O-24: Single Oocyte Secretoma Mapping by NMR-Metabolomics Technology: A Non-Invasive Strategy to Select The Best Oocytes to Fertilize Avoiding Supernumerary Embryos and Increasing Take- Home-Baby-Rate after IVF

Background Current strategies based on random selection of MII-oocytes to fertilize appear unsatisfactory in selecting the best number and the most vital oocytes to fertilize especially in poor responder women in which “chronological age” does not mismatch with “biological age”. The metabolomics- profiling approach, evaluating the final products of cell regulatory process (genome/transcriptome/proteome), may represent a non-invasive, cost-effective tool to improve oocyte selection. The aim of the project was to increase the take-home-baby-rate after ARTs by generating an algorithmic and predictive model able to discriminate real time which and how many oocyte should be fertilized to reduce the number of frozen supernumerary embryos, to avoid multiple embryotransfer policy and to optimize the cost-effectiveness of IVF cycle by increasing the cumulative pregnancy rate. MaterialsAndMethods We performed 35 ART cycles by standard GnRH antagonist flexible short-protocol using rFSH plus rLH for controlled ovarian stimulation and rhCG for ovulation induction in women expected poor responders since biological age does not mismatch with chronological age. Once all oocytes were retrieved, they were denuded by standard procedure with hyaluronidase solution and incubated in individual drops [containing N-2-hydroxyethylpiperazine-N0- 2-ethanesulfonic acid-(HEPES)-buffered human tubal fluid (HTF)] at 37°C in 5%-CO2, 5%-O2, 90%-N2 and 98% humidity for 4-hours prior to ICSI. After incubation, oocyte medium(5μl) was collected from all drops and analyzed by Nuclear- Magnetic-Resonance-Spectroscopy after a standard preparation of samples. Biostatistics evaluation of obtained spectral analysis of oocytes secretoma was performed by univariate and multivariate analysis of Principal Components Analysis and Partial Least Square fitting. Statistical analysis of general features, clinical parameters and biostatistics information was performed by SPSS software. Results Metabolomic information from 128 oocytes secretoma showed strong significant differences in metabolites of glucoma (glucose, pyruvate, lactate) lipidoma (endocannabinoids, lysophosphatidic acid, and prostaglandins) and proteoma (histidine, tyrosine, alanine, creatine) between fertilized oocytes from those not fertilized. In addition, significant differences in metabolites were also collected from the comparison of fertilized oocytes secretome between ones generating blastocyst and evolutive pregnancy and those generating blastocyst and non-evolutive pregnancy. Conclusion Metabolic screening of collected oocytes may allow biologist and clinicians to discriminate between the pool of retrieved oocytes those with high fertilization rate, potentially generating high viable embryos and subsequently ongoing pregnancy. The developing technologies based on predictive metabolic screening may improve the success of single embryo-transfer policy (avoiding multiple pregnancy and increasing the cumulative pregnancy rate) starting from the single oocyte fertilization (minimizing the embryos frozen policy and increasing the gamete frozen policy). These innovations may finally result in cost-effectiveness improvement of IVF treatments in poor responders women (increasing the overall take home baby rate and reducing costs).