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=Volume 14 - 2023

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 relatively common condition estimated to be a contributory factor in as many as 50% of couples experiencing problems with conception. It is also a key catalyst contributing to increased uptake of assisted reproductive technologies. Traditionally, andrological practice has relied on the use of ostensibly descriptive criteria whereby a male’s fertility status has been defined on the basis of the number of motile, morphologically normal spermatozoa present in their ejaculate. Notwithstanding the widespread adoption of such measures, we have come to appreciate their limitations and that fertility is more accurately predicted on the basis of sperm quality. This realization poses the fundamental question of what constitutes a high quality, fertilization competent spermatozoon. Here, we consider recent advances in our understanding of the mechanistic basis of sperm function that are driving innovations in our ability 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.