Clues to non-invasive implantation window monitoring: Isolation and characterisation of endometrial exosomes

Abstract

Despite the significant advances in the last decades, low implantation rate per transferred embryo still remains a major concern in assisted reproductive techniques, highlighting a need to better characterize endometrial receptivity also by mean of specific biomarkers. Based on physiology and on the intimate contact with endometrium as the tissue of interest, in this study we developed and validated an optimized protocol that uses extracellular vesicles (EVs) recovered from uterine flushings and from a cervical brush, the latter never used until now as an EVs source, as surrogates for endometrial biopsies. This method combines the safety of sampling with the ability to study the expression profile across the uterine cycle. We have compared the yield and composition of EVs recovered from different biofluids samples and fractions thereof, opting for chemical precipitation as the EV isolation procedure, assuring the highest yield without introducing any bias in specific EV recovery. Moreover, collected EVs, in particular exosome-like vesicles, express putative endometrial markers, such as glycodelin A and receptors for estrogen and progesterone, thus confirming their endometrial origin. We also identified uterine flushing EVs, in particular those recovered from its mucous fraction, as the richest source of endometrial transcripts, likely correlated to cellular (epithelial) origin of these vesicles. Finally, our pilot quantitative assessment of three endometrial gene profiles, in samples collected at different time points along the luteal phase, revealed the fluctuations apparently recapitulating gene expression variability prior reported during the menstrual cycle. Unlike tissue biopsy that is subjected to inter-and intra-sample differences, our data suggest that EVs from liquid biopsies (from uterine flushings and a cervical brush) obtained through less-invasive procedures, can be substrate to detect and track the tissue representative expression profiles, better depicting the total endometrium complexity.