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Title page for ETD etd-03282016-155111

Type of Document Master's Thesis
Author Heaster, Tiffany Marie
URN etd-03282016-155111
Title Optical imaging of cell cycle-driven tumor heterogeneity
Degree Master of Science
Department Biomedical Engineering
Advisory Committee
Advisor Name Title
Melissa C. Skala Committee Chair
Frederick R. Haselton Committee Member
  • fluorescence lifetime
  • tumor dormancy
  • metabolic imaging
  • quiescence
  • cellular heterogeneity
  • cancer
Date of Defense 2016-03-31
Availability unrestricted
Metabolism-driven changes in cell cycle status introduce a high degree of cellular heterogeneity within a tumor, presenting challenges with evasion from drug treatment and increased likelihood of tumor recurrence. Accordingly, fluorescence lifetime imaging (FLIM) exhibits sensitivity to intracellular activity of metabolic co-enzymes NAD(P)H and FAD. To address the issue of heterogeneity, we first characterized metabolism of proliferating, quiescent, and apoptotic cell populations by measuring the optical redox ratio (NAD(P)H fluorescence intensity divided by FAD intensity), as well as NAD(P)H and FAD fluorescence lifetimes using two-photon microscopy and time-correlated single photon counting. Our hypothesis that populations exhibiting varying cell cycle activity can be metabolically distinguished was supported upon comparison of metabolic properties (fluorescence lifetimes and redox ratio) of Kasumi-1 human acute myeloid leukemia (AML) myeloblasts untreated (proliferation) and treated with JQ1, an inhibitor of cell cycle progression (quiescence), and cytarabine, a common AML chemotherapy (apoptosis). All populations displayed significant differences in measurements of redox ratio (p<0.001), NAD(P)H lifetime (p<0.01), and FAD lifetime (p<0.05). Additionally, we observed metabolic separation between each subpopulation within heterogeneous samples. Two-population (proliferating and quiescent) or three-population samples (proliferating, quiescent, and apoptotic) with varying plating proportions were prepared to generate heterogeneous populations. Subpopulations were distinguished by application of a linear combination model derived from partial least squares discriminant analysis. This resulted in high classification accuracy upon comparison of experimental samples and resolved populations. These results establish a threshold for differentiating mixed populations and may allow further characterization of tumor progression and drug resistance mechanisms influenced by this heterogeneity.
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