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.

Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues.

Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance of three commercial iST platforms on serial sections from tissue microarrays (TMAs) containing 23 tumor and normal tissue types for both relative technical and biological performance. On matched genes, we found that 10x Xenium shows higher transcript counts per gene without sacrificing specificity, but that all three platforms concord to orthogonal RNA-seq datasets and can perform spatially resolved cell typing, albeit with different false discovery rates, cell segmentation error frequencies, and with varying degrees of sub-clustering for downstream biological analyses. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field.

Viruses and Metabolism: The Effects of Viral Infections and Viral Insulins on Host Metabolism.

Over the past decades, there have been tremendous efforts to understand the cross-talk between viruses and host metabolism. Several studies have elucidated the mechanisms through which viral infections manipulate metabolic pathways including glucose, fatty acid, protein, and nucleotide metabolism. These pathways are evolutionarily conserved across the tree of life and extremely important for the host's nutrient utilization and energy production. In this review, we focus on host glucose, glutamine, and fatty acid metabolism and highlight the pathways manipulated by the different classes of viruses to increase their replication. We also explore a new system of viral hormones in which viruses mimic host hormones to manipulate the host endocrine system. We discuss viral insulin/IGF-1-like peptides and their potential effects on host metabolism. Together, these pathogenesis mechanisms targeting cellular signaling pathways create a multidimensional network of interactions between host and viral proteins. Defining and better understanding these mechanisms will help us to develop new therapeutic tools to prevent and treat viral infections.