal, allowing detection and quantification in positive ion mode, thus, resulting in an increase in sensitivity in determination. Another approach is the derivatization of the double bond of unsaturated FAs, which aims to identify the double bond location. The present review summarizes the various classes of reagents developed for FA derivatization and discusses their applications in the liquid chromatography-MS (LC-MS) analysis of FAs in various matrices, including plasma and feces. In addition, applications for the determination of eicosanoids and fatty acid esters of hydroxy fatty acids (FAHFAs) are discussed.

MS-FINDER Assisted in Understanding the Profile of Flavonoids in Temporal Dimension during the Fermentation of Pu-erh Tea

Flavonoid represents a significant class of secondary metabolites in Pu-erh tea with benefits to human health. For a rapid and complete discovery of such compounds, we established a data mining workflow that integrates software MS-DIAL, MS-FINDER, and molecular networking analysis. As a result, 181 flavonoids were tentatively annotated including 22 first found in Pu-erh tea, and two of them were potentially new molecules. The dynamic alteration of these flavonoids during Pu-erh fermentation was further investigated. They all showed a trend of first increasing and then decreasing. Moreover, statistical analysis showed that the first to third pile turnings of the fermentation process had a greater impact on the changes of flavonoids. Partial metabolic pathways were proposed. This study provides a quick and automatic strategy for flavonoid profiling. The temporal dimension of flavonoids during fermentation may serve as a theoretical basis for Pu-erh tea manufacturing technology and study on substance foundation.

Fully Automated Quantitative Measurement of Serum Organic Acids via LC-MS/MS for the Diagnosis of Organic Acidemias: Establishment of an Automation System and a Proof-of-Concept Validation

Gas chromatography-mass spectrometry has been widely used to analyze hundreds of organic acids in urine to provide a diagnostic basis for organic acidemia. However, it is difficult to operate in clinical laboratories on a daily basis due to sample pretreatment processing. Therefore, we aimed to develop a fully automated system for quantifying serum organic acids using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The pretreatment CLAM-2030 device was connected to an LC-MS/MS system for processing serum under optimized conditions, which included derivatizing serum organic acids using 3-Nitrophenylhydrazine. The derivatized organic acids were separated on a reverse-phase Sceptor HD-C column and detected using negative-ion electrospray ionization multiple reaction monitoring MS. The automated pretreatment-LC-MS/MS system processed serum in less than 1 h and analyzed 19 serum organic acids, which are used to detect organic acidemias. The system exhibited high quantitative sensitivity ranging from approximately 2 to 100 µM with a measurement reproducibility of 10.4% CV. Moreover, a proof-of-concept validation of the system was performed using sera from patients with propionic acidemia (n = 5), methylmalonic acidemia (n = 2), and 3-methylcrotonylglycinuria (n = 1). The levels of marker organic acids specific to each disease were significantly elevated in the sera of the patients compared to those in control samples. The automated pretreatment-LC-MS/MS system can be used as a rapid in-hospital system to measure organic acid levels in serum for the diagnosis of organic acidemias.

METABOLOMICS REVEALS BIOMARKERS IN HUMAN URINE AND PLASMA TO PREDICT CYP2D6 ACTIVITY

Background and Purpose

Individualized assessment of cytochrome P450 2D6 (CYP2D6) activity is usually performed through phenotyping following administration of a probe drug to measure the enzyme's activity. To avoid any iatrogenic harm (allergic drug reaction, dosing error) related to the probe drug, the development of non‐burdensome tools for real‐time phenotyping of CYP2D6 could significantly contribute to precision medicine. This study focuses on the identification of markers of the CYP2D6 enzyme in human biofluids using an LC‐high‐resolution mass spectrometry‐based metabolomic approach.

Experimental Approach

Plasma and urine samples from healthy volunteers were analysed before and after intake of a daily dose of paroxetine 20 mg over 7 days. CYP2D6 genotyping and phenotyping, using single oral dose of dextromethorphan 5 mg, were also performed in all participants.

Key Results

We report four metabolites of solanidine and two unknown compounds as possible novel CYP2D6 markers. Mean relative intensities of these features were significantly reduced during the inhibition session compared with the control session (n = 37). Semi‐quantitative analysis showed that the largest decrease (−85%) was observed for the ion m/z 432.3108 normalized to solanidine (m/z 398.3417). Mean relative intensities of these ions were significantly higher in the CYP2D6 normal–ultrarapid metabolizer group (n = 37) compared with the poor metabolizer group (n = 6). Solanidine intensity was more than 15 times higher in CYP2D6‐deficient individuals compared with other volunteers.

Conclusion and Implications

The applied untargeted metabolomic strategy identified potential novel markers capable of semi‐quantitatively predicting CYP2D6 activity, a promising discovery for personalized medicine.

Narrative review of metabolomics in cardiovascular disease

Cardiovascular diseases are accompanied by disorders in the cardiac metabolism. Furthermore, comorbidities often associated with cardiovascular disease can alter systemic and myocardial metabolism contributing to worsening of cardiac performance and health status. Biomarkers such as natriuretic peptides or troponins already support diagnosis, prognosis and treatment of patients with cardiovascular diseases and are represented in international guidelines. However, as cardiovascular diseases affect various pathophysiological pathways, a single biomarker approach cannot be regarded as ideal to reveal optimal clinical application. Emerging metabolomics technology allows the measurement of hundreds of metabolites in biological fluids or biopsies and thus to characterize each patient by its own metabolic fingerprint, improving our understanding of complex diseases, significantly altering the management of cardiovascular diseases and possibly personalizing medicine. This review outlines current knowledge, perspectives as well as limitations of metabolomics for diagnosis, prognosis and treatment of cardiovascular diseases such as heart failure, atherosclerosis, ischemic and non-ischemic cardiomyopathy. Furthermore, an ongoing research project tackling current inconsistencies as well as clinical applications of metabolomics will be discussed. Taken together, the application of metabolomics will enable us to gain more insights into pathophysiological interactions of metabolites and disease states as well as improving therapies of patients with cardiovascular diseases in the future.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS

Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.