Theses and Dissertations

Permanent URI for this collectionhttps://hdl.handle.net/10217/100359

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Embargo
Immune-activated cellular therapies for osteoarthritis and the role of immune recognition of joint antigens
(Colorado State University. Libraries, 2024) Linde, Peter E., author; Dow, Steven, advisor; Pezzanite, Lynn, advisor; Regan, Dan, committee member; Easley, Jeremiah, committee member; McGilvray, Kirk, committee member
Osteoarthritis (OA) is a progressive, degenerative condition that affects over 550 million people worldwide – a 113% increase since 1990. Despite this high prevalence, there remains a lack of effective treatment options that improve quality of life without risk of adverse effects. Recent evidence supports that OA is a multifactorial condition in which the immune system plays a key role to perpetuate chronic inflammation. Cellular therapies to treat OA have emerged as an option, with mixed results reported in terms of efficacy. Heterogeneity within stromal cell populations has been proposed to be partially responsible for the observed variability in therapeutic responses, particularly in the context of variably inflamed recipient environments such as that seen in OA. Pre-activation, or 'inflammatory licensing' of mesenchymal stromal cells (MSC) through priming their respective ligands has been proposed as a means to generate a homogeneous population of immunomodulatory MSCs – thereby potentially improving their therapeutic consistency in the inflammatory environment of OA. The work in this defense addresses three primary aims: 1) to further investigation the role of the adaptive immune system in OA, investigating autoantibody production to synoviocytes and chondrocytes in OA progression, 2) to evaluate further mechanistically how innate immune pathway activation of mesenchymal stromal cell therapy modulates interactions of MSC with synovium and cartilage to mitigate OA progression, and 3) to examine alternate connective tissue sources of MSC for cell expansion as regenerative therapies. With the lifetime likelihood to develop symptomatic knee OA currently 45% and increasing, the need to develop improved strategies towards disease-modification is critical.
Embargo
Interdisciplinary techniques in protein binding prediction and crystal engineering
(Colorado State University. Libraries, 2024) DeRoo, Jacob Benjamin, author; Reynolds, Melissa, advisor; Snow, Christopher D., advisor; Reardon, Ken, committee member; Zabel, Mark, committee member
This dissertation explores the integration of interdisciplinary methods such as advanced robotic automation, machine learning, and hybrid materials synthesis to dual protein engineering challenges: predicting protein-peptide binding specificity and the preparation of crystalline protein materials. The first chapter introduces a computational pipeline, PAbFold, based on AlphaFold2, designed to predict linear antibody epitopes from a given antigen sequence. This method provides a rapid and cost-effective alternative to traditional experimental techniques for epitope mapping, significantly lowering the financial barrier for laboratories. By accurately identifying binding sites on target proteins, PAbFold enhances the understanding of antibody-antigen interactions, facilitating the development of diagnostic and therapeutic antibodies in a more accessible manner. The second chapter presents an innovative approach to protein crystallization scale-up utilizing the Opentrons 2 liquid handling robot. This automation not only reduces manual labor and variability in crystallization experiments but also makes high-throughput crystallization more accessible to a broader range of laboratories by decreasing costs. Traditional high throughput protein crystallization liquid handling robots are priced around $75,000; the Opentrons 2 costs around $15,000. By employing Python scripts for precise control of the Opentrons 2, the study demonstrates successful crystallization of model and non-model proteins, highlighting the potential of automated systems in structural biochemistry to democratize access to high-quality protein crystals. The third chapter delves into the creation of hybrid materials by combining metal-organic frameworks (MOFs) with porous protein crystals. The research demonstrates the feasibility of embedding MOF domains within protein crystals, potentially opening new avenues for applications in catalysis, gas storage, and chemical warfare agent detoxification. By developing a new class of hybrid materials, this work contributes to making advanced structural biochemical research. Together, these chapters illustrate a modern interdisciplinary approach that embraces machine learning and automation in service of the engineering of peptide-binding proteins and crystalline protein materials. The integration of automation, computational predictions, and hybrid materials offers a promising path toward more efficient and innovative solutions in biochemical research, while significantly lowering the cost barriers, thereby increasing accessibility for researchers worldwide.
Open Access
Cellulose nanocrystals extracted from hemp agro-waste as a potential coating for titanium medical devices
(Colorado State University. Libraries, 2024) Heacock, Jesse Andrew, author; Li, Yan Vivian, advisor; Kipper, Matt, committee member; Liu, James, committee member
Increases in biowaste worldwide have created a unique opportunity to extract natural polymers for a variety of uses. Over the last few decades, hemp has increased in popularity as a desirable industrial agricultural plant. Increases in hemp production for commercial product use have increased the amount of hemp agro waste (HAW). This agro-waste is a potentially great source for extraction of the natural polymer cellulose and the creation of a circular economy. Cellulose can be found ubiquitously in plants and has gained great interest as an alternative to synthetic polymers. In this work, cellulose nanocrystals (CNCs) were extracted from HAW using a one-step ammonium persulfate (APS) oxidation method. APS oxidation was used due to its reduction in hazardous wastes, making it a more environmentally friendly method for CNC extraction compared to other chemical methods. HAW, specifically the woody core of hemp known as hurd, underwent CNC extraction and the properties of the final product were analyzed. Depending on the initial source and method of extraction, CNCs properties have been shown to vary, creating a potential for selectivity when applying CNCs for different uses. It was found that changes in reaction time directly impact CNCs size, surface properties, final product mass, and hydrophilicity. Of note, as reaction time increased from 8 hours to 48 hours, the size of the nanocrystals significantly decreased in length and width. While other properties, such as mechanical strength, morphology, surface charge, and cytotoxicity, underwent no statistically significant changes due to increases in reaction time. The results suggest that HAW is a good source for CNC extraction and that changes in APS oxidation can allow for selective tuning of some CNC properties.
Open Access
Advancing impulsive Raman spectroscopy and microscopy for biological applications
(Colorado State University. Libraries, 2024) Smith, David R., author; Bartels, Randy, advisor; Wilson, Jesse, advisor; Tobet, Stuart, committee member; Jost, Dylan, committee member
Chemically sensitive, label-free spectroscopy and microscopy is a critical tool for the study of many complex and dynamic biological systems. The development of the impulsive stimulated Raman scattering (ISRS) techniques in this thesis represent important steps forward in addressing the ability to interrogate Raman vibrations in complex and scattering samples, particularly low frequency Raman modes.
Open Access
Development of fluidic devices to facilitate more accessible monitoring of human health
(Colorado State University. Libraries, 2024) Cherwin, Amanda E., author; Henry, Charles S., advisor; Tobet, Stuart A., advisor; Snow, Christopher, committee member; Abdo, Zaid, committee member
In December of 2023, the World Health Organization (WHO) Director-General Tedros Adhanom Ghebreyesus outlined the 'Five P's' of global health priorities: Promoting health, Providing health, Protecting health, Powering health, and Performing for health. Despite the mantra of 'prevention is better than cure,' many countries still prioritize treating the sick over proactive health promotion, leading to inadequate prevention of non-communicable diseases (NCDs). Access to healthcare services poses a significant barrier to early recognition and treatment of health issues, particularly in low-income communities. To address these challenges, harnessing the power of science and technology becomes imperative. Powering health involves leveraging scientific research and collaboration to understand disease mechanisms better. Physiologically relevant models, such as microfluidic systems, offer insights into disease progression. Microfluidics, especially when combined with 2D and 3D culture systems, enhances functionality by mimicking physiological conditions. These devices provide cost-effective solutions for diagnostic challenges, bridging the gap between in vitro and in vivo studies. Protecting health requires a deeper understanding of organ systems. Chapter 2 examines a microfluidic model of the gut, an organ that plays a critical role in maintaining overall health. Two devices are discussed, an organotypic device for maintaining ex vivo gut tissue explants, and an electrochemical sensor module for monitoring relevant molecules such as oxygen or hydrogen peroxide within the tissue media. Dysbiosis in the gut microbiome has been linked to various pathologies, emphasizing the need for accurate models for studying gut barrier integrity. Ex vivo models using microfluidic devices offer promising avenues for studying disease mechanisms. The devices described in Chapter 2 serve as an effective model of the intestinal barrier that can be closely monitored in real-time. Providing health involves making effective healthcare solutions universally accessible. Point-of-care (POC) diagnostics, facilitated by microfluidics, enable rapid and cost-effective disease detection. Capillary-driven flow microfluidic devices enhance accessibility by eliminating the need for bulky external pumps, making POC testing feasible even in resource-limited settings. Combining the concepts of Powering and Providing health leads to the development of innovative diagnostic devices. Capillary-driven flow microfluidics enables the development of portable devices for diagnosing conditions from viscous sample matrices like blood and saliva. These devices offer less invasive and more accessible alternatives to traditional diagnostic methods, potentially revolutionizing healthcare delivery. Chapter 3 describes a capillary flow device used to quantify levels of two salivary biomarkers (Galectin-3 and S100A7) correlated to Heart Failure (HF) outcomes. This rapid, noninvasive, accessible POC test can drastically improve the quality of life for HF patients, particularly in rural and resource-limited areas. Using an electrochemical detection method, we demonstrate successful multiplexed detection of both biomarkers in spiked buffer solutions. Chapter 4 focuses on microfluidic devices probing rheological properties of whole blood related to Sickle Cell Disease (SCD) and clotting using capillary flow. For the SCD device, our goal was to develop a low-cost Point-of-Care (POC) multiplexed device for rapid and accurate identification of SCD phenotypes using three key reagents tied to altered sickle cell blood rheology: calcium chloride, sodium metabisulfite, and adenosine diphosphate. We developed an integrated device where whole blood reacts with reagent pads, enabling rapid assessment of a patient's SCD phenotype to inform appropriate treatment. We also introduced the Paper-based Clotting Analysis Test (PCAT) for efficient, low-cost analysis of primary hemostasis. Current methods for monitoring hemostasis are expensive and slow. Our capillary flow device uses whole blood moving at high flow rates for sustained durations to induce thrombus formation. This dissertation bridges the gap between effective health monitoring and accessibility through fluidic devices using either pump-driven or capillary-driven flow. Chapters detail the development of microfluidic systems for monitoring intestinal barrier function, detecting biomarkers in saliva for Heart Failure prognosis, and processing blood samples for Sickle Cell Disease phenotyping and clotting analysis. Ultimately, these devices hold the potential to transform healthcare management, particularly in underserved communities.
Open Access
Stochastic modeling to explore the central dogma of molecular biology and to design more informative single-molecule, live-cell fluorescence microscopy experiments
(Colorado State University. Libraries, 2024) Raymond, William Scott, author; Munsky, Brian, advisor; Stasevich, Timothy J., committee member; Snow, Christopher D., committee member; Ben-Hur, Asa, committee member; Krapf, Diego, committee member
Despite being described nearly a century ago, the Central Dogma of Molecular Biology still harbors many intricacies and mysteries that scientists have yet to unravel. With the convergence of many multidisciplinary scientific advances such as stronger computing power, next-generation sequencing, machine learning, and single-cell and single-molecule experiments, cellular biologists have never had more investigative power. These complex methods often are used in tandem--necessitating a closer relationship between computational biologists, computer scientists, and bench top experimentalists. As practice of this emerging dynamic, my corpus of work spans multiple areas within computational and quantitative biology with the goal to facilitate better computational tools to interpret and design experiment. For my main work at Colorado State University, I have developed the open source Python package "RNA sequence to Nascent protein simulator," rSNAPsim, to simulate Nascent Chain Tracking experiments and used it as a backbone for an entire experiment simulation pipeline to check experiment design feasibility. The rSNAPsim software provides start-to-finish capabilities for model design, model fitting, and model selection so that experimentalists can fit a mechanistic model to the Nascent Chain Tracking single-mRNA translation experiments. Along with this main work, I have provided computational modeling efforts on live-cell data on the first two steps of the Central Dogma, DNA transcription and mRNA translation. For the final entry in my corpus, I have used my interdisciplinary skills acquired at CSU to do machine learning based ncRNA riboswitch classification and discovery within the human genome; This work provides the broader scientific community with a starting point for searching for this important secondary structure within humans, where it has not been described as of time of writing.
Embargo
Anomalous diffusion of mRNA in the cytoplasm of HeLa cells
(Colorado State University. Libraries, 2024) Roessler, Ryan, author; Krapf, Diego, advisor; Stasevich, Tim, committee member; Prasad, Ashok, committee member
Information about the diffusive motion of RNA would provide insights into intracellular structures and functions, as well as gene expression and genetic regulation. We study the motion of individual messenger RNA molecules in the cytoplasm of HeLa cells. RNAs are imaged in live cells via total internal reflection (TIRF) microscopy. In order to visualize individual RNA molecules expressing the MYH9 gene, they were labeled via MS2 stem loops bound to coat proteins tagged with the HaloTag-JF646 fluorophore. We then used single-particle tracking to obtain trajectories of individual molecules. Trajectories were analyzed in terms of their mean-squared displacement (MSD) and power spectral density (PSD). We observed non-ergodic, subdiffusive behavior, with statistics that depend on observation time, i.e., aging. Additionally, we observe stochastic switching between two mobility states with an order of magnitude difference in diffusivity. This switching process is responsible for the aging nature of the system. When compared to the cytoplasmic motion of synthetic nanoparticles, the analysis of RNA trajectories gives rise to discrepancies that raise questions about specific intracellular interactions.
Open Access
Exploiting noise, non-linearity, and feedback to differentially control multiple different cells using a single optogenetic input
(Colorado State University. Libraries, 2023) May, Michael P., author; Munsky, Brian, advisor; Stasevich, Tim, advisor; Krapf, Diego, committee member; Shipman, Patrick, committee member
Motivated by Maxwells-Demon, we propose and solve a cellular control problem in which the exploitation of stochastic noise can break symmetry between two cells and allow for specific control of multiple cells using a single input signal. We find that a new type of noise-exploiting controllers are effective and can remain effective despite coarse approximations to the model's scale or extrinsic noise in key model parameters, and that these controllers can retain performance under substantial observer-actuator time delays. We also demonstrate how SIMO controllers could drive two-cell systems to follow different trajectories with different phases and frequencies by using a noise-exploiting controller. Together, these findings suggest that noise-exploiting control should be possible even in the case where models are approximate, and where parameters are uncertain. Having demonstrated the potential of noise-enhanced feedback control through computational modeling, we have also begun the next steps toward automating microscopy to implement this potential in experimental practice. Specifically, we demonstrate a new integrated pipeline to automate the image collection including: (i) quickly search in two-dimensions to find fields of view with cells of desired phenotypes, (ii) targeted collection of three-dimensional image data for these chosen fields of view, and (iii) streamlined processing of the collected images for rapid segmentation, spot detection and tracking, and cell/spot phenotype quantification.
Open Access
Engineered mRNA therapeutic encoding beta-catenin increased bone formation in a murine tibial fracture model
(Colorado State University. Libraries, 2023) Nelson, Anna Laura, author; Ehrhart, Nicole, advisor; Bahney, Chelsea, advisor; Huard, Johnny, committee member; Popat, Ketul, committee member; Prawel, David, committee member
Fractures continue to be a global economic burden and impaired fracture healing cases, like delayed and non-union, occurring in about 14% of all tibial shaft fractures. Current treatments to aid in fracture healing involve surgical interventions and osteoanabolic, bone-morphogenetic protein-2 (BMP-2), yet is challenged supraphysiological doses and adverse side effects. Given the limited treatment options available, there remains a clinical need to develop injectable therapeutics to accelerate fracture healing in impaired fracture healing cases. Mechanistic data reveals β-catenin as a molecular driver in endochondral ossification. The central hypothesis for this dissertation is a stabilized, non-destructive β-catenin mRNA delivered locally in the fracture callus can accelerate fracture healing in a murine tibia fracture healing model. Using mRNA therapeutically continues to be challenged with stability and immunogenicity of the mRNA. To circumvent these limitations, delivery carriers have been employed to maximize gene stability, minimize off-target effects, and reduce immunogenicity. Recent advancements in liposomal technologies have led to the development of lipid nanoparticles (LNPs), leading to successful clinical translation of several novel and highly effective therapies, like SARS-CoV-2 vaccine. Alternative delivery carriers have emerged involving use of mineral coated microparticles (MCMs) as a biomimetic and biocompatible system to deliver liposomes at the site of a fracture in a controlled manner. Here, we explore mRNA delivery carriers for fracture healing applications, including manufactured cationic liposomes, MCMs, LNPs and a combination of these carriers. Manufactured liposome, Lipofectamine™, was found to be prolong transfection when tested in a murine fracture model in vivo as compared to TransIT Transfection Reagent. Using Lipofectamine™ to deliver mRNA, chemically-doped MCMs enhanced transfection and stimulated bone in vitro when delivered in chondrocytes. When testing these platforms in a murine tibia fracture model, chemically-doped MCM did not promote bone expression through testing RNA in the fracture callus for bone-related genes and through histomorphometry of the fracture callus 2 weeks post-fracture. The chemically doped MCM was found to prolong transfection of reporter gene, firefly luciferase mRNA, in vivo when compared to other treatment groups including the liposome and mRNA complex (lipoplex) alone. Ionizable-based LNPs are positively charged at a low pH and net neutral at physiological pH. Two FDA-approved ionizable phospholipids, MC3 and SM-102, were used to generate ionizable LNPs. First, MC3 LNP was tested for transfection capacity when combined with MCMs. While chemically-doped MCMs when combined with firefly luciferase mRNA encapsulated MC3 LNPs showed improved transfection in vitro, no improvements in transfection efficacy were found in vivo. Next, MC3 and SM-102 LNPs were then complexed with reporter gene, firefly luciferase mRNA to test transfection potential, immunogenicity, fracture interference and biodistribution in vitro and in a murine fracture healing model. SM-102 LNPs showed enhanced transfection efficacy in vitro, prolonged transfection in vivo, minimal fracture interference in vivo and showed no localized inflammatory response in the murine fracture callus. Ex-vivo IVIS images of main organs revealed no biodistributive effects when delivering SM-102 complexed with mRNA locally to the site of the fracture callus. Capitalizing on prior mechanistic data showing β-catenin's critical role in chondrocyte to osteoblast transdifferentiation, a non-destructive β-catenin, β-cateninGOF, mRNA transcript was generated using nucleoside modification, N1-methyl-pseudouridine, and cap analog, CleanCap. When testing the generated β-cateninGOF mRNA encapsulated in SM-102 LNPs in vitro for bioactivity, downstream canonical Wnt genes were significantly upregulated. When testing SM-102-β-cateninGOF mRNA therapeutic in murine tibia fracture model, more bone and less cartilage composition compared to PBS control was determined when analyzing histomorphometry at 25 and 45 μg concentrations at 2 weeks post-fracture. To further confirm SM-102-β-cateninGOF mRNA therapy's capabilities to promote bone in vivo, μCT was performed revealing significantly more bone volume over total volume with 45 μg dose as compared to PBS control. Taken together, we generated a novel mRNA based therapeutic encoding a non-destructive β-catenin mRNA and optimized ionizable LNP, SM-102, to maximize transfection efficacy with a localized delivery. This SM-102-β-cateninGOF mRNA therapeutic may accelerate fracture healing in a murine tibia fracture healing model.
Open Access
Understanding and leveraging mechanical forces in haemostasis
(Colorado State University. Libraries, 2023) Macleod Briongos, Iain, author; Bark, David, advisor; Olver, Christine, committee member; Popat, Ketul, committee member; Henry, Charles, committee member
Cardiovascular disease accounts for one third of deaths worldwide, of which over 75% are in low- and middle-income countries. Platelets and von Willebrand Factor play a central role in haemostasis and in cardiovascular diseases, being involved in both excess clotting that causes heart attacks and strokes, and excess bleeding. Herein, we outline a method for leveraging paper microfluidics with the aim of developing a WHO ASSURED criteria compliant point-of-care device for the diagnosis of von Willebrand Disease, as well as work to increase the understanding of mechanical force generation by platelets through Traction Force Microscopy.
Open Access
Using finite state projection and Fisher information to improve single-cell experiment design to gain better understanding of DUSP1 transcription dynamics
(Colorado State University. Libraries, 2023) Cook, Joshua A., author; Munsky, Brian, advisor; Chong, Edwin, committee member; Ghosh, Soham, committee member
Many recent studies have combined fluorescent biochemical labels, single-cell microscopy, and discrete stochastic modeling to understand and predict how organisms react to environmental changes to control gene expression. The experimental data used in these studies is often collected using intuitively-designed applications of techniques such as single-cell immunnocytochemistry (ICC) to measure protein expression and transport or single-molecule Fluorescence in situ Hybridization (smFISH) to measure the number and position of transcribed mRNA. Once collected, these single-cell data are then analyzed using discrete stochastic models, often based on the framework of the Chemical Master Equation (CME), which can be solved using the Finite State Projection (FSP) algorithm. Unfortunately, these experiments can be expensive and labor intensive to perform, primarily due to long imaging and image analysis times, and it is not clear how these experiments must be designed to obtain the most information when their results are later analyzed using the FSP techniques. The recently discovered Finite State Projection based Fisher information Matrix (FSP-FIM) provides a potential and practical solution to this experiment design challenge by providing direct estimates for how well any potential experiment should be expected to constrain parameters for a given model or set of models. In this report, we examine this challenge of experiment design in the situation where multiple different types of experiments (i.e., ICC and smFISH) are possible, for different time points, for different numbers of measurements per time point, for different environmental inputs, and for different assumed models and combinations of unknown parameters. We extend the previous FSP-FIM theory to address these multiple challenges, and we introduce new computational tools in the form of advances to the Stochastic System Identification Toolkit (in Mathworks Matlab) that allow users to easily and efficiently compute the FSP and FIM for each of these circumstances. Using experimental smFISH data, we demonstrate the effectiveness of the FSP tools to quantitatively reproduce the single-cell transcription dynamics of the Dual Specific Phosphatase 1 (DUSP1) gene under stimulation by Dexamethasone (Dex), and we show how the FSP-FIM can be used to design optimal combinations of ICC and smFISH to further improve quantification of this gene regulatory process, including predicting the optimal allocation of measurement times to obtain the most amount of information from each experiment. To probe the generality of our results, these FSP and FSP-FIM analyses are conducted for different models, under different assumptions on known and unknown parameters, and under different drug dosage regimens. The approach developed in this work is expected to have substantial impact on how computational models can be employed to improve the selection and design of future single-cell experiments.
Open Access
Stress, structure, and function of the embryonic heart
(Colorado State University. Libraries, 2023) Gendernalik, Alex L., author; Bark, David, advisor; Garrity, Deborah, committee member; Puttlitz, Christian, committee member; Heyliger, Paul, committee member
Embryonic heart development is a complex process that requires the coordination of hemodynamic stress and tissue morphogenesis. Improperly timed or distorted mechanical cues can cause reverberating malformations that result in congenital heart defects (CHDs) or embryo death. CHDs occur in ~1% of live births. Only ~20% have a known genetic origin. Altered mechanical signaling or hemodynamics is likely a common contributor to CHD prevalence. Significant research using animal models has shown that altered hemodynamics causes varied malformations. Mechanical properties describe the underlying tissue architecture, which dictates how the heart transmits and reacts to stress. This work aims to quantify and map the mechanical properties to understand how they direct the formation of specific heart structures and function. Furthermore, we seek to understand how mechanical properties change in response to altered hemodynamics. We hypothesize that mechanical properties indicate regions of eventual structure formation, are sensitive to altered hemodynamics, and dictate the pumping method that the heart uses to drive blood flow. We test this hypothesis through three aims. In aim 1, we describe a novel technique in which we use controlled pressurization to deform the embryonic zebrafish heart. We measure deformation in two-dimensions and identify constitutive models of the embryonic heart tissue. Finite element analysis is used to validate our findings in three dimensions. In this aim, we establish that controlled pressurization is a valid technique for inducing measured deformation of the heart. Through this, we determine that the zebrafish myocardial stiffness is on the order of 10 kPa. In aim 2, we further develop our pressurization technique by measuring deformation in three dimensions using confocal microscopy. Furthermore, we use a morpholino antisense oligonucleotide (MO), gata1, to alter embryonic zebrafish heart hemodynamics by blocking development of red blood cells, thus decreasing the viscosity and arterial pressure based on Poiseuille's law. Upon mapping strain in three dimensions, we find that strain throughout the heart is variable, with specific regions of low and high strain from 2 to 3 days post-fertilization (dpf). Low arterial pressure in gata1 MO embryos resulted in significantly increased strain compared to controls, indicating that altered hemodynamics cause altered mechanical properties in the developing embryonic heart. In aim 3, we seek to determine if the zebrafish early embryonic heart tube drives blood flow through peristalsis or impedance-type pumping. We attempt to directly induce impedance pumping by cannulating the atrial inlet of the heart tube after halting contractions and applying a controlled pressure pulse. Additionally, we use precisely controlled temperatures to increase heart rate, thus increasing arterial pressure. As temperature is increased, we use high speed imaging to analyze the contractile motion and resulting blood flow in the tube heart. Furthermore, we describe a previously unknown response whereby the traveling endocardial closure shortens with increased arterial pressure. In this aim, we fail to find evidence of impedance-type pumping but cannot preclude it contributes to blood flow. In summary, our pressurization technique can be used to map strain in the zebrafish embryonic heart; altering hemodynamics by reducing arterial pressure results in decreased stiffness of the embryonic heart myocardium, and endocardial closure length in the embryonic zebrafish heart tube shortens as arterial pressure and heart rate increase.
Open Access
Non-ionizing tomographic imaging modalities for bedside lung monitoring
(Colorado State University. Libraries, 2023) Vieira Pigatto, Andre, author; Mueller, Jennifer L., advisor; Wilson, Jesse, committee member; Rezende, Marlis, committee member; Wang, Zhijie, committee member
The need for an accurate and non-ionizing imaging modality for pulmonary assessment of patients undergoing mechanical ventilation due to respiratory failure has increased due to COVID. The ability to quickly detect the development of pathologies at an early stage is highly desirable and could help reduce the incidence of complications. It is also clear that mechanical ventilation can cause ventilator-induced lung injuries, which can be avoided by adequately optimizing the positive end-expiratory pressure to induce alveolar recruitment while preventing hyperinflation. Here, I will explore two non-ionizing pulmonary imaging systems that could be used as monitoring systems in the intensive care unit: Ultrasound Computed Tomography (USCT) and Electrical Impedance Tomography (EIT). The most comprehensive part of this research is the development of a Low-Frequency USCT system, which was motivated by recent studies demonstrating that acoustic waves transmitted at frequencies between 10 kHz and 750 kHz penetrate the lungs and may be useful for thoracic imaging. A novel transducer based on Tonpilz was designed, characterized, and calibrated through vibrational, electrical, and acoustic measurements, and a flexible belt that holds up to 32 transducers was constructed. A Verasonics Vantage 64 Low-frequency Research Ultrasound system was programmed to collect data by transmitting and receiving signals at frequencies of 125 and 156 kHz. The data collection and processing algorithms were developed in MATLAB, and the system was tested on phantom and vertebrate animal experiments; image reconstructions were conducted using a Time-Of-Flight algorithm. As a secondary study, SMA-1, COVID, and regular patients were imaged and analyzed using EIT technology; these results are shown through journal and conference articles presented in the Appendix A and C of this document.
Open Access
Investigation of environmental factors on the intranuclear landscape of mesenchymal stromal cells
(Colorado State University. Libraries, 2022) Kaonis, Samantha, author; Ghosh, Soham, advisor; Johnstone, Brian, committee member; Popat, Ketul, committee member; Dow, Steven, committee member
Mesenchymal stromal cells (MSC), also known as mesenchymal stem cells, are popular candidates for tissue engineering and regenerative medicine. They can differentiate into many tissue types, and they can also help in regeneration through their trophic and immunomodulatory properties. Despite being investigated thoroughly for the last four decades and being under clinical trial in more than a thousand FDA approved studies, their application in clinics is very limited. One of the most important challenges in using MSC is that after harvesting from the patient, they need to be expanded to millions of cells for successful clinical outcomes. During this process, MSCs lose their differentiation potential, and trophic and immunomodulatory properties. In this thesis, I investigated the potential mechanisms of how environmental factors cause the MSC to divert from their phenotype during the expansion process. Subsequently, I intervened these mechanisms to achieve high quality MSCs without compromising the number of cells, i.e., their proliferation potential. Specifically, I investigated how two critical biophysical factors - mechanical stiffness and oxygen concentration of the MSC environment affects the cell phenotype and function through mechanisms involving epigenetic modifiers, transcription factors, and the chromatin architecture. First, the regulation of mechanics-induced population heterogeneity in MSCs was examined. Plastic culture and fibrotic conditions post-transplantation experienced by the MSC is completely different from the natural biomechanical niche of the MSC. Accordingly, the role of the mechanical environment has been shown to be a critical determinant of MSC gene expression and function. In this study, we report that human bone marrow-derived primary MSC population becomes phenotypically heterogenous when they experience an abnormal mechanical environment, compared to their native environment. Using a newly developed technique to quantify the heterogeneity, we provide evidence of phenotypical heterogeneity of MSC through high-resolution imaging and image analysis. Additionally, we provide mechanistic insight into the origin of such substrate mechanics-driven heterogeneity, which is further determined by the cell-cell mechanical communication through the substrate. In the second study, we investigated how the chromatin architecture and epigenetic landscape changes in MSCs by the substrate mechanical stiffness, thus causing a shift from the MSC phenotype. Using high-resolution confocal microscopy and advanced image analysis we identified the key epigenetic drivers in the mechanical stiffness mediated chromatin organization changes. Subsequently, we targeted several components of a proposed mechanobiological pathway to achieve MSCs with higher growth factor secretion without compromising their proliferation. The outcome of these studies might provide mechanism-driven design principles to the molecular, cellular and tissue engineering researchers for the rational design of MSC culture conditions and scaffolds, thus improving their functional outcome. Finally, the effect of oxygen concentration on MSC proliferation and performance wereexplored. Culture under physiological oxygen concentration (physioxia) can increase the proliferation of MSCs through a pathway initiated by the stabilization of the hypoxia-inducible factor-1 (HIF-1). Stabilized HIF-1α translocates into the nucleus, triggering the transcription of target genes conducive to MSC activity and proliferation. However, stabilized HIF-1α also triggers the p21 pathway causing cell cycle arrest, decreasing the MSC proliferation thereby limiting the beneficial effect of physioxia. Maintaining low oxygen conditions can be challenging, especially at a large scale, so rational exploitation and selective manipulation of such pathways through biochemical means has the potential to culture MSCs easily at scale. In this work, we created a mathematical model to predict optimal physioxic culture parameters to achieve the highest MSC proliferation. Through analysis of a gene downstream of the HIF-1 pathway, we also compared standard physioxic culture (2% O2) to treatment with deferoxamine mesylate (DFO), a physioxia-mimicking drug. The outcomes of this study might provide the rationale for MSC culture under standard hyperoxic conditions with only a simple addition of a combination of drugs to the culture medium to improve the scalability of MSC culture. Together, the results of the work will identify the mechanistic details of culture environment factors that play a role in determining the phenotype of MSCs during in vitro expansion process. The combination of these techniques to optimize MSC culture in vitro has the potential to resolve the current impediment to the clinical success of MSC therapies.
Open Access
Uncovering details of the electrical properties of cells
(Colorado State University. Libraries, 2022) Nejad, Jasmine E., author; Lear, Kevin L., advisor; Tobet, Stuart, committee member; McGrew, Ashley K., committee member; Simske, Steve, committee member
The electrical properties of cells have long been studied by scientists across many fields, yet there are still major gaps in our understanding of the intrinsic properties of many types of cells, such as parasite eggs, as well as the detailed electrical behavior of excitable cells, such as neurons. This work aims to provide insights into how these properties can be measured and how machine learning can be used to advance our understanding of these phenomena. The first part of this work discusses the development of a microfluidic impedance cytometer for the enumeration and classification of parasite eggs isolated from fecal samples. Current diagnostics in parasitology rely on the manual counting of eggs, cysts, and oocysts on microscope slides that have been isolated from fecal samples. These methods depend on trained technicians with expertise in the preparation of samples and detection of parasites on these slides, which increases cost and turnaround times for diagnosis. This leads many farmers and ranchers to opt to pool fecal samples from multiple animals to save time and labor. In cattle herds, resistance is often due to underdosing, which can be caused by treating all animals to an average weight or treating by the calendar instead of targeted deworming. This blanket use of anthelmintics, or anti-parasitic medication, is leading to concerns about anthelmintic resistance, which would cause major issues in the livestock industry, as well create unforeseen ecological imbalances. The developed microfluidic system provides a proof-of-concept for a microfluidic impedance cytometer capable of measuring the impedance of parasite eggs at multiple frequencies, simultaneously, as each of the eggs passes through a microfluidic channel past a sensing region. This region consists of parallel electrodes on the top and bottom of the channel, allowing for measurement of the voltage across the channel. When an egg passes through, the signal is interrupted, leaving a distinct profile of the electrical properties at each frequency over time. This system shows proof-of-concept of the impedance measurements at 500kHz and 10MHz and provides insights for further exploration of these properties, with the eventual use of machine learning algorithms for discrimination of parasite eggs from debris, and differentiation of parasite genera. The second part of this work discusses machine learning classification of neuronal subtypes based on features extracted from patch-clamp recordings from adult mice, using data acquired from publicly available databases. Classification of neuronal subtypes has been a continuously progressing area of neuroscience, building on advancements in our understanding of the morphology, physiology, and biochemistry of different neurons, and contributing to the accuracy and repeatability of action potential and neuronal circuit models. This work explores the use of k-nearest neighbors, support vector machine, decision tree, logistic regression, and naïve Bayes algorithms for classification of fast-spiking or regular-spiking neurons from the hippocampus or the primary somatosensory cortex. K-nearest neighbors shows the most accurate classification of these groups, using action potential width, amplitude, and onset potential as features (inputs into the algorithm), with the addition of a measure of rapidity (acceleration near action potential onset) showing major increases in classification accuracy. Of the three methods for measuring rapidity, inverse of the full width at half of the maximum of the second derivative of the membrane potential (V̈m) (IFWd2), inverse of the half width at half of the maximum of V̈m (IHWd2), and the slope of the phase plot (V̇m vs. Vm) near AP onset (phase slope), including the phase slope measure of rapidity increased the accuracy to nearly perfect (weighted f1-score > 0.9999). In addition, the use of phase slope and action potential width as the only features for classification produces measures of accuracy, weighted f1-scores, of >0.9996. The results show the value of rapidity in action potential dynamics, the distinct difference between rapidity in APs generated by hippocampal neurons relative to cortical neurons, and low standard deviations for rapidity values in cortical neurons (fast- and regular-spiking). These findings have potential implications for understanding the ion channel dynamics in action potential initiation and propagation, which can improve the modeling of action potentials and neuronal circuits.
Open Access
Sensing via signal analysis, analytics, and cyberbiometric patterns
(Colorado State University. Libraries, 2022) Anderson, Wesley, author; Simske, Steve, advisor; Lear, Kevin, committee member; Volckens, John, committee member; Carter, Ellison, committee member
Internet-connected, or Internet of Things (IoT), sensor technologies have been increasingly incorporated into everyday technology and processes. Their functions are situationally dependent and have been used for vital recordings such as electrocardiograms, gait analysis and step counting, fall detection, and environmental analysis. For instance, environmental sensors, which exist through various technologies, are used to monitor numerous domains, including but not limited to pollution, water quality, and the presence of biota, among others. Past research into IoT sensors has varied depending on the technology. For instance, previous environmental gas sensor IoT research has focused on (i) the development of these sensors for increased sensitivity and increased lifetimes, (ii) integration of these sensors into sensor arrays to combat cross-sensitivity and background interferences, and (iii) sensor network development, including communication between widely dispersed sensors in a large-scale environment. IoT inertial measurement units (IMU's), such as accelerometers and gyroscopes, have been previously researched for gait analysis, movement detection, and gesture recognition, which are often related to human-computer interface (HCI). Methods of IoT Device feature-based pattern recognition for machine learning (ML) and artificial intelligence (AI) are frequently investigated as well, including primitive classification methods and deep learning techniques. The result of this research gives insight into each of these topics individually, i.e., using a specific sensor technology to detect carbon monoxide in an indoor environment, or using accelerometer readings for gesture recognition. Less research has been performed on analyzing the systems aspects of the IoT sensors themselves. However, an important part of attaining overall situational awareness is authenticating the surroundings, which in the case of IoT means the individual sensors, humans interacting with the sensors, and other elements of the surroundings. There is a clear opportunity for the systematic evaluation of the identity and performance of an IoT sensor/sensor array within a system that is to be utilized for "full situational awareness". This awareness may include (i) non-invasive diagnostics (i.e., what is occurring inside the body), (ii) exposure analysis (i.e., what has gone into the body through both respiratory and eating/drinking pathways), and (iii) potential risk of exposure (i.e., what the body is exposed to environmentally). Simultaneously, the system has the capability to harbor security measures through the same situational assessment in the form of multiple levels of biometrics. Through the interconnective abilities of the IoT sensors, it is possible to integrate these capabilities into one portable, hand-held system. The system will exist within a "magic wand", which will be used to collect the various data needed to assess the environment of the user, both inside and outside of their bodies. The device can also be used to authenticate the user, as well as the system components, to discover potential deception within the system. This research introduces levels of biometrics for various scenarios through the investigation of challenge-based biometrics; that is, biometrics based upon how the sensor, user, or subject of study responds to a challenge. These will be applied to multiple facets surrounding "situational awareness" for living beings, non-human beings, and non-living items or objects (which we have termed "abiometrics"). Gesture recognition for intent of sensing was first investigated as a means of deliberate activation of sensors/sensor arrays for situational awareness while providing a level of user authentication through biometrics. Equine gait analysis was examined next, and the level of injury in the lame limbs of the horse was quantitatively measured and classified using data from IoT sensors. Finally, a method of evaluating the identity and health of a sensor/sensory array was examined through different challenges to their environments.
Open Access
Neural network security and optimization for single-person authentication using electroencephalogram data
(Colorado State University. Libraries, 2022) Andre, Naomi, author; Simske, Steve, advisor; Mueller, Jennifer, committee member; Lyons, Michael, committee member
Security is an important focus for devices that use biometric data, and as such security around authentication needs to be considered. This is true for brain-computer interfaces (BCIs), which often use electroencephalogram (EEG) data as inputs and neural network classification to determine their function. EEG data can also serve as a form of biometric authentication, which would contribute to the security of these devices. Neural networks have also used a method known as ablation to improve their efficiency. In light of this info, the goal of this research is to determine whether neural network ablation can also be used as a method to improve security by reducing a network's learning capabilities to include authenticating only a given target, and preventing adversaries from training new data to be authenticated. Data on the change in entropy of weight values of the networks after training was also collected for the purpose of determining patterns in weight distribution. Results from a set of ablated networks to a set of baseline (non-ablated) networks for five targets chosen randomly from a data set of 12 people were compared. The results found that ablated maintained accuracy through the ablation process, but that they did not perform as well as the baseline networks. Change in performance between single-target authentication and target-plus-invader authentication was also examined, but no significant results were found. Furthermore, the change in entropy differed between both baseline networks and ablated networks, as well as between single-target authentication and target-plus-invader authentication for all networks. Ablation was determined to have potential for security applications that need to be expanded on, and weight distribution was found to have some correlation with the complexity of an input to a network.
Open Access
The selective de-identification of ECGs
(Colorado State University. Libraries, 2022) Akhtar, Musamma, author; Simske, Steven, advisor; Wang, Zhijie, committee member; Vans, Marie, committee member
Biometrics are often used for immigration control, business applications, civil identity, and healthcare. Biometrics can also be used for authentication, monitoring (e.g., subtle changes in biometrics may have health implications), and personalized medical concerns. Increased use of biometrics creates identity vulnerability through the exposure of personal identifiable information (PII). Hence an increasing need to not only validate but secure a patient's biometric data and identity. The latter is achieved by anonymization, or de-identification, of the PII. Using Python in collaboration with the PTB-XL ECG database from Physionet, the goal of this thesis is to create "selective de-identification." When dealing with data and de-identification, clusters, or groupings, of data with similarity of content and location in feature space are created. Classes are groupings of data with content matching that of a class definition within a given tolerance and are assigned metadata. Clusters start without derived information, i.e., metadata, that is created by intelligent algorithms, and are thus considered unstructured. Clusters are then assigned to pre-defined classes based on the features they exhibit. The goal is to focus on features that identify pathology without compromising PII. Methods to classify different pathologies are explored, and the effect on PII classification is measured. The classification scheme with the highest "gain," or (improvement in pathology classification)/ (improvement in PII classification), is deemed the preferred approach. Importantly, the process outlined can be used in many other systems involving patient recordings and diagnostic-relevant data collection.
Open Access
316L stainlesss steel modified via plasma electrolytic oxidation for orthopedic implants
(Colorado State University. Libraries, 2022) Michael, James A., II, author; Popat, Ketul C., advisor; Li, Vivan, committee member; Sampath, Walajabad S., committee member
316L stainless steel (SS) is widely used biomaterial for implantable devices and is estimated to the base material for 60% of implantable devices. However, one challenge of the material is the inhomogeneity of the surface morphology which may influence the adhesion process of host cells and bacteria. One method to create a uniform surface of 316L SS is plasma electrolytic oxidation (PEO). PEO creates an oxide layer on the outer surface thus changing the surface topography on the microscale. PEO process on SS functions by anodizing the surface via direct current in electrolyte solution. Preliminary research found that a continuous direct current over a time manufactured undesirable samples, to overcome this challenge the use of pulse timings was utilized during fabrication. This research aimed to answer the questions how do PEO modifications effect cellular adhesion and viability, and how do PEO modifications affect bacteria adhesion and viability. PEO modified 316L SS surfaces were characterized and its effects on the adhesion, morphology, and differentiation of adipocyte derived stem cells, along with the adhesion and morphology of Staphylococcus aureus was investigated.
Open Access
Odor encoder: computational design of a novel allosteric enzyme activation system for providing enhanced olfactory abilities to trained odor detecting sentinel animals
(Colorado State University. Libraries, 2022) Scroggins, Michael, author; Snow, Christopher, advisor; Peebles, Christie, advisor; Gentry-Weeks, Claudia, committee member
From the perfume of a flower, to the aroma of a favorite food, to what for bioengineers is the all-to-familiar smell of E. coli, olfactory senses play in important role in how animals interact with the world around them. An offensive odor can inform us that an object is unsafe to eat or be around, a familiar scent can recall memories of events from decades in our past, and even our natural body odors can affect our mating selection preferences. Yet there are many chemicals, both natural and synthetic, for which we do not possess the ability for olfactory detection. An everyday example of this is the natural gas that we use in our homes and which is naturally odorless, but which is commonly spiked with the odorant tert-butyl mercaptan (TBM) to provide the characteristic sulfuric smell we associate with natural gas. Because of this added odorant we can rapidly detect a leaking gas via the smell of the TBM and address the situation as needed to ensure the safety of ourselves and our community. Unfortunately, there are some hazardous and odorless chemicals which we cannot simply spike with an odorant molecule, and for these situations it would be ideal to have alternative options for facilitating a rapid olfactory detection. Therein lies the goals of the Odor Encoder project; to create enhanced olfactory abilities via a conditionally activated enzyme which produces a smellable product in the presence of a target odorless molecule. The approach to achieving this goal was creation of a genetically modified bacterial organism which could be engineered for conditional expression of an odorant producing enzyme in-situ within the nasal microbiome of trained odor detecting animals. The odorant producing enzyme chosen for this purpose was salicylic acid methyltransferase, a.k.a SAMT, which produces the characteristic odorant molecule methyl salicylate via methylation of salicylic acid. The probiotic E. coli strain Nissle 1917 was selected as the bacterial organism for inoculation of the nasal microbiome, and an expression plasmid was created which could produce both salicylic acid and methyl salicylate from endogenously produced metabolites via dual expression of SAMT along with a salicylate synthase enzyme known as irp9. Conditional production of methyl salicylate was achieved via two methods. The first method involved conditional enzyme expression via use of a riboswitch specific to the small molecule theophylline. The second method involved conditional enzyme activity via constitutive expression of a crippled form of SAMT which may potentially have its enzymatic activity restored via theophylline induced allosteric activation. The allosteric rescue method utilized computational design methods to design novel theophylline-specific allosteric cavities in SAMT, and theophylline induced allosteric reactivation of enzyme activity will be investigated via production and screening of the computationally designed enzyme library.