Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche, Ancona, Italy.
9.baby, Family and Fertility Center, Via Dante, 15, Bologna, Italy.
Mol Hum Reprod. 2018 Nov 1;24(11):521-532. doi: 10.1093/molehr/gay035.
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.
STUDY DESIGN, SIZE, DURATION: 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.
PARTICIPANTS/MATERIALS, SETTING, METHODS: 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.
LIMITATIONS, REASONS FOR CAUTION: 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.
STUDY FUNDING/COMPETING INTEREST(S): The study was funded by GFI 2014 grant. The authors declare that there is no conflict of interest.
人类壁层颗粒细胞（GCs）的分子和代谢特征是否与卵母细胞命运相关？
通过对选定的生物标志物进行分析，这些标志物由 GC 傅里叶变换红外微光谱（FTIRM）分析定义，揭示了壁层 GCs 的代谢特征与相应卵母细胞命运之间的密切关系。
在 ART 中，卵母细胞的选择主要基于对其形态特征的主观观察；尽管最近进行了一些努力，但这种做法的成功率仍然不尽人意。FTIRM 是一种成熟的振动技术，最近已应用于评估人类多个实验模型中的卵母细胞质量。
研究设计、规模、持续时间：从接受 IVF 治疗的 55 名女性的单个卵泡抽吸中获得 GCs，并在知情同意的情况下进行研究。通过 FTIRM 对 GCs 进行分析，以回顾性地将其光谱特征与其伴侣卵母细胞的命运相关联。该研究于 2016 年 3 月至 2017 年 9 月进行。
参与者/材料、设置、方法：根据以下纳入标准选择患者：年龄<40 岁；不吸烟；无卵巢不孕诊断（仅输卵管、特发性和男性不孕）；月经周期规律（25-30 天），第 3 天的 FSH<10IU/I；经处理后精子总活动计数≥300.000；获得的卵母细胞数≥8 个。根据相应卵母细胞的临床结局，GCs 被回顾性分类为以下实验组：临床妊娠（CP）、受精失败（FF）、胚胎发育失败（EDF）和着床失败（IF）。所有样本均采用 FTIRM 技术进行分析。通过几种特征选择程序（因子判别分析（FDA）上的“留一法”、变量特征化方法和多元逻辑回归方法与多项逻辑模型）得出不同卵母细胞结局的光谱生物标志物特征。还应用了方差分析（ANOVA）、置换多元方差分析（permuted multivariate ANOVA）、FDA 和主成分坐标的典型分析等统计工具来验证鉴定的光谱生物标志物。
共回收了 284 个 GCs 样本，并回顾性地将其分类为 FF（N=92）、EDF（N=113）、IF（N=56）和 CP（N=23）。从属于 CP、FF、EDF 和 IF 实验组的 GCs 的光谱谱图中，通过几种特征选择程序（P<0.0001）确定了 17 个光谱生物标志物。然后通过应用多元工具来验证这些生物标志物将 GCs 样本分离到四个实验组的能力。FDA 沿着 F1 轴（62.75%的区分度）清楚地区分了能够（CP、IF 组）或不能（FF、EDF 组）发育成胚胎的卵母细胞的 GCs；F2 轴（24.14%的区分度）将产生妊娠的胚胎（CP）与着床失败的胚胎（IF）区分开来。从该分析获得的混淆矩阵（总正确性百分比=80.25%）指出，不能发育成胚胎的卵母细胞（FF、EDF）的 GCs 比能够产生可育胚胎的卵母细胞（CP、IF）的 GCs 更好地得到了特征化。ANOVA（P<0.05）分析指出：每个实验组都表现出特定的大分子特征，归因于 GCs 的不同生物学和代谢特征；这些代谢特征可能与不同的卵母细胞命运有关，但与患者特征无关，因为从同一个患者中我们获得了具有不同代谢特征的 GCs。
局限性、谨慎的原因：该研究基于一个小样本量，但提供了概念验证，即 GCs 的代谢特征与相应卵母细胞的命运相关。应该在更大的患者队列中进一步测试生成的模型，以评估这种方法的潜在预测价值。最终，应该在 RCT 中测试该方法的有效性。
FTIRM 分析人类 GCs 的首次研究证明了一种更好地理解卵泡细胞和卵母细胞之间分子串扰的方法，并为提高人类 IVF 成功率确定了潜在的光谱生物标志物。
研究资金/利益冲突：该研究由 GFI 2014 资助。作者声明没有利益冲突。
