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Study Design

Chapter 3 — Building Your Metabolomic Study

As we discussed in the previous chapter of this guide, conducting a successful metabolomics analysis and interpretation begins with a well-designed study. There are a number of factors to consider when designing your study to ensure your protocol delivers high-quality results for drawing actionable insights. In this chapter, we’ll go over each of these factors, breaking the process down into simple steps with clear, easy-to-follow advice. Additionally, we’ve created a two-page study design tool that you can download, print, and use for your next study.

Study Approach

The first thing you must do before designing your study is to define the objective of your study. Are you interested in the impact of a treatment across time? Do you simply want to identify metabolite differences between healthy and diseased subjects? Or do you want to identify biomarkers?

Once you define the objective of your study, the next step is to determine whether you will perform untargeted (global) or targeted analysis, or a combination of the two. Global metabolomics approaches are semi-quantitative and consider all output metabolite data, while targeted studies utilize panels of known metabolites to quantify the amounts of those specific metabolites in the sample(s) of interest (absolute quantitation).

For those interested in identifying novel biomarkers or seeking to understand the presence of a broad set of metabolites, global metabolomics is the best choice. If, however, you know exactly what you’re looking for and want to know how much of a specific metabolite or groups of metabolites are present in your sample, you’ll need a targeted approach. At Metabolon, we provide targeted panels for amino acids, bile acids, central carbons, complex lipids, fatty acids, short-chain fatty acids, and more. And, of course, we can help you develop custom target panels specific to your research goals.

Often, researchers opt for a two-pronged approach, starting with a global approach and then using targeted metabolomics to further understand key analytes identified via global analysis. Regardless of the approach or combination of approaches you choose, it’s important to define this study parameter at the outset, as this will inform the sample types and quantities required to make data collection as straightforward as possible.

Study Aims

Defining your study aims is also critical to ensure you design your study effectively. Most studies detect, identify, quantify, and, finally, correlate metabolites to specific variables to answer the research question. Experiments should ideally have a spectrum of time points, doses, and/or phenotypic/disease severity, enabling them to detect salient “cause and effect” metabolic changes.

We recommend that researchers collect samples at time points, doses, and/or exposures that induce mild, moderate, and severe experimental effects. Additionally, be sure to collect a complete set of metadata—including not only biological information (such as disease state, age, sex, etc.) but also technical information, such as who performed sample collection, which freezer samples were stored in, and anything else that could be a source of variation. There is nothing worse than observing metabolite patterns but being unable to correlate those patterns with a specific variable.

Study Size

Once your objective and aims are defined, you’ll need to determine what sample types you’re going to collect and adequate sample quantities. In the next chapter of this guide, we take a deep dive into the different sample types you can collect for metabolite studies. But whether you choose to collect cells in culture, stool, plasma, serum, urine, tumor biopsies, or other biological fluid/tissue types (or a combination), you’ll need to ensure you collect a sufficient volume of each sample to ensure you can capture biologically relevant metabolite patterns. We recommend the following:

  • Isolated cells: 100 µL
  • Biological fluids (plasma, urine, etc.): 150 to 200 µL
  • Tissues: 50 to 100 mg

Our study directors are ready to work with you to help you determine the appropriate number of samples to collect based on your study design and to ensure you collect enough sample volume for metabolomics analysis.

Study Power

To uncover statistically significant results, your study must be appropriately powered—meaning that you’ve collected enough samples to overcome biological and technical variation. Metabolon scientists have combined decades of experience with thousands of metabolite studies on hundreds of sample types, and we’ve developed a basic set of guidelines, outlined in our Study Design Tool, to ensure your study is adequately powered.

Various aspects of your study design can impact power, however. More samples than the recommended amount for your sample type may be needed if you collect samples from multiple sites, use a mixed population, or expect subtle treatment effects. On the other hand, you may need fewer samples to obtain the same power if you’re collecting across multiple time points, collecting multiple samples from the same individual, or expect your treatment effect to be very strong. Metabolon’s study directors work with each customer individually to not only appropriately design their study but to ensure that the study is adequately powered.

Study Controls

As with any scientific study, controls are a critical component of your metabolomics study. Incorporating an appropriate control for each variable tested in your experiments will ensure that you can answer your scientific question correctly. It’s also good practice to never include a variable for which you cannot also include a proper control. Examples of controls include vehicle (i.e., saline, placebo) controls for drug studies, samples from healthy individuals for disease studies, and wild-type animals for transgenic animal-based studies. Technical controls should also be utilized at the sample processing step. Metabolon includes appropriate controls for every study to ensure instruments are performing properly and to identify (and remove) universal sources of variation.

Physical controls help researchers determine whether patterns in their data are specific to their variables of interest or not (if they are present in control samples, too, they are meaningless in the context of the scientific question being asked). But controls are only one way to deal with study variation. Sample normalization is also critical for deriving biological insights—but it must be performed with utmost care to preserve biological variation while reducing non-biological variation. Metabolon has developed industry-leading approaches for sample normalization, ensuring that any study you do with us extracts maximum insights.

Setting Results Expectations

Having a good idea upfront about what type of data you can expect to receive and how long it will take to receive that data will help plan follow-up experiments and collect samples for those studies at the right time as well as budget for any necessary analysis software/tools.

As we discussed briefly in Chapter 2 of this guide, the output of mass spectrometers is typically a matrix (i.e., a large table full of rows and columns) not yet formatted for data analysis with third-party tools. And while some instruments do come with basic analysis software, results are basic and surface level, inappropriate for generating complex analyses and gaining deep insights.

Metabolon customers have the option to receive only data or different levels of analysis and interpretation, depending on their existing analysis capabilities and capacity and whether they’ve selected global metabolomics or a targeted assay. Every single Metabolon customer, regardless of the assay(s) they select, receives a formatted Client Data Table that is ready for import and analysis with third-party tools such as R and Python.

If you desire help with data analysis and interpretation, it’s important to set appropriate expectations for what you’ll receive and how long it will take. Metabolon not only has the capacity to process hundreds of samples at a time, ensuring you aren’t waiting months for your data table, but also has developed various analysis and interpretation reporting tools, such as the MyMetabolon Portal and the Lipid Surveyor data visualization tool which are available with specific assays. We discuss these tools in greater detail in the next chapter of this guide.

We also often continue to work with customers after initial data interpretation and delivery to more deeply analyze their datasets and design and execute follow-up studies. These collaborative efforts can last months or even years and have led to the publication of over 3,000 scientific presentations and publications. If this is the type of partnership you’d like with Metabolon, be sure you tell your study director right away so together you can set appropriate expectations and considerations, especially for studies that contribute to larger and/or long-term projects.

Mapping and Executing Your Study

Once you’ve addressed all of the considerations outlined above, you’re ready to mockup your study design and prepare to execute your study. Our Study Design tool is a great way to get started, but our study directors will also provide you with a study design mockup once they’ve consulted with you on your project. You can review the mockup together with your study director, identifying any areas of concern and addressing those prior to collecting your samples.

What’s Next?

Once you’ve mocked up and are confident in your study design, you’ll be ready to start your experiments and collect samples. In the next few chapters of this guide, we’ll discuss the various sample types you can collect and specific considerations to ensure you get high-quality data from them, how to appropriately collect, store, transport, and prepare your samples, and the ins-and-outs of study analysis, interpretation, and insights with Metabolon.

metabolomics study design success guide

Continue to Chapter 4 - Sample Types for Metabolomics

In this chapter, we will take a closer look at the most common types of samples used in metabolomic studies. Many of the samples we will cover are human or animal in origin but can also include environmental samples, such as soil and water. By the end of this chapter, you’ll be familiar with the most important factors to consider when processing samples for metabolomics research.

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