Label-Free Optical Imaging and Quantitative Algorithms to Assess Living Biological Systems.

Abstract

Non-perturbing tools that provide reliable, information-rich assessments of living biological systems can inform clinical practice and improve patient health. In this dissertation, we developed label-free nonlinear optical molecular imaging to provide spatially-resolved, non-perturbing, quantitative functional assessments of 1) living cell lines, 2) primary human cells, and 3) tissue-engineered constructs manufactured with primary cells. Quantitative analytic methods were developed to account for the high inter-patient variability in primary human cells freshly harvested from distinct donors. The FDA strictly regulates the manufacture of tissue-engineered constructs, requiring assessment of product effectiveness and safety prior to release for patient treatment. We addressed this clinical need by developing quantitative methods to assess local tissue structure and biochemistry using label-free nonlinear optical molecular microscopy. Optical measures characterized morphologic and functional differences between controls and stressed constructs. Rigorous statistical analysis accounted for variability between patients. The technique reliably differentiated controls from stressed constructs from 10 batches/patients with P-value < 0.01. Further, the optical metrics strongly correlated with a standard WST-1 cell viability assay (P-values < 0.001 for 5 batches/patients). Unlike the standard methods, which are reliable but destructive, label-free optical assessments are both non-invasive and reliable. Thus, such optical measures could serve as reliable manufacturing release criteria for cell-based tissue-engineered constructs prior to human implantation. Label-free fluorescence lifetime imaging microscopy (FLIM) images consist of spatial and temporal information. The traditional method to analyze FLIM is iterative fitting, which is time-consuming and requires prior knowledge of the sample. Clinical practitioners require an analytical and simple-to-operate method to interpret FLIM images. Thus, extended phasor analysis algorithms were developed. The algorithms characterized tissue constituents with better differentiation (P-value < 0.001 for 5 batches/patients) than the standard fitting method (P-value = 0.048 for 5 batches/patients). In addition, time-gated FLIM with various gating schemes was analyzed with the developed phasor analysis algorithms to monitor intracellular lifetime variation. In summary, the developed algorithms could advance future FLIM applications in clinic.