Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics

Norm ISWSVR Enhanced Data Repeatability and Accuracy in Large-Scale Targeted Quantification Metabolomics

Targeted quantification metabolomics provides dynamic insights across various domains within the life sciences. Nevertheless, maintaining high-quality data obtained through liquid chromatography-mass spectrometry presents ongoing challenges. It is essential to develop normalization methods to correct for unwanted variations in metabolomic profiling such as batch effects and analytical drift. In this study, we assessed the normalization efficacy of Norm ISWSVR in targeted quantification metabolomics by comparing it with IS normalization and SERRF normalization. Consequently, Norm ISWSVR demonstrated exceptional efficacy in mitigating batch effects and reducing the relative standard deviation of quality control samples, in addition to correcting signal drift. Following normalization with Norm ISWSVR, the number of metabolites suitable for quantification increased with high precision. Collectively, Norm ISWSVR proves to be a robust and reliable method for enhancing data quality in targeted metabolomics, establishing itself as a promising approach for metabolomics research.

Simultaneous profiling of mercapturic acids, glucuronic acids, and sulfates in human urine

Humans are constantly exposed to both naturally-occurring and anthropogenic chemicals. Targeted mass spectrometry approaches are frequently used to measure a small panel of chemicals and their metabolites in environmental or biological matrices, but methods for comprehensive individual-level exposure assessment are limited. In this study, we applied an integrated library-guided analysis (ILGA) with ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) to profile phase II metabolites, specifically mercapturic acids (MAs), glucuronic acids (GAs), and sulfates (SAs) in human urine samples (n = 844). We annotated 424 metabolites (146 MAs, 171 GAs, 107 SAs) by querying chromatographic features with in-house structural libraries, filtering against fragmentation patterns (common neutral loss and ion fragment), and comparing mass spectra with in-silico fragmentations and external spectral libraries. These metabolites were derived from over 200 putative parent compounds of exogenous and endogenous sources, such as dietary compounds, benzene/monocyclic substituted aromatics, pharmaceuticals, polycyclic aromatic hydrocarbons, bile acids/bile salts, and 4-hydroxyalkenals associated with lipid peroxidation process. Further, we performed statistical analyses on 214 metabolites found in more than 75% of samples to examine the association between metabolites and demographic characteristics using integrated network analysis, principal component analysis (PCA), and multivariable linear regression models. The network analysis revealed four distinct communities of 37 positively correlated metabolites, and the PCA (using the 37 metabolites) presented 4 principal components that meaningfully explained at least 80% of the variance in the data. The multivariable linear regression models showed some positive and negative associations between metabolite profiles and certain demographic variables (e.g., age, sex, race, education, income, and tobacco use).

Mechanism of P-Hydroxy Benzyl Alcohol Against Cerebral Ischemia Based on Metabonomics Analysis

Stroke is the leading cause of death and disability worldwide, with ischemic stroke accounting for the majority of these. HBA is the active ingredient in Gastrodia elata and has potential therapeutic effects on central nervous system diseases. In this study, the cell model of cerebral ischemia was replicated by the culture method of oxygen-glucose deprivation/reoxygenation, and the rat model of vascular dementia was established by the two-vessel occlusion method. Metabolomics technology was employed to analyze the metabolic changes in ischemic neurons induced by HBA, and potential therapeutic targets were verified. The protective effects of HBA on ischemic neurons and their mitochondria were examined through multiple indicators, and the related mechanisms were verified. HBA can improve post-ischemic cognitive impairment in rats, and its mechanism is related to the regulation of the choline-activated phospholipase D2/Sirtuin 1/peroxisome proliferator-activated receptor-γ coactivator 1α pathway to improve mitochondrial function and reduce autophagic activity to maintain mitochondrial homeostasis. It is concluded that HBA has a protective effect on neuronal damage and cognitive impairment caused by cerebral ischemia by regulating key metabolites and signaling pathways, and that it provides a new molecular target for the treatment of cerebral ischemia.

EigenRF: an improved metabolomics normalization method with scores for reproducibility evaluation on importance rankings of differential metabolites

Screening differential metabolites is of great significance in biomarker discovery in metabolomics research. However, it is susceptible to unwanted variations introduced during experiments. Previous normalization methods have improved the accuracy of inter-group classification by eliminating systematic errors. Nonetheless, the classification ability of differential metabolites obtained through these methods still requires further enhancement, and the reproducibility evaluation on importance rankings of differential metabolites is often disregarded. The EigenRF algorithm was developed as an improvement over the previous metabolomics normalization method referred to as EigenMS, which aims to normalize metabolomics data. Furthermore, scoring metrics, including the local consistency (LC) and overall difference (OD) scores, were introduced to evaluate the reproducibility of importance rankings of differential metabolites from a dual perspective. After conducting validation on three publicly accessible datasets, the EigenRF method has demonstrated enhanced classification ability of differential metabolites as well as improved reproducibility. In summary, EigenRF enhances the reliability of differential metabolites in metabolomics research, benefiting the further exploration of molecular mechanisms underlying biological alterations in complex matrices. The EigenRF algorithm was implemented in an R package: https://www.github.com/YangHuaLab/EigenRF.

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, this review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.