PFI-TT: Development of Metabolic Imaging to Improve Treatment of Infertility

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The broader impact/commercial potential of this PFI project is primarily the advancement of the commercialization of a system to improve clinical in vitro fertilization (IVF). IVF is currently the most effective treatment for infertility, but success rates for IVF remain low, with only ~30% of cycles resulting in live birth which, in turn, leads to high financial and emotional costs. Improving the success rate of IVF would help IVF patients and the approximately 10% of couples who suffer from infertility, and would lead to broad societal and economic benefit. The proposed work makes use of advanced imaging and computational image analysis, and has a tight integration between biology, physics, engineering, clinical medicine, and commercialization. Undergraduate, graduate students, and postdocs, will benefit from this interdisciplinary environment and training. This effort will help increase the involvement of women and underrepresented minorities in science, innovation, and technological commercialization.The proposed project tests and develops the use of metabolic imaging as a means to non-invasively measure oocyte quality. Metabolic imaging of oocytes holds great promise for improving IVF success rates: the current inability to determine oocyte quality is one of the major impediments to better IVF treatment, and oocyte quality is believed to be determined by oocyte metabolism. Previous and preliminary data demonstrated that metabolic imaging does not harm mouse oocytes and that it can be used to measure biologically meaningful metabolic differences between different mouse oocytes. The proposed project will expand on that work to investigate the safety and utility of the technology on in vitro matured human oocytes (clinically useless, otherwise discarded material). It is necessary to perform such safety tests before attempting to use metabolic imaging in a clinical setting. This work will also provide information on the metabolism of human oocytes, which is of fundamental import, with broad application for development, infertility, and birth defects. Furthermore, a clinical instrument must be robust and integrate into the clinical workflow. A key challenge is image and data analysis. Thus, this project also seeks to develop and validate fully automated image and data analysis procedures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.