Mass Spectrometry-based Neuroproteomics : Deciphering the Human Brain Proteome

Mammalian brain is challenging to study due to its heterogeneity and complexity. However, recent advances in molecular imaging, genomics and proteomics have contributed significantly to achieve insights into molecular basis of brain function and pathogenesis of neurological disorders. Efficient sample preparation is an integral part of a successful mass spectrometry (MS)-based proteomics. Apart from the identification, quantification of proteins is needed to investigate the alterations between proteome profiles from different sample sets. Therefore, this thesis investigates optimizing and application of the MS compatible sample preparation techniques for the identification and quantification of proteins from brain tissue. The central objective of this thesis was (i) to improve the extraction of proteins as well as membrane proteins (MPs) from the brain tissue and (ii) to apply the optimized method along with the stable isotope dimethyl labeling (DML) and label-free (LF) MS approaches for the relative quantification of the brain proteome profiles during neurological conditions such as Alzheimer’s disease (AD) and traumatic brain injury (TBI). First study described in this thesis is focused on the qualitative aspects for the brain tissue sample preparation. The optimized extraction buffers from first study containing n-octyl-β-glucopyranside or triton X-114 were used in the further quantitative studies to extract the proteins from patient (AD or TBI) and control human brain samples. Triton X-114 has additional advantage of separating MPs into a micellar phase. Therefore we also investigated the possibility to apply this in combination with DML quantitation approach for enrichment of low abundant MPs from AD brains. AD and TBI causes severe socio-economic burden on the society and therefore there is a need to develop diagnostic markers to detect the early changes in the pathology of the disease. Analytical tools and techniques applied and discussed in this thesis for neuroproteomics applications proved to be powerful and reliable for analyzing complex biological samples to generate high-throughput screening and unbiased identification and quantitation of disease-specific proteins that are of great importance in understanding the disease pathology.