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dc.contributor.advisorJoel Voldman.en_US
dc.contributor.authorApichitsopa, Nicha.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-09-03T17:41:49Z
dc.date.available2020-09-03T17:41:49Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127012
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 96-106).en_US
dc.description.abstractUtility of single-cell biophysical markers is often limited due to the low-specificity nature of biophysical markers and lack of existing techniques which can test multiple biophysical characteristics for single cells. To address this challenge, I developed a multiparameter intrinsic cytometry approach which integrates multiple label-free biophysical measurements into a versatile (can combine techniques across domains) and readily extensible (to measure more than two biophysical markers) platform for single cell analysis. The proposed multiparameter cell-tracking intrinsic cytometry utilizes label-free microfluidic techniques to manipulate cells such that information regarding their biophysical properties can be extracted from their spatiotemporal positions. Furthermore, this technique utilizes cell tracking to extract and associate the biophysical markers for single cells. The specific instantiation of the cytometry platform can measure up to five intrinsic markers of cells, and has facilitated the quantitative investigation of label-free cell profiles and classification of cell types and functional states. The applications of this approach were extended by leveraging digital holographic microscopy and deep learning technologies to monitor cells in a large field of view, enabling rapid and high-throughput assessment of biophysical phenotypes.en_US
dc.description.statementofresponsibilityby Nicha Apichitsopa.en_US
dc.format.extent106 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleLarge-area cell-tracking cytometry for biophysical measurements of single cellsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1191624346en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-09-03T17:41:48Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentEECSen_US


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