Imaging Of Metabolism In 3d Culture By Flim PDF Download

Fluorescence Lifetime Imaging Microscopy (FLIM) based visualization of endogenous autofluorescence can provide a plethora of information about cellular biochemical microenvironment. In particular, FLIM of autofluorescent metabolic coenzyme NADH and its relative binding status has been shown to be highly sensitive to fluxes in cellular microenvironment. Accurate estimation of lifetime changes can help us better understand response of biological system to external stimuli, for exampling creating metabolic maps of NADH fluxes during a cellular wounding event. There has been significant improvement in FLIM instrumentation, analysis in the last decade and it has been applied to range of biological applications such as cancer detection, imaging metabolism, protein-protein interaction, tissue characterization, etc. Much of this work has been using FLIM as a contrast to compare normal and diseased conditions. However, there is growing interest in using FLIM to help characterize cellular identity and functional state. There is great need for new and ideally label-free approaches that could distinguish cell types. This is especially of need with immune cell types where it can be very challenging to discern identity and function. Here, we have investigated NADH FLIM as potential biomarker for microglia activation to characterize its functional state. Additionally, we have developed instrumentation and software-oriented technique to augment existing FLIM imaging and analysis methods. In this thesis, we aim to address various aspects of FLIM imaging which can be divided in three broad areas. i) Novel application of NADH FLIM in biology, ii) advanced quantitative analysis to improve FLIM based post-processing, iii) develop novel FLIM based instrumentation. First, we explore NADH FLIM as a potential tool to develop label free probing technique for characterizing microglia in the context of the Central Nervous System (CNS). Using NADH FLIM to compare lifetime of surveillant versus activated microglia, we found that, activated microglia has significantly different NADH lifetime compared to resting microglia cell. Second, we developed a label free technique to identify surveillant microglia in mixed cell culture and brain tissue using FLIM combined with Artificial Neural Network (ANN). Third, we developed hardware and software to improve FLIM acquisition which could lead to better FLIM based characterization and develop cellular signature in future. We demonstrated that using an optical fiber to introduce dispersion to existing multiphoton FLIM system can add simultaneous spectral/lifetime acquisition capability reducing the need to add additional detection pathway for spectra/lifetime imaging. Additionally, we developed open source FLIM acquisition software which can be eventually extended to add runtime FLIM analysis or custom FLIM acquisition. In future, these microglia visualization technique along with machine learning approach can be used to probe microglia in CNS associated diseases such as Alzheimer's disease, Parkinson's disease, and better understand their role in those diseases and might lead to better therapeutic technique.