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Profiling of cell-substrate interactions using single cell fluororeporter imaging & modeling
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Tresier, Matthew David. Profiling of cell-substrate interactions using single cell fluororeporter imaging & modeling. Retrieved from https://doi.org/doi:10.7282/T3MW2HB3

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TitleProfiling of cell-substrate interactions using single cell fluororeporter imaging & modeling
NameTresier, Matthew David (author); Moghe, Prabhas (chair); Kohn, Joachim (co-chair); Knight, Doyle (internal member); Androulakis, Ioannis (internal member); Goss Kinzy, Terri (outside member); Rutgers University; Graduate School - New Brunswick
Date Created
Other Date2009-10 (degree)
SubjectBiomedical Engineering, Cell interaction
Extentxiii, 213 p. : ill.
DescriptionThis 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.
NotePh.D.
NoteIncludes bibliographical references (p. 186-211)
Noteby Matthew David Treiser
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3MW2HB3
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work
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