MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data.

Summary: The MetaboAnalyst web application has been widely used for metabolomics data analysis and interpretation. Despite its user-friendliness, the web interface has presented its inherent limitations (especially for advanced users) with regard to flexibility in creating customized workflow, support for reproducible analysis, and capacity in dealing with large data. To address these limitations, we have developed a companion R package (MetaboAnalystR) based on the R code base of the web server. The package has been thoroughly tested to ensure that the same R commands will produce identical results from both interfaces. MetaboAnalystR complements the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data. Availability and implementation: MetaboAnalystR is freely available from https://github.com/xia-lab/MetaboAnalystR.

MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data.

The widespread application of next-generation sequencing technologies has revolutionized microbiome research by enabling high-throughput profiling of the genetic contents of microbial communities. How to analyze the resulting large complex datasets remains a key challenge in current microbiome studies. Over the past decade, powerful computational pipelines and robust protocols have been established to enable efficient raw data processing and annotation. The focus has shifted toward downstream statistical analysis and functional interpretation. Here, we introduce MicrobiomeAnalyst, a user-friendly tool that integrates recent progress in statistics and visualization techniques, coupled with novel knowledge bases, to enable comprehensive analysis of common data outputs produced from microbiome studies. MicrobiomeAnalyst contains four modules - the Marker Data Profiling module offers various options for community profiling, comparative analysis and functional prediction based on 16S rRNA marker gene data; the Shotgun Data Profiling module supports exploratory data analysis, functional profiling and metabolic network visualization of shotgun metagenomics or metatranscriptomics data; the Taxon Set Enrichment Analysis module helps interpret taxonomic signatures via enrichment analysis against >300 taxon sets manually curated from literature and public databases; finally, the Projection with Public Data module allows users to visually explore their data with a public reference data for pattern discovery and biological insights. MicrobiomeAnalyst is freely available at http://www.microbiomeanalyst.ca.

Molecular Epidemiology of a Vancomycin-Intermediate Heteroresistant Staphylococcus epidermidis Outbreak in a Neonatal Intensive Care Unit.

Coagulase-negative staphylococci (CoNS) have become the leading cause of bloodstream infections (BSIs) in intensive care units (ICUs), particularly in premature neonates. Vancomycin-intermediate heteroresistant CoNS (hVICoNS) have been identified as sources of BSIs worldwide, and their potential to emerge as significant pathogens in the neonatal ICU (NICU) remains uncertain. This study describes the molecular epidemiology of an outbreak of vancomycin-heteroresistant (hV) Staphylococcus epidermidis central-line-associated BSI (CLABSI) in a single tertiary care NICU and compares it to a second tertiary care NICU that had not been associated with an outbreak. Between November 2009 and April 2014, 119 S. epidermidis CLABSIs were identified in two tertiary care NICUs in Quebec, Canada. Decreased vancomycin susceptibility was identified in about 88% of all collected strains using Etest methods. However, discrepancies were found according to the Etest and population analysis profiling-area under the concentration-time curve (PAP-AUC) methods used. All strains were susceptible to linezolid, and a few isolates were nonsusceptible to daptomycin. Great genetic diversity was observed within the collection, with 31 pulsed-field gel electrophoresis (PFGE) patterns identified. The outbreak strains were all determined to be heteroresistant to vancomycin and were polyclonal. The study identified two major clones, PFGE patterns E and G, which were found in both NICUs across the 5-year study period. This suggests the persistence of highly successful clones that are well adapted to the hospital environment. hV S. epidermidis seems more common than currently realized in the NICU, and certain hV S. epidermidis clones can become endemic to the NICU. The reservoirs for these clones remain unknown at this time, and identification of the reservoirs is needed to better understand the impact of hV S. epidermidis in the NICU and to inform infection prevention strategies. In addition, there is a need to investigate and validate hV determination protocols for different species of CoNS.

Molecular markers from different genomic compartments reveal cryptic diversity within glaucophyte species.

Glaucophytes are the least studied of the three major Archaeplastida (Plantae sensu lato) lineages. It has been largely recognized that comprehensive investigations of glaucophyte genetic and species diversity will shed light on the early evolution of photosynthetic eukaryotes. Here we used molecular phylogenetics and genetic distance estimations of diverse molecular markers to explore strain and species diversity within the glaucophyte genera Cyanophora and Glaucocystis. Single gene and concatenated maximum likelihood analyses of markers from three different genetic compartments consistently recovered similar intrageneric genetic groups. Distance analyses of plastid (psbA and rbcL) and mitochondrial (cob and cox1) genes, and the nuclear internal transcribed spacer (ITS) region, revealed substantial genetic divergence between some Cyanophora paradoxa and Glaucocystis nostochinearum strains. The genetic distances estimated between some glaucophyte strains currently considered the same species are similar or greater than divergence values calculated between different species in other unicellular algae, such as certain green algae and diatoms. The analyzed molecular markers are prospective candidates for future studies of species diversity in glaucophytes. Overall, our results unveil previously unrecognized cryptic diversity within Cyanophora and Glaucocystis species.

Identifying glycan consumers in human gut microbiota samples using metabolic labeling coupled with fluorescence-activated cell sorting.

The composition and metabolism of the human gut microbiota are strongly influenced by dietary complex glycans, which cause downstream effects on the physiology and health of hosts. Despite recent advances in our understanding of glycan metabolism by human gut bacteria, we still need methods to link glycans to their consuming bacteria. Here, we use a functional assay to identify and isolate gut bacteria from healthy human volunteers that take up different glycans. The method combines metabolic labeling using fluorescent oligosaccharides with fluorescence-activated cell sorting (FACS), followed by amplicon sequencing or culturomics. Our results demonstrate metabolic labeling in various taxa, such as Prevotella copri, Collinsella aerofaciens and Blautia wexlerae. In vitro validation confirms the ability of most, but not all, labeled species to consume the glycan of interest for growth. In parallel, we show that glycan consumers spanning three major phyla can be isolated from cultures of sorted labeled cells. By linking bacteria to the glycans they consume, this approach increases our basic understanding of glycan metabolism by gut bacteria. Going forward, it could be used to provide insight into the mechanism of prebiotic approaches, where glycans are used to manipulate the gut microbiota composition.

NLRP5 mediates mitochondrial function in mouse oocytes and embryos.

Unraveling molecular pathways responsible for regulation of early embryonic development is crucial for our understanding of female infertility. Maternal determinants that control the transition from oocyte to embryo are crucial molecules that govern developmental competence of the newly conceived zygote. We describe a series of defects that are triggered by a disruption of maternal lethal effect gene, Nlrp5. Previous studies have shown that Nlrp5 hypomorph embryos fail to develop beyond the two-cell stage. Despite its importance in preimplantation development, the mechanism by which the embryo arrest occurs remains unclear. We confirmed that Nlrp5 mutant and wild-type females possess comparable ovarian germ pool and follicular recruitment rates. However, ovulated oocytes lacking Nlrp5 have abnormal mitochondrial localization and increased activity in order to sustain physiological ATP content. This results in an accumulation of reactive oxygen species and increased cellular stress causing mitochondrial depletion. Compromised cellular state is also accompanied by increased expression of cell death inducer Bax and depletion of cytochrome c. However, neither genetic deletion (Bax/Nlrp5 double knockout) nor mimetic interference (BH4 domain or Bax inhibitory peptide) were sufficient to alleviate embryo demise caused by depletion of Nlrp5. We therefore conclude that lack of Nlrp5 in oocytes triggers premature activation of the mitochondrial pool, causing mitochondrial damage that cannot be rescued by inactivation of Bax.

Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets.

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.

TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome.

Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host-gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host-microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.

Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data.

MetaboAnalyst ( www.metaboanalyst.ca ) is an easy-to-use, comprehensive web-based tool, freely available for metabolomics data processing, statistical analysis, functional interpretation, as well as integration with other omics data. This chapter first provides an introductory overview to the current MetaboAnalyst (version 4.0) with regards to its underlying design concepts and user interface structure. Subsequent sections describe three common metabolomics data analysis workflows covering targeted metabolomics, untargeted metabolomics, and multi-omics data integration.

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.

MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min.

Generation of an in vitro model of the outer annulus fibrosus-cartilage interface.

Current treatments for degenerative disc disease do not restore full biological functionality of the intervertebral disc (IVD). As a result, regenerative medicine approaches are being developed to generate a biological replacement that when implanted will restore form and function of the degenerated IVD. Tissue-engineered models to date have focused on the generation of nucleus pulposus and annulus fibrosus IVD components. However, these tissues need to be integrated with a cartilage endplate in order for successful implantation to occur. The purpose of this study was to generate an in vitro annulus fibrosus-cartilage interface model which would enable us to better understand the biological and biomechanical implications of such interfaces. It was hypothesized that in vitro-formed outer annulus fibrosus (OAF) and cartilage tissues would integrate in direct-contact coculture to yield an interface containing extracellular matrix with aspects resembling the native OAF-CEP interface. In vitro-formed tissues were generated using bovine OAF cell-seeded angle-ply, multi-lamellated polycarbonate urethane scaffolds and articular chondrocytes, which were then placed in direct-contact coculture. 2-week old OAF tissues integrated with 3-day old cartilage by 1 week of coculture. Immunohistochemical staining of 2-week interfaces showed that distributions of collagen type I, collagen type II, and aggrecan were similar to the native bovine interface. The apparent tensile strength of the in vitro interface increased significantly between 2 and 4 weeks of coculture. In summary, an annulus fibrosus-cartilage interface model can be formed in vitro which will facilitate the identification of conditions required to generate an entire tissue-engineered disc replacement suitable for clinical use.

Transforming Growth Factor ß Enhances Tissue Formation by Passaged Nucleus Pulposus Cells In Vitro.

The nucleus pulposus (NP) is composed of NP and notochord cell. It is a paucicellular tissue and if it is to be used as a source of cells for tissue engineering the cell number will have to be expanded by cell passaging. The hypothesis of this study is that passaged NP and notochordal cells grown in three-dimensional (3D) culture in the presence of transforming growth factor ß (TGFß) will show enhanced NP tissue formation compared with cells grown in the absence of this growth factor. Bovine NP cells isolated by sequential enzymatic digestion from caudal intervertebral discs were either placed directly in 3D culture (P0) or serially passaged up to passage 3 (P3) prior to placement in 3D culture. Serial cell passage in monolayer culture led to de-differentiation, increased senescence and oxidative stress and decreases in the gene expression of NP and notochordal associated markers and increases in de-differentiation markers. The NP tissue regeneration capacity of cells in 3D culture decreases with passaging as indicated by diminished tissue thickness and total collagen content when compared with tissues formed by P0 cells. Immunohistochemical studies showed that type II collagen accumulation appeared to decrease. TGFß1 or TGFß3 treatment enhanced the ability of cells at each passage to form tissue, in part by decreasing cell death. However, neither TGFß1 nor TGFß3 were able to restore the notochordal phenotype. Although TGFß1/3 recovered NP tissue formation by passaged cells, to generate NP in vitro that resembles the native tissue will require identification of conditions facilitating retention of notochordal cell differentiation. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:438-449, 2020.

Correlations between the Composition of the Bovine Microbiota and Vitamin B12 Abundance.

Vitamin B12 is synthesized by prokaryotes in the rumens of dairy cows-and this has implications in human nutrition since humans rely on consumption of dairy products for vitamin B12 acquisition. However, the concentration of vitamin B12 in milk is highly variable, and there is interest in determining what causes vitamin B12 variability. We collected 92 temporally linked rumen, fecal, blood, and milk sample sets from Holstein cows at various stages of lactation fitted with rumen cannula and attempted to define which bacterial genera correlated well with vitamin B12 abundance. The level of vitamin B12 present in each sample was measured, and the bacterial population of each rumen, fecal, and milk sample (n = 263) was analyzed by 16S rRNA-targeted amplicon sequencing of the V4 region. The bacterial populations present in the rumen, small intestine, and milk were highly dissimilar. Combined diet and lactation status had significant effects on the composition of the microbiota in the rumen and in the feces. A high ruminal concentration of vitamin B12 was correlated with the increased abundance of Prevotella, while a low ruminal concentration of vitamin B12 was correlated with increased abundance of Bacteroidetes, Ruminiclostridium, and Butyrivibrio The ultimate concentration of vitamin B12 is controlled by the complex interaction of several factors, including the composition of the microbiota. Bacterial consumption of vitamin B12 in the rumen may be more important in determining overall levels than bacterial production.IMPORTANCE In this paper, we examined the microbiome of the bovine rumen, feces, and milk and attempted to understand how the bacterial communities at each site affected the production and movement of vitamin B12 around the animal's body. It was determined that the composition of the bovine rumen microbiome correlates well with vitamin B12 concentration, indicating that the rumen microbiota may be a good target for manipulation to improve production of this important vitamin.

Fc gamma receptor signaling in mast cells links microbial stimulation to mucosal immune inflammation in the intestine.

Microbes and microbial products are closely associated with the pathogenesis of inflammatory bowel disease (IBD); however, the mechanisms behind this connection remain unclear. It has been previously reported that flagellin-specific antibodies are increased in IBD patient sera. As mastocytosis is one of the pathological features of IBD, we hypothesized that flagellin-specific immune responses might activate mast cells that then contribute to the initiation and maintenance of intestinal inflammation. Thirty-two colonic biopsy samples were collected from IBD patients. A flagellin/flagellin-specific IgG/Fc gamma receptor I complex was identified on biopsied mast cells using both immunohistochemistry and co-immunoprecipitation experiments; this complex was shown to co-localize on the surfaces of mast cells in the colonic mucosa of patients with IBD. In addition, an ex vivo study showed flagellin-IgG was able to bind to human mast cells. These cells were found to be sensitized to flagellin-specific IgG; re-exposure to flagellin induced the mast cells to release inflammatory mediators. An animal model of IBD was then used to examine flagellin-specific immune responses in the intestine. Mice could be sensitized to flagellin, and repeated challenges with flagellin induced an IBD-like T helper 1 pattern of intestinal inflammation that could be inhibited by pretreatment with anti-Fc gamma receptor I antibodies. Therefore, flagellin-specific immune responses activate mast cells in the intestine and play important roles in the pathogenesis of intestinal immune inflammation.

Immunoglobulin kappa and immunoglobulin lambda are required for expression of the anti-apoptotic molecule Bcl-xL in human colorectal cancer tissue.

OBJECTIVE: Aberrant expression of immunoglobulin (Ig) by cancer cells has been documented in a number of malignant tumors but its biological significance is unclear. Cancer cells overexpress anti-apoptotic molecules such as Bcl-xL. The present study aimed to examine the role of expression of Ig light-chain Igk and Iglambda in maintaining the high levels of Bcl-xL in colorectal cancer cells. MATERIAL AND METHODS: Thirty patients with colorectal cancer were recruited to this study. Expression of Igk, Iglambda and Bcl-xL in surgically removed cancer tissue was examined by immunohistochemistry and/or flow cytometry. Using the HT29 cell line as a study platform, RNA interference (RNAi) was employed to knock out the genes of Igk and Iglambda in the cancer cell line; the expression of Bcl-xL in HT29 cells was subsequently analyzed. RESULTS: Human colorectal cancer cells, but not normal colorectal tissue, expressed both Igk and Iglambda in the cytoplasm. High levels of Bcl-xL were detected in cancer cells. Using RNAi to knock out the genes of Igk and/or Iglambda, Bcl-xL expression in HT29 cells was significantly suppressed and the cells became apoptotic. CONCLUSION: The results suggest that expression of Igk and Iglambda is required to stabilize Bcl-xL expression in cancer cells.

MetaboAnalystR 2.0: From Raw Spectra to Biological Insights.

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis.

MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.