The central nervous system (CNS) encompasses complex groups of cells that coexist to form the functional units of brain mainly comprising three cell types: neurons, oligodendrocytes and astrocytes. The network of these cells through interaction between each other leads to formation of complex organizations such as nodes of ranvier, paranodes, juxtaparanodes etc. Therefore, regions of molecular complexity in brain have different protein/lipid combinatorial complexities. Isolation and enrichment of these domains/regions are crucial for the maximum representation of the protein complement,which otherwise goes undetected due to its miniscule amounts in the whole brain homogenate. Thus preparation of enriched fractions representing the proteins of these complexities is important for detection of molecules of pathogenic and diagnostic significance in diseases. Apart from enrichment, the proteins from the enriched fractions have to be solubilized into an appropriate sample format suitable for resolving into its individual protein components. This holds the key to identification of novel molecules important in neurodegenerative diseases. Thus, sample processing for understanding the organizational and functional proteomics becomes an extremely important step for fruitful results to avoid neuroproteomics biases. This review focuses on the functional units of brain, significance of certain molecules in these organizations and how current techniques are meaningfully employed towards neuroproteomics. In addition, we review how custom techniques of sample preparation for protein analysis are being tailored to address issues for a better analysis of protein complement from specialized regions of the functional units of brain in discovering biomarkers pertaining to neurodegenerative diseases. © 2013 Bentham Science Publishers.
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Krishnakumar N. Menon, Haridasan, A., Xavier, T., Kundargi, R., and Nair, S., “Neuroproteomics: Are We Biased in Our Representation of Molecular Targets Associated with Specific Domains? Implications in Biomarker Discovery”, Current Proteomics, vol. 10, pp. 56-66, 2013.