Blastocoel fluid RNA predicts pregnancy outcome in assisted reproduction
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Background and objective
Assessing embryo quality objectively and with greater precision remains a key challenge in assisted reproductive technologies (ART). RNA molecules excreted by the embryo are detectable in embryo spent media (ESM) and blastocoel fluid (BF). Their potential for predicting pregnancy outcome is however unknown. The aim of the study was to determine whether embryo excreted RNA can serve as biomarkers to predict pregnancy outcome in ART.
Material and methods
the primary outcome of this multicenter study was to develop a prediction model for implantation outcome based on mRNA molecules in ESM and BF. We included 255 couples undergoing ART with single embryo transfers between 2018 and 2019 in four IVF clinics in Sweden. ESM and BF were collected from individually cultured embryos. RNA extraction was performed using our novel protocol developed for ESM and BF. To identify the mRNA signature, each sample underwent mRNA sequencing (Smart-seq3), followed by differential gene expression analysis. A machine learning (ML) approach was applied with the mRNA genes in BF as input data to create a prediction model for pregnancy results measured as a positive or negative urine human chorionic gonadotropin (hCG) test result 14 days after embryo transfer.
Results
After pre-processing and quality controls, we included ESM from 105 cleavage stage and 230 blastocyst stage embryos and BF from 75 blastocyst stage embryos in the analysis. In total we identified 345 and 1132 mRNA genes in ESM from cleavage and blastocyst embryos respectively and 1157 mRNA genes in BF. There was a significant difference in RNA abundancy of 108 mRNA genes (p-value<0.05; fold change +2/-2) between hCG positive and negative embryos in BF but not in ESM. Consequently, the mRNA data from BF was selected as input data to train different ML models to predict pregnancy result after embryo transfer. The most optimal ML model combined L2 Regularized Logistic Regression with differentially abundant genes as feature and principal component analysis for preprocessing. The ML model predicted pregnancy result with a sensitivity of 83% and a specificity of 59% (AUC of 76% and accuracy of 69%). Testing of the model in an independent subset of 9 samples, not included in the training dataset, demonstrated a sensitivity of 60%, specificity of 75%, and accuracy of 67% in prediction of pregnancy result.
Conclusion
For the first time, we demonstrate that RNA molecules can be comprehensively mapped in individual ESM and BF samples using a highly sensitive RNA isolation and sequencing protocol. Moreover, mRNA in BF can be used to predict pregnancy outcome of individual embryos in ART using a ML approach. Our novel prediction model has the potential to enhance embryo selection, thereby improving the success rate of ART treatment. In the future, larger clinical trials are needed to validate the robustness of the method and compare it against standard care.
