Pre-analytical Challenges in Clinical Metabolomics: A Textbook PDF

Pre-analytical challenges in clinical metabolom- ics: from bedside to bench Isabelle Kohler 1 Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 2 Center for Analytical Sciences Amsterdam (CASA...

Pre-analytical challenges in clinical metabolom- ics: from bedside to bench Isabelle Kohler 1 Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 2 Center for Analytical Sciences Amsterdam (CASA), Amsterdam, The Neth- erlands 3 Co van Ledden Hulsebosch Center (CLHC), Amsterdam Center for Foren- sic Science and Medicine, Amsterdam, The Netherlands Corresponding author: Dr. Isabelle Kohler, Division of Bioanalytical Chem- istry, Vrije Universiteit Amsterdam, de Boelelaan 1105, 1081 HV Amster- dam, The Netherlands. Email address: [email protected]. Table of Contents 1. Introduction 2. Whole blood, plasma, and serum 2.1. Whole blood 2.2. Serum and plasma 2.2.1. Serum collection 2.2.2. Plasma collection 2.3. Sample handling 2.3.1. Collection tubes 2.3.2. Clotting time and temperature in serum collection 2.3.3. Clotting agents and separator gels 2.3.4. Anticoagulants in plasma collection 2.3.5. Centrifugation step 2.3.6. Hemolysis 2.3.7. Storage and pre-analytical temperatures 2.4. Comparison between plasma and serum at the metabolome level 3. Urine 3.1. Sample handling 3.1.1. Sample collection 3.1.2. Temperature during collection and transport 3.1.3. Storage, freeze-thaw cycles, and analysis 4. General recommendations 5. Take-home message 6. Acknowledgements 7. References 8. Tables 9. Figure captions 1 What you will learn in this chapter “Trailer starts” The importance of pre-analytical conditions in the metabolomics and lipidomics workflow; The different pre-analytical steps that should be considered when optimizing a method, including sample collection, handling and storage; The effects of inadequate pre-analytical conditions on the metab- olome and lipidome; General practical recommendations for adequate collection, han- dling and storage of biological samples, focusing on blood and urine. ”Trailer ends” Abstract The field of metabolomics has seen tremendous improvements since its introduction two decades ago, notably with the emergence of highly sen- sitive and selective analytical instruments and the implementation of so- phisticated data analysis procedures. A joint effort among the metabolom- ics community has also fostered the use of standardized protocols, aiming for the generation of high-quality data and increasing the confidence in both identification and quantitation of metabolites in body fluids and tis- sues. However, an important aspect of the metabolomics workflow that still often remains neglected is the pre-analytical phase, i.e., all the steps occurring between the sample collection at the hospital or study ward and its actual analysis in a laboratory. Indeed, all pre-analytical conditions – from the type of collection tube to the sample handling temperature – can impact the stability of metabolites, potentially leading to inaccurate re- sults. This Chapter discusses the pre-analytical considerations relevant to the analysis of blood-based matrices (i.e., whole blood, serum, and plasma) and urine, highlighting the effects of inadequate procedures on the metab- olome and lipidome composition. For each step, general practical recom- mendations are given to help enhancing the stability of metabolites through the entire pre-analytical phase and, in turns, the overall quality of the metabolomics data. Keywords Metabolomics, blood, urine, sample storage, sample handling, sample col- lection, pre-analytical conditions 2 1 Introduction Metabolomics, i.e., the analysis of all intermediates and end-products of the metabolism, has raised a lot of interest over the last decade in multiple fields, notably clinical research. Tremendous improvements have been achieved with not only the emergence of highly powerful analytical instru- ments for targeted and untargeted analyses, but also the development of sophisticated data analysis pipelines, allowing to process a higher amount of complex data. These improvements have led to an increased confidence in the quality of the metabolomics data acquired, resulting in a more wide- spread use of this approach in clinical research. However, there is one as- pect of the entire metabolomics workflow that remains often neglected, namely, the pre-analytical phase. Indeed, even with the most sophisticated instruments or powerful bioinformatic tools, a poor data quality will be ob- tained if the pre-analytical steps are not carefully planned and monitored. The pre-analytical step englobes all steps performed between the moment the sample is collected from a subject until the sample preparation and actual analysis. The pre-analytical variables notably encompass the type of sample collected, time of collection, sample collection tubes, presence of additives, conditions for transport, temperature of transport, time before storage, storage temperature, and number of freeze-thaw cycles. Even slight variations in the pre-analytical step can significantly influence the sample integrity and stability of analytes, thereby influencing the data quality and resulting in wrong data interpretation. The clinical conse- quences of inadequate pre-analytical conditions are therefore high. Most critical steps take place outside of the laboratory where samples are ana- lyzed, which highlights the importance to discuss the pre-analytical consid- erations with all collaborators involved and implement standard operating procedures (SOPs) prior to the start of a metabolomics study. Moreover, many large-scale metabolomics studies rely on samples stored in biobanks, which shows the importance for biobanks to guarantee and monitor the quality of biological samples and, in turns, the scientific outcome of subse- quent studies. This Chapter focuses on pre-analytical considerations in the analysis of blood-based matrices (i.e., whole blood, plasma, and serum) and urine, dis- cussing the effects that inadequate pre-analytical conditions may have on the metabolome and lipidome. Each pre-analytical step, from bedside to bench, is discussed. For blood-based matrices, as special attention is given to the selection of anticoagulants (plasma) or addition of clotting agents (serum), the metabolome differences between plasma and serum, the im- pact of collection tubes, the temperature at which each step should be per- formed and its influence on the metabolome and lipidome, and the effect hemolysis can have on the quality of the measurements performed with nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS). For urine, the focus will be on the sampling time, use of additives, and stability at different temperatures during sample collection, handling, transport, and storage. This Chapter concludes with a list of general recom- mendations for pre-analytical considerations to ensure the highest data quality possible. 3 2 Whole blood, plasma, and serum Blood-based matrices, i.e., whole blood, plasma, and serum, are the most frequently used matrices in clinical metabolomics. Blood shows the ad- vantages of reflecting in vivo physiological states influenced by genetics, epigenetics, lifestyle, environmental factors, and drugs, while being rela- tively easily collected (1, 2). This is therefore not surprising that blood- based matrices are often biobanked for future studies, which can take place up to years or decades after the sample collection. This highlights the importance for adequate collection, handling, and long-term storage pro- cedures. Blood is usually collected in venipuncture tubes, and three types of matri- ces can be collected, i.e., whole blood, serum and plasma. 2.1. Whole blood Whole blood is the most information-rich biofluid but represents a very complex matrix due to its high content in cellular components, i.e., red blood cells (RBCs), white blood cells (WBCs), and platelets. Whole blood therefore contains both intracellular and extracellular pools of metabo- lites, which comes with additional challenges. For example, the activity of blood cells remain present after whole blood has been collected and ex- posed to room temperature, which affects the sample quality and integrity. Moreover, the analysis and identification of potential biomarker candi- dates can be complicated by membrane components that originate from the process of cell lysis and isolation (3, 4). Finally, standardized protocols for preparing whole blood are missing. For all these reasons, plasma and serum are typically preferred over whole blood for metabolomics studies. 2.2. Serum and plasma 2.2.1. Serum collection Serum is obtained when blood is collected in collection tubes without the addition of anticoagulants. Upon collection, the blood coagulation cascade is activated, with the rapid transformation of fibrinogen into fibrin, which harnesses cells in blood, resulting in the formation of a fibrin- and platelet- based blood clot (5). After centrifugation, the supernatant (i.e., serum) is collected, while blood cells are eliminated. All the metabolites produced in the body remain in serum, except for fibrinogen and coagulation factors. 2.2.2. Plasma collection In order to obtain plasma, blood is collected in a tube containing an antico- agulant. With the presence of an anticoagulant, the coagulation cascade is not activated, and no fibrin clot is formed. After centrifugation, the super- natant, i.e., plasma, can be collected, while the blood cells remain in the pellets. The composition of plasma is nearly identical to the circulating one, 4 except for the presence of the anticoagulant (5). Compared with serum, plasma contains a significant number of platelets, which may interfere with the analysis. 2.3. Sample handling 2.3.1. Collection tubes The types of tubes used to collect blood samples can have a significant im- pact on the metabolome and should thus be carefully assessed before starting any metabolomics studies. The venipuncture collection tubes are usually made of plastic (rather than glass). In addition to clot activators (see section 2.3.3.) or anticoagulants (see section 2.3.4.), these tubes can also contain a polymer-based gel that provides an easier separation of the supernatant (plasma or serum) from cellular components after centrifugation. The release of plasticizers from these tubes into samples can have a deleterious effect on the quality of the analysis, as they affect the ionization process in MS. A relevant example is polyethylene glycol detected in samples collected with lithium heparinate tubes and serum blood collection tubes, which led to the presence of a typ- ical ion cluster in the MS spectrum (6). This chemical noise was probably explained by the presence of plastic beads in both tubes. Moreover, the additives present in these tubes may contain interfering compounds, which can also affect the quality of the data (6, 7). These in- terferents are anticipated to be manufacturers-dependent, which high- lights the need to stick to one manufacturer and type of tube within a study. The type of collection tube used within a study (and potential addi- tives) should be carefully selected and tested prior to the study to assess whether they are suitable for the expected application. Besides tubes, the influence of pipette tips and other plastic products, as well as solvents used, should also be evaluated during the planning phase. Finally, an adequate labeling of each tube is obviously necessary. It is worth mentioning that not all labels or markings resist to very low temperatures (-80 °C or lower) during long-term storage. It is therefore important to take adequate measures to avoid detached labels or unreadable markings. 2.3.2. Clotting time and temperature in serum collection After blood sampling, billions of cells are still highly active in the tube, re- leasing, uptaking, and metabolizing metabolites. In serum analysis, the time between the collection and the separation of cells with the serum and the temperature at which this process occurs can therefore have a strong impact on the metabolome and lipidome. The ideal clotting time should be between 30 and 60 min. If the clotting time is too short, the coagulation may be incomplete and the serum may still contain cellular elements. On the other hand, a longer coagulation time may result in the lysis of the cells in the clot, which will release cellular components in the serum. The coag- 5 ulation process needs to take place at room temperature, but lower tem- peratures (i.e., 4°C) are recommended for the subsequent steps once the clot has been formed, as the activity of cellular metabolism is reduced at those temperatures (6, 8). It is therefore recommended to cool samples at 4°C after the formation of the clot, and perform the transport, handling and centrifugation at this temperature. Overall, the clotting time and tem- perature should be strictly controlled and identical for all samples within a metabolomics study. This is particularly important in multi-center studies, where samples are drawn at different places. Compared with serum, plasma does not require any clotting step at room temperature. Plasma samples can thus be placed onto ice quickly after col- lection, which is a major advantage compared to serum collection. 2.3.3. Clotting agents and separator gels Clot-activating agents (e.g., silicate-based agents, thrombin) can also be present in the tube, which allows for a shorter clotting time (< 30 min), but at higher costs. However, separator gels and clot-activators may interfere with the analysis, as demonstrated with NMR (2) and LC-MS (1). 2.3.4. Anticoagulants in plasma collection Choosing the adequate anticoagulant for plasma collection and analysis represents a crucial step in the metabolomics workflow, as it can signifi- cantly influence the quality of the data. Common anticoagulants used in clinical settings include heparin, ethylenediaminetetraacetic acid (EDTA), citrate, and EDTA fluoride. These anticoagulants all prevent the formation of the coagulation clot but with a different mechanism. As chelating agent, EDTA inhibits several enzymes, which is advantageous to prevent ex-vivo enzymatic reactions (6). On the other hand, EDTA and citrate, both chela- tors of calcium ions, have shown to lead to the formation of sodium and potassium formate clusters that can lead to significant matrix effects in LC- MS or CE-MS analysis (8). Moreover, citrate is an endogenous metabolite involved in multiple metabolic pathways (notably the TCA cycle) and should therefore be avoided if citrate or related metabolites belong to the tar- geted analytes. Both EDTA and citrate cause strong interference in the NMR spectrum and are therefore less suitable (2, 8). In addition, EDTA and citrate elute at early times in reversed-phase liquid chromatography (RPLC), which can lead to significant ion suppression or enhancement for poorly retained analytes. Heparin seems to cause less issues related to ma- trix effects under standard chromatographic conditions (8), but this also depends on the counterion used for this anticoagulant (i.e., Na+ or Li+). Mei et al. for instance observed different matrix effects between Na-hepa- rin and Li-heparin plasma, where Li-heparin led to higher matrix effects (7). Yin et al. showed that the presence of Li+ can increase the signal of plastic polymer and lead to significant matrix effects (6). Overall, since matrix ef- fects are very dependent on the experimental conditions, it remains diffi- cult to establish general recommendations, as the optimal anticoagulant also strongly depends on the metabolomics applications and target list. 6  2.3.5. Centrifugation step The centrifugation force applied to separate erythrocytes, leucocytes and platelets from the serum/plasma is also relevant, as excessive forces (> 4500 g) may lead to hemolysis and should thus be avoided (8). On the other hand, the centrifugation force and time should be high and long enough, respectively, to ensure a proper separation of cells from the supernatant. Typically, whole blood samples are centrifuged at 1500-4000 g for 5-10 min. With these conditions, red and white blood cells will be removed, but a significant number of platelets may remain in the supernatant. This means that the plasma conventionally obtained in clinical labs is consid- ered “platelet-poor” plasma. The generation of platelet-free plasma, i.e., with less than 10’000 platelets per microliter of plasma, requires a more complex procedure and is usually not used in clinical routine or by biobanks (8). 2.3.6. Hemolysis Hemolysis is defined as the rupture of RBC and release of their contents into the surrounding fluid, which can happen in vivo or ex vivo. Ex vivo he- molysis is one of the most common pre-analytical variables and can happen due to many reasons, e.g., inadequate venipuncture and blood sampling (i.e., too strong aspiration or inadequate diameter of the needle), inade- quate cooling during transport (i.e., whole blood transported at a temper- ature lower than 4 °C), or harsh shaking of whole blood. Hemolysis can lead to a massive release of compounds (e.g., proteins/enzymes, metabolites and electrolytes) into plasma or serum, which can have a pronounced ef- fect on the blood metabolome and lipidome (1, 8, 9). The metabolome in hemolyzed samples does not only differ from that of non-hemolyzed sam- ples, but hemolyzed samples also show a great variability than non-hemo- lyzed samples (6, 8). Hemolyzed samples are not always visible, as only concentrations of free hemoglobin above 0.3 g/L (18.8 mM) can be detected by a naked eye (pink to red color) (1). To date, here is no test or approach that can be used to reliably identify hemolytic samples (8). This shows the need for adequate procedures that limits the chances of hemolyzed samples, since the metab- olome will be already affected even with a slight and non-visible hemolysis. 2.3.7. Storage and pre-analytical temperatures Since sample measurement is most of the time not done on the same day than the sample collection, samples need to be adequately stored until fur- ther analysis, i.e., for weeks, months or even years. Similar to hemolysis, storage conditions (short- and long-term), including temperature and freeze-thaw cycles, can have a huge impact on the metabolome composi- tion. The temperature at which the samples are handled and prepared on the day of their analysis is also extremely important. 7 The ideal long-term storage conditions are to quickly freeze and store sev- eral sample aliquots at -80 °C in specific laboratory freezers or biobanks (6, 8). At -80 °C, most of the metabolites have shown acceptable stability (i.e., no alterations or ≤15%) for months or even years (5-10 years) (10-14). Li- pids are also usually stable at this temperature if butylated hydroxytoluene (BHT), which acts as free radical scavenger, is added to the samples directly after plasma/serum separation (11). In large-scale metabolomics studies, it is frequent to analyze samples that have been stored for different periods of times at 80 °C. This may affect the results because of differences in stor- age duration. However, the expected variability in storage time (i.e., ≤15%) is typically much lower than the biological and inter-individual variability, and thus usually acceptable (8). Typically, -80 °C freezers are not available in study ward or in hospitals. Samples are then often stored temporarily (for hours or days) in -20 °C freezers prior to their transfer to -80 °C freezers. However, a residual enzy- matic activity at -20 °C cannot be excluded, which leads to (significant) al- teration of the metabolome (1). Notably, short-term storage at -20 °C has shown to be critical for lipids – and this even for short periods of time; it should therefore be avoided (11, 15, 16). An interesting and straightfor- ward strategy to increase the stability of bioactive lipids at -20 °C proposed by Giera and co-workers involves the addition of methanol to the samples prior to storage (i.e., protein precipitation), which can preserve sample in- tegrity by preventing (non-)enzymatic degradation (11). In any cases, if a storage step at -20 °C is inevitable, stability studies are recommended prior to the actual study to evaluate the effects of such temperature on the metabolome and lipidome levels. The time between the collection and freezing procedure is also important, as it is often not possible to immediately process the samples in the minutes after a sample has been drawn. Nevertheless, the metabolome and lipidome are expected to remain mostly stable for up to 2h after blood collection when tubes are stored at 4 °C (8, 17). However, samples should be as quickly as possible centrifuged after collection to collect the plasma or serum and store it at -80 °C. Aliquoting of samples into smaller aliquot directly after collection is recom- mended. However, it is not uncommon that some aliquots need to be thawed and frozen a number of times dependent on the number of exper- iments that need to be performed – especially for the most interesting samples. The composition of the metabolome and lipidome may be af- fected by the number of freeze-thaw cycles, as the samples will be exposed at a higher temperature during the thawing process. For instance, signifi- cant changes of the plasma and serum metabolome have been observed when freeze-thawing samples four times at room temperature for 30 min followed by refreezing at −80 °C (12, 18), but metabolites were found to be more stable if the thawing procedure was performed at 4 °C instead of room temperature (6, 19). The metabolite classes mostly sensitive to re- peated freeze-thaw cycles are metabolites from the lipid and carbon center metabolism, antioxidants, nucleotides, and volatile metabolites (8). Blood samples should therefore be thawed at a temperature ≤ 10 °C to prevent significant changes in the metabolome and lipidome composition (6, 20). 8 Overall, these results show the importance adequately assess the number and volume of the aliquots prior to a study, depending on the planned ex- periments, and avoid mixing aliquots within a specific study. Once samples have been thawed and are ready for sample preparation and analysis, the temperature at which all steps are performed can also signifi- cantly influence the composition of the metabolome. This has been inves- tigated by Nishiumi et al., who evaluated the evolution of the concentra- tions of a panel of metabolites (i.e., cations, anions, and lipids) at room temperature (30 min) and cold temperature (i.e., 4 °C for 8 h) in plasma samples (21). The results are shown in Fig. 1. Some metabolites, such as hypoxanthine, showed dramatic changes in their concentrations at both room and cold temperature. Notably, plasma concentrations of hypoxan- thine were 2-times higher after storage at room temperature for 15 min only. Another interesting example is pyruvic acid and the trimethylsilylated derivative of glycerol, whose plasma concentrations increased at room temperature over the time but decreased at cold temperature, respec- tively (21). The instability of some metabolites at room temperature and/or 4 °C has been also reported in other studies (6, 8). Once more, this high- lights the importance of investigating the effect of the temperature on the metabolites or metabolite classes of interest in preliminary studies – not only for long-term storage but also during actual analysis at the bench side, after samples have been thawed. 2.4. Comparison between plasma and serum at the metabolome level Recent studies have investigated the differences at the metabolome and lipidome levels between serum and plasma, as well as with different coag- ulants. For instance, using NMR, Vignoli et al. found that 75% of the me- tabolites quantified showed significantly different concentrations between citrate plasma, EDTA plasma, and serum (2). Notably, amino acids showed higher levels in serum samples, which may be explained by the inhibitory effect of anticoagulants on plasma proteolytic activities and the release of amino acids by activated platelets during the coagulation procedure. More- over, lactate and pyruvate concentrations were higher and lower in serum, respectively, compared with plasma samples. A hypothesis for this differ- ence is an ongoing glycolysis during the process of blood clotting. Finally, higher levels of acetone, acetic acid, and formic acid were reported in plasma, likely due to their presence in the collecting tubes or the anticoag- ulant solution (2). Another study from Yu et al. compared the concentration of 122 metabo- lites in serum and EDTA plasma samples analyzed using the AbsoluteIDQ TM kit p150 from Biocrates Life Sciences, based on flow injection analysis- MS (22). Eighty-five percent of those metabolites showed higher concen- trations in serum (more than 20% difference for arginine, serine, phenylal- anine, glycine, and some lysophosphatidylcholines), while the composition of plasma samples was found to be more stable and repeatable than that 9 of serum. The higher concentrations observed in serum samples may be particularly interesting to detect and quantify low-abundant metabolites. Sotelo-Orozco investigated the difference in the metabolome between se- rum and plasma with different anticoagulants using NMR spectroscopy (23). Heparin and EDTA plasma led to a very similar metabolome than se- rum, with less than 10% of the targeted 50 metabolites showing a differ- ence. Fluoride plasma showed a higher difference, with 11 metabolites pre- senting different levels compared with serum. On the other hands, citrate and acid citrate dextrose plasma samples showed major differences com- pared with serum, largely due to interfering peaks on the NMR spectra is- sued from the anticoagulants themselves (citrate and glucose). Interest- ingly, most amino acids and derivatives showed higher concentrations in serum compared with plasma – whatever the anticoagulant used, which is also supported by other studies (21-23). The type of anticoagulant used can also impact the metabolome and lip- idome differently depending on the temperature. An interesting example comes from the study of Hahnefeld et al., who showed that the amounts of the endocannabinoids 1-arachydonoyl glycerol, 2-arachydonoyl glycerol, and arachidonoyl ethanolamide (anandamide) increased more markedly in K3EDTA plasma after storage on ice for 20 min (by 60, 95 and 30%, respec- tively) than with sodium fluoride/citrate plasma (24). Endocannabinoids are known to be prone to ex vivo formation and extra caution during sam- ple handling and storage should therefore be taken when targeting this class. Finally, Giera and co-workers evaluated the effects of two anticoagulants, namely, K2EDTA and sodium heparin on the concentrations of oxylipins and PUFAs in plasma (11). They observed that certain eicosanoids and leukotri- enes, i.e., LTB4, LTE4, TXB2, 5-HETE and 12-HETE, were much higher in hep- arin samples compared with EDTA samples. Some of these analytes were even not detected in EDTA plasma (i.e., LTB4 and LTE4). 3 Urine Urine is largely used in clinical metabolomics studies due to the ease of collection and the relatively large quantities that can be collected. As the major route of excretion of the body, urine is a very rich matrix, but almost cell-free and with very low protein content. Therefore, it usually requires less sophisticated sample preparation prior to the analysis than other pro- tein-rich matrices, such as plasma or serum. However, similar to blood- based matrices, pre-analytical conditions can have a significant impact on the quality of the metabolomics data. This is particularly relevant in dis- eased patients, as the composition of urine can then significantly change and will be more impacted by pre-analytical conditions, due to the possible higher concentrations of proteins/enzymes and presence of metabolic ac- tive cellular compounds, such as RBC, WBC, bacteria, yeasts, and oxalate crystals (8, 25). For this population, urine test strips can be considered to assess the presence these variables, representing an easy and quick option 10 that can be performed directly after collection to assess the presence of such compounds possibly affecting the metabolome (8). 3.1. Sample handling 3.1.1. Sample collection The collection of urine samples is relatively easy and, thus, does not require highly trained staff. Samples are typically collected in plastic containers. Similar to blood collection, it is important to ensure that the container does not release interferents during the analysis and that urinary metabolites do not adsorb to the container surface (8). Compared with blood, urinary me- tabolites are more prone to non-specific adsorption to the collection con- tainer, as less proteins are available to bind to metabolites and keep them in solution. In immobilized patients, a catheter can be used to collect sam- ples, while adsorbent pads can be put in diapers for the collection of urine samples in newborn or pediatric patients. One important aspect recom- mended during the usual procedure is to collect urine midstream, which is expected to lead to a lower contamination from epithelial cells and bacte- ria compared with the collection of the first stream (9, 26). Indeed, a sig- nificant amount of metabolic active epithelial cells present on the genital surface are flushed with the first stream urine, resulting in contaminated samples even for healthy donors. Since urine can be easily collected, it is not uncommon that the sample collection is done by patients or study participants, which may lead to ad- ditional errors. In this case, an adequate training of the participants based on SOPs is recommended (8, 9). Compared with blood, the composition of urine varies significantly over the time course of the day, a characteristic that needs to be considered for sample collection. Five approaches can be considered for urine collection, i.e., (i) collection of random samples, (ii) collection of spot samples (at spe- cific times), (iii) pool of 24-h samples, (iv) collection of first morning void (urine obtained directly after getting up), and (v) collection of second morning urine (9, 27). Each of these approaches shows advantages and dis- advantages for metabolomics and requires different considerations. Col- lection of first morning void shows the advantages that subjects have been typically fasting for several hours before the collection, which lower the influence of the last meal and/or medication on the sample composition. The second morning urine, collected in a fasted state between 7 and 10 am, shows a different metabolic profile and is even less impacted by the diet of previous day, which is advantageous (28). Random samples are col- lected at any time of the day and will therefore be strongly impacted by the time of collection and effects of meals or other interventions. Spot sam- ples are usually considered in standardized excretion monitoring studies. Finally, 24-h samples (i.e., the collection of all urine voids obtained in 24h and pooled together) are preferred to compensate for the large intra-indi- vidual variability (due to circadian rhythm and diet) in urine composition over the 24-h period. However, they rely on the compliance of the study participants who should not miss any sample and keep the urine collector 11 cool during the whole collection period (9, 27, 29). Among all these options, the best consensus for metabolomics, as suggested by Lehman and co- workers, seems to be the second morning urine collected after an over- night fast until sample collection (8, 28). 3.1.2. Temperature during collection and transport Temperature during collection and transport of urine samples remains a relevant pre-analytical factor for urine analysis, whether urine is collected at the hospital, the study ward, or at home by the patient themselves. Dur- ing a 24-h collection and/or transport of urine samples, the effects of tem- perature on the metabolome and lipidome composition are difficult to pre- dict, as they depend on the targeted analytes. At room temperature, degradation of metabolites will quickly occur for many metabolites, yet not all (9, 30). Some metabolites did not show relevant changes in their urine concentrations at temperatures of 4-10 °C for up to 72 h (31-34), while oth- ers were more significantly impacted (31). However, it is worth mentioning that most of the studies investigating the effects of temperature on the urinary metabolome focused on healthy individuals and did not include urine collected on diseased patients, which contains enzymes and cellular compounds that can have a profound effect on the metabolome (8). Overall, to ensure the lowest risk of metabolite degradation, urine samples should be aliquoted and stored immediately at -80 °C (or -20 °C if not avail- able), where the stability of metabolites is mostly ensured (9, 27, 35). How- ever, this is often not possible for 24-h samples or second morning urine samples collected at the study participant’s place. In this case, urine sam- ples should be kept at 4 °C during (repeated) sample collection and sample transport to reduce enzymatic activities, microbial growth, and limit me- tabolite degradation, prior to long-term storage at -80 °C. The addition of stabilizers, such as borate, thymol, or sodium azide may be considered to further increase the stability of urine samples and prevent bacterial growth. However, they can lead to interference in both MS- and NMR-based approaches, can complex – in the case of borate – with some metabolites, and can introduce artifacts by changing the pH and ionic strength of urine (8, 27, 35). They are therefore not commonly used and not recommended, unless needed for a specific metabolite. 3.1.3. Storage, freeze-thaw cycles, and analysis Urine samples should be stored at -80 °C as soon as possible following col- lection. Numerous studies have reported that the urinary metabolome is not significantly altered at this temperature, and this for a long period of time (up to years) (8, 9, 35, 36). This is particularly the case in untargeted approaches, where the effect of storage typically does not impact the prin- cipal component analysis and samples classification (27). In targeted ap- proach, it is wise to perform a preliminary study to evaluate the actual sta- bility of the target metabolite classes at this temperature. 12 Similar to blood-based matrices, the number of freeze-thaw cycles should be reduced to its minimum. The effect of freeze-thaw cycles on metabolites stability depends on the target analyte: for instance, acylcarnitines and hexose, as well as urea urinary levels were significantly altered after two and three cycles, respectively (31, 37). Another example includes L-isoleu- cine, phenylalanine and 2,3,4-tri-O-acetyl-D-xylono-1,5-lactone, whose uri- nary levels were significantly altered after three freeze-thaw cycle (38). Nevertheless, for many metabolites, the effects of the number of freeze- thaw cycles seem to be negligeable (39). The temperature of urine samples is not only important during collection, transport, and storage, but also during the actual analysis. It is indeed com- mon to analyze large batches of samples, where (prepared) samples stay for a long period of time in the LC autosampler or the NMR cooling rack. A rule of thumb here is to store samples at 4-10 °C for no longer than 48 h (9). A relevant strategy to improve the (long-term) stability of urine is to re- move cellular debris, bacteria, and other particulate matters prior to stor- age using centrifugation and/or filtration. The European Consensus Expert Group Report for laboratory management of samples in biobanks recom- mends a combined use of mild pre-centrifugation (i.e., 1000-4000×g at 4 °C) and filtration as pre-treatment prior to storage at -80 °C (36). The cen- trifugation force should be mild to avoid lysis of cells and release of cellular component in urine, but high enough to be effective enough (40). 4 General recommendations Pre-analytical conditions can have a significant impact on the metabolome and lipidome composition. They should therefore be carefully investigated prior to any metabolomics study to ensure the highest data quality. Gen- eral recommendations can be established for both blood-based matrices and urine, but many experimental conditions also depend on the targeted analytes, which again highlights the need for preliminary studies. A crucial aspect is to discuss the pre-analytical conditions between all col- laborators involved, i.e., clinical doctors, study nurses, analytical chemists, bioinformaticians, etc. Each step should be carefully documented in SOPs, monitored, and reproduced in different collection locations in case of mul- ticentric studies. In the latter cases, clear guidelines should be established regarding the material that needs to be used (e.g., collection tubes, pipette tips, etc.), which should ideally be purchased from the same vendor. Fi- nally, the pre-analytical conditions should also be detailed in scientific arti- cles, so that results can be more easily replicated or validated. Very often, the analytical chemists have access to biobanked samples and does not have any influence on the study design and pre-analytical consid- erations. In this context, it is essential that the most detailed information on the pre-analytical conditions is made available and considered during the data analysis and interpretation. 13 Suggestions to minimize pre-analytical issues for blood and urine samples are summarized in Table 1, adapted from Lehmann (8) and Yin et al. (6, 41). “Take-Home Message starts” 5 Take-Home Message The pre-analytical phase is of utmost importance in metabolomics for the generation of high-quality data. Each pre-analytical step, e.g., type of collection tube, type of anticoagulants, or temperature of storage, can significantly influence the stability of metabolites, thereby influencing the composition of the metabolome and lipidome. General recommendations are difficult to draw, as the effects often depend on the targeted metabolites, but common guidelines can be follow to increase the overall stability of metabolites and ensure sample intergrity. In any cases, preliminary tests should be performed prior to any metabolomics study to investigate the effects of the pre-analytical conditions. Pre-analytical conditions should always be carefully discussed between all partners involved in a metabolomics study – from bedside to bench. Clear guidelines and SOPs should be established and followed; any deviation to the guidelines should be reported Details on the pre-analytical steps should be included in research articles. “Take-Home Message ends” 6 Acknowledgements Marloes van Os is acknowledged for her help in collecting and processing the relevant literature. 14  7 References 1. Hernandes VV, Barbas C, Dudzik D. A review of blood sample handling and pre-processing for metabolomics studies. Electrophoresis. 2017;38(18):2232-41. 2. Vignoli A, Tenori L, Morsiani C, Turano P, Capri M, Luchinat C. Serum or Plasma (and Which Plasma), That Is the Question. J Proteome Res. 2022;21(4):1061-72. 3. Denery JR, Nunes AA, Dickerson TJ. Characterization of differences between blood sample matrices in untargeted metabolomics. Anal Chem. 2011;83(3):1040-7. 4. Kondoh H, Kameda M, Yanagida M. Whole Blood Metabolomics in Aging Research. Int J Mol Sci. 2020;22(1). 5. Plebani M, Banfi G, Bernardini S, Bondanini F, Conti L, Dorizzi R, et al. Serum or plasma? An old question looking for new answers. Clin Chem Lab Med. 2020;58(2):178-87. 6. Yin P, Peter A, Franken H, Zhao X, Neukamm SS, Rosenbaum L, et al. Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin Chem. 2013;59(5):833-45. 7. Mei H, Hsieh Y, Nardo C, Xu X, Wang S, Ng K, et al. Investigation of matrix effects in bioanalytical high-performance liquid chromatography/tandem mass spectrometric assays: application to drug discovery. Rapid Commun Mass Spectrom. 2003;17(1):97-103. 8. Lehmann R. From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids. Anal Bioanal Chem. 2021;413(22):5567-85. 9. Bi H, Guo Z, Jia X, Liu H, Ma L, Xue L. The key points in the pre- analytical procedures of blood and urine samples in metabolomics studies. Metabolomics. 2020;16(6):68. 10. Koch E, Mainka M, Dalle C, Ostermann AI, Rund KM, Kutzner L, et al. Stability of oxylipins during plasma generation and long-term storage. Talanta. 2020;217:121074. 11. Jonasdottir HS, Brouwers H, Toes REM, Ioan-Facsinay A, Giera M. Effects of anticoagulants and storage conditions on clinical oxylipid levels in human plasma. Biochim Biophys Acta Mol Cell Biol Lipids. 2018;1863(12):1511-22. 12. Pinto J, Domingues MR, Galhano E, Pita C, Almeida Mdo C, Carreira IM, et al. Human plasma stability during handling and storage: impact on NMR metabolomics. Analyst. 2014;139(5):1168-77. 13. Haid M, Muschet C, Wahl S, Römisch-Margl W, Prehn C, Möller G, et al. Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C. J Proteome Res. 2018;17(1):203-11. 14. Wagner-Golbs A, Neuber S, Kamlage B, Christiansen N, Bethan B, Rennefahrt U, et al. Effects of Long-Term Storage at -80 °C on the Human Plasma Metabolome. Metabolites. 2019;9(5). 15. Pottala JV, Espeland MA, Polreis J, Robinson J, Harris WS. Correcting the effects of -20 °C storage and aliquot size on erythrocyte 15 fatty acid content in the Women's Health Initiative. Lipids. 2012;47(9):835-46. 16. Metherel AH, Aristizabal Henao JJ, Stark KD. EPA and DHA levels in whole blood decrease more rapidly when stored at -20 °C as compared with room temperature, 4 and -75 °C. Lipids. 2013;48(11):1079-91. 17. Reis GB, Rees JC, Ivanova AA, Kuklenyik Z, Drew NM, Pirkle JL, et al. Stability of lipids in plasma and serum: Effects of temperature-related storage conditions on the human lipidome. J Mass Spectrom Adv Clin Lab. 2021;22:34-42. 18. Fliniaux O, Gaillard G, Lion A, Cailleu D, Mesnard F, Betsou F. Influence of common preanalytical variations on the metabolic profile of serum samples in biobanks. J Biomol NMR. 2011;51(4):457-65. 19. Goodman K, Mitchell M, Evans AM, Miller LAD, Ford L, Wittmann B, et al. Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics. Metabolomics. 2021;17(3):31. 20. Pizarro C, Arenzana-Rámila I, Pérez-del-Notario N, Pérez-Matute P, González-Sáiz JM. Thawing as a critical pre-analytical step in the lipidomic profiling of plasma samples: New standardized protocol. Anal Chim Acta. 2016;912:1-9. 21. Nishiumi S, Suzuki M, Kobayashi T, Yoshida M. Differences in metabolite profiles caused by pre-analytical blood processing procedures. J Biosci Bioeng. 2018;125(5):613-8. 22. Yu Z, Kastenmüller G, He Y, Belcredi P, Möller G, Prehn C, et al. Differences between human plasma and serum metabolite profiles. PLoS One. 2011;6(7):e21230. 23. Sotelo-Orozco J, Chen SY, Hertz-Picciotto I, Slupsky CM. A Comparison of Serum and Plasma Blood Collection Tubes for the Integration of Epidemiological and Metabolomics Data. Front Mol Biosci. 2021;8:682134. 24. Hahnefeld L, Gurke R, Thomas D, Schreiber Y, Schäfer SMG, Trautmann S, et al. Implementation of lipidomics in clinical routine: Can fluoride/citrate blood sampling tubes improve preanalytical stability? Talanta. 2020;209:120593. 25. Kirwan JA, Brennan L, Broadhurst D, Fiehn O, Cascante M, Dunn WB, et al. Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative). Clin Chem. 2018;64(8):1158-82. 26. Emwas AH, Roy R, McKay RT, Ryan D, Brennan L, Tenori L, et al. Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis. J Proteome Res. 2016;15(2):360-73. 27. Fernández-Peralbo MA, Luque de Castro MD. Preparation of urine samples prior to targeted or untargeted metabolomics mass- spectrometry analysis. TrAC. 2012;41:75-85. 28. Liu X, Yin P, Shao Y, Wang Z, Wang B, Lehmann R, et al. Which is the urine sample material of choice for metabolomics-driven biomarker studies? Anal Chim Acta. 2020;1105:120-7. 16 29. Kim K, Mall C, Taylor SL, Hitchcock S, Zhang C, Wettersten HI, et al. Mealtime, temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment. PLoS One. 2014;9(1):e86223. 30. Wang X, Gu H, Palma-Duran SA, Fierro A, Jasbi P, Shi X, et al. Influence of Storage Conditions and Preservatives on Metabolite Fingerprints in Urine. Metabolites. 2019;9(10). 31. Rotter M, Brandmaier S, Prehn C, Adam J, Rabstein S, Gawrych K, et al. Stability of targeted metabolite profiles of urine samples under different storage conditions. Metabolomics. 2017;13(1):4. 32. Budde K, Gök Ö N, Pietzner M, Meisinger C, Leitzmann M, Nauck M, et al. Quality assurance in the pre-analytical phase of human urine samples by (1)H NMR spectroscopy. Arch Biochem Biophys. 2016;589:10- 7. 33. Lauridsen M, Hansen SH, Jaroszewski JW, Cornett C. Human urine as test material in 1H NMR-based metabonomics: recommendations for sample preparation and storage. Anal Chem. 2007;79(3):1181-6. 34. Roux A, Thévenot EA, Seguin F, Olivier MF, Junot C. Impact of collection conditions on the metabolite content of human urine samples as analyzed by liquid chromatography coupled to mass spectrometry and nuclear magnetic resonance spectroscopy. Metabolomics. 2015;11(5):1095-105. 35. González-Domínguez R, González-Domínguez Á, Sayago A, Fernández-Recamales Á. Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics. Metabolites. 2020;10(6). 36. Yuille M, Illig T, Hveem K, Schmitz G, Hansen J, Neumaier M, et al. Laboratory management of samples in biobanks: European consensus expert group report. Biopreserv Biobank. 2010;8(1):65-9. 37. Saude EJ, Sykes BD. Urine stability for metabolomic studies: effects of preparation and storage. Metabolomics. 2007;3(1):19-27. 38. Pasikanti KK, Ho PC, Chan EC. Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites. Rapid Commun Mass Spectrom. 2008;22(19):2984-92. 39. Gika HG, Theodoridis GA, Wilson ID. Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine: sample stability under different handling and storage conditions for metabonomics studies. J Chromatogr A. 2008;1189(1-2):314-22. 40. Bernini P, Bertini I, Luchinat C, Nincheri P, Staderini S, Turano P. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. J Biomol NMR. 2011;49(3- 4):231-43. 41. Yin P, Lehmann R, Xu G. Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem. 2015;407(17):4879-92. 17  8 Tables Table 1. Suggestions of experimental procedures to minimize pre-analytical in metabolom- ics. Adapted from (8) and (6, 41). Parameter Blood Urine Test the suitability of material: collection tubes, pipette tips, vials, etc. Preliminary tests Use the same brands between collection sites Harmonize sample labelling EDTA or heparin plasma (avoid the Midstream second morning first tube of the drawing sequence) urine Sample collection Cool directly after collection (4 °C, Cool directly after collection (4 continuous cooling) °C, continuous cooling) Exclude results obtained from hemo- Hemolysis lyzed samples Transportation Continuous cooling at 4 °C (ice water or cold pack) Centrifugation 2500 × g at 4 °C for 10 min, within 2 h after collection Transfer supernatant in cryotubes as fast as possible while maintaining After centrifugation temperature at 4 °C, followed by snap-freezing Storage At -80 °C Thawing At 4 °C Number of freeze-thaw Limit the number of freeze-thaw cycles to the minimum cycles Record number of freeze-thaw cycle for each sample Monitor every step and document any deviation from the protocol Recording Inspect NMR and MS data for unexpected signal intensities during the entire analysis 18  9 Figure Captions Figure 1 Longitudinal changes in the plasma levels of different metabo- lites depending on the storage conditions, i.e., room tempera- ture (25 °C) and cold temperature (4 °C). The plots show the geometric mean ration of the levels of each metabolite in plasma to the level seen at baseline, i.e., 0 min at room tem- perature or 1 h at cold temperature. A. Analysis with gas chro- matography – mass spectrometry (GC-MS); B. Analysis of cati- onic metabolites with liquid chromatography – mass spectrometry (LC-MS); C. Analysis of anionic metabolites with LC-MS; and D. Analysis of lipids with LC-MS. Reprinted from (21) with permissions. 19 Figure 1 20