AUTHOR=Nixon Brett , Schjenken John E. , Burke Nathan D. , Skerrett-Byrne David A. , Hart Hanah M. , De Iuliis Geoffry N. , Martin Jacinta H. , Lord Tessa , Bromfield Elizabeth G. TITLE=New horizons in human sperm selection for assisted reproduction JOURNAL=Frontiers in Endocrinology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1145533 DOI=10.3389/fendo.2023.1145533 ISSN=1664-2392 ABSTRACT=

Male infertility is a commonly encountered pathology that is estimated to be a contributory factor in approximately 50% of couples seeking recourse to assisted reproductive technologies. Upon clinical presentation, such males are commonly subjected to conventional diagnostic andrological practices that rely on descriptive criteria to define their fertility based on the number of morphologically normal, motile spermatozoa encountered within their ejaculate. Despite the virtual ubiquitous adoption of such diagnostic practices, they are not without their limitations and accordingly, there is now increasing awareness of the importance of assessing sperm quality in order to more accurately predict a male’s fertility status. This realization raises the important question of which characteristics signify a high-quality, fertilization competent sperm cell. In this review, we reflect on recent advances in our mechanistic understanding of sperm biology and function, which are contributing to a growing armory of innovative approaches to diagnose and treat male infertility. In particular we review progress toward the implementation of precision medicine; the robust clinical adoption of which in the setting of fertility, currently lags well behind that of other fields of medicine. Despite this, research shows that the application of advanced technology platforms such as whole exome sequencing and proteomic analyses hold considerable promise in optimizing outcomes for the management of male infertility by uncovering and expanding our inventory of candidate infertility biomarkers, as well as those associated with recurrent pregnancy loss. Similarly, the development of advanced imaging technologies in tandem with machine learning artificial intelligence are poised to disrupt the fertility care paradigm by advancing our understanding of the molecular and biological causes of infertility to provide novel avenues for future diagnostics and treatments.