The preanalytical phase is a major component of total laboratory quality. Different studies show that preanalytics account for up to 93% of laboratory errors within the entire diagnostic process . Important preanalytical variables among others are different storage conditions for human biological samples. Nowadays, there is an increasing interest in sample storage and biobanking of serum and plasma for routine diagnostics and clinical biomarker studies . Numerous small- to large-sized biobanks are installed in hospitals, research, and other facilities across the world, ranging from normal refrigerators to complex storage systems with lowest temperatures incurring enormous efforts and costs . However, although there are recommendations and guidelines referring to adequate storage conditions [4–6], only a limited number of laboratory parameters have been systematically investigated. In addition, data on the stability of biomarkers at different storage temperatures are often incomplete and sometimes contradictory [7, 8]. For example, stability data for factor VIII differ between 20% loss of activity after 3 days of storage at –20 °C and <5% loss of activity after 3 months of storage at the same temperature [5, 6, 9, 10]. Finally, because of the heterogeneity in study designs and data interpretation, comparison of results of biomarker stability between studies is often difficult. These facts may explain the difficulty for scientists in choosing the right storage system for their own scope and for interpreting the data of previously stored samples.
Another limiting factor is the lack of studies that have systematically evaluated the impact of storage temperatures below –80 °C. The majority of data including the guidelines of the World Health Organization (WHO) or of the United German Association for Clinical Chemistry and Laboratory Medicine (DGKL) refer only to temperatures up to –20 °C [5, 6]. A limited number of studies tested temperatures down to –80 °C [10–16], and only individual studies tested temperatures below –80 °C [17, 18]. Indeed, single studies described the instability of biomarkers such as matrix metalloproteinase 9 (MMP-9), factor XI, and factor V when stored at –80 °C, resulting in recommendations that biobanking should be performed at even lower temperatures [9, 10, 14]. Storage below –80 °C almost always involves the use of liquid nitrogen, leading to higher storage costs and making automation exceedingly challenging. It is therefore important to define which biomarkers require storage at such low temperatures. However, to the best of our knowledge, there are no studies that have systematically verified the benefit of storage temperatures lower than –80 °C for biomarkers in serum or plasma samples.
In addition, the potential effects of short-term thermal exposure of frozen samples have not been investigated so far. Short-term thermal exposure may occur while removing individual samples from a biobank, for example, when taking out boxes of samples stored in conventional –80 °C freezers for sample picking at room temperature (RT). To minimize the influence of such short-term thermal exposures during sorting processes, expensive and highly complex cooling systems with working rooms at very low temperatures have recently been commercially introduced. However, it is not known whether such short-term thermal exposure during sorting and picking of samples affects biomarker stability.
We therefore designed a comprehensive study, analyzing the effects of different biobanking conditions on a broad range of individual biomarkers and biomarker classes, selected based on the variability of clinical areas, chemical structures, and stability according to literature. The focus of the study was laid on short-term storage for up to 90 days. The principal goals were to evaluate whether storage stability differs between the biomarker classes and whether there is any benefit of very low storage temperatures (<–80 °C) compared to storage at –80 °C. Furthermore, we tested whether short-term thermal exposure has an effect on the stability of biomarkers.
Materials and methods
Sample collection, processing, and storage
We used anonymized leftover samples from patients of the University Hospital of Leipzig after routine analysis at the Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics. The use of leftover samples was approved by the Ethics Committee of the University of Leipzig (082-10-19042010) and complied with the World Medical Association Declaration of Helsinki regarding the ethical conduct of research involving human subjects. In order to gain sufficient volumes, serum and citrated plasma samples from different patients were pooled, leading to 10 different pools of serum and 10 different pools of citrated plasma. The 10 pools of serum and citrated plasma included five pools from samples from intensive care patients and five pools from outpatients, in order to cover broader concentration ranges of the different parameters. Pools were aliquoted (300-μL samples) into 2-mL polypropylene tubes (Sarstedt, Nümbrecht, Germany; article number 72.694.007, external diameter 10.8 mm). One serum and one plasma aliquot from each pool were immediately analyzed (baseline) (Supplemental Data Table 1, which accompanies the article at http://www.degruyter.com/view/j/cclm.2014.52.issue-5/issue-files/cclm.2014.52.issue-5.xml). Other aliquots were packed in carton cryoboxes (Carl Roth, Karlsruhe, Germany) and immediately stored under different storage conditions (Figure 1A and B). Briefly, to analyze the influence of storage time and temperature on biomarker stability, aliquots of serum and citrated plasma were stored for 7, 30, and 90 days at 4±2 °C in a standard laboratory refrigerator (Liebherr, Bulle, Switzerland), at –20±2 °C in a laboratory freezer (Liebherr), at –80±2 °C in a low-temperature freezer (Thermo Fisher Scientific, Waltham, MA, USA), and at <–130 °C in a cryostorage device (ASKION C-line hermetic storage, Askion, Gera, Germany) (Figure 1A). To analyze the influence of short-term thermal exposure on biomarker stability, additional aliquots were stored for 90 days at –20±2 °C, –80±2 °C, and <–130 °C, but were exposed 3 or 12 times to higher temperatures for 5 min. Within these 5 min, carton cryoboxes were opened and exposed to higher air temperatures as detailed in Figure 1B to simulate picking of individual samples in biobanking systems. Furthermore, temperatures were measured in the serum of single 300 μL serum aliquots located at the center or at the corner of cryoboxes after 5 min of thermal exposure using a pt1000 thermosensor and a Voltcraft K202 datalogger thermometer (Conrad Electronic, Hirschau, Germany). A storage temperature of <–130 °C and a short-term thermal exposure up to –100 °C were additionally chosen because these conditions represented the biobanking conditions of a cryostorage device used in this study. This included a storage tank for conserving samples in the gas-phase above liquid nitrogen (temperature <–130 °C) and a cooled working room (temperature –100 °C), permitting a cold chain with permanent temperatures of ≤–100 °C. All samples were thawed at RT for 20 min before analysis. After vortexing, analyses were performed within 2 h after storage removal.
A comprehensive spectrum of biomarkers comprising different clinical areas, chemical structures, and showing different stability according to the literature was selected to systematically test different biobanking conditions (Table 1). This spectrum included 32 individual biomarkers, which could be grouped into 10 classes comprising electrolytes, enzymes, metabolites, metabolically inert proteins, complement factors, ketone bodies, hormones, cytokines, coagulation factors, and sterols.
Analytical methods and corresponding instruments for the different parameters tested.
Total calcium (Ca)
|Ion selective electrode, photometry||COBAS 8000 automated analyzer (Roche, Basel, Switzerland)|
|Enzymes||Alanine aminotransferase (ALT) (EC 18.104.22.168)||Photometry||COBAS 8000 automated analyzer (Roche)|
|Glutamate dehydrogenase (GLDH) (EC 22.214.171.124)|
|Lactate dehydrogenase (LDH) (EC 126.96.36.199)|
|Creatine kinase (CK) (EC 188.8.131.52)|
|Lipase (EC 184.108.40.206)|
|Matrix metalloproteinase 9 (MMP-9) (EC 220.127.116.11)||Immunoassay (Millipore)||Bio-Plex 200 (Bio-Rad Laboratories, Hercules, CA, USA)|
|Photometry||COBAS 8000 automated analyzer (Roche)|
|Inert proteins||Total protein|
|Photometry||COBAS 8000 automated analyzer (Roche)|
|Immunoglobulin G (IgG)||Turbidimetry||COBAS 8000 automated analyzer (Roche)|
|Complement factors||Complement C3 (C3)||Turbidimetry (C3c antibody)||COBAS 8000 automated analyzer (Roche)|
|Ketone bodies||Total ketone bodies|
|Photometry||COBAS 111 automated analyzer (Roche)|
|Hormones||Insulin||Chemiluminescence||Liaison automated analyzer (Diasorin, Saluggia, Italy)|
|Free thyroxine||Electrochemiluminescence||COBAS 8000 automated analyzer (Roche)|
|Cytokines||Tumor necrosis factor-α (TNF-α)||Immunoassay (Millipore)||Bio-Plex 200 (Bio-Rad)|
|Coagulation factors||Factor V|
Free protein S
|Photometry||ACL Top 700 CTS automated analyzer (Instrumentation Laboratory, Kirchheim, Germany)|
|Mass spectrometry (MS)||API 4000 (AB SCIEX, Framingham, MA, USA)|
Electrolytes, enzymes, metabolites, proteins, complement C3 (C3), ketone bodies, and hormones from serum samples and coagulation factors from plasma samples were analyzed according to the Guidelines of the German Medical Association and ISO 15189 . Analytical methods and the corresponding instruments are shown in Table 1. With the exception of insulin, which was only analyzed in five serum pools from outpatients for each storage condition, analyses of other parameters were performed in 10 pools. Commercial control samples were analyzed to determine interassay coefficients of variation (CV) for each parameter calculated by 10 single values at 10 different days accumulated over a 90-day period.
Tumor necrosis factor-α and MMP-9
Tumor necrosis factor-α (TNF-α) was analyzed in 10 serum pools (5 pools from critically ill patients and 5 pools from outpatients) and MMP-9 only in 5 serum pools from outpatients for each storage condition. TNF-α and MMP-9 were determined with commercial enzyme immunoassays (EIA) (human cardiovascular panel 3 and human MMP panel 2; EMD Millipore) (Table 1). Analyses were performed according to the manufacturer’s instructions. In order to determine the CVs, we stored serum aliquots of a single serum sample in liquid nitrogen before the beginning of the study. For determination of the CV, TNF-α and MMP-9 were analyzed each time from new aliquots at days 0, 7, 30, and 90, respectively.
Sterols were analyzed in 10 serum pools for each storage condition as previously described . Briefly, sterols were analyzed using an API 4000 mass spectrometer (AB SCIEX) with atmospheric pressure photo ionization and multiple reaction monitoring in positive ion mode. Control serum samples were analyzed after 0, 7, 30, and 90 days to determine the CVs.
Each measured value of the different time points (7, 30, and 90 days) was calculated as a percentage of its baseline value (day 0) (Supplemental Data Figure 1). The means and the standard deviations for each storage condition were calculated. We defined instability as a percentage deviation of results from measurements after a given period of time compared to measurements at baseline according to ISO Guide 30 . We considered two approaches to define substantial changes reflecting instability due to storage effects. (a) We related results to the CVs of the respective analytical method [22, 23] by using the formula for reference change values (RCV): RCV=21/2×Z× (CV2+CVI2)1/2 . CVI is a measure for the within-subject biological variation, and Z is the critical value for the standard normal distribution appropriate to the probability. We set CVI to 0, as only stored samples were evaluated, reflecting a non-existing biological variation. We set Z=2.12, representing a 97% confidence interval value for bi-directional changes, resulting in an RCV value of 3 CVs. (b) In order to take multiple testing into account, only changes with p values <0.01 (paired t-test) were considered as significant [25, 26]. In total, only results with p-values <0.01 and changes >3 CVs compared to baseline were defined as substantial.
Influence of storage time and temperature on biomarker stability
Occurrence of substantial changes
No substantial changes were observed for any investigated parameter when stored for 90 days at –80 °C or <–130 °C in comparison to baseline (Figure 2). In contrast, substantial storage effects were observed in all biomarker classes after storage at temperatures of 4 °C and/or –20 °C (Figure 2), except for sterols measured by mass spectrometry (MS), which remained unaffected at all conditions. All biomarker classes contained individual biomarkers not showing any substantial differences after 90 days of storage, with the exception of C3 and TNF-α, which had been chosen as the single representatives for complement factors and cytokines, respectively. In addition to the sterols measured by MS, no substantial effects were seen for lipase, MMP-9, albumin, 3-hydroxybutyric acid, insulin, and factor XI, which remained stable over the complete range of temperatures (4 °C to <–130 °C) for up to 90 days. Interestingly, the enzymes alanine aminotransferase (ALT) (EC 18.104.22.168), lactate dehydrogenase (LDH) (EC 22.214.171.124), and creatine kinase (CK) (EC 126.96.36.199) showed substantial changes at –20 °C but not at 4 °C, when stored for 7 days. These changes did not occur at lower temperatures, suggesting a specific effect of –20 °C storage. No systematic differences in parameter stability of samples from intensive care patients and outpatients reflecting different parameter concentrations were observed (data not shown).
Effect size and direction of substantial changes
We observed marked differences when evaluating the effect sizes and directions of effects for the different biomarker classes. All biomarker classes revealed either only decreases or only increases for all parameters (Supplemental Data Figure 2). Electrolytes, metabolites, and proteins only showed minor increases (<20%) over 90 days (Figure 3; Supplemental Data Table 2). Moderate increases (<45%) were observed for complement factors and hormones. High decreases (<70%) were observed for enzymes and ketone bodies. Finally, cytokines and coagulation factors showed very high decreases (<96%).
Influence of short-term thermal exposure on parameter stability
To simulate sample sorting and picking of frozen samples, which usually occurs at higher temperatures, we tested the effects of multiple 5-min thermal exposures to different temperatures over the course of 90 days (Figure 1B). Within these 5 min, serum temperatures in single tubes located in the center or at the corner of cryoboxes rose from –18 °C to –9 °C or –18 °C to –5 °C (storage temperature –20 °C→exposure to RT), from –81 °C to –67 °C or –78 °C to –23 °C (–80 °C→RT), from –79 °C to –70 °C or –78 °C to –53 °C (–80 °C→–20 °C), from –181 °C to –148 °C or –181 °C to –107 °C (<–130 °C→RT), and from –175 °C to –150 °C or –184 °C to –129 °C (<–130 °C→–100 °C). Only slight effects of short-term thermal exposure on parameter stability could be found. Three of 32 parameters (cholesterol measured by photometry, TNF-α, and factor VIII) revealed substantial changes only after short-term thermal exposure (Figure 4). These effects could only be observed after short-term thermal exposure from the different storage temperatures to RT, but not when exposed to –20 °C or –100 °C. For cholesterol and TNF-α, these changes were at the threshold to substantial effects (changes lower than 3.5 CVs to baseline).
As a main result, no substantial changes were seen for any parameter when stored at –80 °C or <–130 °C. Therefore, we conclude that storage at <–130 °C and –80 °C is equally well suited for short-term biobanking of a broad range of biomarkers. In contrast, substantial changes were observed for individual biomarkers in most biomarker classes when samples were stored at 4 °C and/or –20 °C. These effects were not clearly related to the biomarker class but appeared to be specific for the individual biomarker, because almost all biomarker classes contained biomarkers with and without substantial changes. However, class-specific effects were found when considering directions of substantial changes. Another major result of this study was that short-term thermal exposures (5 min), which might occur when removing individual samples from a biobank, had no measurable influence on the stability of most laboratory parameters or classes. In order to give scientists an instrument to choose appropriate storage temperatures for different laboratory parameters, we provide recommendations for adequate temperatures for storage up to 90 days based on the results of this study (Table 2; for details, see also Supplemental Data Table 4).
Proposed storage temperatures for up to 90 days based on results of this study.
|Group||Parameter||Proposed temperaturea for storage up to 90 days|
|MMP- 9||≤4 °Cd|
|Cholesterol (by photometry)||≤–20 °C|
|Proteins||Total protein||≤–20 °C|
|Complement factors||C3||≤–80 °C|
|Ketone bodies||Total ketone bodies||≤–80 °C|
|3-Hydroxybutyric acid||≤4 °C|
|Free thyroxine||≤–20 °C|
|Coagulation factors||Factor V||≤–80 °Cd|
|Factor VII||≤–20 °C|
|Factor VIII||≤–80 °C|
|Factor XI||≤4 °Ce|
|Free protein S||≤–20 °Cb|
|Free cholesterol||≤4 °C|
|Cholesterol ester||≤4 °C|
|Total cholesterol (by MS)||≤4 °C|
aFor further details, see Supplemental Data Table 4. b–eConsidering also existing literature, the recommended storage temperature would be b<–20 °C [5, 6], c≤–20 °C [5, 6], d<–80 °C [9, 14] and e<4 °C [5, 6].
The present study considered a number of aspects that have only been partially covered in previous publications. (a) To the best of our knowledge, our study evaluated the stability of the largest number of biomarkers after storage of more than 72 h, thereby using the same evaluation criteria for all biomarkers. A recently published study with 81 biomarkers evaluated parameter stability only after storage for ≤72 h and at storage temperatures of ≥4 °C . (b) We tested the four most common storage temperatures (including storage at –80 °C and <–130 °C). (c) Short-term thermal exposure, simulating sorting and picking of samples, was also evaluated. (d) Our results are expressed relative to the CVs of individual parameters [22, 23]. We have set the threshold for substantial changes at >3 CVs. Although this approach does not allow detecting little differences in parameter stability, it assures that observed increases and decreases are due to true storage effects . Most studies such as Woodhams et al.  or Heins et al.  did not refer to CVs, although analyses were performed at different days and the CVs might have been high. Thus, differences due to interassay variation may simulate storage effects in some cases, even for higher differences up to 20%. (e) This study takes the multiple testing problem into account, whereas most studies with numerous parameters did not address this topic [8, 10, 13, 27]. We addressed this issue by setting the level of significance at 0.01 [25, 26]. (f) Finally, investigated parameters were analyzed by actual laboratory methods. The analytical method used in parameter stability studies is important, as the stability of laboratory parameters sometimes differs among analytical methods [12, 28].
There were biomarkers in all classes that showed substantial changes after 90 days of storage at 4 °C and/or –20 °C. Only in the parameter group of sterols measured by MS no substantial changes were observed at any temperature. This might be explained by the higher CV in the sterol group measured by MS (CV cholesterol: 5.45% by MS vs. 1.95% by enzymatic assay). Based on higher CVs in the MS group, small storage-dependent increases at 4 °C are concealed, therefore not leading to substantial changes. Therefore, when focusing on the occurrence of substantial changes, only enzymes were clearly different from other parameter classes: only representatives of this group revealed substantial changes when stored for 7 days at –20 °C, but not when stored at 4 °C or –80 °C and below (Figure 2). There are some studies describing comparable effects for ALT and LDH [16, 29], and the term “cold denaturation” has been coined for this phenomenon . It is explained by a change in the contact of free energy between water and nonpolar groups at colder temperatures, thereby weakening the hydrophobic interaction of proteins, leading to changes in the tertiary structure [31, 32]. Although this phenomenon was also observed for other proteins than enzymes, such as monoclonal antibodies , it could be assumed that small changes within the tertiary structure would have a larger effect on enzyme activity, compared to antigenic properties.
While only these few differences were observed between the diverse biomarker classes concerning the occurrence of substantial changes, class-specific effects were seen when evaluating levels or directions of changes. Electrolytes, metabolites, and proteins including sterols showed only minor storage effects, and all substantial changes were increases. This might be due to evaporation of the samples during storage. While some studies reported similar results [8, 33], others observed slight decreases for parameters within these groups [7, 22]. In any case, strong changes in the parameters within these groups are unlikely to appear . Other parameter groups (enzymes, complement factors, ketone bodies, hormones, cytokines, and coagulation factors) showed moderate to very high changes, and all substantial changes were decreases, except for the parameters C3 and free thyroxine. Storage-dependent increases have been observed for different complement factors such as C3c and C4c [5, 6, 34], and for different free hormones such as free thyroxine and free triiodothyronine . C3 is fragmented to C3c , resulting in increases when measuring with C3c antibodies (as it was done in our study) and in decreases when measuring with C3 antibodies . Increases in free hormones may result from degradation of protein-bound hormones to free hormones . Particularly high changes were seen for some parameters of the coagulation factors and for TNF-α. Our study is in line with the study by Plumhoff et al. , which showed that factor VIII is very unstable when stored not only at 4 °C but also at –20 °C . In contrast, according to the WHO, factor VIII should be stable for 2 weeks when stored at –20 °C . For cytokines, different observations were made. TNF-α was suggested as one of the most unstable cytokines , with both increases and decreases observed during sample storage [11, 17, 36, 37]. The marked decrease in this study seen at 4 °C at two different time points and not at lower temperatures argues for a real storage effect on this parameter (Figure 3).
It should be noted that some results of single parameters were discrepant in comparison to other studies. For some parameters (lipase, glucose, insulin, factor XI, glutamate dehydrogenase, protein S, factor V, and MMP-9), other authors suggested lower storage temperatures [5, 6, 9, 10, 14] than those proposed in Table 2 based on the results of this study. Particularly interesting are the different results for the stability of MMP-9 seen both in our study and in the study by Rouy et al. . While Rouy et al.  observed strong decreases after 3 months at storage temperatures of –80 °C when using an immunoassay with monoclonal antibodies, no substantial changes were found in our study at any temperature using an immunoassay with polyclonal antibodies (Figure 2). It is tempting to speculate that differences in the alternative analytical method might be responsible for this discrepancy.
Another major aim of this study was to evaluate whether short-term thermal exposure has an influence on the stability of common laboratory parameters or classes. Temperatures were chosen according to usual storage practices. Most clinicians and scientists use freezers with temperatures of –20 °C and –80 °C, and some store samples in the gas phase above liquid nitrogen. Samples are mostly removed at room temperature. However, biobanks sometimes store samples in automated storage systems at –80 °C with a working room temperature of –20 °C (e.g., –80 °C Tube Stores from Liconic); other biobanks store samples in semi-automated storage systems in the gas phase above liquid nitrogen at <–130 °C with a cooled working room temperature of –100 °C (e.g., ASKION C-line hermetic storage). There were only very few parameters that revealed substantial changes after short-term thermal exposure (Figure 4). Slight effects were observed for cholesterol, TNF-α, and factor VIII, but only when exposed to RT. However, the increases after short-term thermal exposure for TNF-α when stored at –20 °C might not be due to storage effects. The observed increases were hardly above the threshold to substantial changes and, furthermore, are implausible, because of the decreases seen for this parameter when samples were stored for 90 days at 4 °C. It is therefore tempting to speculate that high variances of the method may be the reason for this observation.
The results of this paper appear to be plausible, because for each biomarker class, substantial changes in all investigated biomarkers and storage conditions were always in the same direction, showing that the evaluation scheme is appropriate. Moreover, our results often agree with the literature (where available), indicating that the study protocol used in this study is adequate. The study protocol may therefore serve as a model for future studies evaluating the stability of further biomarkers. While the scope of this study was to evaluate short-term storage for up to 90 days, it would be interesting to investigate the effects of longer storage times, especially when evaluating a possible benefit of <–130 °C storage in comparison to –80 °C storage. More pronounced differences might be seen after long-term storage of several parameters.
In conclusion, this is, to the best of our knowledge, the first study evaluating a large set of parameters from several parameter groups with several storage temperatures, including –80 °C and <–130 °C storage, with the same evaluation protocols. The results of this study lead to the conclusion that a storage temperature of –80 °C is excellent for short-term storage of most biomarkers. Moreover, this study shows that the common practice of short-term thermal exposures of stored aliquots for up to 5 min do not affect the concentrations of most biomarkers, particularly when using storage systems with working room temperatures of ≤–20 °C. These findings may help scientists to decide which liquid biobanking system would be adequate for their scope.
This publication was supported by LIFE–Leipzig Research Center for Civilization Diseases, Universität Leipzig. LIFE is funded by the European Union, the European Regional Development Fund (ERDF), the European Social Fund (ESF), and the Free State of Saxony within the framework of the excellence initiative. Part of this work was funded by the German Ministry of Education and Research (BMBF) within its project SP2 m4-Biobank Alliance (01EX1020B).
Conflict of interest statement
Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article. Research funding played no role in thestudy design; in the collection, analysis, and interpretationof data; in the writing of the report; or in the decision tosubmit the report for publication.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
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