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Project SummaryThe overall objective of this research project is to develop a novel approach for high throughputscreening of individual cells based on holographic imaging. To achieve this goal, we propose toimplement a new quantitative phase imaging modality, holographic cytomtery, whichincorporates several novel technical advances to enable high throughput imaging. Holographiccytometry (HC) will bring the high sensitivity of quantitative phase microscopy (QPM) to imagingof cells flowing through microfluidic devices. While QPM has been used for cell analysispreviously, typically only a handful of cells have been imaged. To enable significant applicationof QPM for fundamental cell biology and clinical studies, it is necessary to move to a highthroughput implementation.Technical advances needed to realize the high resolution HC system include use of high speedline scan cameras, microfluidic chips with multiple parallel channels, and light from a pulsed lasersource to enable stroboscopic illumination. In order to efficiently analyze and process this dataset, rapid analysis software will be developed that leverages the highly parallel processingcapabilities of graphics processing units and machine learning algorithms to enable automatedclassification.The proposed HC method can be applied to imaging a wide range of flowing cells. Todemonstrate the utility of the approach, we will initially target the measurement of cancerousprogression due to environmental toxicant exposure. We have conducted a preliminary studythat shows QPM can detect early changes in the biomechanical properties of cells due to arsenicexposure. In the proposed project, we seek to develop QPM based biomarkers of pre-cancerouschange that will enable rapid assessesment. QPM has not been implemented in such a format todate and thus is not yet a feasible approach for clinical or research studies.To meet the goal of high throughput imaging with QPM, the following Specific Aims areproposed: 1. Develop new instrumentation for high speed imaging using off axis digitalholography. 2. Implement high throughput analysis methods based on machine learning 3. Testand validate high throughput system with pilot studies of heavy metal exposed epithelial cells toshow the approach can detect early pre-cancerous changes due to environmental toxicantexposure. Upon completion of this project, we will have realized a high throughput imagingcytometry system for research and clinical applications.

Wax, Adam
Duke University
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