Metabolomic profiling of human embryo during pre-implantation in vitro fertilization non-invasive approach

In vitro fertilization (IVF) is a standard protocol used to treat infertility. However, the probability of successful embryo implantation during IVF is very low. Most of the IVF clinics depend on morphological scoring by embryologists to select high-quality embryos capable of implantation. But morphological scoring has only around 30% successful pregnancy rate. In this study, I investigated the potential of a new embryo scoring method based on measuring the change in culture media's metabolomic profiles. I analyzed 71 culture media samples with known pregnancy outcomes from two different culture media by ultraperformance liquid chromatography (UPLC) coupled with ultrahigh-resolution and accuracy mass spectrometer. I used a newly developed on-the-fly dynamic data acquisition technique to increase the percentage of metabolite compounds with MS2 fragmentation spectra. To identify potential metabolomic pregnancy biomarkers, we used a combination of statistical analysis techniques like principal component analysis (PCA), differential analysis (volcano plots), and trend charts. We used Molecular Formula Calculator software, ChemSpider, and mzCloud databases to assign the molecule formula and chemical structure for the detected significant biomarkers. Also, we applied in-silico fragmentation and FISh scoring to validate the chemical structures of the identified biomarkers. Using PCA, we did not find any apparent clustering for pregnant or non-pregnant samples, but we could locate a few outliers' spectra. However, with volcano plots, we were able to identify a set of up-regulated biomarkers that are associated with non-pregnancy and down-regulated biomarkers that are associated with pregnancy in both media. Utilizing the KEGG and Metabolika databases, we recognized two possible metabolomics pathways. This study can improve selecting viable embryos, which will lead to an increase in the success rate of IVF. It will also provide a better understanding of human embryos' metabolomic biochemical pathways during the preimplantation stage.