Research Project
7107560
[unreadable] DESCRIPTION (provided by applicant): The generation of biologically relevant proteomics data requires samples consisting of homogenous cell populations, in which no unwanted cells of different types and/or development stages obscure the results. The problem is compounded for the analysis of tissue biopsies, since many different cell types are typically present, and small numbers of abnormal cells may lie within or adjacent to unaffected areas. While methods such as laser capture microdissection (LCM) enable the isolation of homogeneous subpopulations of cells, proteomic analysis of LCM-procured specimens is severely constrained by the very low amounts of sample generated. Furthermore, LCM is known to result in significant protein sample alterations due to fixation and staining procedures, further limiting the ability to perform effective proteome studies from human astrocytoma biopsies. To avoid the limitations of established proteome techniques for analyzing protein extracts obtained from microdissection-procured tissue specimens, an effective discovery-based proteome platform has recently been developed at Calibrant. This proteome platform, called Gemini, combines a unique multidimensional separation system with customized back-end bioinformatics tools, and enables ultrasensitive analysis of minute protein amounts extracted from cells captured by tissue microdissection. This project further aims to employ a novel, laser-free microdissection technique pioneered by our collaborator, Dr. Zhengping Zhuang at the National Institute of Neurological Disorders and Stroke, capable of providing enriched, high quality, and reproducible tissue samples. Proteome analysis of glioma malignancies, including grade II astrocytoma, grade III anaplastic astrocytoma, and grade IV glioblastoma multiforme, will be performed using a set of fresh frozen brain biopsies (n = 4 for each tumor grade) and in triplicates for each tissue specimen. The results of comparative proteomics studies are expected to reveal (i) relevant tumor-associated biological markers and networks, (ii) molecular relationships between different astrocytomas, and (iii) the molecular mechanisms that drive the progression of astrocytomas, e.g. grade II to grade III. [unreadable] [unreadable] [unreadable]
