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4 February 2026

Bridging the Gap: A Scoping Review of Pre-Analytical Variability in Biofluid Metabolomics

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1
Centre for Science, Technology and Medicine, Keele University, Keele ST5 5BG, UK
2
Oswestry Keele Orthopaedic Research Group (OsKOR), Robert Jones and Agnes Hunt Orthopaedic Hospital Foundation Trust, Oswestry SY10 7AG, UK
3
Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7BE, UK
*
Author to whom correspondence should be addressed.
Appl. Biosci.2026, 5(1), 10;https://doi.org/10.3390/applbiosci5010010
This article belongs to the Special Issue Feature Reviews for Applied Biosciences
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Abstract

Metabolic profiling enables comprehensive characterisation of the small molecules that are part of the biochemical composition of biological fluids. The most widely profiled biofluids include serum and plasma. Additionally synovial fluid provides a direct reflection of the metabolomic environment of joints and holds promise for biomarker discovery in arthropathies. However, the reproducibility of metabolomics data is highly sensitive to pre-analytical variation, and at the present time, standardised protocols for synovial fluid remain underdeveloped. This review aims to identify and evaluate the existing literature on effects of biofluid pre-analytical handling treatments on metabolic profiles. This review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A search was carried out to identify studies employing LC-MS, GC-MS, and NMR spectroscopy for the investigation of factors including sample collection variables, pre-centrifugation conditions, centrifugation parameters, post-centrifugations conditions, sample storage conditions, and freeze/thaw cycling. Best practice recommendations emerging from this review include the use of additive free serum and heparin plasma tubes, the centrifugation of samples within two hours of collection, immediate storage of samples at −80 °C, and avoidance of repeated freeze/thaw cycling. However, while pre-analytical influences have been extensively characterised for plasma and serum, evidence for synovial fluid remains limited. Overall, the findings highlight the existing recommendations for plasma and serum and demonstrate the need for standardised pre-analytical protocols and validation of quality control markers to advance synovial fluid metabolomics.

1. Introduction

Metabolomics is the comprehensive measurement of all metabolites and low molecular weight molecules (metabolites) that are found in a biological specimen to understand the biochemistry and physiological state [1,2]. As the downstream products following genomic and proteomic processes, the metabolic profile (also referred to as untargeted metabolic phenotyping, metabolomics, or metabotyping) is representative of both endogenous and exogenous factors such as a person’s diet, lifestyle, age, therapeutic interventions, and exposome, as well as the gut microbiome [3,4,5]. This makes metabolomics ideal for the characterisation of complex phenotypes, understanding mechanisms involved in disease pathophysiology, the discovery of biomarkers for disease monitoring, and understanding treatment outcomes [6,7,8,9]. Metabolomic platforms include liquid chromatography or gas chromatography coupled with mass spectrometry (LC-MS or GC-MS), as well as nuclear magnetic resonance (NMR) spectroscopy [10,11], all having different advantages. NMR is not as sensitive as GC and LC-MS platforms, and its applications are generally limited to the identification of highly abundant metabolites (≥1 µM) compared to GC/LC-MS approaches that can be used to profile metabolites at low concentrations (typically in the nano–picomolar range depending on the instrument used and in some targeted applications down to the femtomolar range) and offer a higher resolution (∼103–104), allowing for the profiling of low abundance metabolites including small metabolites (<650 Daltons) [12,13]. However, NMR is considered a non-destructive technique, generally requiring less sample preparation prior to analysis, therefore preserving metabolite structure and more readily enabling accurate metabolite quantification [14]. GC and LC-MS approaches can be applied for the analysis of metabolites present at low abundance. GC-MS has been shown to perform better when profiling volatile and thermally stable metabolites, whilst LC-MS outperforms GC-MS in the profiling of lipophilic and higher molecular weight metabolites [15,16,17].
Some of the most widely metabolomic profiled samples are bloods, including serum and plasma [18,19], some of which are large scale cohort studies [20,21]. Metabolomic profiling of blood samples is valuable for understanding the systemic responses and contributors of a disease status/phenotype [22]. For biomarker analysis, the detection of markers, which can be assessed via a simple blood test, is optimal for clinical utility [23,24,25]. However, for certain diseases, analysis of tissue-specific biofluids can be more informative of the disease status [26]. In recent years, the metabolomics of synovial fluid has grown in popularity, particularly for the diagnosis and study of disease mechanisms associated with joint pathologies such as osteoarthritis (OA), rheumatoid arthritis (RA), posttraumatic arthritis, and joint infections, as it is a locally derived biofluid that directly reflects the biochemical environment of the joint space [27,28,29,30,31,32,33]. Unlike blood-based biofluids, synovial fluid composition is influenced by resident and infiltrating cells, cartilage and bone turnover, inflammatory mediators, and mechanical loading [34,35,36]. Across biofluids, a significant challenge in metabolomics studies lies in the preservation of the authentic in vivo metabolite profile. This is because endogenous enzymatic activity continues following sample collection, and subsequent pre-analytical handling steps may further contribute towards changes that distort the metabolite levels [37]. As an extreme example, metabolic flux rates in central carbon metabolism are very fast: within glycolysis in trypanosomes, glucose is transformed into pyruvate in under a second [38]. Clearly biofluids cannot be analysed in these timescales; therefore, one must be pragmatic particularly if collecting samples in a clinical setting.
Pre-analytical factors such as the collection of the sample (e.g., time of collection, blood tubes used for collection, etc.), transport conditions, storage conditions, and sample handling can all impact the stability of the metabolome, therefore introducing variability and making the data collected less reproducible [39,40]. As a result, it is crucial to understand how delays in sample processing and the conditions these samples are exposed to impact the metabolome and our interpretation of the data. Additionally, synovial fluid also presents distinct pre-analytical challenges including low sample volumes (median total knee volume = 3.05 mL), high viscosity due to the presence of hyaluronic acid (3–4 mg/mL), variable dilution of the biofluid due to sample collection procedures, and limited opportunities for rapid processing in clinical settings [41,42,43]. These characteristics mean that pre-analytical factors established for blood-based metabolomics may not be directly transferrable to synovial fluid, underscoring the need for a focused evaluation of pre-analytical handling practices specific to synovial fluid metabolomics. The aim of this review is to identify and assess the current literature on the effects of pre-handling treatments on metabolic profiling of biofluids (whole blood, plasma, serum, and synovial fluid). By mapping the scope and depth of existing research, the review will highlight knowledge gaps and provide biofluid sample handling recommendations for untargeted metabolomics.

2. Materials and Methods

This review was conducted and reported according to the PRISMA-ScR statement and guidance [44]. The PRISMA-ScR checklist is presented in Supplementary Table S1. Searches were performed using a Boolean search string in online databases PubMed and Web of Science (Table S2). In addition to database searches, a snowballing approach was used to identify additional relevant studies. Reference lists of included papers were screened manually to capture studies missed in the initial search. These studies were screened against the same inclusion and exclusion criteria as the database identified records.

2.1. Inclusion and Exclusion Criteria

Only studies that tested the effects of pre-analytical processing using untargeted metabolomic profiling approaches (LC-MS, GC-MS, or NMR) of selected biofluids were included. Both human and animal studies were included. Only studies analysing synovial fluid, whole blood, serum, and plasma samples were included. Non-English language and studies where the full text was unavailable were excluded, and review/abstract only papers were excluded.

2.2. Data Extraction and Analysis

Full text reviewing of all papers meeting the inclusion criteria and data extraction was carried out by two independent researchers (Y.L and S.S). The data extracted included the type of biofluid analysed, source of the biofluid, pre-analytical factors tested, technique used to analyse samples, and the outcomes. Disagreements between the reviewers in the screening were resolved through discussion with a third reviewer (C.H.H). The data were summarised by describing the variables tested in the study and results concerning the impact on the metabolome in a table format.

3. Results

3.1. Study Characteristics

The PRISMA flowchart (Figure 1) outlines the study selection process. The initial search identified a total of 1042 studies. Following the removal of duplicate records (n = 174), the remaining records were screened by title and abstract. During title and abstract screening, records were excluded if they were not related to the metabolomic analysis of relevant biofluids (plasma, serum, or synovial fluid), or if they did not apply NMR, LC, or GC-MS analytical platforms. Full text reports were sought for retrieval for the remaining records (n = 135). These records were assessed for eligibility, and studies were excluded at this stage if they did not meet the inclusion criteria including the use of untargeted metabolomic profiling approaches and the experimental evaluation of pre-analytical variables (n = 89). A total of 46 studies were identified to meet the inclusion criteria, with an additional study identified via snowballing, resulting in a total of 47 studies included in the review. The majority of studies analysed blood-derived biofluids including plasma and serum, while a smaller subset focused specifically on synovial fluid. Analytical platforms employed across studies included NMR, GC-MS, and LC-MS, either individually or in combination. Studies included were published between the years 2000 and 2025.
Figure 1. PRISMA flow diagram illustrating the study selection process. The diagram outlines the number of studies identified, screened, excluded, and included in the final review.

3.2. Sample Collection Variables

Twelve papers (Table 1) explored the impact that blood collection tubes and anticoagulants have on the metabolomic profile.
In NMR studies, all common tube types used in blood collection (EDTA, citrate, heparin, and serum) consistently introduced intense background signals; however, the region of the signal varied depending on the anticoagulant [45]. Serum and heparin tubes (plasma) have been associated with demonstrating increased concentrations of lactate when analysed using NMR, whereas K2-EDTA and K3-EDTA (plasma) tubes cause increases in acetate and pyruvate levels, respectively [46]. Heparin (plasma) tubes generated broad overlapping peaks that are more difficult to distinguish from endogenous signals, compared to the characteristic peaks generated by EDTA tubes, which makes them easier to exclude [47]. Citrate and ACD (plasma) tubes have been found to typically present with intense signals overlapping with endogenous metabolites including citrate and glucose signals, therefore making it difficult to exclude these signals from analysis [48], which is important when there are differences within central carbon metabolite pathways and, in particular, glycolysis and TCA.
In MS based studies, the use of anticoagulants directly impacts the ionisation efficiency and causes matrix effects. This has been observed in relation to citrate and EDTA plasma tubes, as they form sodium and potassium formate clusters and can additionally cause ion suppression or the enhancement of metabolites co-eluting with citrate and EDTA peaks [49]. However, EDTA is a chelating agent; therefore, it can bind to interfering Ca2+ and Mg2+ ions, which is useful when analysing carbohydrates using electrospray ionisation (ESI) MS, as it removes metal adducts and enhances carbohydrate peaks [50,51]. The use of heparin plasma tubes, on the other hand, has been consistently identified to be associated with minimal contamination and matrix interference when samples are analysed using LC-MS; therefore, they are recommended over citrate and EDTA tubes [52,53,54]. With regards to GC-MS analysis, of the two papers identified to apply this analytical technique, the results suggest that, like with the NMR and LC-MS results, the peaks generated by EDTA are easily identifiable and can be excluded from analysis, but the peaks generated by the presence of sodium heparin overlap with endogenous metabolites [55].
Serum tubes when analysed using LC-MS have been found to exhibit increased concentrations of key endogenous metabolites including hypoxanthine, sphingosine-1-phosphate, linolenyl carnitine, and xanthine, where they were found to increase their concentrations. These metabolites have been proposed as biomarkers of coagulation and as indicators of delayed sample processing when serum is stored at room temperature, further highlighting the need for careful sample handling when interpreting biologically relevant changes of these specific metabolites, so as to distinguish them from artefactual concentration shifts [54,56]. GC-MS analysis of serum samples revealed that the presence of polymeric gels in serum collection tubes primarily impact metabolites involved in amino acid metabolism (alanine, proline and threonine), glycerolipid metabolism (monopalmitin and monostearin), and those involved in primary pathways including aconitic acid and lactic acid [57].
Together, these results suggest that the type of anticoagulant used in blood tubes was identified as a common source of variation in metabolomic data across both NMR and MS platforms. Acting as a source of exogenous compounds, anticoagulants can contaminate the sample causing matrix effects that can mask or alter endogenous metabolite signals, therefore potentially skewing the interpretation of the results. Notably, no studies were identified that directly investigated the impact of sample collection variables such as collection containers on synovial fluid metabolomic profiles. This contrasts with the extensive body of evidence available for blood-derived biofluids, therefore highlighting a clear gap in the literature underscoring the need to investigate how sample collection practices may influence synovial fluid metabolomics.
Table 1. Summary of studies investigating the impact of blood collection tube types and anticoagulants on metabolomic profiles. Results highlight differences introduced by tube additives and materials across serum and plasma samples using both NMR and MS platforms.

3.3. Pre-Centrifugation Conditions

Pre-centrifugation conditions including temperature, time delay, light exposure, and heat treatment were frequently explored as sources of pre-analytical variation in metabolomics workflows. Twenty-one papers (Table 2) were identified, which explored these factors, with 12 studies applying NMR [45,46,59,60,61,62,63,64,65,66,67,68], five applying LC-MS only [58,69,70,71,72], two applying GC-MS only [73,74], and two applying both LC-MS and GC-MS [40,75]. Delayed processing time and storage temperature prior to centrifugation were two commonly explored themes within this category.
NMR-based studies [45,46,59,60,61,62,63,64,65,66,67,68] agreed that the delayed processing time significantly impacted the metabolome in both plasma and serum samples; however, the degree of impact was time- and anticoagulant-dependent. One study found that plasma samples collected using tubes containing fluoride/oxalate additives maintained metabolite stability when stored at 4 °C for 24 h [59]. Studies have identified common metabolites that can act as potential biomarkers of delayed sample processing including glucose and lactate or the ratio between glucose and lactate in both plasma and serum samples [45,62,65,67,68]. Prolonged pre-processing time (>4 h) was found to result in increased lactate and decreased glucose concentrations particularly in serum samples. Ornithine was also identified in plasma samples as a potential marker of delayed processing time [61]. However, these results can also be influenced by the temperature the samples are stored at, with studies suggesting an interplay between storage temperature and duration. Generally, studies were in agreement that the storage of plasma and serum samples at either 4 °C or at room temperature (20–25 °C) for up to 48 h significantly influences individual metabolite abundances as opposed to alterations in the global multivariate structure of the metabolome; however, the degree of impact is more pronounced in samples stored at room temperature therefore the recommendation would be to limit the time between plasma or serum collection and centrifugation and store samples at 4 °C during this time [46,61,62,63,65,66]. Ornithine, pyruvate, and lactic acid were specifically identified as temperature sensitive markers with altered levels observed when samples were stored at 22 °C but were more stable at 4 °C [61,65]. One study found that the storage of samples at 4 °C can lead to decreased levels of short-chain fatty acids (SCFA); however, this effect could be reversed via the warming of the sample to 22 °C prior to centrifugation [61]. This study also evaluated the impact of light exposure during the pre-centrifugation delay period in plasma samples; however, no significant changes were observed in the metabolome of light-exposed samples compared to those shielded from the light, suggesting minimal interference from light exposure [61]. One study assessed the effect of heat activation (56 °C for 30 min) on plasma samples, which caused significant alterations in metabolite profiles including a significant decrease in α-1-acid-glycoprotein and increases in triglycerides, lactate, alanine, and glycerol; therefore, it should be avoided [46]. These variables should also be tested for serum and synovial fluid samples.
In MS-based metabolomic analysis (GC and LC-MS) [40,58,69,70,71,72,73,74,75], pre-centrifugation conditions were found to significantly impact both plasma and serum samples; however, these factors primarily affected plasma samples. Delays in sample processing were found to significantly impact the metabolome. In plasma, studies demonstrated that delays of up to 4 h had minimal effects on lipid classes including phosphatidylinositol, sphingomyelin (SM), and hexosylceramides, but prolonged delays of 20–48 h significantly impacted metabolites involved in glycolysis/gluconeogenesis, the TCA cycle, γ-glutamyl amino acid metabolism, and the glutathione pathway [58,69,70,71,72]. Key metabolites identified in studies as markers of processing delays in plasma samples included glucose, lactate, pyruvate, fumarate, 5-oxoproline, and the ratio between arginine and ornithine [70,72]. The storage temperature prior to centrifugation was also found to significantly influence plasma metabolome stability, with studies identifying temperature sensitive metabolites. At room temperature (20–25 °C), plasma samples presented with significant changes in lactic acid, ascorbic acid, sucrose, pyruvic acid, and hypoxanthine levels [73,75]. One study also found elevated lipid levels associated with EDTA plasma when stored at room temperature, suggesting the storage temperature can influence lipid metabolism in plasma [58]. Based on these findings, pre-centrifugation delays should be minimised to under 3 h with storage at 4 °C for the metabolomic analysis of plasma samples when applying MS-based analytical techniques. In serum samples, pre-centrifugation delays of up to 24 h were found to have minimal impact on the metabolome when samples were stored at 4 °C with samples demonstrating no detectable variability in metabolite concentrations compared to those frozen immediately following collection [74]. Increasing the clotting time of serum samples (up to 2 h) was found to significantly impact the metabolome particularly impacting amino acids and peptides possibly associated with the breakdown of red blood cells (RBCs) or proteolysis [40]. These findings suggest that the serum clotting time should be standardised and minimised with samples then being stored at 4 °C for up to 24 h or frozen immediately to maintain metabolite integrity.
Despite multiple studies evaluating the impact of pre-centrifugation conditions on plasma and serum metabolomic profiles, no studies were identified that directly assessed these effects in synovial fluid. This highlights a clear gap in the existing literature, particularly considering the distinct physiochemical properties of synovial fluid, which may limit the direct transferability of findings from blood-derived biofluids.
Table 2. Overview of studies assessing the effect of pre-centrifugation factors on the metabolome stability of plasma and serum samples using NMR- and MS-based analytical techniques.

3.4. Centrifugation Parameters

Centrifugation is a key step in the preparation of biofluids for metabolomic analysis, used to separate cells from the fluid component before storage and analysis [76]. Three studies [66,77,78] (Table 3) were identified to explore this theme in plasma, serum, and synovial fluid samples using NMR- and MS-based approaches. The findings from studies applying NMR and MS (LC-MS) on plasma and serum samples were mixed. Jobard et al. [66] compared centrifugation temperature (20 °C vs. 4 °C), speed (2000× g vs. 3000× g), and time (10 vs. 20 min) on plasma and serum NMR profiles, where no significant changes were observed as a result of these parameters. Lesche et al. [77] compared speed (1500× g vs. 3000× g) and time (5 vs. 10 min) at 20 °C using both NMR and LC-MS on plasma samples. Both the centrifugation speed and time altered the plasma metabolomic profiles. The study demonstrated that the centrifugation conditions influence glutamine levels, as measured by NMR analysis. These differences were attributed to residual platelets remaining in the samples centrifuged at 1500× g for 10 min, resulting in higher glutamine concentrations compared with samples centrifuged at 3000× g for 5 min. In LC-MS analysis, sphingomyelin SM (40:1) and the sodium adduct of sphingomyelin SM (42:2) were identified as key contributors to the separation of samples processed under the two centrifugation conditions in the sPLS-DA model [77].
Anderson and colleagues [78] compared the impact of centrifuging synovial fluid samples prior to freezing using NMR analysis. The study found that the centrifuging of synovial fluid prior to freezing was beneficial and resulted in improved sample clarity and analytical reproducibility; therefore, it is a recommended step for the metabolomic analysis of synovial fluid [78].
Table 3. Summary of the studies evaluating the influence of centrifugation parameters on plasma, serum, and synovial fluid metabolomes.

3.5. Post-Centrifugation Handling and Freezing Delays

Studies using NMR spectroscopy (Table 4) have assessed the impact of the time of delay to freezing and the temperature during the delay on plasma and serum metabolomes. One study found short delays of <1 h did not cause significant changes to plasma and serum metabolomes, suggesting that a short post-processing delay may be tolerable [66]. However, moderate to long delays between 2 and 24 h have been found to result in progressive metabolite degradation, especially in serum samples [62]. Delays of 2 h significantly impacted serum lipid concentrations, whilst delays of up to 24 h were associated with significant decreases in triglycerides, citrate, proline, choline, histidine, and lactic acid concentrations in both plasma and serum samples [45,62,67]. Studies also noted that the impact of post-centrifugation processing delays can also be influenced by the type of anticoagulant tube used, with proline and choline levels demonstrating better stability in EDTA plasma tubes compared to fluoride/oxalate treated plasma [62]. Additionally, one study suggested that metabolites in lithium–heparin plasma tubes demonstrated higher tolerance to post-centrifugation delays compared to EDTA tubes [67]. Overall, plasma samples were found to generally be more stable than serum samples to post-centrifugation delays; therefore, they are recommended over serum samples in NMR workflows that involve delayed freezing. These findings suggest that the period between sample centrifugation and freezing represents a critical window during which continued enzymatic activity, oxidation, and metabolite degradation can still occur; therefore, delays in sample processing following centrifugation should be limited. Ideally, samples should be frozen immediately at −80 °C post-centrifugation where possible, and delays should be limited to <1 h particularly for serum samples.
Four studies (Table 4) were identified that had applied MS analysis to test the impact of post-centrifugation delay. One study tested the impact of a delayed freezing time of 3 days at room temperature, followed by 9 days at 4 °C, on serum samples using both GC and LC-MS analytical techniques [79]. The results of the study indicated significant metabolite class specific alterations, which are perhaps not surprising given the long delays during sample processing; however, overall, the metabolome demonstrated stability despite the prolonged delay [79]. Similar findings were observed in a study that stored serum samples at 4 °C for up to 24 h before freezing [40]. In another study, the impact of plasma sample drying following centrifugation was assessed, where the time of drying was identified to significantly influence the metabolome, with metabolites exhibiting rapid degradation or generation over the first 48 h at room temperature. Plasma metabolites were significantly impacted by extended drying periods including methionine sulfoxide, glutamic acid, and histidine [80]. The short-term storage of plasma samples (up to 16 h) at either 4 or 12 °C resulted in less variation compared to plasma samples stored at room temperature [81]. These findings suggest that the plasma metabolome is more stable when stored at low temperatures following centrifugation, whilst serum samples demonstrate broader temperature stability even under prolonged delays.
Despite multiple studies assessing post-centrifugation handling and freezing delays in plasma and serum, no studies have directly examined these effects in synovial fluid.
Table 4. Summary of the studies exploring the impact of post-centrifugation sample handling and delays in freezing on the metabolome.

3.6. Storage Conditions

The long-term preservation of the metabolome is crucial for ensuring biofluids are viable for metabolomic analysis after they have been kept in either short- and long-term storage. Clinical samples, such as from trials, have to be collected over a period of time prior to all samples being available for metabolomics analysis; hence, storage is unavoidable. Further, samples collected through biobanks may have been stored for long time periods. Variations in storage conditions can potentially lead to chemical degradation or enzymatic activity, which can alter the metabolomic profile of the sample. Four studies (Table 5) were identified that assessed the impact of the storage duration, temperature, and sample handling on the metabolite stability using NMR spectroscopy. One study investigated the impact of long-term storage (2–15 years) at −20 °C on animal-derived plasma samples [82]. This study was able to identify a selective few metabolites that demonstrated significant correlations with storage durations including betaine CH3 group, betaine CH2 group, acetoacetate, and imidazole compounds, which significantly decreased over time, and glycerol and glucose, which significantly increased over time [82]. The short-term storage of plasma samples was investigated in one study, which tested the impact of storage at room temperature and at −20 °C for 7 days and 1 month. The results demonstrated that, following storage at room temperature for 2.5 h, plasma samples exhibited significant alterations in lipid profiles, and storage at −20 °C for 1 month led to increased concentrations of proline and glucose levels. No significant changes were observed in samples stored at −20 °C for 7 days [47]. Two studies investigated the impact of short-term storage at −80 °C on both plasma and serum metabolomes. The results of both studies agreed, with no significant changes being observed in the metabolomes [47,66]. These findings suggest that the short-term storage of plasma samples can be done at −20 °C, avoiding storage at room temperature, but early transfer to −80 °C is preferable, and long-term storage of both plasma and serum samples should be at −80 °C. The quenching and extraction of the plasma metabolome was also explored in one study prior to freezing [83]. This study tested the use of methanol, ethanol, or a mixture of methanol and chloroform on sample extraction followed by incubation on ice. The results demonstrated that the use of a methanol: chloroform mix improved the extraction of coenzymes including NAD+; however, extended incubation on ice increased the conversion of NAD+ to NADH, decreased ATP levels, and sped up coenzyme degradation. These findings suggest that the rapid quenching of the metabolome prior to sample freezing is ideal for capturing labile coenzymes; however, prolonged incubation on ice should be avoided if quantifying redox cofactors and ATP [83].
Six studies (Table 5) were identified that had applied MS for the analysis of plasma and serum samples. Of these identified studies, the impact of long-term storage was assessed only on the plasma metabolome, where it was found that the metabolome demonstrated stability for up to 7 years in −80 °C; however, prolonged storage beyond 7 years was associated with significant metabolomic alterations [53,80,84,85,86]. The short-term storage of plasma samples at room temperature has been shown to cause substantial metabolomic alterations; therefore, it should be avoided where possible [80]. Metabolite stability was found to be class-specific, with specific metabolite classes such as oxylipins identified to demonstrate stability across a range of storage conditions [80,85]. Only one study investigated the impact of storage on serum samples, which found that the short-term storage of serum samples at either 4 °C or 22 °C for up to 24 h caused notable changes in amino acid concentrations suggesting the short-term temperature sensitivity of the serum metabolome [87].
While the majority of studies focused on plasma and serum, notably, two studies were identified that examined the impact of storage conditions on synovial fluid metabolomics using NMR platforms. In one study, animal-derived synovial fluid samples were either snap frozen in liquid nitrogen followed by storage in −80 °C or frozen directly in either −20 °C, −80 °C, or on dry ice. Samples that were snap frozen first, prior to freezing at −80 °C, demonstrated the least variability in metabolite profile compared to other freezing techniques, suggesting rapid freezing can help preserve the synovial fluid metabolome [78]. Two studies evaluated the short- and long-term storage of synovial fluid samples [88,89]. The results suggested that short-term storage at room temperature or at 4 °C for up to 12 h primarily affects metabolites associated with energy synthesis, whilst long-term storage at either −75 °C or −80 °C resulted in significant changes in metabolite concentrations. When stored at −80 °C for 10–12 months, significant decreases in concentrations of 3-hydroxybutyrate, acetate, succinate, creatine, dimethyl sulfone, and N-N dimethylglycine were observed and an increase in acetoacetate [88]. Storage at −75 °C for 1 year resulted in decreased concentrations of glucose, N-acetyl glycoproteins, CH2-chain, and CH3-terminal and resonances of lipoproteins, valine, leucine, and isoleucine concentrations [89]. These findings suggest that the short-term storage of synovial fluid samples should be limited, with samples being stored at −80 °C if stored for periods exceeding 12 h.
Table 5. Summary of the studies exploring the impact of samples storage on plasma, serum, and synovial fluid metabolomes.

3.7. Freeze/Thaw Cycling

Biofluids undergoing F/T cycles are a common occurrence in metabolomic studies due to routine sample storage, transport, and the use of the same samples for multiple analytical procedures. The F/T process represents a critical source of pre-analytical variability in metabolomic studies. Each cycle and thawing can induce mechanical disruption, enzymatic reactivation, and chemical degradation, thus potentially affecting the concentration and detectability of metabolites. This section summarises the findings from studies that investigated the number of F/T cycles, thawing conditions, and sample-specific vulnerabilities. Only three studies (Table 6) were identified that applied NMR analysis for plasma, serum, and synovial fluid. One study evaluated the effect of up to 10 F/T cycles on serum samples with results demonstrating metabolome stability up until 5 F/T cycles [68]. Samples that underwent >5 F/T cycles demonstrated significantly altered concentrations of choline, glycerol, methanol, and ethanol, therefore acting as potential markers of F/T cycling [68]. In plasma the highest metabolomic variation was found to occur after four or more F/T cycles [47]. Synovial fluid analysis revealed the metabolome remained stable for up to 10 F/T cycles; however, eight metabolites were found to be significantly impacted by repeated F/T cycling, with changes observed following the first F/T cycle [88,89]. These include 2-ketoisovalerate, glycosaminoglycan, acetaminophen glucuronide, formate, arginine, glutamate, phenylalanine, and acetaminophen glucuronide [88]. Overall, these results suggest limiting F/T cycling of all three biofluids to no more than four cycles in order to preserve metabolome integrity.
Nine studies (Table 6) were identified that had applied MS platforms for the analysis of plasma and serum samples. In a study assessing the impact of up to three F/T cycles on plasma samples, five metabolites were identified that were consistently affected across all cycles including bilirubin (E, E), dihydroorotate, maltose, glycerol 3-phosphate, and sphingosine [90]. Another study assessed the impact of up to five cycles specifically on the stability of plasma oxylipins, with results suggesting higher stability of esterified oxylipin compared to free oxylipins [85]. In another study, up to four F/T cycles did not significantly impact the plasma metabolome [56]. A study assessing the impact of up to 10 F/T cycles on plasma and serum samples observed changes in both biofluids; however, the highest change was observed in plasma samples, with F/T primarily affecting metabolic pathways associated with peptides, low molecular weight lipids, and glycerolipid metabolites in plasma [91]. Pathways found to be impacted in both plasma and serum samples included pathways of cofactors and vitamins such as biliverdin [91]. In contrast, however, plasma samples that underwent thawing during transportation were found to demonstrate significant increases in the concentrations of amino acids, fatty acids, glycerol metabolites, and purine and pyrimidine metabolites [92]. However, several metabolites that demonstrate stability were also identified including glutamine, glycine, isoleucine, leucine, taurine, valine, and TCA cycle intermediates [92]. Serum metabolomic analysis demonstrated both resilience and vulnerability to F/T cycling depending on the metabolites measured. Up to three F/T cycles were found to significantly impact the concentrations of 11 amino acids including histidine, leucine, isoleucine, methionine, phenylalanine, glutamate, tryptophan, valine, taurine, tyrosine, and ornithine [87]. Lipids, however, were found to remain stable across three F/T cycles if samples were stored at −80 °C and underwent lipoprotein fractionation prior to freezing [93]. Serum samples exposed to up to five F/T cycles demonstrated the highest metabolomic alterations within the first three cycles. Notably, this study also reported that donor gender influenced the extent of the metabolomic variation observed following F/T cycling, an aspect that has not been as widely explored. These findings suggest that gender may represent an underexplored factor affecting sample stability and requires further research [94]. Additionally, one study evaluated the impact of the thawing temperature on serum samples and found that shorter thawing times of <20 min at room temperature had less impact on metabolite levels compared to longer thaws in the refrigerator or on ice [40]. Overall, the results suggest that plasma samples are more sensitive to F/T cycling compared to serum samples, suggesting the influence of the sample matrix on resilience against F/T cycling. Based on these findings, no more than three F/T cycles are recommended for both plasma and serum samples with consistent thawing protocols. The effect of F/T cycling on MS metabolomic analysis of synovial fluids is an area for further investigation.
Table 6. Summary of the studies investigating the impact of freeze/thaw cycling on the stability of plasma, serum, and synovial fluid metabolomes.

3.8. Post Storage Handling

Two studies (Table 7) were identified that evaluated the effects of delays between sample thawing and final analytical processing on the stability of metabolite profiles in plasma and serum samples [65,90]. One study applied NMR and observed the impact of a 24 h delay in buffer addition and NMR acquisition following thawing; delays resulted in changes in histidine and phenylalanine concentrations in plasma and serum samples [65]. In serum samples, histidine concentrations were found to decrease, and phenylalanine was found to increase following buffer (mean β values relative to reference = −0.00052 mmol/L, p = 2.2 × 10−4 and 0.0043 mmol/L, p = 2.1 × 10−5, respectively) and NMR (−0.00094 mmol/L, p = 0.28 and 0.0091, p < 1 × 10−26 respectively) acquisition delays. Plasma (EDTA) samples also followed a similar trend as the serum, with histidine concentrations decreasing and phenylalanine concentrations increasing after buffer (−0.013 mmol/L, p = 4.8 × 10−11 and 0.0043 mmol/L, p = 2.1 × 10−5, respectively) and NMR acquisition delay (−0.0044, p = 1.4 × 10−3 and 0.0091 mmol/L, p < 1 × 10−26, respectively) [65]. The other study applying MS analysis on plasma samples investigated the storage of samples on ice for up to 6 h prior to analysis following thawing. The results revealed no significant changes to the metabolome, suggesting the metabolome remains stable for up to 6 h following thawing if kept cool [90]. However, given the limited number of studies and differences in analytical platforms and experimental designs, further investigations are required before definitive conclusions can be drawn regarding post-thaw handling effects.
Table 7. Summary of the studies exploring the impact of post storage handling on plasma and serum metabolomes.

4. Discussion

This review examined the impact of pre-analytical variables on the metabolomic profile of biological samples, focusing on plasma, serum, and synovial fluid. Accurate and reproducible metabolomic profiling depends on the preservation of in vivo metabolite concentrations, yet these are easily altered by sample handling factors such as the collection tube type, processing delays, temperature, storage conditions/duration, F/T cycling, and post storage steps. While specific effects varied depending on the analytical platform (NMR, GC- or LC-MS) used and biofluid type, several general patterns emerged.
Studies exploring sample collection agreed that anticoagulants introduce intense signals and interfere with endogenous metabolites. Heparin plasma and plain serum tubes (without additives or gels) were identified as the most compatible for metabolomics workflows, demonstrating the least interference. Delays in sample centrifugation following collection, especially when blood is stored at room temperature, were found to contribute towards major shifts in glycolysis-related metabolites, whilst prompt cooling and processing within two hours mitigated these changes. Centrifugation is a necessary step to obtain plasma and serum; studies testing the impact of centrifugation parameters found they have a relatively minor impact on plasma and serum metabolomes. However, one study identified centrifugation to be a beneficial step in the metabolomic analysis of synovial fluid, where it may not be typically applied. Post-centrifugation delays, even as short as 1–2 h, were found to cause oxidative degradation, with serum samples appearing more vulnerable than plasma. Sample storage conditions were also found to play a critical role: overall, samples stored at −80 °C demonstrated the highest stability both short and long term, whilst storage at −20 °C or room temperature was associated with progressive metabolite degradation. However, further investigations are required to assess the impact of long-term storage at −80 °C on the serum metabolome Additionally, none of the studies included in this review investigated the impact of the storage of the metabolome at −196 °C. F/T cycling of plasma and serum, particularly more than three cycles, was found to induce changes in specific amino acids, lipids, and redox sensitive metabolites, while short thawing times at room temperature were found to be less damaging than prolonged thawing on ice or refrigerator.
Based on the findings of this review, several best practice recommendations emerge (illustrated in Figure 2):
(1)
Plain serum collection tubes and heparin plasma tubes free of gels and citrate additives should be used for the collection of blood.
(2)
Blood samples are recommended to be centrifuged within 2 h of collection and stored on ice during this period.
(3)
Following centrifugation, samples should be frozen immediately at −80 °C and aliquoted to avoid multiple F/T cycles. Where F/T cycling is unavoidable, samples having undergone more than three cycles should be avoided for metabolomic analysis.
(4)
Metabolomic workflows should be standardised across study sites, with adequate documentation of pre-analytical handling conditions to support reproducibility and aid in cross-study comparisons.
Notably, several metabolites were identified to be consistently affected across pre-analytical handling variables. These metabolites were repeatedly impacted across different sample types (plasma and serum), platforms (NMR, GC-MS, and LC-MS), and handling steps. Their consistent appearance highlights both a vulnerability in metabolomic workflows but also a potential opportunity for developing internal quality control markers. These included glycolysis intermediates (glucose, lactate, and pyruvate) [88,89,90,91,92,93], redox-active compounds (histidine, proline, and choline) [95,96,97,98], and amino acids (arginine, ornithine, taurine, and glutamate) [99,100], each differentially affected by different pre-analytical handling conditions. This core set of metabolites may serve as candidate markers of sample quality, whose incorporation into routine quality control pipelines can enable researchers to flag compromised samples, correct for batch effects, or improve reproducibility across studies.
Figure 2. Pre analytical workflow highlighting best practice recommendations for the handling of blood based biofluids and synovial fluid for metabolomic analysis as well as highlighting evidence gaps for synovial fluid.
Despite these important insights, several limitations emerge in the current literature. The majority of identified studies focused on plasma and serum and addressed only a narrow range of pre-analytical variables. In several cases, findings were based on small sample sets thus limiting the generalisability of these results. Additional heterogeneity in study designs, sample preparation protocols, and analytical platforms make it difficult to synthesise findings quantitatively or compare across studies. Importantly, synovial fluid, a biofluid of growing interest in joint disease research remains under-investigated, with only three papers identified in this review. The absence of studies exploring sample collection variables, pre-centrifugation conditions, and post-centrifugation conditions on the synovial fluid metabolome contrasts with the body of evidence available for plasma and serum, which indicate that these factors can influence the metabolome. The unique biochemical profile of synovial fluid may render it more or less susceptible to degradation than plasma or serum, but this remains largely unknown. Addressing this gap is essential to ensure that the metabolomic analysis of synovial fluid yields reliable, reproducible, and clinically meaningful results.

5. Conclusions

In conclusion, while substantial progress has been made characterising the effects of pre-analytical handling on plasma and serum metabolomes, further work is required to standardise protocols, validate quality control markers, and expand the evidence base to include less studied biofluids like synovial fluid. In addition, we would advocate the inclusion of quality assurance and quality control practices throughout the whole metabolomics pipeline, with the inclusion of pooled quality control samples where possible, following the discussions outlined by Broadhurst and colleagues [101].

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/applbiosci5010010/s1, Table S1: Preferred Reporting Items for Scoping Reviews checklist; Table S2: Search strategies with keywords and MeSH terms.

Author Contributions

Conceptualisation, C.H.H.; methodology, Y.L. and S.S.; formal analysis, Y.L. and S.S.; investigation, Y.L. and S.S.; resources, K.T.W., R.G. and C.H.H.; data curation, Y.L., S.S. and C.H.H.; writing—original draft preparation, Y.L.; writing—review and editing, Y.L., A.B., R.G., K.T.W., J.P. and C.H.H.; visualisation, Y.L.; supervision, A.B., R.G., K.T.W., J.P. and C.H.H.; project administration, Y.L. and C.H.H.; funding acquisition, C.H.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to thank Keele University for funding this studentship. The sponsors had no involvement in the study design, data collection and interpretation or preparation of the manuscript. This study has been delivered through the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of Keele University, the MRC, the NIHR, or the Department of Health and Social Care. For the purposes of open access, the author has applied Creative Commons Attribution (CC-BY) license to any accepted author manuscript version arising from this submission.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PRISMA-ScrPreferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
LC-MSLiquid chromatography coupled with mass spectrometry
GC-MSGas chromatography coupled with mass spectrometry
NMRNuclear magnetic resonance
OAOsteoarthritis
RARheumatoid arthritis
EDTAEthylenediaminetetraacetic acid
ACDAcid citrate dextrose
SCFAShort-chain fatty acids
SMSphingomyelin
RBCsRed blood cells

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