Biobanking of CSF : international standardization to optimize biomarker development
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Clinical biochemistry. - Toronto, Ont.
, p. 288-292
University of Antwerp
Cerebrospinal fluid (CSF) reflects pathophysiological aspects of neurological diseases, where neuroprotective strategies and biomarkers are urgently needed. Therefore, biobanking is very relevant for biomarker discovery and evaluation of neurological diseases. Important and unique features of CSF biobanking are intensive collaboration in international networks and the tight application of standardized protocols. The current adoption of standardized protocols for CSF and blood collection as presented in this review enables biomarker studies in large cohorts of patients and controls. Another topic of this review is the selection of control groups, which influences the outcome of biomarker investigations. Control groups in CSF biobanks mainly consist of different disease controls. This is in part due to the fact that lumbar punctures are mostly performed for clinical indications and rarely for research purposes only, as it is a relatively invasive procedure. Moreover, there is a lack of homogenous criteria and definition of control groups. We therefore propose uniform consensus definitions for such control groups in biomarker research, i.e. Healthy controls (HC), Spinal anesthesia subjects (SAS), Symptomatic controls (SC), Inflammatory Neurological Disease Controls (CINDC), Peripheral Inflammatory Neurological Disease Controls (PINDC) and Non-inflammatory Neurological Disease Controls (NINDC). Another important aspect of CSF biobanking is quality control. Systematic studies to address effects of pre-analytical and storage variation on a broad range of CSF proteins are needed. In conclusion, biomarker research in neurodegenerative diseases has entered a new era due to the collaborative and multicenter efforts of many groups. The streamlining of biobanking procedures, including quality control, and the selection of optimal control groups for investigating biomarkers are important improvements to perform high quality biomarker studies.