Design and implementation of a custom imaging mass spectrometer.

Mass spectrometry (MS) has been demonstrated as one of the most robust and capable analytical tools for chemical analysis. As such, MS has found application across practically every field of science today. One notable area where MS is used today is in the analysis of biological samples, such as tissues, where MS has been utilized for lipidomic, proteomic, metabolomic, etc. The work contained in this document outlines the author’s contribution to an ongoing project for developing a novel class of imaging mass spectrometer towards the goal of subcellular imaging. Construction of the proposed instrument required a large amount of engineering to ensure the instrument was capable of achieving subcellular mass spectral imaging. In many cases, commercially available instrument components did not meet the specifications of the project and thus custom-engineered solutions were necessary. While subcellular mass spectral imaging has not yet been achieved with this instrument, development of most of the underlying subsystems has been completed. Chapter Two describes many of the system, their function, and how they achieved the necessary specifications for subcellular mass spectral engineering. Construction of mass spectrometers require an understanding of ion trajectories which can be solved either analytically or numerically. While analytical solutions are able to account for ion trajectories under ideal conditions, they are mathematically complex and limited in application to arbitrary geometries. Numerical solutions are mathematical approximations that, when given sufficient computational resources, capable of approaching analytical solutions in accuracy but for arbitrary geometries. In Chapter Three, a technique called Segmented Monte Carlo (SMC) is introduced for unsupervised optimization and characterization of ion optics in mass spectrometers, including the mass spectrometer outlined in this document. Throughout the design and implementation of this instrument it was observed that instrument automation and control was a recurring difficulty. Many different instrument control platforms were investigated, and each was found unsuitable for the scope and requirements of this project. In Chapter Four, an instrument control platform called Data Station One (DSO) is introduced as a solution to this problem. Chapter Five completes the story by discussing future research and outlines a plan for completion of the instrument.