Date of Award

Spring 3-25-2017

Level of Access Assigned by Author

Open-Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biological Engineering

Advisor

Michael D. Mason

Second Committee Member

Paul Millard

Third Committee Member

Rosemary Smith

Additional Committee Members

Scott Collins

Kevin Mills

Abstract

One of the current fundamental objectives in biomedical research is understanding molecular and cellular mechanisms of disease progression. Recent work in genetics support the stochastic nature of disease progression on the single cell level. For example, recent work has demonstrated that cancer as a disease state is reached after the accumulation of damages that result in genetic errors. Other diseases like Huntingtons, Parkinsons, Alzheimers, cardiovascular disease are developed over time and their cellular mechanisms of disease transition are largely unknown. Modern techniques of disease characterization are perturbative, invasive and fully destructive to biological samples. Many methods need a probe or enhancement to take data which alters the biochemistry of the cells and may not be a true representations of cellular mechanisms. Current methods of characterizing disease progression cannot measure dynamics of a process but rely on an average state of a system at a fixed endpoint. They track cellular changes at a population level that rely on static ensemble averages that compare the same population at different time points or populations exposed to different stimuli. Ensemble averaging obscures spatiotemporal and dynamic molecular and cellular mechanism information by only measuring changes before and after disease transitions which neglects mechanistic information. This type of snap shot measurement contains no information regarding the transition into a disease state. The use of an ensemble averages ignores single cell level changes by assuming cells in a population are similar. In reality individual cell-to-cell variability in the same cell population can cause one cell to transition to disease state while another cell does not. Fluctuations are indicators of disease and if cellular processes are not studied spatiotemporally then key molecular changes are undetected. If the path to disease progression is known on an individual cell level, then treatments can be modified to alleviate or prevent disease through early detection. The aim of this thesis is to quantitatively and dynamically measure a biomedical sample on the single cell level without destroying or manipulating it significantly to characterize cellular mechanisms. The technique developed uses microRaman Spectroscopy to analyze molecular signatures of single cells and compare differences between signatures of cells in different populations.

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