Examining Felis catus granulosa cell responses and extracellular vesicle characteristics under biomimetic gonadotropin stimulation in a microfluidic system

Introduction Granulosa cells are essential regulators of follicular development and oocyte competence, yet the effects of dynamic gonadotropin exposure on granulosa cell function and extracellular vesicle (EV)-mediated communication remain poorly understood. This study aimed to develop a microfluidic platform that more closely mimics in vivo gonadotropin patterns of natural estrus and ovulation stimulation induction cycles, and investigated domestic cat granulosa cell responses under these controlled, dynamic conditions. Methods Domestic cat granulosa cells were cultured in a microfluidic platform under dynamic gonadotropin profiles designed to replicate natural estrus and ovulation stimulation induction cycles. Cell viability and steroidogenic activity were assessed, and generated granulosa cell-derived EVs (gcEVs) were characterized by size, morphology, and tetraspanin (CD9 and CD81) expression relative to native follicular fluid EVs (ffEVs). Functional assays evaluated the effects of gcEVs produced under dynamic versus static gonadotropin treatments on gene expression in recipient granulosa cells. Result Granulosa cells maintained in-chip viability greater than 88% and steroidogenic activity with the production of progesterone across all gonadotropin treatment groups. The gcEVs exhibited size, morphology, and CD9 and CD81 expression comparable to native ffEVs. Functional assays demonstrated that gcEVs generated under dynamic versus static gonadotropin regimens differentially modulated CASP3 and STAR gene expression in recipient granulosa cells, whereas CYP19A1 expression was unaffected. Discussion These findings establish a granulosa cell-specific microfluidic model for investigating hormone-regulated EV communication under various gonadotropin exposure profiles. This platform provides a foundation for advancing our understanding of granulosa cell function and EV-mediated signaling, with potential applications in developing EV-based strategies to improve outcomes in assisted reproductive technologies.