Nobody wants to invest thousands of dollars and countless hours of effort in a metabolomics study that turns out to be a flop. So, what steps can researchers take to boost the chances of obtaining robust and reliable results?

The first crucial ingredient is to use standardized and quality-controlled quantitative metabolomics technology.

The second is to understand and control potential confounding factors during the pre-analytical phase. This helps to ensure good sample quality, which is a prerequisite for reliable metabolomics results.

The molecular composition of a biological sample is constantly changing. Some molecules – especially metabolites – are unstable and may be subject to oxidation, aggregation or degradation.

Even ex vivo, these dynamic changes need to be kept to a minimum to best reflect the subject’s physiological status. If essential pre-analytical steps and standardized experimental protocols aren’t meticulously controlled and followed, the results could be inconsistent or corrupted by unwanted artifacts in the data (Ulaszewska et al. 2019).

Is sample type important?

Serum, plasma and urine are the most common sample types in metabolomic studies. Every sample type has its own advantages and disadvantages, so it’s important to choose the most appropriate sample matrix for the target metabolites and study aims (Wang et al. 2018).

For example, there’s not much difference between the lipidomic profiles of serum and plasma, but the profiles of polar metabolites are significantly dependent on sample type (Jørgenrud et al. 2015).

The type of anticoagulant should also be carefully selected, as explained later in this article. If the sample matrix is not the right fit for the research question, and if protocols aren’t adhered to, the study won’t generate high-quality data (Jørgenrud et al. 2015).

What are the main considerations before sampling?

Multiple factors affect the results of a metabolomics study, even before sample collection has begun. For example, alcohol consumption, drug intake, physical exercise, fasting status and diseases can all influence an individual’s metabolic profile.

Studies have shown that alterations in the amino acid profile, especially that of branched-chain amino acids, are associated with obesity (Morris et al. 2012). Furthermore, both pathological and molecular subtypes of diseases can differ in their metabolic profiles (Fan et al. 2016; Wojakowska et al. 2015).

Therefore, the study protocol should specify which information has to be recorded and how. The collected metadata needs to be available for downstream data analysis. Defining these factors in advance is crucial, especially for large-scale, longitudinal studies.

Some important confounding factors that may affect metabolomics results are (Davies et al. 2014; Hardikar et al. 2020):

• Fasting status
• Age
• Gender
• Body mass index (BMI)
• Circadian and physiological rhythm
• Time of sample collection
• Diet, incl. alcohol consumption
• Physical exercise
• Sleep deprivation
• Drugs
• Diseases

What are the main considerations for blood sample collection?

Samples are often taken by different people in different centers. The use of a standardized sample collection protocol (and strict adherence to these protocols) may help to prevent deviations in results (Kirwan et al. 2018; Paglia et al. 2018).

Plasma samples are collected using a set of anticoagulants, including ethylenediaminetetraacetic acid (EDTA), heparin, sodium fluoride/potassium oxalate (NaF/KOx), and sodium citrate. The type of anticoagulant has a strong effect on the concentration of several metabolites.

For example, sodium citrate impairs the reliable determination of citric acid and compounds with similar elution times (Jørgenrud et al. 2015). Anticoagulant residues in the final extract can also affect the final mass spectrometry analysis by forming sodium and potassium formate clusters and causing ion suppression and enhancement (Barri et al. 2013; Jørgenrud et al. 2015).

Choosing the right anticoagulant for metabolomics is still a topic under discussion, and there is no universal answer. Nevertheless, the Human Serum Metabolomics Association (HUSERMET) recommends the use of heparin for metabolomics research (Dunn et al. 2011).

What are the main considerations during blood sample processing?

Before centrifugation and short-term storage

For all omics studies, the general rule is to cool the sample whenever possible. Cooling slows down metabolic activity, thus preserving the sample while it awaits the next preparation step (e.g. centrifugation).

Never freeze a blood sample before centrifugation if you aim to prepare a plasma or serum sample. Freezing breaks cell membranes, resulting in the release of intracellular contents in the sample.

Before centrifugation, the temperature at short-term storage during sample processing can affect the metabolic profile (Hebels et al. 2013; Jørgenrud et al. 2015). Due to continuous blood cell metabolism and release of intracellular compounds, exposure of whole blood to room temperature (RT) for more than two hours is a major risk in the pre-analytical phase (Bi et al. 2020).

In one study, incubation of both whole blood and plasma samples at room temperature for six hours resulted in significantly altered levels of metabolites compared to 0°C and 4°C, respectively (Cao et al. 2019).

Analyses with EDTA plasma samples showed large differences in the stability of specific metabolites, especially lipids, when stored at RT for four days. Degradation of lipids starts already after one day (Jørgenrud et al. 2015).

A similar situation was observed in serum samples. Several studies demonstrated that metabolites are not stable at RT or even at -20°C after being stored for one month (Haid et al. 2018; Hernandes et al. 2017; La Frano et al. 2018).

Following a prolonged storage at RT, about 20% of the metabolites were significantly increased, while 4% were decreased (Kamlage et al. 2018). In fact, degradation and metabolite profile changes began after just 12 hours storage at RT. For example, glutamate levels increase when stored at RT (Kamlage et al. 2014).

Even on ice, certain changes were visible (Anton et al. 2015). This shows that the time window between coagulation and storage is critical for metabolite stability. Luckily there are a few metabolites that may be used as indicators to evaluate pre-analytical sample quality (Schwarz et al. 2019).

Serum is one of the most common sample types used for metabolomic studies. In the first step to obtain serum, whole blood is incubated at RT for 30-60 minutes for coagulation. If clotting time is shorter, coagulation may be incomplete; if it is longer, hemolysis can occur. Both may lead to altered metabolic profiles (Olshansky et al. 2022).

In large-scale multicenter studies, samples may need to be shipped. This introduces another pre-analytical pitfall and may cause delay in sample processing and variability between samples based on different shipping conditions (Breier et al. 2014).

Therefore, fast processing of the sample is recommended. Researchers must pay attention to temperature and clotting time to prevent variations in this step.

While artefacts from pre-analytics may arise, one way to limit their impact on a study is to harmonize the pre-analytical steps and randomize samples during preparation to avoid introducing pre-analytical bias such as batch effects.

Hemolysis

When working with blood samples, hemolysis is another frequent pre-analytical confounding factor (Blanckaert et al. 2008). Hemolysis is the release of hemoglobin and other intracellular components, including metabolites and enzymes from erythrocytes, following disruption of the cell membrane.

Although hemolysis typically results in a red color, it can be hard to spot and requires careful visual inspection during sample preparation.

Considering that intracellular metabolite concentrations can be more than 10 times higher than extracellular, hemolysis can lead to a significant increase in many metabolite concentrations in plasma or serum.

Yin et al. found that 69 metabolites changed significantly in hemolytic samples (Yin et al. 2013). Thus, hemolysis should be avoided, and hemolytic samples should be flagged for statistical data analysis and their results interpreted with caution.

After centrifugation

When preparing plasma, the plasma supernatant should be carefully removed after centrifugation without touching the buffy layer, to avoid contamination with blood cells that can otherwise affect the plasma metabolome (Kamlage et al. 2018).

The ratio of blood sample volume to anticoagulant can also have an effect on the metabolite concentrations in plasma samples. Drawing variable sample volumes may change this proportion and may cause unwanted variations in results (Olshansky et al. 2022).

What to consider when collecting urine samples

Urine collection is relatively easy compared to plasma and serum. But there are still several factors that may cause variability of metabolite profiles (Stevens et al. 2019).

The concentration of metabolites may change up to 15-fold in the same sample volume due to physiological factors, water intake, and external environment (Warrack et al. 2009). Usually, urine samples are collected by the patient. This may introduce variations regarding collection time, sterilization, and sample volume.

As a solution, collection time and fasting status should be specified, an antibacterial additive or filtration should be used to prevent contamination, and normalization strategies should be applied to overcome the volume issue; e.g. normalizing to creatinine concentration (Emwas et al. 2016).

The European Consensus Expert Group Report has published several recommendations for urine biobanks (Yuille et al. 2010).

Long-term sample storage and metabolite stability

After the sample has been processed, it should be transferred to -80°C or lower for long-term storage as quickly as possible to suppress enzymatic activities affecting metabolite levels. However, even at -80°C, certain plasma metabolites are unstable after five years (Haid et al. 2018).

This is mainly due to mechanical degradation. Since most long-term storage of serum, plasma, and urine samples is at -80°C, the decrease in sample quality should be taken into account for long-term studies (Abuja et al. 2015; Jurowski et al. 2017; Rotter et al. 2017; Wagner-Golbs et al. 2019).

Another issue related to sample storage is the frequency of freezing and thawing. Reports on the stability of metabolites during freeze-thaw cycles vary (Abuja et al. 2015; Gratton et al. 2016; Helmschrodt et al. 2014; Moriya et al. 2016).

To minimize the effects on the metabolome, freeze-thaw cycles should be limited, and all samples in each study should be treated consistently, with quality controls applied (Cao et al. 2019).

One way to limit the number of free-thaw cycles is to prepare multiple aliquots in independent storage tubes. This allows to thaw only a small portion of the original sample for a given measurement. The downside is that sample volume also influences the speed of sample degradation during long-term storage.

As always with pre-analytical considerations, the specifics of the study (number of expected measurements, volume required, etc…) are the main driver to choose the optimal long-term storage conditions to ensure sample quality.

Pre-analytics: all’s well that starts well

In summary, pre-analytical confounding factors can have a significant effect on some metabolites and should be kept in mind when comparing metabolomics results. Robust, standardized and detailed sample collection and processing protocols are essential to prevent unwanted variations and to ensure data quality.

All confounding factors and deviations from the study protocol should be recorded and accessible for statistical data analysis. A homogenously treated sample set will be a promising starting point for reliable and reproducible results from a metabolomics study.

If you’d like expert advice on which sample matrix to use for your metabolomic study and how to handle it, please contact us.