New objective measurements of semen wave motion are associated with fertility in sheep

Abstract

In sheep, wave motion in semen is currently used by AI centres to select ejaculates for insemination. Despite its low cost, convenience and established ability to predict fertility, the subjectivity of this assessment is a limiting factor for its applicability. The aims of the present study were to establish an objective method for the analysis of wave motion and to assess the associations of objective parameters with fertility after cervical insemination. Collective sperm motion in undiluted semen was observed by phase contrast microscopy at low magnification in a 100-µm deep glass chamber. Images of moving dark waves over a grey background were recorded and analysed by the optic flow method, producing several velocity-related parameters. Turbulence was assessed from the motion of fluorescent polystyrene beads. Among objective parameters, optical flow entropy and the average speed of beads were both able to discriminate ejaculates suitable for insemination. Two synthetic variables of optic flow and bead motion and a global objective variable were computed from linear combinations of individual parameters and compared with the subjective motion score for their predictive value. These were as efficient as the wave motion score for assessing fertility and can be proposed for the assessment of ram semen in routine AI procedures.

The characteristics of proteome and metabolome associated with contrasting sperm motility in goat seminal plasma

“…It has been reported that sperm motility is tightly associated with the results of AI [6][7][8] . Furthermore, seminal plasma plays a critical role in sperm motility regulation [9][10][11][12][13] .…”

“…Instantly after collecting, we counted volume of semen and observed semen color. Mass motility was rst assessed by observing the wave motion pattern of fresh undiluted semen 76,77 . However, the assessment of mass motility is subjectively carried out on the basis of the experience and knowledge of the technicians, so it is only a rough assessment.…”

“…Those defectively mobile or immobile sperm are usually unfruitful or sterilized except that some assisted reproductive techniques are used 6 . Some AI centers are currently using wave motion of semen as a main index to choose ejaculates for AI in sheep 7 . Furthermore, mass motility has been con rmed to be closely related to fertility in sheep 8 .…”

Global Methylation and Protamine Deficiency in Ram Spermatozoa Correlate with Sperm Production and Quality but Are Not Influenced by Melatonin or Season

“…Regardless of whether sperm populations demonstrate extreme hyper-or hypomethylation, there appears to consistently be a relationship with motility, suggesting that spermatozoa on either end of the methylation spectrum demonstrate impaired motility. Given that sperm motility is a key characteristic used to determine ejaculate fertility in the sheep production industry [69,70], external factors that alter motility are of interest. In this case, we cannot conclude upon any causation behind the relationship between global methylation and sperm motility but can speculate that this may partially be attributed to the other correlations revealed in this study.…”

Computer-assisted sperm analysis and reproductive science; a gift for understanding gamete biology from multidisciplinary perspectives

“…Instrumentation for CASA analysis has developed rapidly alongside our biological understanding, so we wanted to reflect some of this progress. One of the papers (David et al 2018) is interesting because it capitalises on some of the earliest observations made with ram semen; namely, that if an undiluted sample is viewed through a low powered objective, it is usually possible to see a great deal of swirling and wave motion. Although the early pioneers of sperm assessment developed rating systems to rank such sperm behaviour, a group of biologists and mathematicians has developed an unusual CASA approach that quantifies the wave motion to such a degree of accuracy that the data are predictive of AI success in sheep.…”