Protein variability in cerebrospinal fluid and its possible implications for neurological protein biomarker research

Abstract

Cerebrospinal fluid is investigated in biomarker studies for various neurological disorders of the central nervous system due to its proximity to the brain. Currently, only a limited number of biomarkers have been validated in independent studies. The high variability in the protein composition and protein abundance of cerebrospinal fluid between as well as within individuals might be an important reason for this phenomenon. To evaluate this possibility, we investigated the inter- and intraindividual variability in the cerebrospinal fluid proteome globally, with a specific focus on disease biomarkers described in the literature. Cerebrospinal fluid from a longitudinal study group including 12 healthy control subjects was analyzed by label-free quantification (LFQ) via LC-MS/MS. Data were quantified via MaxQuant. Then, the intra- and interindividual variability and the reference change value were calculated for every protein. We identified and quantified 791 proteins, and 216 of these proteins were abundant in all samples and were selected for further analysis. For these proteins, we found an interindividual coefficient of variation of up to 101.5% and an intraindividual coefficient of variation of up to 29.3%. Remarkably, these values were comparably high for both proteins that were published as disease biomarkers and other proteins. Our results support the hypothesis that natural variability greatly impacts cerebrospinal fluid protein biomarkers because high variability can lead to unreliable results. Thus, we suggest controlling the variability of each protein to distinguish between good and bad biomarker candidates, e.g., by utilizing reference change values to improve the process of evaluating potential biomarkers in future studies.

A comprehensive systematic review of CSF proteins and peptides that define Alzheimer’s disease

“…We initially identified APOE, TTHY, Osteopontin (OSTP) and Cystatin-C (CYTC) which are among the most abundant proteins in normal CSF [18] as the protein species whose levels change the most often in the context of AD. However, these 5 proteins appear both as increased and decreased in different proteomic studies.…”

“…The most recurrent proteins were Apolipoprotein E (APOE), Nerve growth factor inducible (VGF) and Transthyretin (TTHY), being identified in 18, 15 and 14 independent proteomics studies, respectively ( Table 2). In order to provide information on the faddabundance of each protein, the 50 most abundant proteins in the CSF of healthy individuals, according to published sources [18], are designated with $ in Tables 2, 3 and 4.…”

“…With the information extracted from these articles we were able to generate a database with 3221 peptide sequences (Additional file 4: Table S4). Further analysis of this database revealed that 87 peptides [ Table 5 and Additional file 6: Figure S1 (sequences Proteins with the same pattern of expression (toward the same direction) at least in 2 or 3 (bolditalic proteins) independent studies $ Proteins abundant in CSF of healthy individuals (Schilde et al 2018) α Studies that provide Aβ and Tau contents in CSF # APOE genotype to diagnose AD in bolditalic and in red)], which correspond to a total of 13 proteins (2 proteins increased and 11 decreased in AD) maintained a direction change consistent with that observed by proteomics in relation to the AD pathology (Table 5). Other peptides from these proteins showed inconsistent distribution across the different studies (Additional file 5: Table S5).…”

Blood Contamination in CSF and Its Impact on Quantitative Analysis of Alpha-Synuclein

“…Moreover, on protein identification level, significantly lower numbers of protein were detected with a blood level of 1% (data not shown). For proteomics analysis, it is known that the overall variance is influenced by the analytical variance plus the biological variance [24]. Moreover, protein detectability and thus quantifiability are For proteomics analysis, it is known that the overall variance is influenced by the analytical variance plus the biological variance [24].…”

“…For proteomics analysis, it is known that the overall variance is influenced by the analytical variance plus the biological variance [24]. Moreover, protein detectability and thus quantifiability are For proteomics analysis, it is known that the overall variance is influenced by the analytical variance plus the biological variance [24]. Moreover, protein detectability and thus quantifiability are strongly dependent on the overall composition of the sample.…”