Cavalera Federica, Zanoni Mario, Merico Valeria, Bui Thi Thu Hien, Belli Martina, Fassina Lorenzo, Garagna Silvia, Zuccotti Maurizio
Laboratorio di Biologia dello Sviluppo, Dipartimento di Biologia e Biotecnologie "Lazzaro Spallanzani", University of Pavia.
Laboratorio di Biologia dello Sviluppo, Dipartimento di Biologia e Biotecnologie "Lazzaro Spallanzani", University of Pavia; Institute of Biotechnology, University of Helsinki.
J Vis Exp. 2018 Mar 3(133):56668. doi: 10.3791/56668.
Infertility clinics would benefit from the ability to select developmentally competent vs. incompetent oocytes using non-invasive procedures, thus improving the overall pregnancy outcome. We recently developed a classification method based on microscopic live observations of mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage, followed by the analysis of the cytoplasmic movements occurring during this time-lapse period. Here, we present detailed protocols of this procedure. Oocytes are isolated from fully-grown antral follicles and cultured for 15 h inside a microscope equipped for time-lapse analysis at 37 °C and 5% CO2. Pictures are taken at 8 min intervals. The images are analyzed using the Particle Image Velocimetry (PIV) method that calculates, for each oocyte, the profile of Cytoplasmic Movement Velocities (CMVs) occurring throughout the culture period. Finally, the CMVs of each single oocyte are fed through a mathematical classification tool (Feed-forward Artificial Neural Network, FANN), which predicts the probability of a gamete to be developmentally competent or incompetent with an accuracy of 91.03%. This protocol, set up for the mouse, could now be tested on oocytes of other species, including humans.
不孕不育诊所若能通过非侵入性程序筛选出发育能力正常与发育能力异常的卵母细胞，将从中受益，从而改善整体妊娠结局。我们最近开发了一种分类方法，该方法基于对小鼠卵母细胞从生发泡（GV）期到中期II期体外成熟过程的显微实时观察，随后分析在此延时期间发生的细胞质运动。在此，我们展示该程序的详细方案。从完全成熟的窦状卵泡中分离出卵母细胞，并在配备延时分析功能的显微镜下于37°C和5%二氧化碳环境中培养15小时。每隔8分钟拍摄一次照片。使用粒子图像测速（PIV）方法分析图像，该方法为每个卵母细胞计算整个培养期间发生的细胞质运动速度（CMV）曲线。最后，将每个单个卵母细胞的CMV输入一个数学分类工具（前馈人工神经网络，FANN），该工具预测配子发育能力正常或异常的概率，准确率为91.03%。这个为小鼠建立的方案现在可以在包括人类在内的其他物种的卵母细胞上进行测试。
