Single-cell and spatiotemporal profile of ovulation in the mouse ovary.

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

Profiling Transcriptional Heterogeneity with Seq-Well S3: A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq.

Seq-Well is a high-throughput, picowell-based single-cell RNA-seq technology that can be used to simultaneously profile the transcriptomes of thousands of cells (Gierahn et al. Nat Methods 14(4):395-398, 2017). Relative to its reverse-emulsion-droplet-based counterparts, Seq-Well addresses key cost, portability, and scalability limitations. Recently, we introduced an improved molecular biology for Seq-Well to enhance the information content that can be captured from individual cells using the platform. This update, which we call Seq-Well S3 (S3: Second-Strand Synthesis), incorporates a second-strand-synthesis step after reverse transcription to boost the detection of cellular transcripts normally missed when running the original Seq-Well protocol (Hughes et al. Immunity 53(4):878-894.e7, 2020). This chapter provides details and tips on how to perform Seq-Well S3, along with general pointers on how to subsequently analyze the resultant single-cell RNA-seq data.

Single-cell transcriptomics of staged oocytes and somatic cells reveal novel regulators of follicle activation.

In brief: Proper development of ovarian follicles, comprised of an oocyte and surrounding somatic cells, is essential to support female fertility and endocrine health. Here, we describe a method to isolate single oocytes and somatic cells from the earliest stage follicles, called primordial follicles, and we characterize signals that drive their activation. Abstract: Primordial follicles are the first class of follicles formed in the mammalian ovary and are comprised of an oocyte surrounded by a layer of squamous pre-granulosa cells. This developmental class remains in a non-growing state until individual follicles activate to initiate folliculogenesis. What regulates the timing of follicle activation and the upstream signals that govern these processes are major unanswered questions in ovarian biology. This is partly due to the paucity of data on staged follicle cells since isolating and manipulating individual oocytes and somatic cells from early follicle stages are challenging. To date, most studies on isolated primordial follicles have been conducted on cells collected from animal-age- or oocyte size-specific samples, which encompass multiple follicular stages. Here, we report a method for collecting primordial follicles and their associated oocytes and somatic cells from neonatal murine ovaries using liberase, DNase I, and Accutase. This methodology allows for the identification and collection of follicles immediately post-activation enabling unprecedented interrogation of the primordial-to-primary follicle transition. Molecular profiling by single-cell RNA sequencing revealed that processes including organelle disassembly and cadherin binding were enriched in oocytes and somatic cells as they transitioned from primordial to the primary follicle stage. Furthermore, targets including WNT4, TGFB1, FOXO3, and a network of transcription factors were identified in the transitioning oocytes and somatic cells as potential upstream regulators that collectively may drive follicle activation. Taken together, we have developed a more precise characterization and selection method for studying staged-follicle cells, revealing several novel regulators of early folliculogenesis.