Designing a Metabolomics Study

Overview

The three previous chapters discussed instances in which metabolomics provided critical insight or solved a problem that other omics sciences could not have addressed. Some of those studies also showed how using metabolomics alongside other omics sciences can enhance the depth of study findings. In this final chapter we will go over the basic principles of designing a metabolomics study while highlighting Metabolon’s experience, products, and tools that have facilitated the successful completion of many thousands of studies and over 2 million samples analyzed.

Establish a Study Goal

The study goal is a highly important factor to consider when designing a metabolomics study because it will determine whether global or targeted profiling should be used. As a general example, studies aimed at discovery of biomarkers, therapeutic targets, or hypothesis generation are best approached with global profiling, while studies that are testing a hypothesis regarding a specific pathway or mechanism, validating prior study findings, or that require absolute quantitation (providing concentration units) would be served best by targeted profiling.

Choose an Appropriate Sample Type

It is crucial to choose a sample type that contains the metabolites of interest in abundance even if the sample matrix is not commonly profiled. Metabolon has developed protocols for many alternative sample types beyond the ones specifically mentioned in this guide including sebum, sweat, dried blood spots, bile, and hair follicles, among others. Metabolon also offers Sample Matrix Validation for sample types that have yet to be validated for LC/MS metabolomics profiling.

For some studies, it is prudent to analyze more than one sample type to ensure full coverage of the appropriate metabolomes. This was demonstrated by a few studies discussed in this guide. In the study by Zgoda-Pols et al. described in Chapter 3, investigators analyzed mouse brain, plasma, and urine to gain the deepest insight into drug-induced toxicity beyond what traditional biomarkers could provide. In the study by Wu et. al. discussed in Chapter 4, the research team analyzed both plasma and feces to comprehensively evaluate microbiome-metabolome dynamics as they affect progression of type 2 diabetes. Metabolon is well equipped to advise on which sample matrix or combination of matrices would be appropriate for your study.

It is also important to note that not all sample collection options are amenable to metabolomics analysis. Some buffers and sample treatments can have strong deleterious effects on metabolomics data. Metabolon can assist in these discussions and help guide appropriate sample type selection.

Data Interpretation

Metabolomics data is often interpreted using many types of advanced statistical tests. For example, unique compounds that differ significantly between study groups are often identified using student’s t-tests or ANOVAs. The degree of change between metabolic signatures between study groups can further be evaluated using principal component analysis (PCA) and pathway plots. Additionally, the metabolites that contribute the most to an observed change in metabolism can be identified using Random Forest Analysis. Every statistical method is used based on relevance to the study objective. Metabolon uses the aforementioned techniques, along with many others, to ensure accurate interpretation of the data.

We reiterate that interpreting metabolomics data from other providers, especially data from a global profiling study, can feel intimidating due to the sheer number of ion features reported, and low-quality identifications. Metabolon addresses these limitations with its chemo-centric approach, in which extraneous and redundant ion features are omitted from the dataset prior to statistical analysis. Limiting the data set to biologically relevant ion features that are directly associated with high quality identified compounds makes data interpretation more straightforward and maximizes the likelihood of gaining meaningful insight. Metabolon further supports data interpretation with its bioinformatics platform, which allows rapid pathway analysis and integration of genomics data with metabolomics data. Additionally, project findings are analyzed by Metabolon’s PhD level scientists, and their insights are provided in a written report along with all data and statistical analyses, considered a manuscript in hand for those with high publication demands.

Final Conclusions and Metabolon’s Commitment

The studies presented in this guide highlight the essential role of metabolomics in providing actionable insights and advancing our understanding of diverse topics, and we hope that these concepts inspire your own research.

Metabolon is committed to supporting investigators through this journey by providing the tools, data, and guidance needed to interpret complex data into meaningful discoveries. Our PhD-trained scientists have decades of experience designing, executing, and supporting the analysis and interpretation of complex metabolomics data. We would be happy to help you design and execute appropriately powered studies to ensure your data delivers accurate scientific insights and propels your research forward.

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