Adapting ambient ionization mass spectrometry methods for disease characterization and tissue classification

Date

2022-05

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Ambient ionization mass spectrometry (MS) techniques have brought about a new era of clinical MS research by allowing for direct, rapid analysis of biological samples. Methods such as desorption electrospray ionization (DESI) MS enable chemical mapping of biomolecules directly from unprocessed tissue specimens, allowing investigation of disease states and offering insights into dysregulated metabolism. The molecular information obtained can act as a diagnostic fingerprint and be used to discriminate healthy and diseased tissue. Furthermore, handheld ambient MS-based devices such as the MasSpec Pen have recently been developed for analysis of tissues in vivo. These technologies are capable of providing feedback to clinicians in near real-time to aid in surgical decision making, demonstrating potential for translation of MS into a surgical setting. Ambient ionization techniques require minimal sample preparation, allow for rapid analysis of tissue in situ, and can be performed at atmospheric pressure in open air; as such, these techniques have the potential to be easily translated into a clinical workflow. This dissertation describes the development and application of ambient ionization MS methods for disease characterization and tissue classification. Chapter 2 describes the application of DESI-MS imaging to identify altered metabolites in a gene knockout mouse model to help elucidate metabolic processes potentially contributing to the occurrence of neural tube defects. Chapters 3-5 describe the development of ambient ionization MS technologies to address clinical challenges in the diagnosis and treatment of patients with endocrine diseases. In Chapter 3, DESI-MS mass spectral profiles of thyroid tissue sections were used to build statistical models for discriminating benign and malignant tissues. These models were then validated using prospectively collected thyroid fine needle aspiration biopsy samples, demonstrating the capabilities of this method for preoperative classification of thyroid nodules. Next, the performance of the benign thyroid versus papillary thyroid carcinoma classifier was evaluated on an independent dataset collected on a different DESI-MS platform, as outlined in Chapter 4. Chapter 5 discusses the capabilities of the MasSpec Pen for the intraoperative classification of thyroid, parathyroid, and lymph node tissues during cervical endocrine surgeries.

Department

Description

LCSH Subject Headings

Citation