Hernández Haydee O, Montoya Fernando, Hernández-Herrera Paul, Díaz-Guerrero Dan S, Olveres Jimena, Bloomfield-Gadêlha Hermes, Darszon Alberto, Escalante-Ramírez Boris, Corkidi Gabriel
Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, UNAM, Ciudad de México, Mexico.
Laboratorio de Imágenes y Visión por Computadora, Instituto de Biotecnología, UNAM, Cuernavaca, Mexico.
Heliyon. 2024 Feb 23;10(5):e26645. doi: 10.1016/j.heliyon.2024.e26645. eCollection 2024 Mar 15.
The flagellar movement of the mammalian sperm plays a crucial role in fertilization. In the female reproductive tract, human spermatozoa undergo a process called capacitation which promotes changes in their motility. Only capacitated spermatozoa may be hyperactivated and only those that transition to hyperactivated motility are capable of fertilizing the egg. Hyperactivated motility is characterized by asymmetric flagellar bends of greater amplitude and lower frequency. Historically, clinical fertilization studies have used two-dimensional analysis to classify sperm motility, despite the inherently three-dimensional (3D) nature of sperm motion. Recent research has described several 3D beating features of sperm flagella. However, the 3D motility pattern of hyperactivated spermatozoa has not yet been characterized. One of the main challenges in classifying these patterns in 3D is the lack of a ground-truth reference, as it can be difficult to visually assess differences in flagellar beat patterns. Additionally, it is worth noting that only a relatively small proportion, approximately 10-20% of sperm incubated under capacitating conditions exhibit hyperactivated motility. In this work, we used a multifocal image acquisition system that can acquire, segment, and track sperm flagella in 3D+t. We developed a feature-based vector that describes the spatio-temporal flagellar sperm motility patterns by an envelope of ellipses. The classification results obtained using our 3D feature-based descriptors can serve as potential label for future work involving deep neural networks. By using the classification results as labels, it will be possible to train a deep neural network to automatically classify spermatozoa based on their 3D flagellar beating patterns. We demonstrated the effectiveness of the descriptors by applying them to a dataset of human sperm cells and showing that they can accurately differentiate between non-hyperactivated and hyperactivated 3D motility patterns of the sperm cells. This work contributes to the understanding of 3D flagellar hyperactive motility patterns and provides a framework for research in the fields of human and animal fertility.
哺乳动物精子的鞭毛运动在受精过程中起着至关重要的作用。在女性生殖道中，人类精子会经历一个称为获能的过程，该过程会促进其运动能力的变化。只有获能的精子才可能发生超活化，只有那些转变为超活化运动的精子才有能力使卵子受精。超活化运动的特征是鞭毛弯曲不对称，幅度更大且频率更低。从历史上看，临床受精研究一直使用二维分析来对精子运动进行分类，尽管精子运动本质上是三维（3D）的。最近的研究描述了精子鞭毛的几种三维摆动特征。然而，超活化精子的三维运动模式尚未得到表征。在三维空间中对这些模式进行分类的主要挑战之一是缺乏一个真实的参考标准，因为很难直观地评估鞭毛摆动模式的差异。此外，值得注意的是，在获能条件下孵育的精子中，只有相对较小的比例（约10 - 20%）表现出超活化运动。在这项工作中，我们使用了一个多焦点图像采集系统，该系统可以在3D + t中采集、分割和跟踪精子鞭毛。我们开发了一种基于特征的向量，通过椭圆包络来描述精子鞭毛的时空运动模式。使用我们基于3D特征的描述符获得的分类结果可以作为未来涉及深度神经网络工作的潜在标签。通过将分类结果用作标签，将有可能训练一个深度神经网络，根据精子的三维鞭毛摆动模式自动对精子进行分类。我们通过将这些描述符应用于人类精子细胞数据集，并表明它们可以准确区分精子细胞的非超活化和超活化三维运动模式，证明了这些描述符的有效性。这项工作有助于理解三维鞭毛超活化运动模式，并为人类和动物生殖领域的研究提供了一个框架。
