Technology Developments in Biomedical Mass Spectrometry

University Of Kansas Lawrence, Lawrence KS

Investigators

Abstract

Project Summary The central theme of the proposal is developing technologies that will allow for expanded biomedical research using mass spectrometry. One aspect of the work focuses on developing noninvasive sampling techniques and methods for mass spectrometry studies on omics samples; a second research goal is to develop improved tools for analyzing ESI-MS data. The program is well-aligned with the NIGMS mission to “develop innovative technologies to enable discoveries in biomedical research.” The work builds on five years of MIRA support, which included numerous advances in methods development for mass spectrometry and machine learning. In the next five years, we will develop and optimize a completely noninvasive, nonpainful sampling method, for lipidomics studies by mass spectrometry, where participants, from infants to the elderly, can be recruited without any safety concerns, and sample processing is simple and biohazard-free. Best practices in the mass spectral analysis of these samples would be ascertained, and the method would be optimized specifically for use in children and infants, a medically underserved population. The method would be used to study lipid regulation across the healthy lifespan. As an enabling technology, future researchers could use the developed methods to study diseases that display lipid dysregulation, like autism, cystic fibrosis, kidney disease, Parkinson’s disease, and others. As a second focus, we would develop a new tool to accurately assign charge states in ESI-MS data. The approach, based on sound preliminary data, leverages machine learning and shows particular promise over the existing state-of-the-art method for assigning ESI-MS data with higher charge states. Mass spectrometry experiments involving glycoproteomics, top-down studies, disulfide analyses, or cross-linking, would all greatly benefit from this development, since all these fields are underserved by existing automated assignment tools, in part, because of inadequate charge state determination methods. This research program leverages expertise across two fields, mass spectrometry and machine learning, and seeks to develop broadly applicable methods that will (a) allow researchers to better understand the fundamental biological changes observed in lipid regulation across the lifespan, and (b) offer improved tools for biomedical mass spectrometry researchers, so that they can more effectively contribute to human health research.