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Profiling of cell-substrate interactions using single cell fluororeporter imaging & modeling
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Profiling of cell-substrate interactions using single cell fluororeporter imaging & modeling
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ETD_1941
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051914
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eng
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theses
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Biomedical Engineering
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Topic
Cell interaction
Abstract
This dissertation advances the field of biomaterials-based tissue engineering via the development of a single cell imaging approach to profile and predict cellular responses. The methodology at the core of the dissertation characterizes cellular behaviors via the extraction and quantification of cell shape, intensity, textural and spatial distribution features of molecular fluororeporters, referred to as high-content cell imaging.
First, we highlight the use of high-content imaging of cell-based fluororeporters to establish and correlate quantifiable metrics of intracellular cytoskeletal features (e.g., descriptors of actin organization) on a set of model biomaterial substrates. The high-content imaging approach is then applied to spatially graded polymer blend substrates of both continuous roughness and discrete chemical compositions in parallel with high-throughput analyses. The imaging approach allowed us to identify the "global" and “high content” structure-property relationships between cell adhesion and biomaterial properties such as polymer chemistry and topography. The approach also identifies features of the actin-based cytoskeleton that respond to minute chemical modifications of the polymer backbone of a combinatorially derived library. In combination with decision tree and artificial neural network analyses, these 24-hour descriptors are used to predict 3-week material-mediated mineralization.
The high-content imaging approach is complemented with multi-dimensional scaling (MDS) modeling efforts to project amplified variations in cytoskeletal organization that forecast human mesenchymal stem cell lineage commitment before it is detected via traditional assays. Utilizing early quantitative measures of cytoskeletal morphology (morphometrics) and MDS allows the identification of distinct subpopulations of stem cells that emerge from non-linear combinations of cell shape, texture, density and cytoskeletal organizational features. These clusters allow the prediction of long-term differentiation behaviors of stem cells that manifest days to weeks later than the time of morphometric analysis. The proposed platform represents an ideal approach to probe cell-responses to complex microenvironments as it provides: real time measures of stem cell fates and material induced cell responses, cell-by-cell based analysis that captures the heterogeneity of sample populations, and the ability to parse out lineage commitment in stem cells resulting from multiple stimuli.
First, we highlight the use of high-content imaging of cell-based fluororeporters to establish and correlate quantifiable metrics of intracellular cytoskeletal features (e.g., descriptors of actin organization) on a set of model biomaterial substrates. The high-content imaging approach is then applied to spatially graded polymer blend substrates of both continuous roughness and discrete chemical compositions in parallel with high-throughput analyses. The imaging approach allowed us to identify the "global" and “high content” structure-property relationships between cell adhesion and biomaterial properties such as polymer chemistry and topography. The approach also identifies features of the actin-based cytoskeleton that respond to minute chemical modifications of the polymer backbone of a combinatorially derived library. In combination with decision tree and artificial neural network analyses, these 24-hour descriptors are used to predict 3-week material-mediated mineralization.
The high-content imaging approach is complemented with multi-dimensional scaling (MDS) modeling efforts to project amplified variations in cytoskeletal organization that forecast human mesenchymal stem cell lineage commitment before it is detected via traditional assays. Utilizing early quantitative measures of cytoskeletal morphology (morphometrics) and MDS allows the identification of distinct subpopulations of stem cells that emerge from non-linear combinations of cell shape, texture, density and cytoskeletal organizational features. These clusters allow the prediction of long-term differentiation behaviors of stem cells that manifest days to weeks later than the time of morphometric analysis. The proposed platform represents an ideal approach to probe cell-responses to complex microenvironments as it provides: real time measures of stem cell fates and material induced cell responses, cell-by-cell based analysis that captures the heterogeneity of sample populations, and the ability to parse out lineage commitment in stem cells resulting from multiple stimuli.
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electronic resource
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xiii, 213 p. : ill.
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Ph.D.
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Includes bibliographical references (p. 186-211)
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by Matthew David Treiser
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Tresier
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Matthew David
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1981-
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Matthew David Tresier
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Moghe
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Prabhas
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chair
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Prabhas V Moghe
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Kohn
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Joachim
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Joachim Kohn
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Doyle
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Androulakis
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Ioannis
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Ioannis Androulakis
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Goss Kinzy
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Terri
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Terri Goss Kinzy
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-10
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xx
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Rutgers University Electronic Theses and Dissertations
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Graduate School - New Brunswick Electronic Theses and Dissertations
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doi:10.7282/T3MW2HB3
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ETD doctoral
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The author owns the copyright to this work
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Open
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Treiser
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Matthew
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Matthew Tresier
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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