This study has evaluated the performance of a multivariate statistical model to predict embryo implantation potential by processing data from the chemical fingerprinting of culture medium samples used for human embryo culture. The culture medium for 113 embryos from 55 patients undergoing ICSI was collected after embryo transfer. The samples were split into positive (nZ29) and negative (nZ84) implantation groups according their implantation outcomes (100% or 0% implantation). The samples were individually diluted and analyzed by electrospray ionization mass spectrometry (ESI-MS). The m/z ratios and relative abundances of the major ions in each spectrum were considered for partial least square discriminant analysis. Data were divided into two subsets (calibration and validation), and the models were evaluated and applied to the validation set. A total of 5987 ions were observed in the groups. The multivariate statistical model described more than 82% of the data variability. Samples of the positive group were correctly identified with 100% probability and negative samples with 70%. The culture media used for embryos that were positive or negative for successful implantation showed specific biochemical signatures that could be detected in a fast, simple, and noninvasive way by ESI-MS. To our knowledge, this is the first report that uses MS fingerprinting to predict human embryo implantation potential. This biochemical profile could help the selection of the most viable embryo, improving single-embryo transfer and thus eliminating the risk and undesirable outcomes of multiple pregnancies.

A bottom-up label-free mass spectrometric proteomic strategy was used to analyse the protein profiles of the human embryonic secretome. Culture media samples used for embryonic culture of patients undergoing intracytoplasmic sperm injection cycles were selected as a test case for this exploratory proof-of-principle study. The media were stored after embryo transfer and then pooled into positive (n = 8) and negative (n = 8) implantation groups. The absolute quantitative bottom-up technique employed a multidimensional protein identification technology based on separation by nano-ultra-high pressure chromatography and identification via tandem nano-electrospray ionization mass spectrometry with data-independent scanning in a hydrid QqTOF mass spectrometer. By applying quantitative bottom-up proteomics, unique proteins were found exclusively in both the positive- and negative-implantation groups, which suggest that competent embryos express and secrete unique biomarker proteins into the surrounding culture medium. The selective monitoring of these possible secretome biomarkers could make viable procedures using single-embryo transfer.

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Purpose The goal for the present study was to implement a technique for protein extraction and identification in human cumulus cells (CCs). Methods Forty samples of CCs were collected after ovum pick-up from patients undergoing intracytoplasmic sperm injection (ICSI). Samples were split into the blastocyst group (n = 10), including patients in which all embryos converted into blastocysts, and the non-blastocyst group (n = 10), including patients in which none of the embryos reached the blastocyst stage or the positive-pregnancy (n = 10) and negativepregnancy group (n = 10). Proteins were extracted and injected into a liquid chromatography system coupled to a mass spectrometer. The spectra were processed and used to search a database. Results There were 87 different proteins in samples from the blastocyst and non-blastocyst groups, in which 30 were exclusively expressed in the blastocyst group and 17 in the nonblastocyst group. Among the 72 proteins detected in the pregnancy groups, 19 were exclusively expressed in the positive, and 16 were exclusively expressed in the negative-pregnancy group. Conclusions CC proteomics may be useful for predicting pregnancy success and the identification of patients that should be included in extended embryo culture programs.

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