Optimising a Spatial Gene Expression Protocol to Track Progenitor to Theca Cell Differentiation In Situ.

The theca, a highly specialised stromal cell layer that surrounds ovarian follicles, first appears at the secondary stage of ovarian follicle development. While the role of the theca in folliculogenesis is well understood, the spatiotemporal dynamics governing theca cell differentiation are yet to be fully elucidated. Spatial transcriptomics offers a unique insight into the spatiotemporal dynamics of the molecular processes that occur during cell differentiation by capturing mRNA expression patterns within tissues, maintaining their spatial context. The overall objective of this thesis was to optimise a protocol to perform spatial transcriptomics in mouse ovarian tissue. This contributes to a wider project which aims to track the progenitor to differentiated theca cell transition in situ using a mouse model. I refined protocols for collecting ovaries, and various histological techniques (including protocols for freezing, embedding and cryosectioning) that prioritised maintaining the integrity and morphology of ovarian tissue for imaging, while still allowing for the recovery of high-quality RNA from the same sample. As a result, I was able to validate this protocol by collecting and preparing ovarian tissues, using the optimised sample preparation protocol, perform tissue-specific permeabilization optimisation for the STOmics Stereo-seq Transcriptomics kit, and contribute to the final spatial transcriptomic experiment. I prepared a fresh-frozen tissue block containing four ovaries from four mice (n=4), which was of sufficient quality (RNA Integrity Number (RIN) 7.4 ± 0.1) to proceed with the experiment. This sample consistently passed all DNA concentration and fragment size quality control checks throughout the experiment, indicating that the optimised tissue preparation protocol was successful in generating a high quality (both in terms of morphology and RNA quality) sample which was used to prepare the final RNA-seq library. This thesis serves as a valuable resource for future studies using spatial transcriptomics to investigate cellular differentiation within the ovary.