Human cerebrospinal fluid data for use as spectral library, for biomarker research.

Spectral libraries generated by data dependent acquisition (DDA) are a useful tool for the analysis of data created by data independent acquisition (DIA) in mass spectrometry. The quality of DIA analysis is dependent on the quality of the spectral library. We used cerebrospinal fluid (CSF) of patients with Parkinson's disease and healthy controls to create a spectral library of human CSF proteome. To this date, there is no validated CSF biomarker for Parkinson's disease. This data set may therefore be valuable for the future analysis of CSF proteins. Part of the samples consisted of fractions that were separated by gel electrophoresis. After tryptic digestion, all samples were spiked with indexed retention time (iRT) peptides and were measured using a DDA mass spectrometry approach. The here provided data set can be used as a CSF-specific spectral library. Data files generated from the described workflow are hosted in the public repository ProteomeXchange under the identifier PXD013487.

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

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.