A hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Cardiac Substrate Utilization and Relationship to Invasive Exercise Hemodynamic Parameters in HFpEF

The authors conducted transcardiac blood sampling in healthy subjects and subjects with heart failure with preserved ejection fraction (HFpEF) to compare cardiac metabolite and lipid substrate use. We demonstrate that fatty acids are less used by HFpEF hearts and that lipid extraction is influenced by hemodynamic factors including pulmonary pressures and cardiac index. The release of many products of protein catabolism is apparent in HFpEF compared to healthy myocardium. In subgroup analyses, differences in energy substrate use between female and male hearts were identified.

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.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling

BACKGROUND

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.

RESULTS

As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.

CONCLUSIONS

Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

BACKGROUND

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

RESULTS

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.

CONCLUSIONS

Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Cardiovascular disease remains a leading cause of mortality with an estimated half a billion people affected in 2019. However, detecting signals between specific pathophysiology and coronary plaque phenotypes using complex multi-omic discovery datasets remains challenging due to the diversity of individuals and their risk factors. Given the complex cohort heterogeneity present in those with coronary artery disease (CAD), we illustrate several different methods, both knowledge-guided and data-driven approaches, for identifying subcohorts of individuals with subclinical CAD and distinct metabolomic signatures. We then demonstrate that utilizing these subcohorts can improve the prediction of subclinical CAD and can facilitate the discovery of novel biomarkers of subclinical disease. Analyses acknowledging cohort heterogeneity through identifying and utilizing these subcohorts may be able to advance our understanding of CVD and provide more effective preventative treatments to reduce the burden of this disease in individuals and in society as a whole.

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.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.