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CSF Sample Preparation for Data-Independent Acquisition.

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To study changes in neurological diseases and to identify disease-related mechanisms or biomarkers for diagnosis, cerebrospinal fluid (CSF) is frequently used for proteomic-based discovery. In the last years, development and… Click to show full abstract

To study changes in neurological diseases and to identify disease-related mechanisms or biomarkers for diagnosis, cerebrospinal fluid (CSF) is frequently used for proteomic-based discovery. In the last years, development and application of mass spectrometry (MS) techniques have made essential contributions to proteomic studies including protein identification as well as quantification. Until recently, biomarker discovery studies were performed through bottom-up proteomics utilizing data-dependent acquisition. However, drawbacks like stochastic selection of precursor ions cause the exclusion of low-abundant ions from fragmentation as well as from data analysis leading to technical variances among different samples and result in inconsistent data sets. In contrast, data-independent acquisition (DIA) enables almost complete and reproducible quantitative analysis gaining more and more interest as a method for reliable MS-based protein quantification. Besides the utilization of a proper analysis platform, a prerequisite for biomarker studies is the selection of suitable samples and sample processing strategies. Especially for CSF, blood contamination has tremendous impact on the quantitative analysis. In addition, complex processing methods such as protein or peptide fractionation prior to MS analysis can lead to variabilities that affect the reliability of the quantitative results. Here we present methods to evaluate in a first step the CSF quality in regard to blood contamination for the subsequent MS-based sample preparation and finally a DIA method for the analysis of CSF.

Keywords: data independent; analysis; independent acquisition; csf; sample preparation; acquisition

Journal Title: Methods in molecular biology
Year Published: 2019

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