Pregnancy rates in managed horse populations depend on the innate fertility of the mares and stallions involved and on the quality of breeding management. Of course, because a single stallion usually mates many mares, stallion fertility is a critical factor in the overall success of a breeding program. Unfortunately, accurate evaluation of stallion fertility per se requires a large number of normal mares to be mated and is necessarily retrospective. Rather, the ideal is to predict fertility in advance of the stallion's breeding career, and this is currently attempted by way of a thorough physical examination and a routine analysis of semen quality. However, while such a 'breeding soundness examination' identifies stallions that clearly lack the capacity for adequate fertility, it is of limited use for predicting the level of fertility and fails to identify some seriously sub-fertile animals. Similarly, while various sperm function tests (e.g., sperm head morphometry, the hypoosmotic swelling test, glass wool-sephadex filtration, progesterone receptor exposure) have been shown to correlate fairly well with fertility in the field, most examine only a single or a narrow range of the attributes that a sperm must possess if it is to fertilize an oocyte in vivo, and are thus more useful for identifying specific causes of sub-fertility than for predicting the level of fertility. On the other hand, combining the results of the various sperm function tests does improve the reliability of fertility estimation and current research is therefore concentrated on identifying a range of tests that covers as many important sperm attributes as possible but that can be performed rapidly and cheaply. In this respect, flow-cytometry has proven to be an ideal tool because it allows the objective, rapid and simultaneous analysis of a number of properties in a large number of sperm. Moreover, stains are available for an increasing range of sperm characteristics including viability, capacitation and acrosome status, mitochondrial activity and chromatin integrity. Flow-cytometric analysis of sperm with appropriate probes thus offers considerable promise for the prediction of stallion fertility.

The predictive value of semen analysis in the evaluation of stallion fertility.

Regression analyses and motile sperm subpopulation structure study as improving tools in boar semen quality analysis.

We have applied a statistical protocol based on principal component analysis, clustering methods, and discriminant analysis for the identification of sperm subpopulations in computer-assisted sperm analysis (CASA) data. Samples were obtained from the cauda epididymis of 11 Iberian red deer and cryopreserved following a standard protocol. Motility by CASA was analyzed just after sperm recovery, just before freezing, and after thawing, and eight motility descriptors for each individual spermatozoon were recorded. Sperm viability and acrosomal status were also assessed. Subpopulation analysis was performed in four sequential steps: principal component analysis using the eight motility descriptors; nonhierarchical clustering analysis (k-means) using the first two principal components; hierarchical clustering analysis (UPGMA); and selection of the final number of clusters. Three clusters were obtained for each motility analysis: slow and nonlinear; rapid and linear; and rapid, high ALH, nonlinear. We detected variations in the clusters between treatments (initial, prefreezing and postthawed). Indeed, motility increased and linearity decreased in the prefreezing analysis. A discriminant analysis isolated three descriptors that were used again in the same statistical analysis, giving four clusters that resembled the pattern found in the first classification. We also performed a clustering analysis of the males according to prefreezing/postthawed variation of total motility, viability, and acrosomal status. The proportion of the linear subpopulations in the prefreezing treatment, in both clustering analyses, correlated positively with postthawed viability recovery. Our results show that clustering analysis of CASA data gives useful and practical information that is not obtained by conventional sperm analysis.

https://bioone.org/journals/biology-of-reproduction/volume-72/issue-2/biolreprod.104.032730/Sperm-Subpopulations-in-Iberian-Red-Deer-Epididymal-Sperm-and-Their/10.1095/biolreprod.104.032730.pdf

Sperm subpopulations in Iberian red deer epididymal sperm and their changes through the cryopreservation process.

Factors affecting the plasma membrane function of cooled-stored stallion spermatozoa.

A statistical approach using sequentially principal component analysis (PCA), clustering, and discriminant analyses was developed to identify sperm morphometric subpopulations in well-defined portions of the fresh boar ejaculate. Semen was obtained as 2 portions (the first 10 mL of the sperm-rich fraction and the rest of the ejaculate, respectively) and frozen using a conventional protocol. Before freezing, an aliquot was used for computer-assisted sperm morphometry analysis (ASMA). Postthaw quality was evaluated using computer-assisted sperm analysis (CASA), and an annexin-V/PI assay evaluated sperm membranes. The PCA revealed that 3 variables represented more than 78% of the cumulative variance in sperm subpopulations. The clustering and discriminant analyses, based on 5780 individual spermatozoa, revealed the existence of 4 sperm subpopulations. The relative percentage of these subpopulations varied between boar and ejaculate portions. Linear regression models based on measured morphometric characteristics could account for up to 36% of the percentage of intact sperm membranes postthaw. The ASMA protocol used in our study was useful to detect subtle morphometric differences between spermatozoa, and the combination of this analysis with a multivariate statistical procedure gave new information on the biological characteristics of boar ejaculates that is not given by conventional sperm analysis.

https://onlinelibrary.wiley.com/doi/pdf/10.2164/jandrol.05030

A Teresa Rigau

Papers

Identification of sperm subpopulations with specific motility characteristics in stallion ejaculates.