Identification of Sperm Morphometric Subpopulations in Two Different Portions of the Boar Ejaculate and Its Relation to Postthaw Quality

Abstract

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 computerassisted 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.

Sperm head morphometry in ejaculates of adult marmosets (Callithrix jacchus): A model for studying sperm subpopulations and among-donor variations

“…The next step was to perform a non-hierarchical analysis using the k-means model that uses euclidean distances from the quantitative variables after standardization of the data, so the cluster centers were the means of the observations assigned to each cluster. The multivariate k-means cluster analysis was done to classify spermatozoa into a reduced number of subpopulations according to their morphometric descriptors, as described by Peña et al [8]. Spermatozoa that were very close to each other were assigned to the same cluster, whereas spermatozoa that were far apart were put into different clusters.…”

“…Values for all sperm head dimension and shape parameters of marmoset donors were determined to be normally distributed by Kolmogorov-Smirnov normality test [8]. The PC analysis (data matrix consisted of 19 450 observations) rendered seven, three, four, and six PCs, respectively, with eigenvalues above one (depending on the donor analyzed), which accounted for more than 85% of the cumulative variance from the seven initial morphometric parameters (93.5%, 85.0%, 96.3%, and 95.4%, respectively; Table 2).…”

“…larly interesting for future studies regarding detection of different fertility profiles or predictors of sperm quality following cryopreservation procedures, even in other species as rhesus macaque (Macaca mulatta) [33]. Semen analysis should, therefore, be performed to establish the presence of each of these subpopulations, and not only to provide average values for the semen population as a whole [8,15]. The distribution and structure of spermatozoa in each sperm subpopulation varied significantly, comparing various individuals studied.…”

“…The introduction of automated sperm morphometry analysis may solve the problem of subjective evaluations on sperm morphology, because with this technology, semen research has gained objectivity and sensitivity [7]. However, although it is possible to minimize inter-and intraobserver variability with this technique, the classical approach considering the whole ejaculate as a homogeneous population with a normal distribution to assess sperm quality or biophysiological factors is considered erroneous [8]. In that regard, the existence of well-defined sperm subpopulations within mammalian ejaculates is now widely accepted by the scientific community [9,10].…”

“…Thus, there is a substantial loss of information when traditional statistical procedures are applied to the results, because the real distribution of sperm morphometry is not uniform and normal, but rather structured in separate subpopulations [11]. An association between computerized and statistical techniques could allow classifying the overall sperm population of semen samples into homogeneous separated subpopulations, grouping spermatozoa with similar morphometric characteristics [8].…”