Author ORCID Identifier
https://orcid.org/0009-0007-7133-0436
Date of Award
Spring 2026
Document Type
Thesis (Ph.D.)
Department or Program
Engineering Sciences
First Advisor
Britt Goods
Second Advisor
Emme Burgin
Abstract
For decades, women have relied on hormonal contraceptives, yet suffered from various side effects, which have led to discontinuation or compromise on sexual autonomy. The development of novel, less adverse non-hormonal contraceptives represents a critical, unmet need in women’s health. While targeting the localized, finely tuned process of ovulation offers a promising strategy, identifying mechanistically relevant and safe therapeutic targets within the heterogeneous mammalian ovary remains a challenge. Recently, spatial transcriptomics (ST) has emerged as a transformative tool. Capable of preserving tissue architecture, ST can map gene expression directly onto intact ovarian tissue, enabling researchers to capture the dynamic cell states that drive localized events like ovulation. However, there are currently no frameworks to distill ST datasets into actionable, prioritized drug targets. This thesis establishes a gene-to-target translational framework that advances spatial biology from mapping of ovulation to in silico drug discovery. After systemically benchmarking state-of-the-art ST platforms on formalin-fixed, paraffin-embedded (FFPE) tissues, we constructed a high-resolution spatiotemporal mapping of the murine ovulatory cascade, capturing dynamic molecular and morphological shifts. We subsequently developed the OVA scoring matrix that prioritizes target genes by integrating spatial specificity, temporal expression dynamics, and tissue-wide safety profiles. This framework translated our spatiotemporal atlas into viable therapeutic targets, pinpointing Carbohydrate Sulfotransferase 15 (CHST15) as a promising candidate for non-hormonal contraception. Finally, to complete the translational loop, we transitioned CHST15 into an in silico, structure-based drug design workflow, in which we confirmed the target’s structural druggability and identified potential small-molecule inhibitors. Ultimately, this dissertation demonstrates that spatial omics data can be mined to guide drug development. By delivering candidate molecules ready for experimental validation, this work provides a blueprint for resolving target-discovery bottlenecks in reproductive biology and beyond.
Original Citation
The content of chapter 2 is currently under review in Reproduction (submitted 2026) under the title Spatial Transcriptomics in Ovarian Biology Technologies, Computational Challenges, and Biological In sights.
Chapter 3 is reproduced from the following published article: Wang, H., Huang, R., Nelson, J. et al. Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues. Nat Commun 16, 10215 (2025). https://doi.org/10.1038/s41467-025-64990-y. Copyright © 2025 by Springer Nature. Reproduced with permission. The text and figures in this chapter are identical to the published version except for minor formatting adjustments to fit this thesis.
Chapter 4 is reproduced from the following published article: Huang, R., Kratka, C. E., Pea, J., McCann, C., Nelson, J., Bryan, J. P., Zhou, L. T., Russo, D. D., Zaniker-Gomez, E. J., Gandhi, A. H., Shalek, A. K., Cleary, B., Farhi, S. L., Duncan, F. E., & Goods, B. A. (2025). Single-cell and spatiotemporal profile of ovulation in the mouse ovary. PLoS biology, 23(6), e3003193. https://doi.org/10.1371/journal.pbio.3003193. Copyright © 2025. Reproduced with permission. The text and figures in this chapter are identical to the published version except for minor formatting adjustments to fit this thesis.
Recommended Citation
Huang, Ruixu, "From Spatial Omics to Drug Target Discover" (2026). Dartmouth College Ph.D Dissertations. 481.
https://digitalcommons.dartmouth.edu/dissertations/481
