Biology of Reproduction Group, National Wildlife Research Institute (IREC) UCLM-CSIC-JCCM, 02071, Albacete, Spain.
Theriogenology. 2012 May;77(8):1661-72. doi: 10.1016/j.theriogenology.2011.12.011. Epub 2012 Feb 14.
Using Iberian red deer as a model, this study presents a supervised learning method, the Support Vector Machines (SVM), to characterize sperm population structure related with freezability. Male freezability was assessed by evaluating motility, membrane status and mitochondrial membrane potential of sperm after a freezing-thawing procedure. The SVM model was generated using sperm motility information captured by computer-assisted sperm analysis (CASA) from thawed semen, belonging to six stags with marked differences on their freezability. A total of 1369 sperm tracks were recorded for seven kinematic parameters and assigned to four motility patterns based on them: weak motile, progressive, transitional and hyperactivated-like. Then, these data were split in two sets: the training set, used to train the SVM model, and the testing set, used to examine how the SVM method and three other unsupervised methods, a non-hierarchical, a hierarchical and a multistep clustering procedures, performed the sperm classification into subpopulations. The SVM was revealed as the most accurate method in the characterization of sperm subpopulations, showing all the sperm subpopulations obtained in this way high significant correlations with those sperm parameters used to characterize freezability of males. Given its superiority, the SVM method was used to characterize the sperm motile subpopulations in Iberian red deer. Sperm motile data from frozen-thawed semen belonging to 25 stags were recorded and loaded into the SVM model. The sperm population structure revealed that those males showing poor freezability were characterized by high percentages of sperm with a weak motility pattern. In opposite, males showing good freezability were characterized by higher percentages of sperm with a progressive and hyperactivated-like motility pattern and lower percentages of sperm with a weak motile pattern. We also identified a sperm subpopulation with a transitional motility pattern. This subpopulation increased as the freezability of males improved, and may be used as indicative of overall sperm motility.
本研究以伊比利亚红鹿为模型,提出了一种基于支持向量机(Support Vector Machines,SVM)的有监督学习方法,用于描述与可冻存性相关的精子群体结构。通过评估冻融后精子的活力、膜状态和线粒体膜电位来评估雄性的可冻存性。SVM 模型是使用解冻精液的计算机辅助精子分析(CASA)捕获的精子活力信息生成的,这些精液来自于 6 只在可冻存性方面存在明显差异的雄鹿。总共记录了 1369 条精子轨迹,用于七个运动学参数,并根据这些参数将其分配到四个运动模式:弱运动、渐进、过渡和超激活样。然后,将这些数据分为两组:训练集,用于训练 SVM 模型;测试集,用于检查 SVM 方法和其他三种无监督方法(非层次、层次和多步聚类程序)如何将精子分类为亚群。SVM 被证明是描述精子亚群最准确的方法,显示出通过这种方式获得的所有精子亚群与用于描述雄性可冻存性的精子参数高度相关。鉴于其优越性,SVM 方法用于描述伊比利亚红鹿精子的运动亚群。记录了来自 25 只雄鹿的冷冻解冻精液的精子运动数据,并将其加载到 SVM 模型中。精子群体结构表明,那些可冻存性差的雄性的特征是具有弱运动模式的精子比例较高。相反,那些可冻存性好的雄性的特征是具有渐进和超激活样运动模式的精子比例较高,而具有弱运动模式的精子比例较低。我们还鉴定出一种具有过渡运动模式的精子亚群。随着雄性的可冻存性提高,这个亚群的比例增加,并且可能作为整体精子活力的指示。