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Bayesian Inference Frameworks for Fluorescence Microscopy Data Analysis

Description
In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different…

In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscopy data, benchmarking the method on synthetic data. These three frameworks are self-contained, and can be used concurrently so that the fluorescence profile and emitter locations are both considered unknown and, under some conditions, learned simultaneously. The framework I present is flexible and may be adapted to accommodate the inference of other parameters, such as emission photophysical kinetics and the trajectories of moving molecules. My TIRF-specific implementation may find use in the study of structures on cell membranes, or in studying local sample properties that affect fluorescent molecule photon emission rates.
ContributorsWallgren, Ross (Author) / Presse, Steve (Thesis advisor) / Armbruster, Hans (Thesis advisor) / McCulloch, Robert (Committee member) / Arizona State University (Publisher)
Created2019
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Role of Environment on Escherichia coli Velocity

Description
OP50 Esherichia coli is a Gram-negative bacterium with a fast replication rate and can be easily manipulated, making it a model species for many science disciplines. To probe this bacterium’s search strategy, cultures were starved and the cell velocity was probed at various points later in time after perturbing the…

OP50 Esherichia coli is a Gram-negative bacterium with a fast replication rate and can be easily manipulated, making it a model species for many science disciplines. To probe this bacterium’s search strategy, cultures were starved and the cell velocity was probed at various points later in time after perturbing the buffer in which the bacteria were located. To start, we added E.coli OP50 filtrate. In yet another experiment filtrate from a Bdellovibrio bacteriovorus (Gram-negative predator) culture was added to monitor the OP50’s differential response to cues from its environment. Using MATLAB code, thousands of E.coli tracks were measured.
ContributorsSanchez, Alec Jesus (Author) / Presse, Steve (Thesis director) / Gile, Gillian (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05