Date of Award

Spring 2020

Document Type

Open Access Dissertation


Biomedical Engineering

First Advisor

Holly LaVoie


Infertility is a worldwide epidemic often treated with in vitro fertilization. The success of in vitro fertilization is directly dependent upon the quality of oocytes produced during the controlled stimulation of the patient’s ovaries. There is a need for improved methods to allow embryologists to select the most viable embryos with the highest probability of leading to live births since in vitro fertilization success is far from optimal.

Oocyte and subsequent embryo quality are intimately influenced by the granulosa cells which surround the oocyte during follicular maturation. The oocytes require proper signaling and energy production form the surrounding granulosa cells for ideal maturation which will allow the oocyte to fertilize and produce a viable embryo. We hypothesize that mRNA levels of certain genes in granulosa cells will allow for the identification oocytes that will produce euploid embryos and the most viable embryos within a cohort. Secondly, we hypothesize that the rate by which mitochondria from granulosa cells are able to utilize certain metabolic substrates will be able to identify patients that have increased probability of producing high quality embryos leading to live births.

This dissertation attempts to identify a group of genes within individual cumulus cells that show differential mRNA gene expression between oocytes of euploid embryos and oocytes leading to live birth compared to oocytes that do not. From these genes we further attempt to create a model using multiple genes to identify the most viable oocytes within a cohort. This dissertation also attempts to identify metabolic substrates differentially utilized by granulosa cell mitochondria based on patient demographics or embryo development and determine if individual substrates can be used as mitochondrial biomarkers for assisted reproduction. As a whole, the work in this dissertation seeks to provide a panel of biomarkers that can be used by clinicians to better identify embryos that will help improve overall assisted reproductive success.