149 Bovine embryo selection can be improved by the characteristics of secreted extracellular vesicles

Extracellular vesicles (EV) secreted by blastocysts might be relevant to predict competence of embryos produced in vitro. The aim of this study was to develop a model to select competent embryos that combines blastocyst morphokinetics data and morphological parameters of EV secreted during blastulation (Days 5-7.5). Embryos were cultured in groups up to Day 5; morulae were selected and individually cultured in SOFaa depleted of EV until Day 7.5 after IVF. Embryo competence was determined by in vitro post-hatching development up to Day 11. A retrospective classification of blastocyst and culture media was performed based on blastulation time [early (EB) or late (LB)] and competence at Day 11 [competent (C) or non-competent (NC)]. The EV were isolated from culture media of individual embryos, their properties determined by nanoparticle tracking analysis. The model was based on a binary logistic regression to describe the dichotomous-dependent variable of the blastocyst (C=1 and NC=0). A set of independent variables of blastocyst morphokinetics (blastulation time, blastocyst stage, blastocyst quality and blastocyst diameter at Day 7.5) and EV morphological parameters [mean size (ME), mode size (MO) and particle concentration (CO)] were analysed with multiple regression. The analysis generated the coefficients and their standard errors and significance level of an equation to calculate a probability, where values between 0.5 and 1 predict competent embryos. To verify the predictive power of the algorithm, the following indicators were used: the receiver operating characteristic with the determination of area under the curve, percentage correct predictions, and Omnibus tests. Statistical significance was determined at the P<0.05 level. A rough guide for classifying the accuracy of a predictive model is as follows: 0.9 to 1=excellent, 0.8 to 0.9=good, 0.7 to 0.8=fair, 0.6 to 0.7=poor, 0.5 to 0.6=fail. A total of 254 embryos were used in this study; from them, 73 were classified in C-EB, 68 in NC-EB, 61 in C-LB and 52 in NC-LB. Initially, all independent variables were analysed in model 1; the most significant predictors associated with embryo competence were blastocyst stage, blastocyst quality, blastocyst diameter, ME and CO (P<0.05). In model 2 no significant variables were excluded (blastulation time and MO). The statistical test of predictive power indicates that models 1 and 2 achieved a receiver operating characteristic-area under the curve of 0.853 (95% confidence interval, 0.806-0.9; P<0.001) and correct predictions of 77.2 and 77.6%, respectively. When EV characteristics were excluded and the model considers only variables from the embryo, the receiver operating characteristic-area under the curve value was 0.714 (95% confidence interval, 0.651-0.777; P<0.001) and correct predictions was reduced to 65.4. Model 2 was consider the most appropriate from the practical point of view because it avoids disturbing embryo culture during blastulation. The results indicate that incorporating EV properties increases accuracy of embryo selection, supporting the possibility to improve conventional methods by combining blastocyst morphology and characteristics of EV obtained by nanoparticle tracking analysis. This work was supported by Fondecyt 1170310.