Applications of desorption electrospray ionization mass spectrometry imaging for disease characterization from tissue sections and minimally invasive biopsies



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Due to improved therapeutic regimens for cancer, disease grade, stage, and subtype, have become pertinent stratifications for prescribing an optimized treatment plan for every patient. Histopathologists are now being asked to perform increasingly complex disease distinctions from small biopsy samples that may or may not provide the necessary information to make such distinctions. Since inception, mass spectrometry (MS) have been proven to be a powerful analytical tool for disease diagnosis and several MS techniques have been successfully integrated into routine clinical workflows. Over the past two decades, ambient ionization MS, particularly desorption electrospray ionization mass spectrometry imaging (DESI-MSI), has been studied for disease differentiation in a similar manner. Intrinsic advantages of DESI-MSI, such as minimal sample preparation, nondestructive solvent system, and spatial separation of relevant tissue, supports the successful integration of this technology into a pathological workflow. Despite these advantages, research is ongoing to determine the efficacy of DESI-MSI as a tool for intricate disease stratification and biopsy analysis. This dissertation presents the applications of DESI-MSI towards staging advanced disease, classifying preneoplastic lesions, and subtyping cancerous tissue from tissue sections and minimally invasive biopsy material. Chapter 2 discusses the application of DESI-MSI and statistical analyses to understand metabolic dysregulation in primary and metastatic melanoma as well as discusses the performance of statistical classifiers on metastatic melanoma in lymph node tissue. Chapter 3 presents the application of DESI-MSI and statistical classifiers towards differentiation of low grade and high grade preneoplastic pancreatic cysts. Finally, Chapter 4 describes the application of this technique towards the stratification of lung cancer subtypes from tissue sections and biopsy material. In entirety, this work aims to demonstrate the capabilities of the DESI-MSI workflow towards increasingly complex biospecimens and diagnostic challenges commonly confronted in routine clinical environments.



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