The ovarian cellular ecosystem in polycystic ovary syndrome: from cellular heterogeneity to clinical relevance

Polycystic ovary syndrome (PCOS) is one of the most biologically heterogeneous disorders in reproductive endocrinology, extending far beyond the classical features of hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. Although substantial progress has been made in defining the endocrine, metabolic, genetic, epigenetic, and inflammatory mechanisms associated with PCOS, these processes have largely been investigated independently, and their contribution to the marked clinical and biological heterogeneity of the disorder remains incompletely understood. Recent advances in cellular-resolution biology, including single-cell and single-nucleus transcriptomics, spatial omics, and multimodal molecular profiling, together with computational systems biology, enable increasingly detailed characterization of cellular states, spatial organization, and molecular relationships within the ovarian-follicular environment. In this Review, we use the ovarian cellular ecosystem as an operational systems framework to test whether PCOS-associated abnormalities observed in individual ovarian and follicular compartments form reproducible higher-order patterns of multicellular organization. The framework is organized across five measurable domains: cellular composition and states, intercellular signaling relationships, spatial niche organization, cross-compartment molecular coupling, and systemic-ovarian coupling. Current direct human cellular-resolution evidence remains sparse and compartment-specific. Available studies support altered molecular and cellular states within selected ovarian and follicular compartments more strongly than coordinated, spatially organized, or progressive ecosystem-wide remodeling. We critically evaluate how cellular-resolution, spatial, multi-omics, computational and artificial intelligence approaches can be used to test these ecosystem-level hypotheses, explicitly distinguishing direct measurement from computational inference and identifying the validation required for functional or causal interpretation. Finally, we discuss the translational implications of this framework for biologically informed patient stratification, ecosystem-derived biomarker discovery, mechanism-informed therapeutic development, and precision reproductive endocrinology. By integrating current evidence within a falsifiable framework, this Review defines measurable features and validation criteria for testing ovarian ecosystem organization in PCOS and identifies the critical evidence gaps that future cellular-resolution, spatial, and multi-omic studies must address. This provides a practical roadmap for determining whether reproducible ovarian ecosystem states can improve biological stratification, biomarker discovery, or therapeutic target identification beyond compartment-specific abnormalities and established systemic and clinical determinants.