UR7516 CHIMERE Laboratory, University of Picardie Jules Verne, Amiens, France.
Laboratorio de Gametos y Desarrollo Tecnológico, Facultad de Estudios Superiores Iztacala, UNAM, 54090, Tlalnepantla, Estado de México, Mexico.
Cytometry A. 2023 Aug;103(8):655-663. doi: 10.1002/cyto.a.24732. Epub 2023 Apr 5.
The identification of kinematic subpopulations is of paramount importance to understanding the biological nature of the sperm heterogeneity. Nowadays, the data of motility parameters obtained by a computer-assisted sperm analysis (CASA) system has been used as input to distinct algorithms to identify kinematic subpopulations. In contrast, the images of the trajectories were depicted only as examples of the patterns of motility in each subpopulation. Here, python code was written to reconstruct the images of trajectories, from their coordinates, then the images of trajectories were used as input to a machine learning clustering algorithm of classification, and the subpopulations were described statistically by the motility parameters. Finally, the images of trajectories in each subpopulation were displayed in a way we called Pollock plots. Semen samples of boar sperm were treated with distinct concentrations of ketanserin (an antagonist of the 5-HT2 receptor of serotonin) and untreated samples were used as a control. The motility of sperm in each sample was analyzed at 0 and 30 min of incubation. Six subpopulations were found. The subpopulation 2 presented the highest values of velocities at 0 or 30 min. After 30 min of incubation, the ketanserin increased the values of the curvilinear velocity at high concentrations, whereas the linearity and the straight velocity decreased. Our computational model permits better identification of the kinematic subpopulations than the traditional approach and provides insights onto the heterogeneity of the response to ketanserin; thus, it could significantly impact the research on the relationship between sperm heterogeneity-fertility.
运动学亚群的鉴定对于理解精子异质性的生物学本质至关重要。如今，计算机辅助精子分析（CASA）系统获得的运动参数数据已被用作不同算法的输入，以识别运动学亚群。相比之下，轨迹图像仅被描绘为每个亚群运动模式的示例。在这里，我们编写了一个 Python 代码来从坐标重建轨迹图像，然后将轨迹图像用作机器学习分类聚类算法的输入，并通过运动参数对亚群进行统计学描述。最后，我们以我们称之为波洛克图的方式显示每个亚群的轨迹图像。猪精液样本用不同浓度的酮色林（5-羟色胺 2 受体的拮抗剂）处理，未处理的样本用作对照。在 0 和 30 分钟孵育时分析每个样本中的精子运动。发现了六个亚群。亚群 2 在 0 或 30 分钟时具有最高的速度值。孵育 30 分钟后，酮色林在高浓度下增加了曲线速度的值，而直线性和直线速度下降。我们的计算模型比传统方法更能识别运动学亚群，并深入了解对酮色林的反应异质性；因此，它可能会对研究精子异质性与生育力之间的关系产生重大影响。
