Author ORCID Identifier
https://orcid.org/0009-0006-1686-1579
Date of Award
Summer 8-8-2024
Document Type
Thesis (Master's)
Department or Program
Engineering Sciences
First Advisor
Britt A. Goods
Abstract
The ovary is vital for fertility and the maintenance of endocrine homeostasis. It contains heterogeneous cell populations, including oocyte, granulosa, stromal, endothelial, smooth muscle, and immune cell types. Creating a harmonized dataset that enables cross-tissue comparisons, including the ovary, is essential for identifying genes specific to ovarian cell types and for facilitating drug discovery for contraception and fertility purposes. There are very few resources that allow for the assessment of the expression of potential gene targets across human and mouse tissues for female-directed non-hormonal contraceptives. This is because most of these publicly available datasets are from post-menopausal women. Through our curation efforts, we have also found that of datasets derived from pre-menopausal groups, the majority of these are from women with polycystic ovary syndrome or other reproductive diseases.
The emergence of large-scale tissue Atlas efforts, in both human and mouse, coupled with established data integration tools for datasets comprised of millions of cells, allows us to curate data from pre-menopausal women across many tissues. We aim to establish stringent curation guidelines, pipelines for data pre-processing and integration, and data harmonization. We plan to complete curation and comparative analyses of cells from datasets representing more than 10 reference tissues. This resource, when completed, will allow users across the non-hormonal contraceptive research community to query the expression of target genes across all tissues in human and mouse datasets.
Recommended Citation
Nugmanova, Zhanel, "Integrated Reference Single-cell RNA seq Atlas of Healthy Human Female Tissues to Expand the Computational Drug Target Discovery Approaches for Female-Directed Non-hormonal Contraceptives" (2024). Dartmouth College Master’s Theses. 190.
https://digitalcommons.dartmouth.edu/masters_theses/190
