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- All Subjects: Biomedical Engineering
Description
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
Neural tissue is a delicate system comprised of neurons and their synapses, glial cells for support, and vasculature for oxygen and nutrient delivery. This complexity ultimately gives rise to the human brain, a system researchers have become increasingly interested in replicating for artificial intelligence purposes. Some have even gone so far as to use neuronal cultures as computing hardware, but utilizing an environment closer to a living brain means having to grapple with the same issues faced by clinicians and researchers trying to treat brain disorders. Most outstanding among these are the problems that arise with invasive interfaces. Optical techniques that use fluorescent dyes and proteins have emerged as a solution for noninvasive imaging with single-cell resolution in vitro and in vivo, but feeding in information in the form of neuromodulation still requires implanted electrodes. The implantation process of these electrodes damages nearby neurons and their connections, causes hemorrhaging, and leads to scarring and gliosis that diminish efficacy. Here, a new approach for noninvasive neuromodulation with high spatial precision is described. It makes use of a combination of ultrasound, high frequency acoustic energy that can be focused to submillimeter regions at significant depths, and electric fields, an effective tool for neuromodulation that lacks spatial precision when used in a noninvasive manner. The hypothesis is that, when combined in a specific manner, these will lead to nonlinear effects at neuronal membranes that cause cells only in the region of overlap to be stimulated. Computational modeling confirmed this combination to be uniquely stimulating, contingent on certain physical effects of ultrasound on cell membranes. Subsequent in vitro experiments led to inconclusive results, however, leaving the door open for future experimentation with modified configurations and approaches. The specific combination explored here is also not the only untested technique that may achieve a similar goal.
ContributorsNester, Elliot (Author) / Wang, Yalin (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2022
Description
DNA methylation (DNAm) is an epigenetic mark with a critical role in regulating gene expression. Altered clinical states, including toxin exposure and viral infections, can cause aberrant DNA methylation in cells, which may persist during cell division. Current methods to study genome-wide methylome profiles of the cells require a long processing time and are expensive. Here, a novel technique called Multiplexed Methylated DNA Immunoprecipitation Sequencing (Mx-MeDIP-Seq), which is amenable to automation. Up to 15 different samples can be combined into the same run of Mx-MeDIP-Seq, using only 25 ng of DNA per sample. Mx-MeDIP-Seq was used to study DNAm profiles of peripheral blood mononuclear cells (PBMCs) in two biologically distinct RNA viral infections with different modes of transmission, symptoms, and interaction with the host immune system: human immunodeficiency virus1 (HIV-1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Analysis of 90 hospitalized patients with SARS-CoV-2 and 57 healthy controls revealed that SARS-CoV-2 infection led to alterations in 920 methylated regions in PBMCs, resulting in a change in transcription that affects host immune response and cell survival. Analysis of publicly available RNA-Sequencing data in COVID-19 correlated with DNAm in several key pathways. These findings provide a mechanistic view toward further understanding of viral infections. Genome-wide DNAm changes post HIV-1-infection from 37 chronically ill patients compared to 17 controls revealed dysregulation of the actin cytoskeleton, which could contribute to the establishment of latency in HIV-1 infections. Longitudinal DNAm analysis identified several potentially protective and harmful genes that could contribute to disease suppression or progression.
ContributorsRidha, Inam (Author) / LaBaer, Joshua (Thesis advisor) / Murugan, Vel (Thesis advisor) / Plaisier, Christopher (Committee member) / Nikkhah, Mehdi (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2022
Description
According to the World Health Organization, cancer is one of the leading causes of death around the world. Although early diagnostics using biomarkers and improved treatments with targeted therapy have reduced the rate of cancer related mortalities, there remain many unknowns regarding the contributions of the tumor microenvironment to cancer progression and therapeutic resistance. The tumor microenvironment plays a significant role by manipulating the progression of cancer cells through biochemical and biophysical signals from the surrounding stromal cells along with the extracellular matrix. As such, there is a critical need to understand how the tumor microenvironment influences the molecular mechanisms underlying cancer metastasis to facilitate the discovery of better therapies. This thesis described the development of microfluidic technologies to study the interplay of cancer cells with their surrounding microenvironment. The microfluidic model was used to assess how exposure to chemoattractant, epidermal growth factor (EGF), impacted 3D breast cancer cell invasion and enhanced cell motility speed was noted in the presence of EGF validating physiological cell behavior. Additionally, breast cancer and patient-derived cancer-associated fibroblast (CAF) cells were co-cultured to study cell-cell crosstalk and how it affected cancer invasion. GPNMB was identified as a novel gene of interest and it was shown that CAFs enhanced breast cancer invasion by up-regulating the expression of GPNMB on breast cancer cells resulting in increased migration speed. Lastly, this thesis described the design, biological validation, and use of this microfluidic platform as a new in vitro 3D organotypic model to study mechanisms of glioma stem cell (GSC) invasion in the context of a vascular niche. It was confirmed that CXCL12-CXCR4 signaling is involved in promoting GSC invasion in a 3D vascular microenvironment, while also demonstrating the effectiveness of the microfluidic as a drug screening assay. Taken together, the broader impacts of the microfluidic model developed in this dissertation include, a possible alternative platform to animal testing that is focused on mimicking human physiology, a potential ex vivo platform using patient-derived cells for studying the interplay of cancer cells with its surrounding microenvironment, and development of future therapeutic strategies tailored toward disrupting key molecular pathways involved in regulatory mechanisms of cancer invasion.
ContributorsTruong, Danh, Ph.D (Author) / Nikkhah, Mehdi (Thesis advisor) / LaBaer, Joshua (Committee member) / Smith, Barbara (Committee member) / Mouneimne, Ghassan (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Detection of molecular interactions is critical for understanding many biological processes, for detecting disease biomarkers, and for screening drug candidates. Fluorescence-based approach can be problematic, especially when applied to the detection of small molecules. Various label-free techniques, such as surface plasmon resonance technique are sensitive to mass, making it extremely challenging to detect small molecules. In this thesis, novel detection methods for molecular interactions are described.
First, a simple detection paradigm based on reflectance interferometry is developed. This method is simple, low cost and can be easily applied for protein array detection.
Second, a label-free charge sensitive optical detection (CSOD) technique is developed for detecting of both large and small molecules. The technique is based on that most molecules relevant to biomedical research and applications are charged or partially charged. An optical fiber is dipped into the well of a microplate. It detects the surface charge of the fiber, which does not decrease with the size (mass) of the molecule, making it particularly attractive for studying small molecules.
Third, a method for mechanically amplification detection of molecular interactions (MADMI) is developed. It provides quantitative analysis of small molecules interaction with membrane proteins in intact cells. The interactions are monitored by detecting a mechanical deformation in the membrane induced by the molecular interactions. With this novel method small molecules and membrane proteins interaction in the intact cells can be detected. This new paradigm provides mechanical amplification of small interaction signals, allowing us to measure the binding kinetics of both large and small molecules with membrane proteins, and to analyze heterogeneous nature of the binding kinetics between different cells, and different regions of a single cell.
Last, by tracking the cell membrane edge deformation, binding caused downstream event – granule secretory has been measured. This method focuses on the plasma membrane change when granules fuse with the cell. The fusion of granules increases the plasma membrane area and thus the cell edge expands. The expansion is localized at the vesicle release location. Granule size was calculated based on measured edge expansion. The membrane deformation due to the granule release is real-time monitored by this method.
First, a simple detection paradigm based on reflectance interferometry is developed. This method is simple, low cost and can be easily applied for protein array detection.
Second, a label-free charge sensitive optical detection (CSOD) technique is developed for detecting of both large and small molecules. The technique is based on that most molecules relevant to biomedical research and applications are charged or partially charged. An optical fiber is dipped into the well of a microplate. It detects the surface charge of the fiber, which does not decrease with the size (mass) of the molecule, making it particularly attractive for studying small molecules.
Third, a method for mechanically amplification detection of molecular interactions (MADMI) is developed. It provides quantitative analysis of small molecules interaction with membrane proteins in intact cells. The interactions are monitored by detecting a mechanical deformation in the membrane induced by the molecular interactions. With this novel method small molecules and membrane proteins interaction in the intact cells can be detected. This new paradigm provides mechanical amplification of small interaction signals, allowing us to measure the binding kinetics of both large and small molecules with membrane proteins, and to analyze heterogeneous nature of the binding kinetics between different cells, and different regions of a single cell.
Last, by tracking the cell membrane edge deformation, binding caused downstream event – granule secretory has been measured. This method focuses on the plasma membrane change when granules fuse with the cell. The fusion of granules increases the plasma membrane area and thus the cell edge expands. The expansion is localized at the vesicle release location. Granule size was calculated based on measured edge expansion. The membrane deformation due to the granule release is real-time monitored by this method.
ContributorsGuan, Yan (Author) / Tao, Nongjian (Thesis advisor) / LaBaer, Joshua (Committee member) / Goryll, Michael (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2015
Description
While techniques for reading DNA in some capacity has been possible for decades,
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
ContributorsFaucon, Philippe Christophe (Author) / Liu, Huan (Thesis advisor) / Wang, Xiao (Committee member) / Crook, Sharon M (Committee member) / Wang, Yalin (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Solid tumors advance from benign stage to a deadly metastatic state due to the complex interaction between cancer cells and tumor microenvironment (TME) including stromal cells and extracellular matrix (ECM). Multiple studies have demonstrated that ECM dysregulation is one of the critical hallmarks of cancer progression leading to formation of a desmoplastic microenvironment that participates in tumor progression. Cancer associated fibroblasts (CAFs) are the predominant stromal cell type that participates in desmoplasia by depositing matrix proteins and increasing ECM stiffness. Although the influence of matrix stiffness on enhanced tumorigenicity has been well studied, the biological understanding about the dynamic changes in ECM architecture and the role of cancer-stromal cell interaction on ECM remodeling is still limited.
In this dissertation, the primary goal was to develop a comprehensive cellular and molecular level understanding of ECM remodeling due to the interaction of breast tumor cells and CAFs. To that end, a novel three-dimensional (3D) high-density tumor-stroma model was fabricated in which breast tumor cells (MDA-MB-231 and MCF7) were spatially organized surrounded by CAF-embedded collagen-I hydrogel (Aim 1). Further the platform was integrated with atomic force microscopy to assess the dynamic changes in ECM composition and stiffness during active tumor invasion. The results established an essential role of crosstalk between breast tumor cells and CAFs in ECM remodeling. The studies were further extended by dissecting the mode of interaction between tumor cells and CAFs followed by characterization of the role of various tumor secreted factors on ECM remodeling (Aim 2). The results for the first time established a critical role of paracrine signaling between breast tumor cells and CAFs in modulating biophysical properties of ECM. More in-depth analysis highlighted the role of tumor secreted cytokines, specifically PDGF-AA/BB, on CAF-induced desmoplasia. In aim 3, the platform was further utilized to test the synergistic influence of anti-fibrotic drug (tranilast) in conjugation with chemotherapeutic drug (Doxorubicin) on desmoplasia and tumor progression in the presence of CAFs. Overall this dissertation provided an in-depth understanding on the impact of breast cancer-stromal cell interaction in modulating biophysical properties of the ECM and identified the crucial role of tumor secreted cytokines including PDGF-AA/BB on desmoplasia.
In this dissertation, the primary goal was to develop a comprehensive cellular and molecular level understanding of ECM remodeling due to the interaction of breast tumor cells and CAFs. To that end, a novel three-dimensional (3D) high-density tumor-stroma model was fabricated in which breast tumor cells (MDA-MB-231 and MCF7) were spatially organized surrounded by CAF-embedded collagen-I hydrogel (Aim 1). Further the platform was integrated with atomic force microscopy to assess the dynamic changes in ECM composition and stiffness during active tumor invasion. The results established an essential role of crosstalk between breast tumor cells and CAFs in ECM remodeling. The studies were further extended by dissecting the mode of interaction between tumor cells and CAFs followed by characterization of the role of various tumor secreted factors on ECM remodeling (Aim 2). The results for the first time established a critical role of paracrine signaling between breast tumor cells and CAFs in modulating biophysical properties of ECM. More in-depth analysis highlighted the role of tumor secreted cytokines, specifically PDGF-AA/BB, on CAF-induced desmoplasia. In aim 3, the platform was further utilized to test the synergistic influence of anti-fibrotic drug (tranilast) in conjugation with chemotherapeutic drug (Doxorubicin) on desmoplasia and tumor progression in the presence of CAFs. Overall this dissertation provided an in-depth understanding on the impact of breast cancer-stromal cell interaction in modulating biophysical properties of the ECM and identified the crucial role of tumor secreted cytokines including PDGF-AA/BB on desmoplasia.
ContributorsSaini, Harpinder (Author) / Nikkhah, Mehdi (Thesis advisor) / Ros, Robert (Committee member) / LaBaer, Joshua (Committee member) / Kodibagkar, Vikram (Committee member) / Ebrahimkhani, Mohammad (Committee member) / Arizona State University (Publisher)
Created2019
Description
In brain imaging study, 3D surface-based algorithms may provide more advantages over volume-based methods, due to their sub-voxel accuracy to represent subtle subregional changes and solid mathematical foundations on which global shape analyses can be achieved on complicated topological structures, such as the convoluted cortical surfaces. On the other hand, given the enormous amount of data being generated daily, it is still challenging to develop effective and efficient surface-based methods to analyze brain shape morphometry. There are two major problems in surface-based shape analysis research: correspondence and similarity. This dissertation covers both topics by proposing novel surface registration and indexing algorithms based on conformal geometry for brain morphometry analysis.
First, I propose a surface fluid registration system, which extends the traditional image fluid registration to surfaces. With surface conformal parameterization, the complexity of the proposed registration formula has been greatly reduced, compared to prior methods. Inverse consistency is also incorporated to drive a symmetric correspondence between surfaces. After registration, the multivariate tensor-based morphometry (mTBM) is computed to measure local shape deformations. The algorithm was applied to study hippocampal atrophy associated with Alzheimer's disease (AD).
Next, I propose a ventricular surface registration algorithm based on hyperbolic Ricci flow, which computes a global conformal parameterization for each ventricular surface without introducing any singularity. Furthermore, in the parameter space, unique hyperbolic geodesic curves are introduced to guide consistent correspondences across subjects, a technique called geodesic curve lifting. Tensor-based morphometry (TBM) statistic is computed from the registration to measure shape changes. This algorithm was applied to study ventricular enlargement in mild cognitive impatient (MCI) converters.
Finally, a new shape index, the hyperbolic Wasserstein distance, is introduced. This algorithm computes the Wasserstein distance between general topological surfaces as a shape similarity measure of different surfaces. It is based on hyperbolic Ricci flow, hyperbolic harmonic map, and optimal mass transportation map, which is extended to hyperbolic space. This method fills a gap in the Wasserstein distance study, where prior work only dealt with images or genus-0 closed surfaces. The algorithm was applied in an AD vs. control cortical shape classification study and achieved promising accuracy rate.
First, I propose a surface fluid registration system, which extends the traditional image fluid registration to surfaces. With surface conformal parameterization, the complexity of the proposed registration formula has been greatly reduced, compared to prior methods. Inverse consistency is also incorporated to drive a symmetric correspondence between surfaces. After registration, the multivariate tensor-based morphometry (mTBM) is computed to measure local shape deformations. The algorithm was applied to study hippocampal atrophy associated with Alzheimer's disease (AD).
Next, I propose a ventricular surface registration algorithm based on hyperbolic Ricci flow, which computes a global conformal parameterization for each ventricular surface without introducing any singularity. Furthermore, in the parameter space, unique hyperbolic geodesic curves are introduced to guide consistent correspondences across subjects, a technique called geodesic curve lifting. Tensor-based morphometry (TBM) statistic is computed from the registration to measure shape changes. This algorithm was applied to study ventricular enlargement in mild cognitive impatient (MCI) converters.
Finally, a new shape index, the hyperbolic Wasserstein distance, is introduced. This algorithm computes the Wasserstein distance between general topological surfaces as a shape similarity measure of different surfaces. It is based on hyperbolic Ricci flow, hyperbolic harmonic map, and optimal mass transportation map, which is extended to hyperbolic space. This method fills a gap in the Wasserstein distance study, where prior work only dealt with images or genus-0 closed surfaces. The algorithm was applied in an AD vs. control cortical shape classification study and achieved promising accuracy rate.
ContributorsShi, Jie, Ph.D (Author) / Wang, Yalin (Thesis advisor) / Caselli, Richard (Committee member) / Li, Baoxin (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2016
Description
The apolipoprotein E (APOE) e4 genotype is the most prevalent known genetic risk factor for Alzheimer's disease (AD). In this paper, we examined the longitudinal effect of APOE e4 on hippocampal morphometry in Alzheimer's Disease Neuroimaging Initiative (ADNI). Generally, atrophy of hippocampus has more chance occurs in AD patients who carrying the APOE e4 allele than those who are APOE e4 noncarriers. Also, brain structure and function depend on APOE genotype not just for Alzheimer's disease patients but also in health elderly individuals, so APOE genotyping is considered critical in clinical trials of Alzheimer's disease. We used a large sample of elderly participants, with the help of a new automated surface registration system based on surface conformal parameterization with holomorphic 1-forms and surface fluid registration. In this system, we automatically segmented and constructed hippocampal surfaces from MR images at many different time points, such as 6 months, 1- and 2-year follow up. Between the two different hippocampal surfaces, we did the high-order correspondences, using a novel inverse consistent surface fluid registration method. At each time point, using Hotelling's T^2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the non-demented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes.
ContributorsLi, Bolun (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2015