ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 15
Description
This dissertation treats a number of related problems in control and data analysis of complex networks.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
ContributorsWang, Lezhi (Author) / Lai, Ying-Cheng (Thesis advisor) / Wang, Xiao (Thesis advisor) / Papandreoou-Suppappola, Antonia (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2017
Description
The portability of genetic tools from one organism to another is a cornerstone of synthetic biology. The shared biological language of DNA-to-RNA-to-protein allows for expression of polypeptide chains in phylogenetically distant organisms with little modification. The tools and contexts are diverse, ranging from catalytic RNAs in cell-free systems to bacterial proteins expressed in human cell lines, yet they exhibit an organizing principle: that genes and proteins may be treated as modular units that can be moved from their native organism to a novel one. However, protein behavior is always unpredictable; drop-in functionality is not guaranteed.
My work characterizes how two different classes of tools behave in new contexts and explores methods to improve their functionality: 1. CRISPR/Cas9 in human cells and 2. quorum sensing networks in Escherichia coli.
1. The genome-editing tool CRISPR/Cas9 has facilitated easily targeted, effective, high throughput genome editing. However, Cas9 is a bacterially derived protein and its behavior in the complex microenvironment of the eukaryotic nucleus is not well understood. Using transgenic human cell lines, I found that gene-silencing heterochromatin impacts Cas9’s ability to bind and cut DNA in a site-specific manner and I investigated ways to improve CRISPR/Cas9 function in heterochromatin.
2. Bacteria use quorum sensing to monitor population density and regulate group behaviors such as virulence, motility, and biofilm formation. Homoserine lactone (HSL) quorum sensing networks are of particular interest to synthetic biologists because they can function as “wires” to connect multiple genetic circuits. However, only four of these networks have been widely implemented in engineered systems. I selected ten quorum sensing networks based on their HSL production profiles and confirmed their functionality in E. coli, significantly expanding the quorum sensing toolset available to synthetic biologists.
My work characterizes how two different classes of tools behave in new contexts and explores methods to improve their functionality: 1. CRISPR/Cas9 in human cells and 2. quorum sensing networks in Escherichia coli.
1. The genome-editing tool CRISPR/Cas9 has facilitated easily targeted, effective, high throughput genome editing. However, Cas9 is a bacterially derived protein and its behavior in the complex microenvironment of the eukaryotic nucleus is not well understood. Using transgenic human cell lines, I found that gene-silencing heterochromatin impacts Cas9’s ability to bind and cut DNA in a site-specific manner and I investigated ways to improve CRISPR/Cas9 function in heterochromatin.
2. Bacteria use quorum sensing to monitor population density and regulate group behaviors such as virulence, motility, and biofilm formation. Homoserine lactone (HSL) quorum sensing networks are of particular interest to synthetic biologists because they can function as “wires” to connect multiple genetic circuits. However, only four of these networks have been widely implemented in engineered systems. I selected ten quorum sensing networks based on their HSL production profiles and confirmed their functionality in E. coli, significantly expanding the quorum sensing toolset available to synthetic biologists.
ContributorsDaer, René (Author) / Haynes, Karmella (Thesis advisor) / Brafman, David (Committee member) / Nielsen, David (Committee member) / Kiani, Samira (Committee member) / Arizona State University (Publisher)
Created2017
Description
Myocardial infarction (MI) remains the leading cause of mortality and morbidity in the U.S., accounting for nearly 140,000 deaths per year. Heart transplantation and implantation of mechanical assist devices are the options of last resort for intractable heart failure, but these are limited by lack of organ donors and potential surgical complications. In this regard, there is an urgent need for developing new effective therapeutic strategies to induce regeneration and restore the loss contractility of infarcted myocardium. Over the past decades, regenerative medicine has emerged as a promising strategy to develop scaffold-free cell therapies and scaffold-based cardiac patches as potential approaches for MI treatment. Despite the progress, there are still critical shortcomings associated with these approaches regarding low cell retention, lack of global cardiomyocytes (CMs) synchronicity, as well as poor maturation and engraftment of the transplanted cells within the native myocardium. The overarching objective of this dissertation was to develop two classes of nanoengineered cardiac patches and scaffold-free microtissues with superior electrical, structural, and biological characteristics to address the limitations of previously developed tissue models. An integrated strategy, based on micro- and nanoscale technologies, was utilized to fabricate the proposed tissue models using functionalized gold nanomaterials (GNMs). Furthermore, comprehensive mechanistic studies were carried out to assess the influence of conductive GNMs on the electrophysiology and maturity of the engineered cardiac tissues. Specifically, the role of mechanical stiffness and nano-scale topographies of the scaffold, due to the incorporation of GNMs, on cardiac cells phenotype, contractility, and excitability were dissected from the scaffold’s electrical conductivity. In addition, the influence of GNMs on conduction velocity of CMs was investigated in both coupled and uncoupled gap junctions using microelectrode array technology. Overall, the key contributions of this work were to generate new classes of electrically conductive cardiac patches and scaffold-free microtissues and to mechanistically investigate the influence of conductive GNMs on maturation and electrophysiology of the engineered tissues.
ContributorsNavaei, Ali (Author) / Nikkhah, Mehdi (Thesis advisor) / Brafman, David (Committee member) / Migrino, Raymond Q. (Committee member) / Stabenfeldt, Sarah (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Synthetic manipulation of chromatin dynamics has applications for medicine, agriculture, and biotechnology. However, progress in this area requires the identification of design rules for engineering chromatin systems. In this thesis, I discuss research that has elucidated the intrinsic properties of histone binding proteins (HBP), and apply this knowledge to engineer novel chromatin binding effectors. Results from the experiments described herein demonstrate that the histone binding domain from chromobox protein homolog 8 (CBX8) is portable and can be customized to alter its endogenous function. First, I developed an assay to identify engineered fusion proteins that bind histone post translational modifications (PTMs) in vitro and regulate genes near the same histone PTMs in living cells. This assay will be useful for assaying the function of synthetic histone PTM-binding actuators and probes. Next, I investigated the activity of a novel, dual histone PTM binding domain regulator called Pc2TF. I characterized Pc2TF in vitro and in cells and show it has enhanced binding and transcriptional activation compared to a single binding domain fusion called Polycomb Transcription Factor (PcTF). These results indicate that valency can be used to tune the activity of synthetic histone-binding transcriptional regulators. Then, I report the delivery of PcTF fused to a cell penetrating peptide (CPP) TAT, called CP-PcTF. I treated 2D U-2 OS bone cancer cells with CP-PcTF, followed by RNA sequencing to identify genes regulated by CP-PcTF. I also showed that 3D spheroids treated with CP-PcTF show delayed growth. This preliminary work demonstrated that an epigenetic effector fused to a CPP can enable entry and regulation of genes in U-2 OS cells through DNA independent interactions. Finally, I described and validated a new screening method that combines the versatility of in vitro transcription and translation (IVTT) expressed protein coupled with the histone tail microarrays. Using Pc2TF as an example, I demonstrated that this assay is capable of determining binding and specificity of a synthetic HBP. I conclude by outlining future work toward engineering HBPs using techniques such as directed evolution and rational design. In conclusion, this work outlines a foundation to engineer and deliver synthetic chromatin effectors.
ContributorsTekel, Stefan (Author) / Haynes, Karmella (Thesis advisor) / Mills, Jeremy (Committee member) / Caplan, Michael (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2019
Description
Synthetic biology is a novel method that reengineers functional parts of natural genes of interest to build new biomolecular devices able to express as designed. There is increasing interest in synthetic biology due to wide potential applications in various fields such as clinics and fuel production. However, there are still many challenges in synthetic biology. For example, many natural biological processes are poorly understood, and these could be more thoroughly studied through model synthetic gene networks. Additionally, since synthetic biology applications may have numerous design constraints, more inducer systems should be developed to satisfy different requirements for genetic design.
This thesis covers two topics. First, I attempt to generate stochastic resonance (SR) in a biological system. Synthetic bistable systems were chosen because the inducer range in which they exhibit bistability can satisfy one of the three requirements of SR: a weak periodic force is unable to make the transition between states happen. I synthesized several different bistable systems, including toggle switches and self-activators, to select systems matching another requirement: the system has a clear threshold between the two energy states. Their bistability was verified and characterized. At the same time, I attempted to figure out the third requirement for SR – an effective noise serving as the stochastic force – through one of the most widespread toggles, the mutual inhibition toggle, in both yeast and E. coli. A mathematic model for SR was written and adjusted.
Secondly, I began work on designing a new genetic system capable of responding to pulsed magnetic fields. The operators responding to pulsed magnetic stimuli in the rpoH promoter were extracted and reorganized. Different versions of the rpoH promoter were generated and tested, and their varying responsiveness to magnetic fields was recorded. In order to improve efficiency and produce better operators, a directed evolution method was applied with the help of a CRISPR-dCas9 nicking system. The best performing promoters thus far show a five-fold difference in gene expression between trials with and without the magnetic field.
This thesis covers two topics. First, I attempt to generate stochastic resonance (SR) in a biological system. Synthetic bistable systems were chosen because the inducer range in which they exhibit bistability can satisfy one of the three requirements of SR: a weak periodic force is unable to make the transition between states happen. I synthesized several different bistable systems, including toggle switches and self-activators, to select systems matching another requirement: the system has a clear threshold between the two energy states. Their bistability was verified and characterized. At the same time, I attempted to figure out the third requirement for SR – an effective noise serving as the stochastic force – through one of the most widespread toggles, the mutual inhibition toggle, in both yeast and E. coli. A mathematic model for SR was written and adjusted.
Secondly, I began work on designing a new genetic system capable of responding to pulsed magnetic fields. The operators responding to pulsed magnetic stimuli in the rpoH promoter were extracted and reorganized. Different versions of the rpoH promoter were generated and tested, and their varying responsiveness to magnetic fields was recorded. In order to improve efficiency and produce better operators, a directed evolution method was applied with the help of a CRISPR-dCas9 nicking system. The best performing promoters thus far show a five-fold difference in gene expression between trials with and without the magnetic field.
ContributorsHu, Hao (Author) / Wang, Xiao (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2016
Description
Synthetic gene networks have evolved from simple proof-of-concept circuits to
complex therapy-oriented networks over the past fifteen years. This advancement has
greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a
mechanism that cells use to achieve a discrete number of mutually exclusive states in
response to environmental inputs. However, complex contextual connections of gene
regulatory networks in natural settings often impede the experimental establishment of
the function and dynamics of each specific gene network.
In this work, diverse synthetic gene networks are rationally designed and
constructed using well-characterized biological components to approach the cell fate
determination and state transition dynamics in multistable systems. Results show that
unimodality and bimodality and trimodality can be achieved through manipulation of the
signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to
communicate with each other.
Moreover, a synthetic quadrastable circuit is also built and experimentally
demonstrated to have four stable steady states. Experiments, guided by mathematical
modeling predictions, reveal that sequential inductions generate distinct cell fates by
changing the landscape in sequence and hence navigating cells to different final states.
Circuit function depends on the specific protein expression levels in the circuit.
We then establish a protein expression predictor taking into account adjacent
transcriptional regions’ features through construction of ~120 synthetic gene circuits
(operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks.
These combined results illustrate applications of synthetic gene networks to
understand the cell fate determination and state transition dynamics in multistable
systems. A protein-expression predictor is also developed to evaluate and tune circuit
dynamics.
complex therapy-oriented networks over the past fifteen years. This advancement has
greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a
mechanism that cells use to achieve a discrete number of mutually exclusive states in
response to environmental inputs. However, complex contextual connections of gene
regulatory networks in natural settings often impede the experimental establishment of
the function and dynamics of each specific gene network.
In this work, diverse synthetic gene networks are rationally designed and
constructed using well-characterized biological components to approach the cell fate
determination and state transition dynamics in multistable systems. Results show that
unimodality and bimodality and trimodality can be achieved through manipulation of the
signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to
communicate with each other.
Moreover, a synthetic quadrastable circuit is also built and experimentally
demonstrated to have four stable steady states. Experiments, guided by mathematical
modeling predictions, reveal that sequential inductions generate distinct cell fates by
changing the landscape in sequence and hence navigating cells to different final states.
Circuit function depends on the specific protein expression levels in the circuit.
We then establish a protein expression predictor taking into account adjacent
transcriptional regions’ features through construction of ~120 synthetic gene circuits
(operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks.
These combined results illustrate applications of synthetic gene networks to
understand the cell fate determination and state transition dynamics in multistable
systems. A protein-expression predictor is also developed to evaluate and tune circuit
dynamics.
ContributorsWu, Fuqing (Author) / Wang, Xiao (Thesis advisor) / Haynes, Karmella (Committee member) / Marshall, Pamela (Committee member) / Nielsen, David (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2017
Description
Cardiovascular disease (CVD) remains the leading cause of mortality, resulting in 1 out of 4 deaths in the United States at the alarming rate of 1 death every 36 seconds, despite great efforts in ongoing research. In vitro research to study CVDs has had limited success, due to lack of biomimicry and structural complexity of 2D models. As such, there is a critical need to develop a 3D, biomimetic human cardiac tissue within precisely engineered in vitro platforms. This PhD dissertation involved development of an innovative anisotropic 3D human stem cell-derived cardiac tissue on-a-chip model (i.e., heart on-a-chip), with an enhanced maturation tissue state, as demonstrated through extensive biological assessments. To demonstrate the potential of the platform to study cardiac-specific diseases, the developed heart on-a-chip was used to model myocardial infarction (MI) due to exposure to hypoxia. The successful induction of MI on-a-chip (heart attack-on-a-chip) was evidenced through fibrotic tissue response, contractile dysregulation, and transcriptomic regulation of key pathways.This dissertation also described incorporation of CRISPR/Cas9 gene-editing to create a human induced pluripotent stem cell line (hiPSC) with a mutation in KCNH2, the gene implicated in Long QT Syndrome Type 2 (LQTS2). This novel stem cell line, combined with the developed heart on-a-chip technology, led to creation of a 3D human cardiac on-chip tissue model of LQTS2 disease.. Extensive mechanistic biological and electrophysiological characterizations were performed to elucidate the mechanism of R531W mutation in KCNH2, significantly adding to existing knowledge about LQTS2. In summary, this thesis described creation of a LQTS2 cardiac on-a-chip model, incorporated with gene-edited hiPSC-cardiomyocytes and hiPSC-cardiac fibroblasts, to study mechanisms of LQTS2.
Overall, this dissertation provides broad impact for fundamental studies toward cardiac biological studies as well as drug screening applications. Specifically, the developed heart on-a-chip from this dissertation provides a unique alternative platform to animal testing and 2D studies that recapitulates the human myocardium, with capabilities to model critical CVDs to study disease mechanisms, and/or ultimately lead to development of future therapeutic strategies.
ContributorsVeldhuizen, Jaimeson (Author) / Nikkhah, Mehdi (Thesis advisor) / Brafman, David (Committee member) / Ebrahimkhani, Mo (Committee member) / Migrino, Raymond Q (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2021
Description
Annually, approximately 1.7 million people suffer a traumatic brain injury (TBI) in the United States. After initial insult, a TBI persists as a series of molecular and cellular events that lead to cognitive and motor deficits which have no treatment. In addition, the injured brain activates the regenerative niches of the adult brain presumably to reduce damage. The subventricular zone (SVZ) niche contains neural progenitor cells (NPCs) that generate astrocytes, oligodendrocyte, and neuroblasts. Following TBI, the injury microenvironment secretes signaling molecules like stromal cell derived factor-1a (SDF-1a). SDF-1a gradients from the injury contribute to the redirection of neuroblasts from the SVZ towards the lesion which may differentiate into neurons and integrate into existing circuitry. This repair mechanism is transient and does not lead to complete recovery of damaged tissue. Further, the mechanism by which SDF-1a gradients reach SVZ cells is not fully understood. To prolong NPC recruitment to the injured brain, exogenous SDF-1a delivery strategies have been employed. Increases in cell recruitment following stroke, spinal cord injury, and TBI have been demonstrated following SDF-1a delivery. Exogenous delivery of SDF-1a is limited by its 28-minute half-life and clearance from the injury microenvironment. Biomaterials-based delivery improves stability of molecules like SDF-1a and offer control of its release.
This dissertation investigates SDF-1a delivery strategies for neural regeneration in three ways: 1) elucidating the mechanisms of spatiotemporal SDF-1a signaling across the brain, 2) developing a tunable biomaterials system for SDF-1a delivery to the brain, 3) investigating SDF-1a delivery on SVZ-derived cell migration following TBI. Using in vitro, in vivo, and in silico analyses, autocrine/paracrine signaling was necessary to produce SDF-1a gradients in the brain. Native cell types engaged in autocrine/paracrine signaling. A microfluidics device generated injectable hyaluronic-based microgels that released SDF-1a peptide via enzymatic cleavage. Microgels (±SDF-1a peptide) were injected 7 days post-TBI in a mouse model and evaluated for NPC migration 7 days later using immunohistochemistry. Initial staining suggested complex presence of astrocytes, NPCs, and neuroblasts throughout the frontoparietal cortex.
Advancement of chemokine delivery was demonstrated by uncovering endogenous chemokine propagation in the brain, generating new approaches to maximize chemokine-based neural regeneration.
ContributorsHickey, Kassondra (Author) / Stabenfeldt, Sarah E (Thesis advisor) / Holloway, Julianne (Committee member) / Caplan, Michael (Committee member) / Brafman, David (Committee member) / Newbern, Jason (Committee member) / Arizona State University (Publisher)
Created2021
Description
The GGGGCC (G4C2) hexanucleotide repeat expansion (HRE) in the C9orf72 gene is the most common genetic abnormality associated with both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), two devastatingly progressive neurodegenerative diseases. The discovery of this genetic link confirmed that ALS and FTD reside along a spectrum with clinical and pathological commonalities. Historically understood as diseases resulting in neuronal death, the role of non-neuronal cells like astrocytes is still wholly unresolved. With evidence of cortical neurodegeneration leading to cognitive impairments in C9orf72-ALS/FTD, there is a need to investigate the role of cortical astrocytes in this disease spectrum. Here, a patient-derived induced pluripotent stem cell (iPSC) cortical astrocyte model was developed to investigate consequences of C9orf72-HRE pathogenic features in this cell type. Although there were no significant C9orf72-HRE pathogenic features in cortical astrocytes, transcriptomic, proteomic and phosphoproteomic profiles elucidated global disease-related phenotypes. Specifically, aberrant expression of astrocytic-synapse proteins and secreted factors were identified. SPARCL1, a pro-synaptogenic secreted astrocyte factor was found to be selectively decreased in C9orf72-ALS/FTD iPSC-cortical astrocytes. This finding was further validated in human tissue analyses, indicating that cortical astrocytes in C9orf72-ALS/FTD exhibit a reactive transformation that is characterized by a decrease in SPARCL1 expression. Considering the evidence for substantial astrogliosis and synaptic failure leading to cognitive impairments in C9orf72-ALS/FTD, these findings represent a novel understanding of how cortical astrocytes may contribute to the cortical neurodegeneration in this disease spectrum.
ContributorsBustos, Lynette (Author) / Sattler, Rita (Thesis advisor) / Newbern, Jason (Committee member) / Zarnescu, Daniela (Committee member) / Brafman, David (Committee member) / Mehta, Shwetal (Committee member) / Arizona State University (Publisher)
Created2023
Description
The past decade has seen a drastic increase in collaboration between Computer Science (CS) and Molecular Biology (MB). Current foci in CS such as deep learning require very large amounts of data, and MB research can often be rapidly advanced by analysis and models from CS. One of the places where CS could aid MB is during analysis of sequences to find binding sites, prediction of folding patterns of proteins. Maintenance and replication of stem-like cells is possible for long terms as well as differentiation of these cells into various tissue types. These behaviors are possible by controlling the expression of specific genes. These genes then cascade into a network effect by either promoting or repressing downstream gene expression. The expression level of all gene transcripts within a single cell can be analyzed using single cell RNA sequencing (scRNA-seq). A significant portion of noise in scRNA-seq data are results of extrinsic factors and could only be removed by customized scRNA-seq analysis pipeline. scRNA-seq experiments utilize next-gen sequencing to measure genome scale gene expression levels with single cell resolution.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
ContributorsBalachandran, Parithi (Author) / Wang, Xiao (Thesis advisor) / Brafman, David (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2017