Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

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.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Metabolomics and pharmacodynamic analysis unveil the therapeutic role of icaritin on osteoporosis rats

Osteoporosis is a systemic metabolic bone disease characterized by a reduction in bone mass resulting from multifactorial causes. Icaritin (ICT), a flavonoid glycoside, exhibits a multitude of effects on bone tissue. To examine the influence of ICT on bone trabecular loss in vivo, ovariectomized (OVX) rats were utilized. The ability of ICT to mitigate bone trabecular loss and the underlying anti-osteoporotic pathways were assessed using ovariectomy-induced osteoporosis rats. Furthermore, we gain insights into the osteoprotective mechanisms of ICT on osteoporosis by conducting UPLC-Orbitrap-MS-based metabolomics of rat urine. The results of experiments demonstrated a significant attenuation of bone trabecular loss, as well as improvements in biochemical indices, biomechanical parameters, and microstructure in the ICT administered group compared to the OVX group. Moreover, metabolomics results suggested that the ICT treatment adjusted 33 different metabolites, which associated with the metabolism of amino acids, lipids, and energy. The findings suggest that the anti-osteoporosis effect of ICT may be related to the activation of PI3K/AKT signal and the inhibition of TLR4 pathway regulated by metabonomics. These results contribute to a better understanding of the therapeutic potential of ICT in the treatment of osteoporosis.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics

Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.

CHRISTMAS: Chiral Pair Isobaric Labeling Strategy for Multiplexed Absolute Quantitation of Enantiomeric Amino Acids

Amino acids (AAs) in the d-form are involved in multiple pivotal neurological processes, although their l-enantiomers are most commonly found. Mass spectrometry-based analysis of low-abundance d-AAs has been hindered by challenging enantiomeric separation from l-AAs, low sensitivity for detection, and lack of suitable internal standards for accurate quantification. To address these critical gaps, N,N-dimethyl-l-leucine (l-DiLeu) tags are first validated as novel chiral derivatization reagents for chromatographic separation of 20 pairs of d/l-AAs, allowing the construction of a 4-plex isobaric labeling strategy for enantiomer-resolved quantification through single step tagging. Additionally, the creative design of N,N-dimethyl-d-leucine (d-DiLeu) reagents offers an alternative approach to generate analytically equivalent internal references of d-AAs using d-DiLeu-labeled l-AAs. By labeling cost-effective l-AA standards using paired d- and l-DiLeu, this approach not only enables absolute quantitation of both d-AAs and l-AAs from complex biological matrices with enhanced precision but also significantly boosts the combined signal intensities from all isobaric channels, greatly improving the detection and quantitation of low-abundance AAs, particularly d-AAs. We term this quantitative strategy CHRISTMAS, which stands for chiral pair isobaric labeling strategy for multiplexed absolute quantitation. Leveraging the ion mobility collision cross section (CCS) alignment, interferences from coeluting isomers/isobars are effectively filtered out to provide improved quantitative accuracy. From wild-type and Alzheimer's disease (AD) mouse brains, we successfully quantified 20 l-AAs and 5 d-AAs. The significant presence and differential trends of certain d-AAs compared to those of their l-counterparts provide valuable insights into the involvement of d-AAs in aging, AD progression, and neurodegeneration.