Which sample matrix should I use for my metabolomics study?

by | Sep 20, 2022 | Blog, Data analysis, Literature

Among the “Frequently Asked Questions” in metabolomics, one question is sure to pop up: which sample matrix should I use for my study?

Let’s cut to the chase: there is no one-size-fits-all answer to this question. If you were looking for a quick answer, you might want to stop reading now. But if you’re curious about why “it depends” and how you can figure out a good starting point for your study, then read on.

While there’s no single correct approach to figuring out your sample matrix, there certainly are things that could be done wrong. Here, we dive into some of the advantages and disadvantages of the sample matrices you might consider.

The most important question

Before we discuss individual samples, let’s get an even more important question out of the way. What do you want to achieve with your metabolomics study? If you properly define the outcome you desire, the rest often falls neatly into place.

For example, if you want to learn about a biological process that cannot be investigated in human studies, perhaps because it would require multiple tissue samples from sites difficult to access, you have no choice but to move toward animal models or other model systems for the project.

If the goal is to find a stratification biomarker and human biofluids are available, then those biofluids are your logical matrix. If you have other data that seems to make a specific metabolic pathway relevant, such as from transcriptomics experiments, you’d clearly want a metabolomics approach that covers the respective pathways.

The scientific question defines the feasibility of metabolomics from a specific matrix.

An overview of benefits and challenges:

Cells and tissues

+ Detection of local and/or cell/organ-specific effects
– Sampling and extraction
– Simplistic view — no consideration of systemic effects, unclear significance for in-vivo settings

Blood components

+ Easily accessible
+ Integrates signals from the whole organism
– Unclear origin of identified metabolite changes
– Choice of type of blood product can have profound effects on results

Urine and other biofluids

+ Mostly easy to obtain
+ Great resource to study especially for diseases of the organ system from which the sample is collected
– Difficult to standardize; analytical issues (high salt content)

Other non-invasive sample types

+ Many with interesting properties for the analysis of local effects and/or repeated sampling
+ Highly innovative approaches
– Rare use makes validation difficult
– Methodology may not be fully established

Cells and tissues (incl. novel model systems)

The benefit of using cell and tissue sample matrices is obvious. You can directly investigate the cell or organ type that you are interested in. Any signals should be expected to represent the target organ or cell type. Organoids and organ-on-a-chip technologies offer amazing new opportunities for cell- and tissue-based research as well.

Several factors must be considered to obtain meaningful results from cell- and tissue-based studies:

  • Generally, cells and tissues can provide insights into local effects. However, organisms (and biological systems in general) are complex, so one must carefully consider the representativeness of any findings.

  • With tissue samples, variability between locations within organs must be considered. You can counter this problem by using homogenates of samples larger than what the metabolomics method requires, or by measuring tissue samples from multiple sites.

    Most metabolomics studies use fresh tissue rather than fixed tissues, mainly because the fixation step has vast consequences on metabolite levels.

  • In organs that are highly perfused, eliminating blood contamination can also be a step to increase validity.

  • Small changes in the conditions for culturing and/or collection can have a major impact on the findings. If cells change their phenotype and behavior in a culture, they may no longer represent what you are intending to investigate.

    Co-culturing of cell types can be an interesting option to better simulate physiological conditions. The type of medium can also influence the results.

  • Finally, consider the extraction of metabolites from cells and tissues. Metabolites vary considerably in their chemical and physical properties.

    Consequently, one needs to consider whether a single-step extraction works well enough across metabolites of interest, whether a multiple-step extraction protocol could yield better results across metabolite classes, or whether an extraction optimized for polar or apolar is the preferrable option.


Blood components

Blood components are probably the most frequently used matrix in metabolomics studies. As a routinely used clinical sample, it’s the go-to matrix for biomarker studies. Blood represents signals from the whole organism, making it a great matrix for research on complex diseases.

While no one doubts the relevance of blood-based samples as a matrix for metabolomics, there’s intense debate around the exact type of sample to use.

Some proponents in the field prefer a serum matrix because it removes a larger proportion of cellular components, leading to slightly higher metabolite concentrations and thus higher sensitivity in biomarker discovery. For some areas of (targeted) metabolite analyses, such as steroid hormone analysis, serum is the conventional matrix of choice.

Others argue that serum is more prone to pre-analytical issues such as oxidation, making serum-based metabolomics vulnerable to effects of impaired sample quality. In addition, some blood cells remain metabolically active during coagulation and may release metabolites into the fluid component of the sample. For example, platelets can metabolize arachidonic acid and release high amounts of eicosanoids during coagulation. This may limit the relevance of such groups of metabolites if analyzed in serum rather than plasma.

For this reason, many say that plasma should be be preferred over serum. However, it’s not clear which anti-coagulant is most suitable. Ethylenediamine tetraacetic acid (EDTA) plasma is most-commonly used, while citrate plasma is often discouraged for use in LC-MS based metabolomics studies.

Whole blood is rarely used as a matrix for metabolomics because of the potential interference from cellular components. That said, dried blood spot analysis is a well-established metabolomics approach in routine newborn screening (NBS).

Recently, we’ve also seen considerable interest in novel means of biofluid sampling, such as dried plasma spot sampling. This could keep samples (more) stable at room temperature and alleviate logistical barriers to using metabolomics (traditional methods are hampered if blood samples cannot be frozen and transported safely).

Sorted cells or cellular components, plus extracellular vesicles collected from blood samples, can be a very interesting matrix to use to answer specific questions. For example, tumor-secreted extracellular vesicles could provide a non-invasive sample to inform researchers about the metabolome of a tumor. Similarly, metabolomics performed from sorted immune cells can reveal the intricacies of immune regulation.


Urine is generally considered an interesting matrix for metabolomics due to the easy and non-invasive means of sampling. It’s also highly stable compared to blood-based samples, even at higher storage temperatures.

The gold standard is a 24-hour urine sample, but spot urine is also common. Researchers should watch out for the high salt content in urine when performing analysis. In addition, normalizing for creatinine concentrations is a common strategy to improve quality and reduce variability.

It’s also worth noting that in diagnostic settings, you often look for what is not supposed to be there, e.g. glucose or proteins. Many detectable metabolites are deliberately excreted by the organism to maintain homeostasis within the organism. This is a conceptual issue that is also relevant to the use of feces, as will be discussed later.

Other biofluids

  • Saliva and tear fluid are also interesting matrices due to the non-invasive means of sampling, though compared to other biofluids mentioned here, metabolomics in these matrices is in its infancy.
  • Cerebrospinal fluid (CSF) is an interesting surrogate for the central nervous system, but is difficult to sample and it can be very difficult to obtain healthy control samples for ethical reasons.
  • Likewise, bronchoalveolar lavage fluid (BALF) can be a great sample for research on respiratory tract disorders, but sampling challenges make sampling at scale impractical.
  • Sweat could also be a promising matrix due to the easy and non-invasive sampling, but again remains in its infancy.

Other non-invasive sample types

Non-invasive sampling is extremely attractive because it’s accessible, acceptable to study subjects, and easily repeated. Feces and hair are the main matrices to mention here, but we see metabolomics applications described for several other sample types, such as skin lavage, earwax, nasal mucus and nasal lavage, among others.

The use of feces as a matrix for metabolomics studies has seen a steep rise recently. This is partly due to the rise of microbiome studies and acknowledgement by microbiome researchers that metabolomics can enhance functional understanding of host-microbiome interaction. However, ensuring quality in metabolomics studies from fecal samples poses several challenges.

Firstly, the structure and consistency of fecal samples can vary considerably, which affects the abundance of biomolecules. Secondly, both recent and habitual diet can be important confounders. In addition, the method of extraction has a major effect on what is measured in the fecal metabolome.

With separation of fecal water or soft extraction methods, you may be able to mostly base your analysis on the cell-free parts of the fecal sample. With harsher extraction methods you may lyse or rupture microbial cells to varying degrees. This can lead to higher metabolite coverage and be of scientific interest as well.

After all, bacteria interact with the organism extensively and cells may produce and excrete substances that are swiftly taken up by the organism and might thus not be represent in fecal water at significant amounts.

Finally, and most importantly, the fecal metabolome may represent what the organism actively sheds rather than the organism’s metabolic composition. The interaction between the microbiome and the metabolome is more significant in the duodenum than in the lower intestines.

Consequently, the fecal metabolome is a poor surrogate for the metabolome in the upper intestines. The use of cecal duodenal contents may be a better matrix to assess host-microbial interaction, at least in basic research.

In in-vivo human settings, sampling and analyzing samples from the upper intestines has not yet moved beyond proof-of-concept studies and no generally accepted method has evolved yet.

See our article on fecal metabolomics to learn more: Best practices for feces metabolomics – biocrates life sciences ag

Hair is another accessible matrix that’s particularly interesting for disease monitoring because it can capture changes that have occurred over weeks or even months prior to sampling.

Although hair analysis has become an established method for measuring xenobiotics and analyzing selected metabolites such as cortisol, its clinical utility remains in question.

The scarcity of metabolomics studies from hair compared to other indications may make it even harder to confirm the relevance of biomarker signatures. Again, variability is a major concern. This can stem from hair characteristics such as pigmentation, but also from external factors such as hair treatment with shampoo and other chemicals.

There are also no accepted standard procedures specific to the analysis of the hair metabolome, which makes comparison between studies even more difficult.

Concluding thoughts

What’s clear is that metabolomics can be and has been done from a vast variety of sample matrices, each with its pros and cons. In that respect, the question of which sample matrix to use remains as relevant as it is difficult to answer.

Thankfully, if you are considering more than one matrix, you don’t always have to choose: why not combine multiple matrices to optimize results and maximize insights?

Here are a few examples of how combining matrices could be of value:

  • Combining plasma and urine analyses for research on kidney diseases can add insights compared to using only a single matrix, as relative changes between plasma and urine could be biologically meaningful.
  • For biomarker research based on blood components, the question of where the signal originates can be answered by adding tissue of the target organ or multiple organs which are known to contribute to a disease. In other words, combining circulatory biofluids and tissues can provide information on BOTH the local effects and the systemic contribution.
  • Combining metabolomics from fecal samples and plasma can provide clues as to the probable effects of microbiome changes directly in the gastrointestinal tract, and which actions it can exert in systemic circulation.

There’s also plenty to say about combining metabolomics with imaging technologies or other -omics technologies – but we’ll save that for another article.

Before you embark on your metabolomics project, you have some serious thinking to do. Choose wisely, and you’ll be rewarded with exciting findings.

What do you think? What is your favorite matrix and why? Have we missed a matrix that you find important? Reach out to us if you have questions about the methodological considerations for metabolomics studies in the matrix of your interest

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