Quantitative phase imaging for cellular biology

Quantitative phase imaging;Spatial light interference microscopy;diffraction phase microscopy;red blood cell cytometry;cell growth;cycle dependent growth;neuronal network organization;cell proliferation;tomography;sub-cellular tomography;image analysis;interferometry;microscopy

Mir, Mustafa

关键词: Quantitative phase imaging;    Spatial light interference microscopy;    diffraction phase microscopy;    red blood cell cytometry;    cell growth;    cycle dependent growth;    neuronal network organization;    cell proliferation;    tomography;    sub-cellular tomography;    image analysis;    interferometry;    microscopy;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/45668/Mustafa_Mir.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Measuring cellular level phenomena is challenging because of the transparent nature of cells and tissues, the multiple temporal and spatial scales involved, and the need for both high sensitivity (to single cell density, morphology, motility, etc.) and the ability to measure a large number of cells.Quantitative phase imaging (QPI) is an emerging field that addresses this need.New quantitative phase imaging modalities have emerged that provide highly sensitive information on cellular growth, motility, dynamics and spatial organization. These parameters can be measured from the sub-micron to millimeter scales and timescales ranging from milliseconds to days. In this thesis I discuss the development and use of QPI tools and analysis methods to explore several applications in both clinical and research settings. Through these applications I demonstrate that the quantitative information provided by QPI methods allows for analyzing biological systems in an unprecedented manner, creating opportunities to answer longstanding questions in biological sciences, and also enabling the study of phenomena that were previously inaccessible. Here I show results on blood cell analysis, single cell growth, cellular proliferation assays and neural network formation. These results prove that QPI provides unique and important insight into the behavior of biological systems and can be utilized to help address important needs in clinical settings as well as answer fundamental biological questions.