Biobanks can help in the search for biomarkers to diagnose brain diseases. Neurological diseases can be difficult to diagnose as they may have overlapping symptoms. Furthermore, there are currently no diagnostic tests for diseases such as Amylotrophic lateral sclerosis (ALS) and Alzheimer’s disease. Physicians diagnose ALS or Alzheimer’s by ruling out other neurological diseases (1). Doctors currently monitor disease progression in ALS patients using subjective questionnaires (1).
Biomarkers to Diagnose Neurological Diseases
Biomarkers are proteins that change expression in different disease states. Therefore, biomarkers could provide an objective measurement of disease and replace or supplement subjective methods of diagnosing patients with neurological diseases. Biomarkers could also help doctors and clinical researchers to monitor disease progression and response to treatment.
Since the blood-brain barrier separates the brain from circulating blood, proteins in the plasma may not accurately reflect disease processes occurring in the brain. In contrast, cerebrospinal fluid (CSF), which surrounds the brain and spinal cord, may be a source of more reliable biomarkers of neurological disease. Scientists, clinical researchers and physicians are particularly interested in finding robust and reliable CSF biomarkers to track diseases of the brain.
Challenges in the Search for CSF Biomarkers
However, this has not been an easy task. While researchers have identified CSF markers for ALS and Alzheimer’s disease (1,2), these biomarkers have not yet made their way into widespread clinical use.
This reason for this is that different studies have shown marked variability in CSF biomarker levels for particular neurological diseases (3). This is partly because protein levels can be significantly affected by small changes in how researchers collect, process or store biological samples (4). In fact, up to 50-75% of lab errors happen in the preanalytical phase, that is the period when samples are collected and handled prior to running an experiment (3). These preanalytical errors include sample contamination, labelling errors and variations in storage conditions.
Any variation in preanalytical protocols can change the levels of proteins within samples. Inherent sample variability can mask biological variability. This can affect experimental results and thus hinder the search for biomarkers. The problem of sample variability can be compounded when combining samples or data sets collected at different times, by different people or in different places.
Some proteins are very sensitive to preanalytical variability such as changes in storage temperature or freeze-thaw cycles, whereas others are more resistant (3). The problem is that when searching for new biomarkers, researchers don’t always know which types of proteins they are looking for and how sensitive these proteins might be to different variables.
International Guidelines for Biobanking CSF Samples
Scientists can decrease sample variability and optimize biomarker discovery by using standard operating procedures (SOPs) and validated protocols. International consensus guidelines aim to help scientists by providing data and recommendations on the best methods to collect, process and store CSF for biomarker studies (3,4).
As part of the ‘Methods in Molecular Biology’ series, a group of scientists from the Neurochemistry Laboratory at Amsterdam University Medical Center have summarized consensus guidelines for CSF biobanking (3).
These guidelines recommend standardizing and recording all experimental parameters including data and time of sample collection, sample volume, type of needle and collection tube, and centrifugation conditions. All these parameters can affect experimental results. For example, hydrophobic peptides can stick to plastic tubes. Therefore, scientists should use glassware instead of plasticware wherever possible when studying hydrophobic proteins.
The guidelines also recommend dividing samples into aliquots to avoid repeated freeze-thaw cycles which can cause degradation of certain potential biomarkers. Also storing samples at room temperature for extended time periods (up to 48 hours) can also cause some proteins to degrade. Sample volume is another factor that can affect protein concentrations.
Conclusions
CSF biomarkers could be a valuable source of new objective diagnostic tests for neurological diseases. However, to date, various studies have found significant variability in the expression of potential CSF biomarkers. International consensus guidelines aim to reduce this variability by giving scientists standardized, validated protocols to use when collecting and processing CSF samples. Furthermore, by recording all protocols and prenalytical conditions, scientists can work out where any errors or variations occur and use this information to parse which experimental results are likely to be true biological change versus prenalytical variation. Hopefully, adoption of standardized experimental conditions and accurate recording will help scientists discover new biomarkers to help diagnose and monitor neurological diseases.
References
- Bereman et al. Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS. Sci Rep. 2018
- Niemantsverdriet et al. Alzhiemer’s disease CSF biomarkers: clinical indications and rational use. Acta Neurol Belg. 2017
- Hok-A-Hin et al. Guidelines for CSF Processing and Biobanking: Impact on the Identification and Development of Optimal CSF Protein Biomarkers. In: Santamaría E., Fernández-Irigoyen J. (eds) Cerebrospinal Fluid (CSF) Proteomics. Methods in Molecular Biology, vol 2044. Humana, New York, NY 2019
- NCI Best Practices for Biospecimen Resources. National Cancer Institute. 2016 (Online) Accessed June 17, 2019
