Beaton, B. | bioRxiv Science

bioRxiv2026

Single-cell roadmap of bovine oogenesis and somatic niche interactions during fetal ovarian development

The major events of female germline establishment, from primordial germ cell (PGC) specification to assembly of primordial follicles, occur during embryonic/fetal development. This study presents a single-cell RNA-sequencing atlas of the bovine fetal ovary at four gestational timepoints: estimated day 50, and timed pregnancies at days 70, 90, and 120, capturing the progression of PGCs through commitment, meiotic entry, and early oocyte growth. Fourteen transcriptionally distinct cell populations were identified, including stromal, epithelial, endothelial, immune, somatic support cell, and germ cell lineages. Sub-clustering of the germ cell population resolved six developmental stages (PGCs, transitioning oogonia, proliferative oogonia, committed oogonia, meiotic prophase I oogonia, and oocytes), while that of the somatic support cell compartment revealed five granulosa cell subtypes (steroidogenic, pre-granulosa 1, pre-granulosa 2, pre-granulosa 3, and epithelial cells). Trajectory analysis reconstructed the developmental path of PGCs to oocytes, with sequential activation of meiotic and oocyte-specific gene programs. Representation of all six germ cell stages at day 120 pointed to asynchronous oogenesis in the fetal ovary, which was validated and shown to be region-specific by protein immunolocalization. Intercellular signaling networks between germ cells and the somatic niche were mapped, revealing strong interactions through BMP, KIT, IGF, IGFBP, WNT, and MDK pathways with temporal specificity across gestational ages. The bovine germ cell and pre-granulosa cell subtypes demonstrate significant transcriptional parallels with similarly-staged cells from human fetal ovaries, establishing the cow as a reliable model for human germ cell and ovarian development. Collectively, these data provide a developmental roadmap for bovine oogenesis at the single-cell resolution that advances fundamental understanding of gametogenesis and informs strategies for advanced assisted reproduction.

developmental biology↗

bioRxiv2021

Inexpensive monitoring of flying insect activity and abundance using wildlife cameras

O_LIThe ability to measure flying insect activity and abundance is important for ecologists, conservationists and agronomists alike. However, existing methods are laborious and produce data with low temporal resolution (e.g. trapping and direct observation), or are expensive, technically complex, and require vehicle access to field sites (e.g. radar and lidar entomology). C_LIO_LIWe propose a method called "camfi" for long-term non-invasive monitoring of the activity and abundance of low-flying insects using images obtained from inexpensive wildlife cameras, which retail for under USD$100 and are simple to operate. We show that in certain circumstances, this method facilitates measurement of wingbeat frequency, a diagnostic parameter for species identification. To increase usefulness of our method for very large monitoring programs, we have developed and implemented a tool for automatic detection and annotation of flying insect targets based on the popular Mask R-CNN framework. This tool can be trained to detect and annotate insects in a few hours, taking advantage of transfer learning. C_LIO_LIWe demonstrate the utility of the method by measuring activity levels and wingbeat frequencies in Australian Bogong moths Agrotis infusa in the Snowy Mountains of New South Wales, and find that these moths have log-normally distributed wingbeat frequencies (mean = 49.4 Hz, std = 5.25 Hz), undertake dusk flights in large numbers, and that the intensity of their dusk flights is modulated by daily weather factors. Validation of our tool for automatic image annotation gives baseline performance metrics for comparisons with future annotation models. The tool performs well on our test set, and produces annotations which can be easily modified by hand if required. Training completed in less than 2 h on a single machine, and inference took on average 1.15 s per image on a laptop. C_LIO_LIOur method will prove invaluable for ongoing efforts to understand the behaviour and ecology of the iconic Bogong moth, and can easily be adapted to other flying insects. The method is particularly suited to studies on low-flying insects in remote areas, and is suitable for very large-scale monitoring programs, or programs with relatively low budgets. C_LI

ecology↗