METABOLOMIC PROFILING OF HUMAN EMBRYO DURING PRE-IMPLANTATION IN VITRO FERTILIZATION NON-INVASIVE APPROACH
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Abstract
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
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understanding of human embryos' metabolomic biochemical pathways during the preimplantation stage.