|John Timothy Elliott
|Alexander W Peterson
|Anne L. Plant
Induced pluripotent stem cell technologies are powerful new tools for biomedical research and have the potential to revolutionize medicine. The mechanisms by which cells transition from pluripotent to differentiated states is incompletely understood, and correlating measurable parameters to identify efficient culture conditions and release criteria for safe and effective therapies is imperfect. One challenge is the natural biological variability in cell responses across a population. In order to provide better biomarkers of pluripotency and differentiation, data describing the changes in gene expression at the single cell level are needed. In this project, quantitative live cell imaging and image analysis will be used to follow gene expression dynamics, and other phenotypic characteristics, in single cells. Imaging modes that employ fluorescence, transmitted light, quantitative phase, and/or surface plasmon resonance microscopy may be used to acquire different kinds of images on large numbers of iPS cells in culture; machine learning algorithms and other image analysis strategies may be used to extract and test image features as predictors of cell state. Quantitiation of the extent, probability, and dynamics of changes in phenotypic markers over the population will add confidence in the interpretation of biomarkers of pluripotency and differentiation.
Bhadriraju K, et al: “Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies.” Stem Cell Research (17): 122-129, 2016
Peterson, Alexander W.; Halter, Michael W.; Tona, Alessandro; Plant, Anne L. (2014) High Resolution Surface Plasmon Resonance Imaging of Cells. BMC Cell Biology. 15:35 DOI: 10.1186/1471-2121-15-35
Peterson, Alexander, Michael Halter, Anne Plant, and John Elliott (2016) Surface plasmon resonance microscopy: achieving a quantitative optical response. Review of Scientific Instruments, 87:9 DOI: 10.1063/1.4962034
Bioengineering; Quantitative imaging; Stem cell; Pluripotency; Differentiation; Live cell microscopy; Image analysis; Biological noise; Machine learning;