Equine maternal aging affects the metabolomic profile of oocytes and follicular cells during different maturation time points.

Introduction: Oocyte quality and fertility decline with advanced maternal age. During maturation within the ovarian follicle, the oocyte relies on the associated somatic cells, specifically cumulus and granulosa cells, to acquire essential components for developmental capacity. Methods: A nontargeted metabolomics approach was used to investigate the effects of mare age on different cell types within the dominant, follicular-phase follicle at three time points during maturation. Metabolomic analyses from single oocytes and associated cumulus and granulosa cells allowed correlations of metabolite abundance among cell types. Results and Discussion: Overall, many of the age-related changes in metabolite abundance point to Impaired mitochondrial metabolic function and oxidative stress in oocytes and follicular cells. Supporting findings include a higher abundance of glutamic acid and triglycerides and lower abundance of ceramides in oocytes and somatic follicular cells from old than young mares. Lower abundance of alanine in all follicular cell types from old mares, suggests limited anaerobic energy metabolism. The results also indicate impaired transfer of carbohydrate and free fatty acid substrates from cumulus cells to the oocytes of old mares, potentially related to disruption of transzonal projections between the cell types. The identification of age-associated alterations in the abundance of specific metabolites and their correlations among cells contribute to our understanding of follicular dysfunction with maternal aging.

Maturation and culture affect the metabolomic profile of oocytes and follicular cells in young and old mares.

Introduction: Oocytes and follicular somatic cells within the ovarian follicle are altered during maturation and after exposure to culture in vitro. In the present study, we used a nontargeted metabolomics approach to assess changes in oocytes, cumulus cells, and granulosa cells from dominant, follicular-phase follicles in young and old mares. Methods: Samples were collected at three stages associated with oocyte maturation: (1) GV, germinal vesicle stage, prior to the induction of follicle/oocyte maturation in vivo; (2) MI, metaphase I, maturing, collected 24 h after induction of maturation in vivo; and (3) MIIC, metaphase II, mature with collection 24 h after induction of maturation in vivo plus 18 h of culture in vitro. Samples were analyzed using gas and liquid chromatography coupled to mass spectrometry only when all three stages of a specific cell type were obtained from the same mare. Results and Discussion: Significant differences in metabolite abundance were most often associated with MIIC, with some of the differences appearing to be linked to the final stage of maturation and others to exposure to culture medium. While differences occurred for many metabolite groups, some of the most notable were detected for energy and lipid metabolism and amino acid abundance. The study demonstrated that metabolomics has potential to aid in optimizing culture methods and evaluating cell culture additives to support differences in COCs associated with maternal factors.

RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data.

Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature-feature relationships are determined simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatographic-spectrometric platform or feature-finding software.