Gene expression atlas captures where ovulation can go awry | Cornell Chronicle
Back in the late 1960s and early 1970, in what seems like a different lifetime now, I was a senior research assistant in the Oxford University/MRC Neuroendocrinology Research Unit, researching the hormonal control of ovulation in guinea pigs. Two of our tools were radioimmunoassays I had adapted for measuring extremely low levels of a hormone in guinea pig anterior pituitary glands known as luteinizing hormone (LH), and another similar assay for measuring the level of the steroid progesterone in guinea pig blood.
Sadly, having worked for close on two years towards producing a research paper with hundreds of assay results, thousands of microscope slides, hundreds of electron micrographs and a freezer full of samples waiting to be assayed, the government pulled the rug from under our feet by withdrawing our research funding, and I was made redundant, so my work was never published. Disillusioned and with a young family to support, I left research and perused a career in the NHS Ambulance Service instead - but that's a different story, and not relevant to the subject of this blogpost, which illustrates how much science has progressed in the last 50-60 years.
Researchers are no longer researching the hormonal control of ovulation but the fine details of the genetic control of the process of ovulation at the cell level, and what they've found is that the process is far from intelligently designed by anything resembling a perfect, omniscient, omnipotent designer. It is a process that is so complex that it can, and does, go wrong. An intelligent designer who didn't want random women to be unable to shed viable eggs, could have designed a less complicated process, but you can depend on creationism's putative intelligent[sic] designer to never do something simple when there is a far more complicated and wasteful way to achieve the same result.
The research, published a few days ago in Proceedings of the National Academy of Sciences, was led by Iwijn De Vlaminck, associate professor of biomedical engineering in Cornell Engineering, and Yi Athena Ren, assistant professor of animal science in the College of Agriculture and Life Sciences. The paper’s lead author is Madhav Mantri, Ph.D., now a postdoctoral researcher at Stanford University.
The team used a form of RNA tagging to map the gene expressions that occur during ovarian follicle maturation and ovulation in mice.
This spatial transcriptomics map depicts the cell types of a mouse ovary undergoing hormone-induced ovulation
De Vlaminck previously used the imaging method, high-resolution spatiotemporal transcriptomics, to survey the entire spectrum of RNA in mouse tissues, which showed the role of elusive RNA in skeletal muscle regeneration and viral myocarditis. Transcriptomics essentially converts RNA into DNA copies, which are tagged with barcodes that capture their spatial location – data that can then be sequenced into an image. In 2022, De Vlaminck gave a presentation on the myocarditis findings at the 2nd Intercampus Immunology Symposium, which Ren attended. She was intrigued by De Vlaminck’s approach and wondered if it could be applied to one of her chief interests: unraveling the cellular and molecular mechanisms that regulate ovulation. Ovulation requires accurate coordination between female germ cells, called oocytes, and their release via the rupture of ovarian follicles, which provide the environment for oocytes to grow and mature. In mice, this rupture occurs every four to five days; in women, it’s approximately every four weeks. Oocytes expire quickly once they depart the ovary, so the timing of their release is critical.In the years since De Vlaminck’s myocarditis study, the spatial resolution of transcriptomics has significantly improved, from 100 micrometers to 10 microns per pixel – a tenfold enhancement that has resulted in near single-cell resolution. The flip side to obtaining so much data, however, is that parsing it all is daunting. “We had about 10 images and we spent a good 10 months making sense of them,” De Vlaminck said. For each image, the researchers sequenced hundreds of millions of DNA molecules, then translated them into a matrix of gene expression. Every pixel contained the expression level of all 22,000 protein-coding genes in the mouse genome. Multiply that by approximately 100,000 pixels. And that was only the beginning.Ovarian follicles are like launching pads, and the ovary is like the ground control. Together they prepare the eggs for fertilization at the right time and right location. All the different cell types in the ovary must work together through an amazingly complex and dynamic ‘social network’ that involves intricated communication between all cells. That’s the power of Iwijn’s technology. It combines high resolution in both time and space. So those two really capture the essence of ovulation.
Yi A. Ren, co-corresponding author
Assistant professor
Department of Animal Science
Cornell University, Ithaca, NY, USA.Among the findings, the atlas reveals that roughly one hour before an egg is released, the follicles undergo an additional layer of selection to determine which ones will ovulate. This acute process had never been identified before, and when it goes awry, it may lead to reduced ovulation rates and could hinder fertility. The researchers were also able to detect early differentiation markers that decide the different paths cells may take in the ovary. In effect, the atlas captures dynamic cellular and molecular control programs in both the very early and very late stages of ovulation.You have to turn that data into biological findings, look at temporal patterns, fish out specialized cell states and so on. It’s not just like a normal microscopy image where you have the image, and that’s it, you see what you see.
Iwijn De Vlaminck, co-corresponding author
Associate professor of biomedical engineering
Nancy E. and Peter C. Meinig School of Biomedical Engineering
Cornell University, Ithaca, NY, USA.Now De Vlaminck and Ren, who are both faculty with the Cornell Reproductive Sciences Center, plan to extend their collaboration into exploring fertility and ovulation problems associated with obesity and reproductive aging. In the U.S. alone, more than 10% of infertility cases are caused by ovulation failure, the researchers noted, a problem that is exacerbated by increasing obesity, and maternal age.This type of atlas provides so much more detail about where and when all the molecular changes happen in the ovary, details that were difficult to capture using other methodologies. So that may inspire new interventions that target specific molecules we identify, for example, specific genes that are important for fertility management.
Iwijn De Vlaminck.
William Heath Robinson
More detail is provided in the team's open access paper in Proceeding of the National Academy of Science (PNAS):
SignificanceI wonder how many creationist women blamed themselves when they discovered they were infertile, assuming their god had punished them for some wrong thought or transgression of cult rules, when it turns out they should have been blaming their putative designer's incompetence in designing such a hopelessly complicated, error-prone Heath Robinson machine in their ovaries.
Ovulation failure accounts for over 10% of infertility cases in women of reproductive age in the United States. There is therefore an urgent need for a better understanding of ovulation. Yet, the mechanisms of ovulation are exceptionally difficult to study given that ovulation is a highly dynamic process that operates on a time scale of minutes in small spatially confined niches within the ovary. In this manuscript, we apply spatial transcriptomics at high spatial and temporal resolution to generate a molecular atlas of cellular transitions and cell–cell interactions in the mouse ovary. This atlas provides insights into intricate processes that regulate mouse ovarian follicle maturation and ovulation with important implications for advancing therapeutic strategies in reproductive medicine.
Abstract
Ovulation is essential for reproductive success, yet the underlying cellular and molecular mechanisms are far from clear. Here, we applied high-resolution spatiotemporal transcriptomics to map out cell type– and ovulation stage–specific molecular programs as function of time during follicle maturation and ovulation in mice. Our analysis revealed dynamic molecular transitions within granulosa cell types that occur in tight coordination with mesenchymal cell proliferation. We identified molecular markers for the emerging cumulus cell fate during the preantral-to-antral transition. We describe transcriptional programs that respond rapidly to ovulation stimulation and those associated with follicle rupture, highlighting the prominent roles of apoptotic and metabolic pathways during the final stages of follicle maturation. We further report stage-specific oocyte–cumulus cell interactions and diverging molecular differentiation in follicles approaching ovulation. Collectively, this study provides insights into the cellular and molecular processes that regulate mouse ovarian follicle maturation and ovulation with important implications for advancing therapeutic strategies in reproductive medicine.
Ovulation is a complex process that involves extensive molecular, cellular, and structural changes in distinct compartments in ovarian follicles, which culminate in follicle wall rupture and release of oocytes for fertilization. Before ovulation and in response to follicle-stimulating hormone, ovarian follicles undergo a pivotal phase of maturation and transition from the preantral stage to the antral stage. During this transition, mural and cumulus granulosa cells become distinguishable by molecular features. Successful ovulation is predicated on precise coordination and interaction between diverse cell types within the ovary, both temporally and spatially. Temporally, the gene expression processes that are important for ovulation are regulated dynamically on a time scale of minutes to hours (1). Spatially, the formation of follicle rupture sites requires spatially restricted tissue remodeling between granulosa and mesenchymal cells in the surrounding stromal tissues (2). The cellular and molecular mechanisms responsible for controlling this temporal and spatial specificity of follicle rupture, however, remain poorly understood.
Single-cell RNA sequencing has been used extensively to characterize the diversity of cell types and phenotypes in the ovary (3–6). Yet, single-cell RNA sequencing requires tissue dissociation and therefore leads to the loss of spatial information regarding cellular niches, cell–cell interactions, and the interplay between morphological changes and cellular changes in tissues (7, 8). Spatial RNA sequencing has been applied recently to study the cellular organization within the ovary but at limited spatial resolution and without incorporating temporal analyses (9, 10). Here, we address these challenges by implementing spatial RNA sequencing at 10 μm spatial resolution and by profiling gene expression at eight well-defined stages preceding ovulation in mice. To induce ovulation, we used exogenous gonadotropin stimulation in immature mice, a widely used model to study the molecular and cellular mechanisms that regulate ovulation. To support our analysis, we developed analytical approaches to study cell–cell interactions in specific spatial niches within ovarian follicles. Altogether, our work leads to a highly detailed spatiotemporal atlas of ovulation in mice and provides insights into how ovarian follicle maturation and rupture are regulated in time and space.Fig 1.
Spatial transcriptomics profiling of murine ovaries undergoing hormone-induced ovulation. (A) Illustration depicting the internal components of the mouse ovary. (B) Workflow schematic outlining the steps followed in the experiment. PMSG = pregnant mare serum gonadotropin; hCG = human chorionic gonadotropin. Spatial transcriptomics was conducted on tissue sections from three untreated immature ovaries and seven ovaries undergoing hormone-controlled ovulation. (C) Tree diagram showing multilevel hierarchy of different cell types within the ovary. The first level of the tree represents the broad cell-type labels predicted for Slide-seq beads using scRNA-seq data from cycling mouse ovaries. The leaves of the tree represent the fine cell-type labels assigned using canonical markers after unsupervised clustering of bead transcriptomes in individual samples. (D) Spatial transcriptomics maps of three immature ovaries and seven representative ovaries undergoing hormone-controlled ovulation colored by fine-grained cell-type labels. (E–G) Spatial transcriptomics maps showing expression of Pgr, Ptgs2, and Ihh over eight time points before and after ovulation stimulation.
Fig 2.
Spatial and molecular differentiation of distinct granulosa cell layers emerging during the prenatal-to-antral transition. (A) Illustration showing the structural transition from preantral to antral ovarian follicles. (B) Spatial transcriptomics maps of three untreated immature ovaries colored by granulosa cell type. (C) Spatial expression of three immature untreated ovaries showing the expression of previously reported and novel gene markers for preantral, antral, atretic, and mitotic follicles. (D) Spatial transcriptomics maps of immature ovaries colored by follicle type as determined by gene expression of mural granulosa cells within the follicles. (E) Bar plot showing the proportion of cells in various cell cycle phases across different follicle types. (F) Heatmap showing differentially expressed genes in mural granulosa cells across different follicle types. (G) Spatial transcriptomics maps showing spatial organization for the expression of preantral follicle marker, Kctd14; and antral follicle marker, Inhbb. (H) Spatial transcriptomics maps showing the expression of genes specific to granulosa cells in follicles transitioning from the preantral to antral stage: Ank1, Aldh3b1, Irx3, and Cacna1a. (I) Multiplexed RNA FISH staining for preantral marker Kctd14 (yellow), antral marker Inhbb (magenta), and Ank1 gene (cyan) in an untreated immature ovary. The dotted boxes show zoomed-in images of a representative antral follicle. Images shown are representative of five biological replicates.
Mantri, Madhav; Zhang, Hanxue Hannah; Spanos, Emmanuel; Ren, Yi A.; De Vlaminck, Iwijn
A spatiotemporal molecular atlas of the ovulating mouse ovary
Proceedings of the National Academy of Sciences (PNAS) 121(5), e2317418121. DOI: 10.1073/pnas.2317418121
Copyright: © 2024 The authors.
Published by PNAS Open access.
Reprinted under a Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
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