Biomarkers for Alzheimer’s disease (AD) present the potential for early detection and consequently early treatment of the condition. They may also prove useful in monitoring disease progression and as a measure of the effectiveness of new therapies. Growing interest in this field of research has therefore led to numerous studies analysing cerebrospinal fluid (CSF) samples from AD patients to aid clinical diagnosis, research and drug development. In a recent study in Alzheimer’s Research & Therapy, Jamie Toombs, Henrik Zetterberg and colleagues from the Institute of Neurology at University College London, UK, investigate whether biomarker measurements from CSF samples are affected by transfer of these samples between tubes, revealing that a significant reduction in biomarker concentrations is indeed an issue. Toombs and Zetterberg discuss their key findings, potential solutions and what this means for the biomarker research community.
What led to your research interest in biomarker development for Alzheimer’s disease?
Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative condition with a long preclinical phase. Biomarkers offer an exciting opportunity for engaging with detection in this early phase, differentiation throughout the disease course, as well as validating therapeutic efficacy. The recently founded Leonard Wolfson Experimental Neurology Centre (LWENC) aims to “accelerate the development and validation of treatments, open an earlier therapeutic window for intervention, and horizon scan for future therapeutic targets”. To facilitate this, a bio-resource centre is being created, with focus on building an extensive, high-quality sample base. It is in our research interest to establish rigorous standard operating procedures for sample handling, benefiting from, and contributing to, the latest developments in the field.
Your study looked at how the measurement of several Alzheimer’s disease biomarkers in cerebrospinal fluid (CSF) could be distorted by serial tube transfer. How did this investigation come about?
The relationship between amyloid beta monomers and tau isomers forms a cornerstone of the present understanding of AD, and is relevant in a wide range of other neurodegenerative conditions. However, significant differences are known to occur between measurements of target molecules in bio-fluid samples made by different laboratories. This can occur even when using the same samples and the same brand and batch of equipment. During the early testing of the effect of storage volume on core AD biomarkers we noticed that such a discrepancy had occurred among samples received from colleagues in another lab.
The concentrations of amyloid beta 42 (Aβ42) detected were lower than previously identified at the other site despite a shared protocol and close working relationship. Tau results from the same samples were unmodified from the previous estimates, so we reasoned that something had gone awry on the Aβ plate. ELISA assays involve lengthy, multi-step processes during which significant variables or errors may potentially occur at any stage. The assays were repeated and the results were exactly as before. The major results of the volume experiment, independent of which sample was analysed, showed that detectible Aβ42 in a CSF sample decreased as the relative surface area of the stored volume increased. In this context of finding further evidence that Aβ42, but not so much tau, had an apparent tendency to be adsorbed to container surfaces, a possible explanation for the earlier problem presented itself. It was realised that the samples had been transferred between containers a number of times between analysis in the original laboratory and our own. The work of del Campo et al. 2012 (Biomark Med. 2012, 6, 419-430) supported the plausibility of this mechanism, and a decision was made to investigate this hypothesis further.
What were your key findings and were you surprised by them?
Our key findings were:
Transfer of cerebral spinal fluid (CSF) samples between tubes led to approximately a 25 percent decrease per transfer in amyloid beta 42 peptide concentration.
Transfer of CSF samples between tubes led to approximately a 16 percent decrease per transfer in amyloid beta 38 and 40 peptide concentrations.
Transfer of CSF samples between tubes led to a significant decrease per transfer in tau protein concentration, though the magnitude of this (approximately 4 percent) was much smaller than with the amyloid beta peptides, and considered clinically irrelevant.
The introduction of 0.05 percent Tween 20, a non-ionic surfactant, mitigated this effect in all proteins tested, but did not entirely negate it in the amyloid peptides.
The results were not unexpected given our previous experience looking at the effect of storage volume on these proteins (Clin Chem Lab Med. 2013, 51, 2311-2317), and our suspicions regarding inter-laboratory variability. However, the magnitude of amyloid beta peptide loss was certainly greater than we had hoped. Sample handling and sharing practices will need to be adapted accordingly.
What other confounders to accurate biomarker measurement did you find, and how much of an effect do you think they may have?
In a previous study (Clin Chem Lab Med. 2013, 51, 2311-2317), we observed that storage volume can have a significant effect on core AD biomarker protein concentration. Smaller volumes of liquid have a greater proportional surface area than larger volumes, in turn meaning that molecules within solution will come into contact with the container surface in greater or lesser proportions. A range of volumes (50μL 75μL 100μL 125μL 250μL 500μL 1000μL 1500μL) were tested in the same 2mL tube type. Tau proteins were not significantly affected, but an increase of 10μL volume was, on average, associated with an increase of 0.95pg/mL in detected Aβ42. It was observed that the introduction of 0.05 percent Tween 20 at the aliquot making stage effectively neutralised this effect. CSF derived from AD patients and non-AD controls behaved similarly, as did pooled and individual subject CSF.
It is difficult to say exactly what effect such factors may have had, and are having on work within the field more broadly. The results of our studies show that there is at least the potential for incautious or inconsistent sample treatment to be a considerable problem. Awareness of standardisation issues within biomarker-based research is growing and in the last few years a number of other groups have published valuable studies on the subject. We hope that the lasting effect will be of improved accuracy and greater resource efficiency as we evolve our laboratory methods to neutralise such factors, rather than being confounded by them.
Do you think your findings may also be relevant to other hydrophobic biomarkers?
One must be careful when generalising, especially given the relatively limited nature of our study, but it seems reasonable to predict that a biomarker’s hydrophobicity will be a factor in its vulnerability to surface adsorption. It is important to note that the crucial point may be not whether the molecule is or is not considered hydrophobic, but its properties relative to competitors within the solution matrix.
Additionally, whilst the hydrophobicity of a molecule does appear to be important in terms of its interaction with container surfaces and other components of a solution, this is far from the only property governing protein dynamics. Certain proteins may simply bind more easily to certain materials due to compatible structural composition. Also, as we acknowledged in the paper, the method we used does not allow clear distinction to be drawn between direct protein loss to the tube surface and the aggregation of proteins within solution, which could mask the target epitopes and so decrease detection. Such aggregation could be contributed to by hydrophobicity or pH related conformation change, and contributing factors need not be mutually exclusive. Further work is needed to fully investigate the mechanisms involved in the effect observed.
We would certainly encourage other laboratories to test this effect for themselves, perhaps on their own molecules of interest. It would be interesting to know what they find.
What further research is needed in order to address how best to improve the accuracy of biomarker measurement, from initial collection from a patient to lab testing?
It is clear that the biomarker research community have to establish more rigorous and standardised operating procedures for each step of sample handling – from initial collection to final testing. This is a daunting task, as whatever protocols are developed will inevitably have to keep pace with changes in technology, ideas, and new biomarkers. Ideally, whenever a new biomarker is identified, a series of tests to understand its properties in regard to collection and storage factors should be conducted. Research themes of temperature, pH, aggregation, surface interactions, and matrix dynamics are likely to be useful for identifying confounding factors so that steps can be taken to neutralise them and so improve the accuracy of biomarker measurement by proxy.
What’s next for your research?
We are looking forward to continuing the investigation into Tween 20 as a potential stabilising additive to CSF. We are currently in the early stages of a project to compare the stability of patient CSF in the presence and absence of Tween 20 over multiple time points. Additionally, we intend to pursue confirmation of the exact mechanisms behind protein concentration loss in storage, and to define more precisely how non-ionic surfactants interact with our target biomarkers.
In a wider scope, our research aims to assist in the development of reference materials for assay standardisation. Such reference materials would be a considerable boon to biomarker research, therapeutic advancement, and ultimately patient well-being.