Browsing by Author "Payne, Christine K"

For over 200 years, cell function and behavior has been the subject of significant interest. Although microscopic, mammalian cells are fantastically complicated and need to overcome tremendous environmental inertia to maintain homeostasis, such as facilitating ion gradients and intracellular transport, the movement of cargo through a viscous, crowded cytosol. This latter point is especially important because many diseases, including Alzheimer’s disease, are associated with aberrant transport. Thus, determining how a cell responds to environmental stimuli is critical to building an understanding of fundamental biophysics and working toward future curative measures for transport-related disease.This dissertation begins with a high-level, epidemiological perspective on the co-occurrence of pulmonary diseases in the United States with pneumoconiosis to contextualize microscopic cell responses to damage with macroscopic outcomes. Pneumoconiosis is caused by inhaled dusts and nanomaterials, which can become embedded in and inflame lung tissues. We found that pneumoconiosis is associated with increased rates of chronic obstructive pulmonary disease (COPD), lung cancer, and pneumonia at time of death. When we combined pneumoconiosis diagnoses with the known covariate of smoking history, smokers with pneumoconiosis had the highest rate of COPD in our data, indicating a potential synergistic effect of lung damage. Interestingly, we found that smoking history and pneumoconiosis were more associated with lung cancer and pneumonia, respectively. Through presented case studies in non-mining/construction occupations, we note that specific pneumoconioses can occur on local scales, demonstrating that even if nanomaterials are too varied to appear in an aggregate population, the risks of increased disease rate as a result of microscopic lung injury are still present. This is essential for future regulation and policy decisions as nanomaterial production continually increases. To further explore microscopic cell responses to controlled stimuli, we used particle tracking microscopy to follow trafficked organelles and evaluate how the cell uses intracellular transport and reacts to disruptions. The classic approach to analyzing particle tracking data is the mean squared displacement (MSD). Despite its ubiquity, recent work has shown that the MSD is a flawed method because it is unstable with respect to noise, curve fitting choices, and observation window. Here, we present a novel tracking framework that uses a Bayesian changepoint segmentation strategy and then infers population motility from segment velocities. This method avoids the use of MSDs and is efficient and stable in response to trajectories of different quality and length. We demonstrate this software on tracked lysosomes in epithelial cells. We found that there is a clear difference in the frequency of motion for lysosomes depending on where in the cell they were located, with lysosomes in the perinuclear region moving less often than those in the periphery. This is an extremely important finding because it robustly distinguishes these two regions over thousands of lysosome observations and much of the current particle tracking literature ignores region as a factor, potentially exposing any results to selection bias. Separately, we found that the size of lysosomes, which was controlled with sucrose-induced osmotic swelling, had no effect on transport frequency; however, the speed of large lysosomes was slower than with small lysosomes. Next, we generalize our tracking system to include all vesicles, rather than only lysosomes. With these conditions, we present an exciting new result: disruption of the endoplasmic reticulum with palmitate, a fatty acid found at elevated levels in patients with diabetes and obesity, significantly decreases vesicle motility. This effect was independent of any reduction in ATP levels or cell viability and appears to be associated with the distortion of the ER we observed under these conditions. This result points to areas of future research in the biophysical complications associated with these diseases and further underscores recent work detailing the extensive interactions between the ER and endocytic vesicles. Paired with this analysis, we also observed that macromolecular crowding has no effect on directed transport through the reduction of ribosome concentration, indicating that directed intracellular transport is quite efficient despite significant obstacles in the cytosol. Looking further at the cytoskeleton, we show that disruption of actin filaments and microtubules both decrease vesicle motility as expected. However, we found that disruption of intermediate filament organization with withaferin A significantly decreases vesicle motility in a dose-dependent fashion. Unlike microtubules and actin filaments, there are no molecular motors associated with intermediate filaments, so this result may be tied to cytoskeletal interactions and merits further exploration. In summary, this dissertation details analyses that explore and characterize disruptions to cellular homeostasis. We first provide an updated perspective of dust inhalation diseases in the United States to provide context for cell damage on a macroscopic level and advocate for intentional regulation of nanomaterials as production and exposure risk increase. We also demonstrate an effective Bayesian particle tracking analysis alternative to the mean squared displacement, which overcomes the latter’s limitations. We then use this new tool to learn significant new information about intracellular transport, particularly that there are regional distinctions in vesicle behavior and that ER disruption with palmitate causes a dramatic decrease in transport without affecting viability. Overall, we are most excited for the potential for this analysis to be used across a variety of problems and disciplines and look forward to its implementation.

Nanoparticles are being used or implemented in a wide array of applications including health care, cosmetics, automotive, food, beverage, coatings, consumer electronics, and coatings. Despite this widespread use, we are unable to predict how nanoparticles will interact with biological systems. This is important for both nanotoxicity resulting from human exposure to nanomaterials and the development of new nano-based biotechnologies. The first step in the interaction of nanoparticles with biological systems is often with blood, for biomedical applications, or lung fluid, when nanoparticles are inhaled. In both cases, the nanoparticles are exposed to proteins that form a "corona" by adsorbing on the nanoparticle surface. The subsequent cellular response is determined by this protein corona rather than the bare nanoparticle.Our goal is to develop a predictive capability for protein-nanoparticle interactions. This requires lab automation, large datasets, machine learning, and mechanistic studies. We first developed and validated a semi-automated approach to generate, purify, and characterize protein coronas using a liquid handling robot and low-cost proteomics. Using this semi-automated approach, we characterized the formation of protein coronas with increasing incubation time and serum concentration. Incubation time was found to be an important parameter for corona composition and concentration at high incubation concentrations but yielded only a small effect at low serum incubation concentrations. To better understand how the protein corona affects biological responses, we investigated the response of macrophage cells to titanium dioxide nanoparticles as a function of the protein corona. As in our previous work with serum proteins, we measured the concentration and composition of murine lung fluid proteins adsorbed on the surface of titanium dioxide nanoparticles. We found that a low concentration of lung fluid corona, relative to fetal bovine serum and bovine serum albumin coronas, led to an increased expression of cytokines (interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α), and macrophage inflammatory protein 2 (MIP-2)), indicating an inflammation response. This underscores the importance of understanding how the composition and concentration of the protein corona governs organism responses to nanoparticle exposures. Our validated high-throughput lab automation work allowed us to synthesize a library of magnetic nanoparticles and characterize their resulting protein coronas. The resulting nanoparticle dataset has 12 unique NP surfaces, seven surface charges, two core sizes, and two core materials. We used this dataset to generate and characterize, via proteomics, a variety of protein coronas varying incubation concentration and purification methods. We used the resulting proteomic dataset in conjunction with a database of protein physicochemical properties to build a machine learning model that identifies the most important nanoparticle and protein properties for protein corona formation. The model was tested using external datasets and found that it can predict human serum and lung fluid coronas on varying nanoparticle surfaces. Overall, this combination of lab automation, machine learning, and mechanistic analysis suggests that a generalizable understanding of the protein corona formation and its effects is forthcoming.

Bacteria maintain a resting membrane potential which is generated from the ion gradients across the membrane. Membrane potential is important for bacterial functions, such as ATP synthesis, cell transport, cell proliferation and division, cell-cell communication, and antibiotic resistance. Controlling bacterial membrane potential and cell growth has many potential applications for anti-bacterial agents, synthetic biology and living materials. A range of solution-phase agents, such as antibiotics and ionophores, can be used to finely tune the bacterial membrane potential and growth, but these agents all lack spatial control. Developing methods with better spatial control of bacterial membrane potential and cell growth is the original goal of our research. We also try to understand the mechanisms of our control methods and elucidate the relationship between bacterial membrane potential and cell growth.Electric fields have been widely used in neuroscience for modulating neuron activities and treating brain disorders such as Parkinson's disease. They have better spatial precision than solution-phase agents. Previous research mainly used electric fields for electroporation and antibacterial applications. However, the relationship between applied electric field and bacteria growth has not been well characterized. We developed a device which allows the application of an electric field while imaging cell growth and membrane potential change simultaneously. We discovered a range of low frequency voltages that induce slower bacteria growth with increased voltage without killing the cells; subsequently, the bacteria can recover to normal growth levels after removal of the voltage. Hyperpolarization waves can be visually observed during the application of an electric field to bacteria. We identified that gold ions, electrochemically-generated from the gold electrodes in our experiments, are the main cause of the observed slow bacterial growth and hyperpolarization. The speed of the hyperpolarization wave can be modulated by adjusting the applied voltage and frequency, which controls the rate of gold ions electrochemically-generated from electrodes, confirmed by inductively coupled plasma mass spectrometry. Solution-phase gold ion salts were shown to similarly slow bacteria growth and induce hyperpolarization, further validating our observations. To eliminate the effect of side reactions and have better spatial control, we moved to using blue light. Blue light exposure has been demonstrated to hyperpolarize bacteria and encode membrane potential based patterns within a biofilm. Additionally, high doses of blue light can inactivate microbes. Despite this work, using blue light to control bacteria growth at a single cell level has not been previously studied. We have discovered that in the sub-cytotoxic range (30-50 s, 480 nm), longer blue light exposure leads to slower bacterial growth without inducing measurable cell death. Exposure areas can be tightly controlled by moving the light beam of a fluorescence microscope. As a result, complex patterns can be achieved in growing bacterial communities by locally limiting bacteria growth. Our results suggest that the mechanism of blue light control on bacteria growth may be related to hyperpolarization, generation of reactive oxygen species, and increased esterase activities, but future works are needed to decouple these different factors. Notably, we also found that the commonly used Nernstian dye Thioflavin T (ThT) slows the growth of bacteria and may lead to previously-unconsidered experimental artifacts. Cells hyperpolarized by blue light (3 s, 480 nm) internalize more ThT, leading to higher fluorescence signals in these cells than unexposed controls. Additionally, hyperpolarized cells grow slower than control cells in the presence of ThT; however, in the absence of ThT, blue light (3 s, 480 nm) exposed cells do not have much difference of growth compared to unexposed cells. These results suggest that difference of intracellular ThT concentration rather than hyperpolarization is the main reason of slowed bacteria growth when ThT is used as the membrane potential indicator. In summary, we discovered that solution-phase and electrochemically-generated gold ions lead to the hyperpolarization of bacteria and slow cell growth, it provides a new tool for controlling bacterial electrophysiology. This finding may also relate to the antibacterial study of gold nanoparticles. In addition, we figured out that the widely used Nernstian dye ThT slows bacterial growth and causes previously unconsidered experimental artifacts in bacteria growth study. Last, we found that blue light causes slow growth of bacteria and can be used to pattern engineered living materials. Future works are needed to understand the mechanism of the slow bacterial growth induced by blue light.