Does the molecular and metabolic profile of human granulosa cells correlate with oocyte fate? New insights by Fourier transform infrared microspectroscopy analysis.

Abstract

Does the molecular and metabolic profile of human mural granulosa cells (GCs) correlate with oocyte fate? A close relation between the metabolic profile of mural GCs and the fate of the corresponding oocyte was revealed by the analysis of selected biomarkers defined by GC Fourier transform infrared microspectroscopy (FTIRM) analysis. In ART, oocyte selection is mainly based on the subjective observation of its morphological features; despite recent efforts, the success rate of this practice is still unsatisfactory. FTIRM is a well-established vibrational technique recently applied to evaluate oocytes quality in several experimental models, including human. GCs retrieved from single-follicle aspirates were obtained with informed consent from 55 women undergoing controlled ovarian stimulation for IVF treatment. GCs were analysed by FTIRM to retrospectively correlate their spectral features with the fate of the companion oocytes. The study has been conducted between March 2016 and September 2017. Patients were selected according to the following inclusion criteria: age <40 years; non-smokers; no ovarian infertility diagnosis (only tubal, idiopathic and male infertility); regular ovulatory menstrual cycles (25-30 days) with FSH < 10 IU/I on Day 3 of the menstrual cycle; sperm sample with a total motility count after treatment ≥300.000; number of retrieved oocytes ≥8. Based on the clinical outcome of the corresponding oocyte, GCs were retrospectively classified into the following experimental groups: clinical pregnancy (CP), fertilization failure (FF), embryo development failure (EDF) and implantation failure (IF). All samples were analysed by the FTIRM technique. The spectral biomarker signature of different oocyte fates was derived by several feature selection procedures ('Leave-one-out' method on factorial discriminant analysis (FDA), variable characterization method and logistic regression method with the multinomial Logit model). ANOVA, permutational multivariate ANOVA, FDA and canonical analysis of principal co-ordinates statistical tools were also applied to validate the identified spectral biomarkers. In total, 284 GCs samples were retrieved and retrospectively classified as FF: (N = 92), EDF (N = 113), IF (N = 56) and CP (N = 23). From the spectral profiles of GCs belonging to CP, FF, EDF and IF experimental groups, 17 spectral biomarkers, were identified by several feature selection procedures (P < 0.0001). These biomarkers were then validated by applying multivariate tools, to evaluate their ability to segregate GCs samples into the four experimental groups. FDA showed a clear separation along the F1-axis (62.75% of discrimination) between GCs from oocytes able (CP, IF groups) or not (FF, EDF groups) to develop into embryos; the F2-axis (24.14% of discrimination) segregated the embryos that gave pregnancy (CP) from those that failed implantation (IF). The confusion matrix (total percentage of correctness = 80.25%) obtained from this analysis pinpointed that GCs from oocytes unable to develop into embryos (FF, EDF) were better characterized than those from oocytes able to give viable embryos (CP, IF). ANOVA (P < 0.05) analysis pinpointed that: each experimental group showed specific macromolecular traits, ascribable to different biological and metabolic characteristics of GCs; these metabolic features were likely associated with different oocytes fates, but not to patient characteristics, since from the same patient we obtained GCs with different metabolic profiles. The study is based on a small sample size but provides proof of concept that the GCs' metabolic profile is associated with the companion oocyte fate. The generated model should be further tested on a larger cohort of patients, classified in a similar manner, to assess the potential predictive value of this approach. Ultimately, validity of the proposed approach should be tested in a RCT. For the first time, the FTIRM analysis of human GCs has demonstrated an approach to better understand the molecular crosstalk between follicular cells and oocytes and has identified potential spectral biomarkers for improving human IVF success rate. The study was funded by GFI 2014 grant. The authors declare that there is no conflict of interest.