Laboratory of Theriogenology, Graduate School of Veterinary Sciences, Osaka Metropolitan University, Izumisano, 598-8531, Osaka, Japan.
Laboratory of Theriogenology, Graduate School of Veterinary Sciences, Osaka Metropolitan University, Izumisano, 598-8531, Osaka, Japan.
Theriogenology. 2023 Oct 1;209:235-242. doi: 10.1016/j.theriogenology.2023.07.007. Epub 2023 Jul 8.
Nuclear maturation is an essential process in which oocytes acquire the competence to develop further. However, the time required for nuclear maturation during IVM varies among oocytes. Therefore, predicting nuclear maturation speed (NMS) could help identify the optimal timing for IVF and maximize the developmental competence of each oocyte. This study aimed to establish machine learning-based prediction models for NMS using non-invasive indicators during the individual IVM of Japanese Black (JB) beef heifer oocytes. We collected ovaries from abattoirs and aspirated cumulus-oocyte complexes (COCs) from follicles with diameters ranging between 2 and 8 mm. The COCs were matured individually for 18 h, and photographs of each COC were taken at the beginning and every 3 h from 12 h to the end of maturation. After IVM culture, we denuded COCs and stained oocytes to confirm the progression of meiosis. Only oocytes that reached the metaphase II (MII) stage were considered to have a fast NMS. Morphological features, including COC area, cumulus expansion ratio, expansion rate per hour, and expansion pattern, were extracted from the recorded photos and applied to develop prediction models for NMS using machine learning algorithms. The MII rates of oocytes with fast- and slow-predicted NMS differed when the decision tree (DT) and random forest (RF) models were employed (P < 0.05). To evaluate the relationship between predicted NMS by DT and RF models and fertilization dynamics during individual IVF, sperm penetration and pronuclear formation were evaluated at 3, 6, 9, and 12 h after IVF start, following 24 h of IVM. The association between predicted NMS and embryo development was investigated by performing IVC for seven days using microwell culture dishes after 24 h of IVM and 6 h of IVF. Predicted NMS did not show a significant association with fertilization dynamics. However, oocytes with fast-predicted NMS by the RF model exhibited a tendency towards a higher cleavage rate 48 h after IVF start (P = 0.08); no other relationship was found between predicted NMS and embryo development. These findings demonstrate the feasibility of using non-invasive indicators during IVM to develop prediction models for NMS of JB beef heifer oocytes. Although the effect of predicted NMS on embryo development remains unclear, customized treatments based on NMS predictions have the potential to improve the efficiency of in vitro embryo production following individual IVM culture.
核成熟是卵母细胞获得进一步发育能力的必要过程。然而，IVM 期间核成熟所需的时间因卵母细胞而异。因此，预测核成熟速度（NMS）可以帮助确定最佳的 IVF 时间，并最大限度地提高每个卵母细胞的发育能力。本研究旨在使用日本黑（JB）肉牛卵母细胞的个体 IVM 期间的非侵入性指标，建立基于机器学习的 NMS 预测模型。我们从屠宰场收集卵巢，并从直径在 2 到 8 毫米之间的卵泡中抽吸卵丘-卵母细胞复合物（COC）。将 COC 单独培养 18 小时，并在 12 小时至成熟结束时每 3 小时从 COC 开始拍摄一张照片。IVM 培养后，我们剥去 COC 并对卵母细胞进行染色，以确认减数分裂的进展。只有达到中期 II（MII）阶段的卵母细胞才被认为具有快速的 NMS。从记录的照片中提取 COC 面积、卵丘扩张率、每小时扩张率和扩张模式等形态特征，并应用机器学习算法开发 NMS 预测模型。当使用决策树（DT）和随机森林（RF）模型时，快速和慢速预测 NMS 的卵母细胞的 MII 率不同（P<0.05）。为了评估 DT 和 RF 模型预测的 NMS 与个体 IVF 期间受精动力学之间的关系，在 IVM 后 24 小时进行 IVF 开始后 3、6、9 和 12 小时评估精子穿透和原核形成。通过在 IVM 后 24 小时和 IVF 后 6 小时进行微滴培养盘的 7 天 IVC，研究预测的 NMS 与胚胎发育的关系。预测的 NMS 与受精动力学之间没有显著的关联。然而，RF 模型预测的 NMS 较快的卵母细胞在 IVF 开始后 48 小时有较高的卵裂率趋势（P=0.08）；在胚胎发育方面没有发现其他关系。这些发现表明，使用 IVM 期间的非侵入性指标开发 JB 肉牛卵母细胞的 NMS 预测模型是可行的。尽管预测的 NMS 对胚胎发育的影响尚不清楚，但基于 NMS 预测的定制治疗有可能提高个体 IVM 培养后的体外胚胎生产效率。
