Raman Spectrum of Follicular Fluid: A Potential Biomarker for Oocyte Developmental Competence in Polycystic Ovary Syndrome - Huang, Xin; Hong, Ling; Wu, Yuanyuan; Chen, Miaoxin; Kong, Pengcheng; Ruan, Jingling; Teng, Xiaoming; Wei, Zhiyun (Frontiers in Cell and Developmental Biology, 2021)
Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in reproductive women where abnormal folliculogenesis is considered as a common characteristic. Our aim is to evaluate the potential of follicular fluid (FF) Raman spectra to predict oocyte development and pregnancy outcome, so as to prioritize the best promising oocyte for implantation, reducing both physiological and economical burdens of PCOS patients. In addition, the altered metabolic profiles will be identified to explore the aetiology and pathobiology of PCOS. In this study, follicular fluid samples obtained from 150 PCOS and 150 non-PCOS women were measured with Raman spectroscopy. Individual Raman spectrum was analyzed to find biologic components contributing to the occurrence of PCOS. More importantly, the Raman spectra of follicular fluid from the 150 PCOS patients were analyzed via machine-learning algorithms to evaluate their predictive value for oocyte development potential and clinical pregnancy. Mean-centered Raman spectra and principal component analysis showed global differences in the footprints of follicular fluid between PCOS and non-PCOS women. Two Raman zones (993-1,165 cm-1 and 1,439-1,678cm-1) were identified for describing the largest variances between the two groups, with the former higher and the latter lower in PCOS FF. The tentative assignments of corresponding Raman bands included phenylalanine and β -carotene. Moreover, it was found that FF, in which oocytes would develop into high-quality blastocysts and obtain high clinical pregnancy rate, were detected with lower quantification of the integration at 993-1,165 cm-1 and higher quantification of the integration at 1,439-1,678 cm-1 in PCOS. In addition, based on Raman spectra of PCOS FF, the machine-learning algorithms via the fully connected artificial neural network (ANN) achieved the overall accuracies of 90% and 74% in correctly assigning oocyte developmental potential and clinical pregnancy, respectively. The study suggests that the PCOS displays unique metabolic profiles in follicular fluid which could be detected by Raman spectroscopy. Specific bands in Raman spectra have the biomarker potential to predict the oocyte development and pregnancy outcome for PCOS patients. Importantly, these data may provide some valuable biochemical information and metabolic signatures that will help us to understand the abnormal follicular development in PCOS.