Study Design

Chapter 5 — Metabolomics Sample Preparation, Storage, and Transportation

In the previous chapter of this guide, we provided an overview of the sample types that are commonly processed for metabolomics studies and the considerations to make when processing them. Regardless of the sample type, robust sample collection and transportation procedures must be established to preserve sample integrity. Failing to do so raises the risk of data variability, instrument interferences, and metabolite degradation.1 In this chapter, we provide an overview of the common steps underlying sample preparation, storage, and transportation.

Metabolomics Sample Collection and Storage Guidelines

Sample collection is the first execution step in any metabolomics study. The importance of a robust sampling procedure cannot be understated. Improper collection protocols or the use of inappropriate collection devices can adversely impact instrumentation outputs and the resulting metabolomes.2 Researchers also study many kinds of clinical and environmental specimens, each of which contains diverse chemical components and matrices. Thus, the type of sample being studied plays the biggest role in determining appropriate collection and processing procedures. Here are some important considerations as you prepare your collection procedures:

  • Some sample types can change form. Blood samples are prone to coagulation, a process that prevents excess blood loss by changing it into a solid or semi-solid state. While serum samples are obtained after coagulation, plasma collection requires an anticoagulant such as ethylenediaminetetraacetic acid (EDTA) to be added.3 Doing so retains plasma in its liquid form for processing.
  • There can be a delay before freezing samples. A delay between sample collection and processing is common, which can impact metabolomes. In some cases, sample collection methods allow for room temperature storage. Utilizing the validated OMNIMet™•GUT tubes is one way to enable room temperature storage of human fecal samples prior to processing which preserves the metabolomic profile.4 For most other sample types, freezing is more appropriate for storage and should be done as soon as possible after collection.5
  • Ensure that freeze-thaw cycles are minimized. While freezing samples is the gold standard for preserving metabolomes, freeze-thaw cycles are incredibly damaging to the metabolome.6 If possible, samples should be homogenized and aliquoted to avoid damage due to freeze-thaw cycles and reduce intra-sample variation.
  • Some samples may require lyophilization. Lyophilization begins with a freezing step followed by a vacuum step to remove water from the samples. This results in the ice turning into a vapor without passing through the liquid phase. Lyophilization is commonly employed in plant cells to minimize changes in oxidation within the cells.7

All studies performed with Metabolon receive our free Study Success Sample Handling Kit, customized specifically for your study. Included in the kit are barcoded tubes specific to your matrix (ie, biofluids, solid samples, or cell pellets), a barcode scanner, and instructions for mailing your samples to us to ensure they arrive safely.

The Importance of Robust Sample Preparation
Reproducibility remains a critical issue in all areas of research including metabolomics. For instance, inter-lab variation in metabolite profiles can stem from differences in the mass spectrometry equipment or normalization method used.8 Metabolomes are typically analyzed from clinical or environmental specimens as a proxy of the environment being studied. Handling these samples properly reduces the likelihood that technical error produces variation among the metabolomes. Listed below are some factors to consider to minimize variation caused by sample handling:

  • Sample degradation. Several variables inherent to the sample can affect its chemical composition during sample processing. Clinical and biological samples contain enzymes that can degrade metabolites and change their native metabolomes prior to processing the sample(s).9,10 Ensure that samples are appropriately stored and quickly processed to minimize these effects.
  • Bias in metabolomics data. Any sample processing method can affect the metabolites present at the point of sample extraction. Wash steps and chemical processing can wash away or introduce reagent-derived chemicals that alter metabolomic outputs. Even minute variations in sample handling can generate biased results and false positives in serum metabolomes.11 Any robust metabolomics study must document these procedures carefully and define how each sample was processed.
  • Instrument interference. Samples can contain compounds that interfere with a mass spectrometer’s ability to detect metabolites.12 Ion suppression, the most notable of these interferences, occurs when ionized analytes such as salts, drugs, and other endogenous metabolites compete for charge.13 This affects the sets of metabolites detected by a mass spectrometer and can skew metabolome profiles. Sample processing procedures must note the presence of these compounds and plan accordingly.

Put together, having a standardized operating procedure to process samples for metabolome profiling will help you generate trustworthy metabolomic outputs and enable scientifically sound comparisons.

Shipping Considerations for Metabolomics Samples

In many cases, samples must be transported to the lab where they’ll be processed from an outside location. Protocols for sample transport have been developed to minimize their impacts on downstream analyses. To refine this process, scientists have identified several important factors to consider when preparing samples for shipping:

  • Detailed documentation: Every individual sample has unique characteristics, even among groups of samples collected from the same source. Distinguishing between the samples and their origins can help researchers correlate sample characteristics with a patient’s phenotype or delineate environmental processes taking place at a given site. To make this process easier, all samples should have detailed digital manifests to track a sample’s characteristics and origins. A robust project coding system that does not invade a person’s privacy will make this process much easier.
  • Durable sample storage: The outside environment must have as little impact on the sample’s composition as possible during transport. Knowing this, scientists have developed multiple approaches to retain sample characteristics during transport. One such approach, immediate freezing in liquid nitrogen, reduces the risk of metabolic activity from endogenous enzymes. Sample aliquoting before freezing also helps minimize freeze-thaw cycles after sample storage. Using durable sample collection tubes will also ensure sample viability.

At Metabolon, we work with you to develop a shipping protocol to ensure your samples reach us safely. If you store large quantities of samples in a biorepository or biobank, we’ll even work directly with personnel there to prepare and ship your samples.

You can learn more about our sample handling processes, or request a demo sample kit by visiting our Sample Shipment Resources page.

What’s Next?

In this chapter, we took a big-picture view of key factors to consider when developing a sampling procedure. We showed the importance of handling samples carefully, collecting samples promptly, and retaining their chemical characteristics during shipping. While each sample type has unique traits that require separate sample collection procedures, each has similar considerations to ensure the reproducibility of metabolomics data. In the next chapter, we’ll take a similar deep dive into analyzing and interpreting your metabolomics data to extract actionable biological insights.

metabolomics study design success guide

Continue to Chapter 6 - Metabolomics Study Analysis, Interpretation, and Insights

Next comes the exciting moment when your data is returned! As you dive into the numbers, Metabolon scientists will help you turn your results into a story. In this chapter, we will go over the deliverables of your study analysis, interpretation, and insights.


  1. Smith L, Villaret-Cazadamont J, Claus SP, et al. Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions. Metabolites. 2020;10(3):104. doi:10.3390/metabo10030104
  2. Bowen RAR, Remaley AT. Interferences from blood collection tube components on clinical chemistry assays. Biochem Med (Zagreb). 2014;24(1):31-44. doi:10.11613/BM.2014.006
  3. Kiseleva O, Kurbatov I, Ilgisonis E, Poverennaya E. Defining Blood Plasma and Serum Metabolome by GC-MS. Metabolites. 2021;12(1):15. doi:10.3390/metabo12010015
  4. Lim MY, Hong S, Kim BM, Ahn Y, Kim HJ, Nam YD. Changes in microbiome and metabolomic profiles of fecal samples stored with stabilizing solution at room temperature: a pilot study. Sci Rep. 2020;10(1):1789. doi:10.1038/s41598-020-58719-8
  5. Haukaas TH, Moestue SA, Vettukattil R, et al. Impact of Freezing Delay Time on Tissue Samples for Metabolomic Studies. Front Oncol. 2016;6:17. doi:10.3389/fonc.2016.00017
  6. Chen D, Han W, Huan T, Li L, Li L. Effects of Freeze–Thaw Cycles of Blood Samples on High-Coverage Quantitative Metabolomics. Anal Chem. 2020;92(13):9265-9272. doi:10.1021/acs.analchem.0c01610
  7. Papageorgiou V, Mallouchos A, Komaitis M. Investigation of the Antioxidant Behavior of Air- and Freeze-Dried Aromatic Plant Materials in Relation to Their Phenolic Content and Vegetative Cycle. J Agric Food Chem. 2008;56(14):5743-5752. doi:10.1021/jf8009393
  8. Lin Y, Caldwell GW, Li Y, Lang W, Masucci J. Inter-laboratory reproducibility of an untargeted metabolomics GC–MS assay for analysis of human plasma. Sci Rep. 2020;10(1):10918. doi:10.1038/s41598-020-67939-x
  9. Villas-Boas SG, Nielsen J, Smedsgaard J, Hansen MA, Roessner-Tunali U. Metabolome Analysis: An Introduction. John Wiley & Sons; 2007.
  10. Lu W, Su X, Klein MS, Lewis IA, Fiehn O, Rabinowitz JD. Metabolite Measurement: Pitfalls to Avoid and Practices to Follow. Annu Rev Biochem. 2017;86:277-304. doi:10.1146/annurev-biochem-061516-044952
  11. McClain KM, Moore SC, Sampson JN, et al. Preanalytical Sample Handling Conditions and Their Effects on the Human Serum Metabolome in Epidemiologic Studies. Am J Epidemiol. 2020;190(3):459-467. doi:10.1093/aje/kwaa202
  12. Roberts LD, Souza AL, Gerszten RE, Clish CB. Targeted Metabolomics. Curr Protoc Mol Biol. 2012;CHAPTER:Unit30.2. doi:10.1002/0471142727.mb3002s98
  13. Annesley TM. Ion suppression in mass spectrometry. Clin Chem. 2003;49(7):1041-1044. doi:10.1373/49.7.1041

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