A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK)

Categorizing Concentration Confidence: A Framework for Reporting Concentration Measures from Mass Spectrometry-Based Assays

BACKGROUND

Innovation in mass spectrometry-based methods to both quantify and perform discovery has blurred the lines between targeted and untargeted assays of biospecimens. Continuous data-concentrations or intensity values generated from both methods-can be used in statistical analysis to determine associations with health outcomes, but concentration values are needed to compare measurements from one study to another to inform policy making decisions and to develop clinically relevant thresholds. As a single solution for discovery and quantitation, new hybrid-type assays derive concentration values for chemicals or metabolites but with varying degrees of uncertainty that may be greater than traditional quantitative assays. There is no current single standard to guide reporting bioassay concentrations or their uncertainty in concentration values from hybrid assays. Even when measures are robust, obtained with high scientific rigor, and provide valuable data toward risk assessment, unknown uncertainty can lead to bias in interpretation of reported data or omission of reported data that does not meet the strict criteria for absolute quantitation.

OBJECTIVE

The objective of this commentary is to articulate a scheme that enables investigators across bioanalytical fields to easily report analyte measurement assurance on the same scale from quantitative, untargeted, or hybrid assays that include a range of concentration confidences.

DISCUSSION

We propose a simple scheme to report concentrations for targeted and untargeted analytes. Level 1 is a confirmed concentration following established tolerances in a fully quantitative assay while level 5 is a tentative intensity from a typical untargeted assay. This framework enables easy communication of uncertainty in concentration measurements to aid cross-validation, meta-analysis, and extrapolation across studies. It will facilitate interpretation while supporting analytical advancement and allow clear and concise measurement reporting across a broad range of confidences. https://doi.org/10.1289/EHP15465.

Metabolomic and lipidomic profiling of traditional Chinese medicine Testudinis Carapax et Plastrum and its substitutes

Introduction

Chinemys reevesii (Gray) species-sourced Testudinis Carapax et Plastrum (TCP) is an animal-based traditional Chinese medical material, and its decoction or extract possesses multiple pharmacological effects. However, other species-sourced substitutes are sometimes used in the market, potentially impairing the quality and effectiveness of TCP medications. To address this issue, it is very necessary to develop applicable approaches that can accurately differentiate genuine TCP from its counterfeit counterparts.

Methods

In this study, liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomic and lipidomic analyses were performed to comprehensively detect water-soluble metabolites and organic-soluble lipids in water decoctions of genuine TCP and its substitutes, such as Trachemys scripta elegans (Wied)- and Ocadia sinensis (Gray)-sourced tortoise shells. Differential analyses based on fold change (FC), principal component analysis (PCA), and Orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed to assess the differences among TCP decoctions from different origins, as well as between decoctions of TCP samples and the two substitutes. Further, Kyoto Encyclopedia of Genes and Genomes (KEGG) database-based pathway enrichment analysis was performed for differential metabolites and lipids among them. Besides, LC-MS/MS-based absolute quantitative method was used to quantify the amino acid-relevant metabolites in decoctions of TCP and substituted tortoise shell samples.

Results

All told, 1117 water-soluble metabolites (including amino acids, organic acids, nucleotides and their metabolites or derivatives, etc.) and 574 organic-soluble lipids (including glycerolipids, sphingolipids, glycerophospholipids, fatty acids, and sterol lipids) were detected in decoctions of TCP and two substitutes. Comparative analyses revealed that there were significantly differential metabolites and lipids among TCP decoctions from different origins, as well as between decoctions of TCP samples and the two substitutes. Of particular interest, the content of N-methyl-4-aminobutyric acid was lower in the substituted samples than TCP samples. Furthermore, the content of 27 amino acids, 22 amino acid derivatives, and 18 small peptides in the decoctions of TCP and two substitutes were absolutely quantified, constituting up to tens of milligrams per 10 g of tortoise shell.

Discussion

In conclusion, our study provides comprehensive metabolomic and lipidomic information of TCP decoction. However, the current results represent preliminary data, and further extensive research is required to validate these findings.

Synergizing metabolomics and artificial intelligence for advancing precision oncology

Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics integration. In this opinion, we explore recent advances in the application of metabolomics within precision oncology, emphasizing the unique advantages that AI-driven metabolomics offers. We propose that AI not only complements but also amplifies the potential of current platforms, accelerating research progress and ultimately improving patient outcomes. Finally, we discuss the opportunities and challenges involved in translating AI-driven metabolomics into clinical practice for precision oncology.

Metabolomics studies in cushing's syndrome: recent developments and perspectives

INTRODUCTION

Exogenous Cushing's syndrome is the result of long-term exposure to glucocorticoids, while endogenous Cushing's syndrome occurs due to excessive production of glucocorticoids within the body. Cushing's syndrome remains a diagnostic challenge for the treating physician.Mass spectrometry, with its better resolution, detectability, and specificity, paved the way to understanding the cellular and molecular mechanisms involved in several diseases that facilitated the evolution of biomarkers and personalized medicine, which can be applicable to manage Cushing's syndrome as well.

AREAS COVERED

There are only a few reports of mass spectrometry-based metabolomic approaches to endogenous Cushing's syndrome of certain etiologies. However, the application of this approach in the diagnosis of exogenous Cushing has not been explored much. This review attempts to discuss the application of the mass spectrometry-based metabolomic approach in the evaluation of Cushing's syndrome.

EXPERT OPINION

Global metabolomics has the potential to discover altered metabolites and associated signaling and metabolic pathways, which may serve as potential biomarkers that would help in developing tools to accelerate precision medicine. Multi-omics approaches will provide innovative solutions to develop molecular tests for multi-molecule panel assays.

Lipid-mediated resolution of inflammation and survival in amyotrophic lateral sclerosis

Neuroinflammation impacts on the progression of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disorder. Specialized pro-resolving mediators trigger the resolution of inflammation. We investigate the specialized pro-resolving mediator blood profile and their receptors' expression in peripheral blood mononuclear cells in relation to survival in ALS. People living with ALS (pwALS) were stratified based on bulbar versus limb onset and on key progression metrics using a latent class model, to separate faster progressing from slower progressing ALS. Specialized pro-resolving mediator blood concentrations were measured at baseline and in one additional visit in 20 pwALS and 10 non-neurological controls (Cohort 1). Flow cytometry was used to study the GPR32 and GPR18 resolvin receptors' expression in peripheral blood mononuclear cells from 40 pwALS and 20 non-neurological controls (Cohort 2) at baseline and in two additional visits in 17 pwALS. Survival analysis was performed using Cox proportional hazards models, including known clinical predictors and GPR32 and GPR18 mononuclear cell expression. Differential expression and linear discriminant analyses showed that plasma resolvins were able to distinguish phenotypic variants of ALS from non-neurological controls. RvE3 was elevated in blood from pwALS, whilst RvD1, RvE3, RvT4 and RvD1n-3 DPA were upregulated in A-S and RvD2 in A-F. Compared to non-neurological controls, GPR32 was upregulated in monocytes expressing the active inflammation-suppressing CD11b+ integrin from fast-progressing pwALS, including those with bulbar onset disease (P < 0.0024), whilst GPR32 and GPR18 were downregulated in most B and T cell subtypes. Only GPR18 was upregulated in naïve double positive Tregs, memory cytotoxic Tregs, senescent late memory B cells and late senescent CD8+ T cells from pwALS compared to non-neurological controls (P < 0.0431). Higher GPR32 and GPR18 median expression in blood mononuclear cells was associated with longer survival, with GPR32 expression in classical monocytes (hazard ratio: 0.11, P = 0.003) and unswitched memory B cells (hazard ratio: 0.44, P = 0.008) showing the most significant association, along with known clinical predictors. Low levels of resolvins and downregulation of their membrane receptors in blood mononuclear cells are linked to a faster progression of ALS. Higher mononuclear cell expression of resolvin receptors is a predictor of longer survival. These findings suggest a lipid-mediated neuroprotective response that could be harnessed to develop novel therapeutic strategies and biomarkers for ALS.

CE-MS-Based Clinical Metabolomics of Human Plasma

Capillary electrophoresis coupled to mass spectrometry (CE-MS) has emerged as a powerful analytical technique with significant implications for clinical research and diagnostics. The integration of information from CE and MS strengthens confidence in the identification of compounds present in clinical samples. The ability of CE to separate molecules based on their electrophoretic mobility coupled to MS enables the accurate identification and quantification of analytes, even in complex biological matrices such as human plasma.Here, we present a detailed protocol for an untargeted metabolomics study using CE-MS and its application in a study on human plasma from patients suffering Long COVID syndrome. The protocol ranges from sample preparation to biological interpretation, detailing a workflow enabling the analysis of cationic and anionic compounds, metabolite identification, and data processing.

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.

Development and Validation of Methodologies for the Identification of Specialized Pro-Resolving Lipid Mediators and Classic Eicosanoids in Biological Matrices

Lipid mediators, which include specialized pro-resolving mediators and classic eicosanoids, are pivotal in both initiating and resolving inflammation. The regulation of these molecules determines whether inflammation resolves naturally or persists. However, our understanding of how these mediators are regulated over time in various inflammatory contexts is limited. This gap hinders our grasp of the mechanisms underlying the disease onset and progression. Due to their localized action and low endogenous levels in many tissues, developing robust and highly sensitive methodologies is imperative for assessing their endogenous regulation in diverse inflammatory settings. These methodologies will help us gain insight into their physiological roles. Here, we establish methodologies for extracting, identifying, and quantifying these mediators. Using our methods, we identified a total of 37 lipid mediators. Additionally, by employing a reverse-phase HPLC method, we successfully separated both double-bond and chiral isomers of select lipid mediators, including Lipoxin (LX) A4, 15-epi-LXA4, Protectin (PD) D1, PDX, and 17R-PD1. Validation of the method was performed in both solvent and surrogate matrix for linearity of the standard curves, lower limits of quantitation (LLOQ), accuracy, and precision. Results from these studies demonstrated that linearity was good with r2 values > 0.98, and LLOQ for the mediators ranged from 0.01 to 0.9 pg in phase and from 0.1 to 8.5 pg in surrogate matrix. The relative standard deviation (RSD) for inter- and intraday precision in solvent ranged from 5% to 12% at low, intermediate, and high concentrations, whereas the RSD for the inter- and intraday variability in the accuracy ranged from 95% to 87% at low to high concentrations. The recovery in biological matrices (plasma and serum) for the internal standards used ranged from 60% to 118%. We observed a marked ion suppression for molecules evaluated in negative ionization mode, while there was an ion enhancement effect by the matrix for molecules evaluated in positive ionization mode. Comparison of the integration algorithms, namely, AutoPeak and MQ4, and approaches for calculating signal-to-noise ratios (i.e., US Pharmacopeia, relative noise, peak to peak, and standard deviation) demonstrated that different integration algorithms tested had little influence on signal-to-noise ratio calculations. In contrast, the method used to calculate the signal-to-noise ratio had a more significant effect on the results, with the relative noise approach proving to be the most robust. The methods described herein provide a platform to study the SPM and classic eicosanoids in biological tissues that will help further our understanding of disease mechanisms.

Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes

OBJECTIVES

Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA).

METHODS

Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring.

RESULTS

The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included.

CONCLUSIONS

Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.

Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial

BACKGROUND

Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.

AIM OF REVIEW

In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

Multi-omics bioactivity profile-based chemical grouping and read-across: a case study with Daphnia magna and azo dyes

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this 'omics approach are identified, the study illustrates an effective statistical approach for building chemical groups.

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.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data

The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.