Integrating Next-Generation Reproductive Organoids with Genomics, Multi-Omics and Bioengineering to Understand Human Infertility.

Abstract

Infertility affects approximately one in six individuals worldwide and is a highly heterogeneous disorder resulting from the complex interplay of genetic, epigenetic, endocrine, environmental and lifestyle factors. Although numerous genes and molecular pathways involved in female and male infertility have been identified, elucidating the functional consequences of disease-associated variants remains challenging due to the lack of relevant human experimental models. In recent years, reproductive organoids have emerged as powerful three-dimensional systems that recapitulate key structural, cellular and functional characteristics of the ovary, fallopian tube, endometrium, testis and early embryo. Here, we review the current landscape of reproductive organoid models based on a focused analysis of the literature, with particular emphasis on original studies describing their generation, characterization and applications. Beyond modeling tissue development and reproductive physiology, these models provide unique opportunities to investigate infertility-associated mechanisms, gene regulatory networks, cell-cell communication and tissue-specific responses to environmental and pharmacological stimuli. Single-cell and spatial transcriptomics, multi-omics, CRISPR/Cas9 genome editing, artificial intelligence and bioengineering technologies, including organ-on-chip systems, are expanding their potential as next-generation platforms for functional genomics, disease modeling, biomarker discovery and therapeutic screening. However, current reproductive organoids remain simplified representations of native tissues, with limitations in physiological maturity, reproducibility and standardization, while their clinical predictive value remains to be established. Overall, by linking genomic variation with molecular regulation, cellular phenotypes and tissue organization, reproductive organoids represent promising preclinical platforms for understanding human infertility and may ultimately contribute to the development of precision reproductive medicine.