One out of seven couples in Europe suffer from subfertility problems. Assisted reproduction including in vitro fertilization (IVF) has overcome many of the underlying infertility causes. IVF involves crossing sperm with isolated eggs to generate an embryo in vitro. Typically, 5-10 embryos are produced per cycle, and 1 or 2 will be then transferred to a recipient female patient. Despite the great advances in IVF, the number of embryos leading to a live birth is relatively low (~18%).
Often pregnancy rates are artificially increased by transferring multiple embryos to the mother. However, this procedure has the undesirable effect of multiple pregnancies, which generate remarkable obstetrical and neonatal complications, including prematurity, low weight, and it increases forty times the risk to die in early infancy.
Mothers carrying multiple fetuses frequently have repeated hospitalisations and higher rates of caesarean delivery. Therefore, identifying the embryos that are competent for implantation is a critical step in the IVF cycle. However, current methods cannot tell them apart. Time-lapse incubators allow monitoring morphological changes in the embryo development. Pre-implantation genetic testing for aneuploidy discards embryos presenting genetic aberrations. Neither of these two methods showed a significant increase in pregnancy rates in retrospective studies.
To overcome this limitation, here we propose to develop a new imaging method that will provide IVF professionals with a robust and unbiased method to identify the embryos with higher implantation potential. To achieve this goal, we will implement spectroscopy methods to measure the presence and concentration of 6 individual metabolites, taking advantage of their intrinsic fluorescence. These fluorophores have great physiological and metabolic relevance (i.e. retinol or NADH) and can be used as biomarkers, providing a comprehensive picture of the metabolic status of an embryo.
The rich multidimensional data retrieved by spectroscopy can be reduced to a simpler geometrical configuration using multivariate statistics. This will generate plots were individual embryos are easily profiled by their metabolic fingerprint. These fingerprints will be correlated to implantation scores obtained from in vitro culture models. Finally, the imaging and statistic data will be used to generate a predictive algorithm of implantation based on metabolic profiles.
The experiments consider a 12-month tight collaboration between clinicians from DEXEUS Hospital and IBEC researchers. A pipeline of experiments will lead to a proof-of-concept of a new IVF diagnostic technology. We foresee that the technology ensuing from this project will be the basis for a new medical device combining laser illumination with a novel software analysis. The overarching goal is to develop a new tool that enables IVF professionals to select competent embryos, thus increasing the implantation success rate and reducing the time to pregnancy.