Year of Award:
Biospecimen Science Technologies
Other PI or Project Leader:
Direct tissue profiling and imaging mass spectrometry (MS) provides a molecular assessment of numerous expressed proteins within a tissue sample. MALDI MS (matrix-assisted laser desorption ionization) analysis of thin tissue sections results in the visualization of 500-1000 individual protein signals in the molecular weight range from 2000 to over 200,000. These signals directly correlate with protein distribution within a specific region of the tissue sample. The systematic investigation of the section allows the construction of ion density maps, or specific molecular images, for virtually every signal detected in the analysis. Ultimately, hundreds of images, each at a specific molecular weight, may be obtained. To date, profiling and imaging MS has been applied to multiple diseased tissues, including human non-small cell lung tumors, gliomas, and breast tumors. Interrogation of the resulting complex MS data sets using modern biocomputational tools has resulted in identification of both disease-state and patient-prognosis specific protein patterns. These studies suggest that such proteomic information will become more and more important in assessing disease progression, prognosis and drug efficacy. Molecular histology has been known for some time and its value clear in the field of pathology. Imaging MS brings a new dimension of molecular information that specifically focuses on the disease phenotype. One important aspect of the MALDI MS imaging technology is sample preparation and processing. We propose here to further optimize the existing methodologies to maximize the information recovered from the MS analysis of fresh frozen sections, and develop and validate new approaches to investigate solvent fixed biopsies. Next, we propose to further develop and optimize methodologies to measure pharmaceutical compounds by MS in tissue sections. We also propose to further develop and validate protocols for the molecular analysis by MS of cancer cells in fine needle aspirates. Finally, we propose to automate some key aspects of these methodologies.