Home Pre-analytical long-term stability of neopterin and neurofilament light in stored cerebrospinal fluid samples
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Pre-analytical long-term stability of neopterin and neurofilament light in stored cerebrospinal fluid samples

  • Carolina Rosadas ORCID logo and Graham P. Taylor EMAIL logo
Published/Copyright: January 25, 2023

Abstract

Objectives

The aim of this study was to evaluate the impact of long-term sample storage on the concentrations of neopterin and neurofilament light (Nfl) in cerebrospinal fluid (CSF) samples. These are useful markers of neuroinflammation and neuronal damage and have been applied as biomarkers for several neurological diseases. However, different pre-analytical variables have potential to influence results.

Methods

Twenty-one CSF samples donated by patients with HTLV-1-associated myelopathy (HAM) and stored for up to 11 years at −80 °C were retested after three-years for neopterin (n=10) and Nfl (n=11) by ELISA.

Results

There was a strong correlation between the paired results (r>0.98, p<0.0001). Neopterin concentrations (nmol/L) ranged from 12.4 to 64 initially and from 11.5 to 64.4 when retested, with means (SD) of 30 (18.4) 1st test and 33 (19.1) 2nd test. Nfl concentrations (pg/mL) ranged from 79.9 to 3,733 initially and from 86.3 to 3,332, when retested with means (SD) of 1,138 (1,272) 1st test and 1,009 (1,114) at re-test.

Conclusions

Storing CSF samples at −80 °C appears not to impact the quantification of neopterin and Nfl allowing confidence in the reporting of archived samples.

Introduction

Biomarkers are considered “A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention” [1]. Several markers of inflammation and neuronal damage have been proposed as diagnostic or prognostic markers of neurological disease. These biomarkers can be measured in different bodily fluids, such as plasma, serum, urine and cerebrospinal fluid (CSF). Among the markers of neuroinflammation, the quantity of neopterin in CSF has long been used as a biomarker of different infectious and non-infectious neurological conditions, such as multiple sclerosis, hypoxic-ischemic encephalopathy (HIE), human T cell lymphotropic virus type-1 (HTLV-1) -associated myelopathy (HAM), human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND) and cerebral malaria [2], [3], [4], [5], [6]. Interest in neurofilament light (Nfl), a structural axonal protein that is released into CSF following neuronal damage [7] is increasing. Like neopterin, Nfl CSF concentrations are increased in several neurological diseases [8, 9]. Although the quantification of these markers is valuable, pre-analytical variables may impact results [10, 11]. For example, it is known that there is a risk of sample degradation during storage. This is particularly important, when performing retrospective studies that rely on the analysis of stored samples. Therefore, the present research aimed to address whether long-term storage of CSF samples at −80 °C would impact on the concentrations of neopterin and neurofilament light, markers of neuroinflammation and neuronal damage that are widely used in research and increasingly adopted into routine follow-up of patients with neurological conditions.

Materials and methods

Twenty-one anonymised CSF samples, obtained from patients with HAM during their clinical follow-up, were randomly selected and included in the study. These patients attended the National Centre for Human Retrovirology (NCHR), at St Mary`s Hospital, London and had donated samples to the Communicable Diseases Research Tissue Bank following written informed consent (National Research Ethics Reference 20/SC/026). Samples were collected by lumbar puncture and centrifugated (16,000 rcf for 1 min) within 24 h of collection and transported at room temperature. CSF supernatant was stored at −80 °C for 0.5–11 years (Median 3.5 years) before testing for neopterin and for 1–8 years (Median 3) before testing the first time for Nfl. Stored samples were retested, with a three-year interval, after one freeze and thaw cycle, for neopterin (RE59321, IBL International, Hamburg, Germany) (n=10) and neurofilament light (10–7,001, UmanDiagnostics, Umea, Sweden) (n=11) by ELISA (Figure 1). Previous data indicate stability of these markers when submitted to one freeze-thaw cycle. Samples were tested undiluted for neopterin and diluted 1:2 for Nfl. Assays were performed according to the manufacturer’s instructions. Briefly, Nfl ELISA uses two non-competing monoclonal antibodies to Nfl protein. One is immobilized on a solid phase and binds to the Nfl in the sample. The second antibody acts as a detector that interacts with the bound target protein and signals through biotin – horseradish peroxidase avidin binding. The signal obtained corresponds directly to the amount of Nfl which is extrapolated from the standard curve. Neopterin ELISA is a competitive ELISA. In this assay, an unknown amount of antigen in the sample and a fixed amount of enzyme labelled antigen compete for binding at rabbit-anti-neopterin. Both antigen-antibody complexes bind to the goat-anti-rabbit antibody coating the microtiter wells. All unbound antigen is removed by washing and the intensity of the colour developed after the substrate incubation is inversely proportional to the amount of antigen in the sample. The quantity is extrapolated from the manufacturer’s standard curve. All samples were tested in duplicate and the average concentration used for analysis. Mean, standard deviation (SD) and coefficient of variation between the two repeat tests were calculated for neopterin and Nfl for each sample. The concentrations of neopterin and Nfl were compared between the first and second test using paired t test. Correlations were evaluated with the Spearman test, and p<0.05 was considered to reflect statistically significance. Patients with HAM were classified according to disease activity based on the levels of neopterin in CSF, as proposed by Sato and colleagues, as low <6 nmol/L, moderate 6–43 nmol/L and high >43 nmol/L [3]. The concordance between the two results was assessed. Reference change values (RCV) were calculated using the formula RCV=21/2 · Z · (CV(A)2 + CV(I)2)1/2, as previously described [12]. Z is the number of standard deviations appropriate to the probability and set as 1.96 (p<0.05). CV biological (CV(I)) was 0 as there were no resampling.

Figure 1: 
Graphic summary of methods.
Figure 1:

Graphic summary of methods.

Results

Data are shown in the Table 1. Results for both neopterin and Nfl concentration were consistent when comparing the two time-points. Neopterin concentrations ranged from 12.4 to 64 nmol/L in 2018 and from 11.5 to 64.4 nmol/L, when retested in 2021 (Mean [SD]: 2018=30 [18.4]; 2021=33 [19.1], p=0.03). Neurofilament light concentrations ranged from 79.9 to 3,733 pg/mL in 2018 and from 86.3 to 3,332 pg/mL, when retested in 2022 (Mean [SD]: 2019=1,138 [1,272]; 2022=1,009 [1,114], p=0.002). There was a strong correlation between the results obtained at the two time-points for both neopterin and Nfl (Figure 2). The coefficient of variation ranged between 0.4 and 25.7% for neopterin and 5.1–13.3% for Nfl with a mean (SD) for CV of 9 (9.5) and 8 (2.6), respectively and was not impacted by the duration of storage to first test. There was no correlation between duration of storage and the absolute concentration of either biomarker. According to neopterin concentration, three patients had high disease activity, while the remaining seven had moderate disease activity. Most importantly, all results were concordant when retested.

Table 1:

Stability of Neopterin and neurofilament light in CSF samples stored at −80 °C.

Sample ID Year of sample collection Biomarker concentration in CSF
1st testa 2nd testa Mean STD dev CV RCV
Neopterin in CSF, nmol/L

1 2007 49.9 50.8 50.4 0.6 1.2 2.4
2 2007 48.6 60.3 54.4 8.3 15.2 29.8
3 2013 12.4 17.9 15.1 3.9 25.7 50.4
4 2016 12.4 11.5 11.9 0.6 5.3 10.4
5 2016 20.0 20.1 20.0 0.1 0.3 0.6
6 2008 30.0 31.1 30.5 0.8 2.5 4.9
7 2007 64.0 64.4 64.2 0.3 0.4 0.8
8 2017 12.5 17.7 15.1 3.7 24.3 47.6
9 2016 30.4 34.9 32.6 3.2 9.7 19.0
10 2018 19.5 21.2 20.3 1.2 5.9 11.6

Neurofilament light in CSF, pg/mL

11 2018 675.7 610.7 643.2 46.0 7.1 13.9
12 2017 761.5 681.9 721.7 56.3 7.8 15.3
13 2016 431.8 367.4 399.6 45.6 11.4 22.3
14 2018 749.6 646.0 697.8 73.2 10.5 20.6
15 2016 79.6 86.3 83.0 4.7 5.7 11.2
16 2017 250.3 224.3 237.3 18.4 7.7 15.1
17 2016 498.6 451.6 475.1 33.2 7.0 13.7
18 2014 966.0 894.9 930.5 50.3 5.4 10.6
19 2012 3,583.3 3,332.0 3,457.7 177.7 5.1 10.0
20 2014 792.1 717.0 754.6 53.1 7.0 13.7
21 2011 3,733.0 3,091.8 3,412.4 453.4 13.3 26.1
  1. aNeopterin: 1st test 2018, 2nd test 2021; Nfl: 1st test 2019, 2nd test 2022. CSF, cerebrospinal fluid; STD dev, standard deviation; CV, coefficient of variation; RCV, reference change value.

Figure 2: 
Stability of neopterin and neurofilament light in CSF samples stored at −80 °C. Comparison of neopterin and neurofilament light concentration in cerebrospinal fluid samples (CSF) retested with a 3-year interval. (A, D) Values were compared using paired t test and p-values are shown. (C, E) Spearman test was used to assess correlation between the results when retested. Each dot represents one sample.
Figure 2:

Stability of neopterin and neurofilament light in CSF samples stored at −80 °C. Comparison of neopterin and neurofilament light concentration in cerebrospinal fluid samples (CSF) retested with a 3-year interval. (A, D) Values were compared using paired t test and p-values are shown. (C, E) Spearman test was used to assess correlation between the results when retested. Each dot represents one sample.

Discussion

In the present study we assessed the effect of long-term storage on the quantification of the CSF biomarkers neopterin and neurofilament light by ELISA. Historical cohorts and stored samples are needed to establish appropriate cut-off values and for the feasibility and reliability of studies focusing on rare diseases, diseases with long period of incubation or with slow progression, as for example, HTLV-1-associated myelopathy (HAM). In this scenario, to understand if and how long-term storage influences analyte stability is key. Here, the two biomarkers assessed, neopterin and Nfl, were stable during storage at −80 °C, when retested 3-years later. Although there was a statistically significant difference between the means, this difference (3 nmol/L for neopterin, 127 pmol/mL for neurofilament light) was not clinically relevant given the absolute differences in the ranges associated with the described disease states. This is supported by the small coefficient of variation observed for most samples. Only two samples (samples 3 and 8) had a coefficient of variation higher than 20% when retested for neopterin. In both cases the quantity of neopterin had increased when retested, pointing against analyte degradation, and being probably related to inter-assay variability. Although sublimation/evaporation cannot be disregarded, all samples were stored in the same condition. It is important to note that the variation has not impacted clinical classification according to disease activity [3]. This reinforces the importance of monitoring differences in reagents batches to identify potential impact of inter-assay variability in results. The inclusion of inter-assay controls is strongly advised as well as inter-laboratory comparison.

Our results add to the literature about the impact of pre-analytical factors on the reliability of biomarkers concentration. Previous studies have assessed the impact of a range of variables on the concentration of different biomarkers, but data on CSF samples is limited. In addition, long-term stability is usually limited to up to 21 days with only one study showing stability of these biomarkers in serum samples stored for up to 20 years [13]. Here we have reassessed CSF samples with a 3-year interval.

One study evaluated the impact of blood collection tube type, and of ethylene diamine tetraacetic acid (EDTA), plasma delayed centrifugation, centrifugation temperature, aliquot volume, delayed storage, and freeze-thawing cycles on the concentration of different biomarkers such as Nfl, amyloid beta (Aβ)42 and 40, Aβ oligomerization-tendency (OAβ), amyloid precursor protein (APP)699-711, glial fibrillary acidic protein (GFAP), total tau (t-tau), and phosphorylated tau 181. This study found that while the collection tube type resulted in different values of all assessed markers, delayed plasma centrifugation and storage affected Aβ and t-tau only. In addition, centrifugation temperature affected t-tau measurement. All the other markers, including Nfl, were resistant to all handling variations tested [11]. Based on these findings the authors proposed a standardized operating procedure for plasma handling, for the quantification of blood-based biomarkers to be used on research and clinical settings [11]. Others have shown that Nfl is also resistant to delayed freezing of serum and plasma (up to 7 days) [14] and repeated freeze-thaw cycles [10, 15], [16], [17]. There is also data showing that Nfl in serum is not affected by collection tube type, centrifugation temperature, delayed storage after centrifugation [10] nor delayed centrifugation [18]. In addition, a recent study confirmed that NfL is stable in serum and plasma samples stored at −80 °C for up to 20 and 16 years, respectively [13], which accords with our findings for CSF. CSF Nfl is also resistant to up to four freeze-thaw cycles and to blood contamination which may accidently happen during sampling [19]. Interestingly, NfL concentration was stable in CSF stored at −20 °C and 37 °C compared to samples stored at −80 °C, for 21 days. In contrast, Neurofilament heavy (Nfh) concentration in CSF had a significant linear decline when stored at the same conditions. A reduction of 18% was observed at day 21 for samples at −20 °C, 26% when kept at 4 °C and 20 °C, and up to 71% reduction on Nfh was detected when samples were stored at 37 °C [19].

Regarding neopterin, data on the impact of pre-analytical variables are extremely limited. One study showed that neopterin in plasma is resistant to up to 10 freeze-thaw cycles and when kept at temperatures varying from −70 °C to 24 °C for up to 20 days [20]. The same study obtained different concentrations of neopterin in plasma, when using kits from different manufactures [20]. Our study has some limitations, including the small sample size and the lack of baseline data on fresh samples. However, our data confirms the stability of neopterin in stored samples and shows, for the first time, the stability of this marker in CSF stored long-term at −80 °C and we conclude that results from retrospective analyses will be reliable.


Corresponding author: Prof. Graham P. Taylor, Section of Virology, Department of Infectious Disease, Imperial College London, London, UK; and National Centre for Human Retrovirology, St Mary’s Hospital, London, UK, E-mail:

Funding source: Internal funding Section of Virology, Imperial College London

Funding source: GPT is supported by the National Institute of Health Research Imperial Biomedical Research Centre

  1. Research funding: Internal funding Section of Virology, Imperial College London. GPT is supported by the National Institute of Health Research Imperial Biomedical Research Centre.

  2. Author contributions: CR designed and conducted the research and wrote the initial draft. GPT oversaw the project. Both authors wrote the final version and have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as revised in 2013). This study used samples donated to the Communicable Diseases Research Tissue Bank following written informed consent (National Research Ethics Reference 20/SC/026).

References

1. Califf, RM. Biomarker definitions and their applications. Exp Biol Med 2018;243:213–21. https://doi.org/10.1177/1535370217750088.Search in Google Scholar PubMed PubMed Central

2. Carreras, N, Arnaez, J, Valls, A, Agut, T, Sierra, C, Garcia-Alix, A. CSF neopterin and beta-2-microglobulin as inflammation biomarkers in newborns with hypoxic-ischemic encephalopathy. Pediatr Res 2022. https://doi.org/10.1038/s41390-022-02011-0 [Epub ahead of print].Search in Google Scholar PubMed

3. Sato, T, Yagishita, N, Tamaki, K, Inoue, E, Hasegawa, D, Nagasaka, M, et al.. Proposal of classification criteria for HTLV-1-associated myelopathy/tropical spastic paraparesis disease activity. Front Microbiol 2018;9:1651. https://doi.org/10.3389/fmicb.2018.01651.Search in Google Scholar PubMed PubMed Central

4. Barco, A, Orlando, S, Stroffolini, G, Pirriatore, V, Lazzaro, A, Vai, DD, et al.. Correlations between cerebrospinal fluid biomarkers, neurocognitive tests, and resting-state electroencephalography (rsEEG) in patients with HIV-associated neurocognitive disorders. J Neurovirol 2022;28:226–35. https://doi.org/10.1007/s13365-021-01047-y.Search in Google Scholar PubMed

5. Rubach, MP, Mukemba, JP, Florence, SM, Lopansri, BK, Hyland, K, Simmons, RA, et al.. Cerebrospinal fluid pterins, pterin-dependent neurotransmitters, and mortality in pediatric cerebral malaria. J Infect Dis 2021;224:1432–41. https://doi.org/10.1093/infdis/jiab086.Search in Google Scholar PubMed PubMed Central

6. Rajda, C, Galla, Z, Polyák, H, Maróti, Z, Babarczy, K, Pukoli, D, et al.. Cerebrospinal fluid neurofilament light chain is associated with kynurenine pathway metabolite changes in multiple sclerosis. Int J Mol Sci 2020;21:2665. https://doi.org/10.3390/ijms21082665.Search in Google Scholar PubMed PubMed Central

7. Khalil, M, Teunissen, CE, Otto, M, Piehl, F, Sormani, MP, Gattringer, T, et al.. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol 2018;14:577–89. https://doi.org/10.1038/s41582-018-0058-z.Search in Google Scholar PubMed

8. Jessen Krut, J, Mellberg, T, Price, RW, Hagberg, L, Fuchs, D, Rosengren, L, et al.. Biomarker evidence of axonal injury in neuroasymptomatic HIV-1 patients. PLoS One 2014;9:e88591. https://doi.org/10.1371/journal.pone.0088591.Search in Google Scholar PubMed PubMed Central

9. Rosadas, C, Zetterberg, H, Heslegrave, A, Haddow, J, Borisova, M, Taylor, GP, et al.. Neurofilament Light in CSf and plasma is a marker of neuronal damage in HTLV-1-associated myelopathy and correlates with neuroinflammation. J Neurol Neuroimmunol Neuroinflammation 2021;8:e1090. https://doi.org/10.1212/NXI.0000000000001090.Search in Google Scholar PubMed PubMed Central

10. van Lierop, ZYGJ, Verberk, IMW, van Uffelen, KWJ, Koel-Simmelink, MJA, In’t Veld, L, Killestein, J, et al.. Pre-analytical stability of serum biomarkers for neurological disease: neurofilament-light, glial fibrillary acidic protein and contactin-1. Clin Chem Lab Med 2022;60:842–50. https://doi.org/10.1515/cclm-2022-0007.Search in Google Scholar PubMed

11. Verberk, IMW, Misdorp, EO, Koelewijn, J, Ball, AJ, Blennow, K, Dage, JL, et al.. Characterization of pre-analytical sample handling effects on a panel of alzheimer’s disease-related blood-based biomarkers: results from the standardization of alzheimer’s blood biomarkers (SABB) working group. Alzheimers Dementia 2022;18:1484–97. https://doi.org/10.1002/alz.12510.Search in Google Scholar PubMed PubMed Central

12. Fraser, CG. Reference change values. Clin Chem Lab Med 2011;50:807–12. https://doi.org/10.1515/CCLM.2011.733.Search in Google Scholar PubMed

13. Schubert, CR, Paulsen, AJ, Pinto, AA, Merten, N, Cruickshanks, KJ. Effect of long-term storage on the reliability of blood biomarkers for alzheimer’s disease and neurodegeneration. J Alzheim Dis 2022;85:1021–9. https://doi.org/10.3233/jad-215096.Search in Google Scholar PubMed PubMed Central

14. Altmann, P, Ponleitner, M, Rommer, PS, Haslacher, H, Mucher, P, Leutmezer, F, et al.. Seven day pre-analytical stability of serum and plasma neurofilament light chain. Sci Rep 2021;11:11034. https://doi.org/10.1038/s41598-021-90639-z.Search in Google Scholar PubMed PubMed Central

15. Altmann, P, Leutmezer, F, Zach, H, Haslacher, H, Mucher, P, Leutmezer, F, et al.. Serum neurofilament light chain withstands delayed freezing and repeated thawing. Sci Rep 2020;10:19982. https://doi.org/10.1038/s41598-020-77098-8.Search in Google Scholar PubMed PubMed Central

16. Keshavan, A, Heslegrave, A, Zetterberg, H, Schott, JM. Stability of blood-based biomarkers of alzheimer’s disease over multiple freeze-thaw cycles. Alzheimers Dement Diagn Assess Dis Monit 2018;10:448–51. https://doi.org/10.1016/j.dadm.2018.06.001.Search in Google Scholar PubMed PubMed Central

17. Hviid, CVB, Knudsen, CS, Parkner, T. Reference interval and preanalytical properties of serum neurofilament light chain in Scandinavian adults. Scand J Clin Lab Invest 2020;80:291–5. https://doi.org/10.1080/00365513.2020.1730434.Search in Google Scholar PubMed

18. Simrén, J, Ashton, NJ, Blennow, K, Zetterberg, H. Blood neurofilament light in remote settings: alternative protocols to support sample collection in challenging pre-analytical conditions. Alzheimers Dement Diagn Assess Dis Monit 2021;13:e12145. https://doi.org/10.1002/dad2.12145.Search in Google Scholar PubMed PubMed Central

19. Koel-Simmelink, MJA, Vennegoor, A, Killestein, J, Blankenstein, MA, Norgren, N, Korth, C, et al.. The impact of pre-analytical variables on the stability of neurofilament proteins in CSF, determined by a novel validated SinglePlex Luminex assay and ELISA. J Immunol Methods 2014;402:43–9. https://doi.org/10.1016/j.jim.2013.11.008.Search in Google Scholar PubMed

20. Aziz, N, Nishanian, P, Mitsuyasu, R, Detels, R, Fahey, JL. Variables that affect assays for plasma cytokines and soluble activation markers. Clin Diagn Lab Immunol 1999;6:89–95. https://doi.org/10.1128/cdli.6.1.89-95.1999.Search in Google Scholar PubMed PubMed Central

Received: 2022-09-13
Accepted: 2023-01-11
Published Online: 2023-01-25
Published in Print: 2023-06-27

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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