We present here a new metabolomic methodology to predict embryo implantation ability in in vitro fertilization (IVF). In the present study we have included a total of 23 patients scheduled for IVF. Embryos were selected to be transferred by using morphological criteria on day 3 of in vitro culture. The relative amino acid concentrations in the embryo culture media were analyzed by HPLC-MS and HPLC-MS/MS. 1 H NMR metabolomic profiles were also obtained for the embryo culture media. Chemometric models were performed with SIMCA (soft independent modeling of class analogy) for samples from both, non-pregnancy and pregnancy cycles. The metabolic differences between the embryos, with pregnancy and nonpregnancy outcome, can be correlated with the relative amino acid concentrations and with 1 H NMR profiles. We used interval partial least square (iPLS) in order to identify the higher correlation between regions in the 1 H NMR spectra and the embryo implantation capability. The 1 H NMR regions with higher correlation are between 1.2 and 0.5 ppm, that included the signals for cholesterol backbone -C(18)H 3 , -CH 3 and CH 2 groups of triglycerides, cholesterol compounds and phospholipids. Our results can allow building a quick, non invasive, useful and feasible chemometric models in order to identify embryos with a high pregnancy rate and embryos unable to achieve successful pregnancies.

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The search of metabolites which are present in biological samples and the comparison between different samples allow the construction of certain biochemical patterns. The mass spectrometry (MS) methodology applied to the analysis of biological samples makes it possible for the identification of many metabolites. Each obtained signal (m/z) is characteristic of a particular metabolite. However, the mass data (m/z) interpretation is difficult because of the large amount of information that they contain. In this work, we present a relatively simple tool that allows us to deal with the whole of the mass information from the chemometric analysis. The statistical analysis is a key stage in order to identify the metabolites involved in a particular biochemical pattern. We transformed the mass data matrix in a vector. By having the data as a vector, it was possible to keep all the information and also avoid the signals overlapping, which is the major problem when the total ion chromatogram (TIC) is obtained. In the approach proposed here, the mass data (m/z) matrix was split in 100 different TIC in order to avoid the signal overlapping. The 100 chromatograms were concatenated in a vector. This vector, which can be plotted as a continuous (2D pseudospectrum), greatly simplifies for one to understand the subsequent dimensional multivariate analysis. To validate the method, 19 samples from two human embryos culture medium were analyzed by high-pressure liquid chromatography-mass spectrometry (HPLC-MS). Our methodology would be applied to the obtained raw data. Later on, a multivariate analysis was conducted using a robust principal components analysis interval (robPCA) and interval partial least squares algorithm (iPLS). The results obtained allow one to differentiate the two sample populations undoubtedly, although their composition was similar.