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• W2969762605 abstract "Cerebrospinal fluid (CSF) is the fluid of choice to study pathologies and disorders of the central nervous system (CNS). Its composition, especially its proteins and peptides, holds the promise that it may reflect the pathological state of an individual. Traditionally, proteins and peptides in CSF have been analyzed using bottom-up proteomics technologies in the search of high proteome coverage. However, the limited protein sequence coverage of this technology means that information regarding post-translational modifications (PTMs) and alternative splice variants is lost. As an alternative technology, top-down proteomics offers low to medium proteome coverage, but high protein coverage enabling almost a full characterization of the proteins' primary structure. This allows us to precisely identify distinct molecular forms of proteins (proteoforms) as well as naturally occurring bioactive peptide fragments, which could be of critical biological relevance and would otherwise remain undetected with a classical proteomics approach.Here, we describe various strategies including sample preparation protocols, off-line intact protein prefractionation, and LC-MS/MS methods together with data analysis pipelines to analyze cerebrospinal fluid (CSF) by top-down proteomics. However, there is not a unique or standardized method and the selection of the top-down strategy will depend on the exact goal of the study. Here, we describe various top-down proteomics methods that enable rapid protein characterization and may be an excellent companion analytical workflow in the search for new protein biomarkers in neurodegenerative diseases." @default.
• W2969762605 created "2019-08-29" @default.
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• W2969762605 date "2019-01-01" @default.
• W2969762605 modified "2023-10-16" @default.
• W2969762605 title "Top-Down Proteomics Applied to Human Cerebrospinal Fluid" @default.
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• W2969762605 doi "https://doi.org/10.1007/978-1-4939-9706-0_12" @default.
• W2969762605 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31432414" @default.
• W2969762605 hasPublicationYear "2019" @default.
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