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Single cell analysis has become increasingly important in understanding disease onset, progression, treatment and prognosis, especially when applied to cancer where cellular responses are highly heterogeneous. Through the advent of single cell computerized tomography (Cell-CT), researchers and clinicians now have the ability to obtain high resolution three-dimensional (3D) reconstructions of single cells. Yet to date, no live-cell compatible version of the technology exists. In this thesis, a microfluidic chip with the ability to rotate live single cells in hydrodynamic microvortices about an axis parallel to the optical focal plane has been demonstrated. The chip utilizes a novel 3D microchamber design arranged beneath a main channel creating flow detachment into the chamber, producing recirculating flow conditions. Single cells are flowed through the main channel, held in the center of the microvortex by an optical trap, and rotated by the forces induced by the recirculating fluid flow. Computational fluid dynamics (CFD) was employed to optimize the geometry of the microchamber. Two methods for the fabrication of the 3D microchamber were devised: anisotropic etching of silicon and backside diffuser photolithography (BDPL). First, the optimization of the silicon etching conditions was demonstrated through design of experiment (DOE). In addition, a non-conventional method of soft-lithography was demonstrated which incorporates the use of two positive molds, one of the main channel and the other of the microchambers, compressed together during replication to produce a single ultra-thin (<200 µm) negative used for device assembly. Second, methods for using thick negative photoresists such as SU-8 with BDPL have been developed which include a new simple and effective method for promoting the adhesion of SU-8 to glass. An assembly method that bonds two individual ultra-thin (<100 µm) replications of the channel and the microfeatures has also been demonstrated. Finally, a pressure driven pumping system with nanoliter per minute flow rate regulation, sub-second response times, and < 3% flow variability has been designed and characterized. The fabrication and assembly of this device is inexpensive and utilizes simple variants of conventional microfluidic fabrication techniques, making it easily accessible to the single cell analysis community.
ContributorsMyers, Jakrey R (Author) / Meldrum, Deirdre (Thesis advisor) / Johnson, Roger (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
In vitro measurements of cellular respiration have proven to be key biomarkers for the early onset of tumor formation in certain pathological mechanisms.1 The examination of isolated single cells has shown promise in predicting the onset of cancerous growth much earlier than current methods allow.2 Specifically, measurements of the oxygen consumption rates of precancerous cells have elucidated outliers which predict the early onset of esophageal cancer.2 Single cell profiling can fit in to current pathology studies and can serve as a step along the way, much like PCR or gel assays, in detecting biomarkers earlier than current clinical methods.3 Measurement of these single cell metabolic rates is currently limited to 25 cells per experiment. It is the aim of this project to increase throughput from 25 cells to 225 cells per experiment via the implementation of new hardware and software which fit with current methods to allow the same experimental structure. Successful implementation of such methods will allow for more rapid and efficient data collection, facilitating quantitative results and nine times the yield from the same experimental manpower and funding. This document focuses on the implementation ultra high density (UHD) hardware consisting of a pneumatic molar design, angular adjustment features and a mechanical Z-stage. These components have produced the most encouraging results thus far and are the key changes in transitioning to higher throughput experiments.
ContributorsUeberroth, Benjamin Edward (Author) / Kelbauskas, Laimonas (Thesis director) / Ashili, Shashanka (Committee member) / Myers, Jakrey (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2013-05
Description
Esophageal adenocarcinoma (EAC) is one of the most lethal and fastest growing cancers in the United States. Its onset is commonly triggered by metaplastic transformation of normal squamous esophageal epithelial cells to Barrett's esophagus (BE) cells in response to acid reflux. BE patients are believed to progress through non-dysplastic metaplasia and increasing grades of dysplasia prior to EAC. Conventional cancer diagnostic tools rely on bulk-cell analyses that are incapable of identifying intratumoral heterogeneity or rare driver cells that play important roles in cancer progression. An improved single-cell method of cancer diagnosis would overcome this challenge by detecting cancer initiating cells before they progress into untreatable stages. In this study, using EAC as a model, we attempted to identify a more effective method of cancer diagnosis. We quantified the single- and bulk-cell mRNA expression of genes that have been proposed to be instrumental in the progression of EAC through BE. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) analysis was performed on human primary cells to measure the mRNA expression levels of BE- and EAC-associated genes. Our results showed high levels of heterogeneity of CDX2 and TFF3 at the single-cell resolution in human BE and EAC samples. Additionally, while expression of VEGF is generally low at the bulk-cell level, our results showed that a few, rare cells had significantly higher VEGF expression levels than the majority of cells in the EAC sample. In conclusion, we have affirmed that EAC cancer cells, as well as BE cells, show high levels of heterogeneity. Based on the VEGF gene expression pattern, single-cell analysis could potentially be more effective for identifying rare, but essential cells for cancer progression, which could then be targeted for treatment. Future studies will focus on analyzing human samples from thousands of normal and cancer subjects to validate the use of single-cell profiling in cancer.
ContributorsHaeuser, Kelsey Lynn (Author) / Tran, Thai (Thesis director) / Kelbauskas, Laimonas (Committee member) / Gao, Weimin (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2013-12
Description
Background
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
ContributorsNandakumar, Vivek (Author) / Kelbauskas, Laimonas (Author) / Hernandez, Kathryn (Author) / Lintecum, Kelly (Author) / Senechal, Patti (Author) / Bussey, Kimberly (Author) / Davies, Paul (Author) / Johnson, Roger (Author) / Meldrum, Deirdre (Author) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / Biodesign Institute (Contributor) / Center for Biosignatures Discovery Automation (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Department of Physics (Contributor)
Created2012-01-05
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cities in the Global South face rapid urbanization challenges and often suffer an acute lack of infrastructure and governance capacities. Smart Cities Mission, in India, launched in 2015, aims to offer a novel approach for urban renewal of 100 cities following an area‐based development approach, where the use of ICT and digital technologies is particularly emphasized. This article presents a critical review of the design and implementation framework of this new urban renewal program across selected case‐study cities. The article examines the claims of the so‐called “smart cities” against actual urban transformation on‐ground and evaluates how “inclusive” and “sustainable” these developments are. We quantify the scale and coverage of the smart city urban renewal projects in the cities to highlight who the program includes and excludes. The article also presents a statistical analysis of the sectoral focus and budgetary allocations of the projects under the Smart Cities Mission to find an inherent bias in these smart city initiatives in terms of which types of development they promote and the ones it ignores. The findings indicate that a predominant emphasis on digital urban renewal of selected precincts and enclaves, branded as “smart cities,” leads to deepening social polarization and gentrification. The article offers crucial urban planning lessons for designing ICT‐driven urban renewal projects, while addressing critical questions around inclusion and sustainability in smart city ventures.`
ContributorsPraharaj, Sarbeswar (Author)
Created2021-05-07
Description
Attitudes and habits are extremely resistant to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring long-term, massive societal changes. During the pandemic, people are being compelled to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. Going forward, a critical question is whether these experiences will result in changed behaviors and preferences in the long term. This paper presents initial findings on the likelihood of long-term changes in telework, daily travel, restaurant patronage, and air travel based on survey data collected from adults in the United States in Spring 2020. These data suggest that a sizable fraction of the increase in telework and decreases in both business air travel and restaurant patronage are likely here to stay. As for daily travel modes, public transit may not fully recover its pre-pandemic ridership levels, but many of our respondents are planning to bike and walk more than they used to. These data reflect the responses of a sample that is higher income and more highly educated than the US population. The response of these particular groups to the COVID-19 pandemic is perhaps especially important to understand, however, because their consumption patterns give them a large influence on many sectors of the economy.
ContributorsConway, Matthew Wigginton (Author) / Salon, Deborah (Author) / da Silva, Denise Capasso (Author) / Mirtich, Laura Christine (Author)
Created2020-09-03
Description
In the past decades, single-cell metabolic analysis has been playing a key role in understanding cellular heterogeneity, disease initiation, progression, and drug resistance. Therefore, it is critical to develop technologies for individual cellular metabolic analysis using various configurations of microfluidic devices. Compared to bulk-cell analysis which is widely used by reporting an averaged measurement, single-cell analysis is able to present the individual cellular responses to the external stimuli. Particularly, oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) are two key parameters to monitor heterogeneous metabolic profiles of cancer cells. To achieve multi-parameter metabolic measurements on single cells, several technical challenges need to be overcome: (1) low adhesion of soft materials micro-fabricated on glass surface for multiple-sensor deposition and single-cell immobilization, e.g. SU-8, KMPR, etc.; (2) high risk of using external mechanical forces to create hermetic seals between two rigid fused silica parts, even with compliance layers; (3) how to accomplish high-throughput for single-cell trapping, metabolic profiling and drug screening; (4) high process cost of micromachining on glass substrate and incapability of mass production.
In this dissertation, the development of microfabrication technologies is demonstrated to design reliable configurations for analyzing multiple metabolic parameters from single cells, including (1) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can be integrated and sealed to 3 × 3 tri-color sensor arrays for OCR and ECAR measurements; (2) design and characterization of a microfluidic device enabling rapid single-cell trapping and hermetic sealing single cells and tri-color sensors within 10 × 10 hermetically sealed microchamber arrays; (3) exhibition of a low-cost microfluidic device based on plastics for single-cell metabolic multi-parameter profiling. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform.
In this dissertation, the development of microfabrication technologies is demonstrated to design reliable configurations for analyzing multiple metabolic parameters from single cells, including (1) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can be integrated and sealed to 3 × 3 tri-color sensor arrays for OCR and ECAR measurements; (2) design and characterization of a microfluidic device enabling rapid single-cell trapping and hermetic sealing single cells and tri-color sensors within 10 × 10 hermetically sealed microchamber arrays; (3) exhibition of a low-cost microfluidic device based on plastics for single-cell metabolic multi-parameter profiling. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform.
ContributorsSong, Ganquan (Author) / Meldrum, Deirdre R. (Thesis advisor) / Goryll, Michael (Committee member) / Kelbauskas, Laimonas (Committee member) / Wang, Hong (Committee member) / Arizona State University (Publisher)
Created2017
Description
Cell-cell interactions in a microenvironment under stress conditions play a critical role in pathogenesis and pre-malignant progression. Hypoxia is a central factor in carcinogenesis, which induces selective pressure in this process. Understanding the role of intercellular communications and cellular adaptation to hypoxia can help discover new cancer biosignatures and more effective diagnostic and therapeutic strategies. This dissertation presents a study on transcriptomic and metabolic profiling of pre-malignant progression of Barrett's esophagus. It encompasses two methodology developments and experimental findings of two related studies. To integrate phenotype and genotype measurements, a minimally invasive method was developed for selectively retrieving single adherent cells from cell cultures. Selected single cells can be harvested by a combination of mechanical force and biochemical treatment after phenotype measurements and used for end-point assays. Furthermore, a method was developed for analyzing expression levels of ten genes in individual mammalian cells with high sensitivity and reproducibility without the need of pre-amplifying cDNA. It is inexpensive and compatible with most of commercially available RT-qPCR systems, which warrants a wide applicability of the method to gene expression analysis in single cells. In the first study, the effect of intercellular interactions was investigated between normal esophageal epithelial and dysplastic Barrett's esophagus cells on gene expression levels and cellular functions. As a result, gene expression levels in dysplastic cells were found to be affected to a significantly larger extent than in the normal esophageal epithelial cells. These differentially expressed genes are enriched in cellular movement, TGFβ and EGF signaling networks. Heterotypic interactions between normal and dysplastic cells can change cellular motility and inhibit proliferation in both normal and dysplastic cells. In the second study, alterations in gene transcription levels and metabolic phenotypes between hypoxia-adapted cells and age-matched normoxic controls representing four different stages of pre-malignant progression in Barrett's esophagus were investigated. Through differential gene expression analysis and mitochondrial membrane potential measurements, evidence of clonal evolution induced by hypoxia selection pressure in metaplastic and high-grade dysplastic cells was found. These discoveries on cell-cell interactions and hypoxia adaptations provide a deeper insight into the dynamic evolutionary process in pre-malignant progression of Barrett's esophagus.
ContributorsZeng, Jia (Author) / Meldrum, Deirdre R (Thesis advisor) / Kelbauskas, Laimonas (Committee member) / Barrett, Michael T (Committee member) / Bussey, Kimberly J (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2014
Description
Urban areas across the Unites States are facing a housing affordability crisis. One approach some cities and states have taken is to reduce or eliminate single-family zoning. Single-family zoning prevents the construction of more-affordable apartments in vast swaths of the American urban landscape. This policy shift has already occurred in Minneapolis, Sacramento, and Oregon, and is under discussion in California, Massachusetts, and North Carolina, among others. Independent of any effects on housing affordability, changes to land use will have effects on transport. I evaluate these effects using a microsimulation framework. In order for land use policies to have an effect on transport, they need to first have an effect on land use, so I first build an economic model to simulate where development will occur given a loosening of single-family zoning. Transport outcomes will vary depending on which households live in which parts of the region, so I use an equilibrium sorting model to forecast how residents will re-sort across the region in response to the land use changes induced by new land-use policies. This model also jointly forecasts how many vehicles each household will choose to own. Finally, I apply an activity-based travel demand microsimulation model to forecast the changes in transport associated with the forecast changes from the previous models. I find that while there is opportunity for economically-feasible redevelopment of single-family homes into multifamily structures, the amount of redevelopment that will occur varies greatly depending on the exact expectations of developers about future market conditions. Redevelopment is focused in higher-income neighborhoods. The transport effects of the redevelopment are minimal. Average car ownership across the region does not change hardly at all, although residents of new housing units do have somewhat lower car ownership. Vehicles kilometers traveled, mode choice, and congestion change very little as well. This does not mean that upzoning does not affect transport in general, but that more nuanced proposals may be necessary to promote desirable transport outcomes. Alternatively, the results suggest that upzoning will not worsen transport outcomes, promising for those who support upzoning on affordability grounds.
ContributorsConway, Matthew Wigginton (Author) / Salon, Deborah (Thesis advisor) / Pfeiffer, Deirdre (Committee member) / Fotheringham, A Stewart (Committee member) / van Eggermond, Michael AB (Committee member) / Arizona State University (Publisher)
Created2021