Biophysics Doctoral Candidates - Jeff Folz, “Potassium Nanosensor for Photoacoustic Imaging” and Josh Karslake, "Uncovering Hidden Dynamics in Live-Cell Single Molecule Data with Bayesian Statistics"

Jeff Folz, “Potassium Nanosensor for Photoacoustic Imaging” ABSTRACTS: Potassium is one of the most abundant cations found in the body. It is of particular clinical interest due to its implications for cancer treatment. It has been shown that the necrotic core within a tumor can induce local, 5 to 10 fold increases in the potassium concentration, which has been shown to inhibit immune cell function. However, the development of this hyperkalemic state, including rate, extent, and distribution, are not characterized and have strong therapeutic implications. We developed a potassium nanosensor that is capable of performing as a functional photoacoustic contrast agent. The optode is based on the principle of Donnan exclusion in which a pH sensitive dye deprotonates when a potassium ionophore chelates a potassium ion. Our sensor is multimodal, and has been calibrated for absorption, fluorescence, and photoacoustic read-out modes over biologically relevant potassium concentrations: 1mM to 100mM for photoacoustics and 20mM to 1M for fluorescence. The sensor shows exquisite selectivity over common interfering ions, such as sodium, magnesium, and calcium. By introducing primary amines to the surface of the sensor, we have been able to functionalize and target the sensor, both passively through the Enhanced Permeability and Retention effect and actively via the attachment of the tumor-homing F3 peptide. The nanosensor has been applied to ex vivo samples, where it was demonstrated that tumor samples have a relatively large concentrations of potassium compared to healthy tissue.

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Johsh Karslake, "Uncovering Hidden Dynamics in Live-Cell Single Molecule Data with Bayesian Statistics", ABSTRACT: Single-molecule imaging techniques localize and track individual molecules inside living cells with nanometer precision and millisecond timing; this capability has allowed researchers to investigate many open questions across biology. However, single-molecule image analysis is fundamentally limited by a priori model selection, parameter unidentifiability, and other supervisory biases. To address these issues, we have developed an analysis framework for Single-Molecule Tracking data based on nonparametric Bayesian inference. We have validated this method using realistic simulations and simple experimental systems and finally we extend our investigations to determine the model and dynamics of the membrane-bound keystone virulence protein TcpP in live Vibrio cholerae cells.