Development of the single-molecule tracking and fluorescence lifetime techniques for biophysical measurements and biomedical applications

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2022-02-08

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Chen, Yuan-I, Ph. D.

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Abstract

Single-molecule detection allows us to reveal the biophysical features of rare species in complex samples. Direct characterization of annealing/melting kinetics of nucleic acids without the need for synchronization of molecular states can be achieved with single-molecule detection, but the current experiments are not carried out in a native cellular context. In this dissertation, I developed an integrated 3D single-molecule tracking (3D-SMT) and lifetime measurement methods that can follow individual DNA molecules diffusing inside a mammalian cell and observe multiple annealing and melting events on the same molecules (Chapter 2). By comparing the hybridization kinetics of the same DNA strand in vitro, I found the association constants can be 13 to 163-fold higher in the molecular crowding cellular environment. Interestingly, I also found phosphorothioate modified DNA oligonucleotides can be thermodynamically less stable in the cellular environment than expected. There is a need for exploring the causes in the future (Chapter 3). In Chapter 4, I further employed 3D-SMT and fluorescence correlation spectroscopy (FCS) to characterize the diffusivity of an individual JNK2 oligomers in live HeLa cells and demonstrated that JNK2 tetramers can be stable inside mammalian cells. Although total internal reflection fluorescence (TIRF) microscopy with highly inclined thin illumination optical sheet (HILO) mode is the gold standard of single-molecule dynamics and kinetics measurements, it can only allow an imaging depth of up to 10 μm (Chapter 5). When working on the fluorescence lifetime measurement at the single-molecule level, I was realized that the major challenges of using the fluorescence lifetime technique in fundamental research and clinics are the low-speed and poor-accuracy analysis under the low-photon-count conditions (~100 photon counts/pixel). Although fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study the molecular states in the complex cellular environment as the lifetime readings are not biased by the fluorophore concentration or the excitation power, the current methods to generate FLIM images are either computationally intensive or unreliable. In this dissertation, I introduced a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. I demonstrated our model is not only up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation method but also provide more accurate analysis in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells (Chapter 6-7). The future employment of flimGANE in fluorescence lifetime imaging ophthalmoscopy has been described in Chapter 8. In summary, I emphasized that this is the first time we are able to measure the hybridization kinetics inside live cells by our single-molecule tracking technique. This opens a window of opportunity that kinetics signature of molecules can potentially facilitate the oligonucleotide drugs design or disease diagnosis. Additionally, the new lifetime imaging analysis method, flimGANE, is particularly useful in fundamental biological research and clinical applications with its advantages in speed and reliability.

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