Sperm Subpopulations in Iberian Red Deer Epididymal Sperm and Their Changes Through the Cryopreservation Process1

Abstract

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.

Artificial neural networks for the definition of kinetic subpopulations in electroejaculated and epididymal spermatozoa in the domestic cat

“…A previous study on boar semen highlighted the need for a different approach apart from the classical parametric and nonparametric analyses (Abaigar et al 1999). Recently, different multivariate approaches for sperm motility evaluation were proposed (Abaigar et al 1999, 2001, Quintero-Moreno et al 2003, Martinez-Pastor et al 2005a, Nunez-Martinez et al 2006. Irrespective of the statistical procedure used, the aim of multivariate analysis in our study was to consider a global evaluation of all spermatozoa and whole motility parameters by simplifying the number of individuals and descriptors Neural networks clustering for feline sperm without losing their information content (Abaigar et al 1999).…”

Subpopulation pattern of eel spermatozoa is affected by post-activation time, hormonal treatment and the thermal regimen

“…In the present study, we have adapted an unsupervised cluster analysis from previous studies on 92 sperm classification (Martinez-Pastor et al, 2005b;Martínez-Pastor et al, 2008; Domínguez-Rebolledo 93 et al, 2011), in order to discover the subpopulation structure of European eel sperm, and to apply this 94 information to improve our knowledge on the effect of thermal and hormonal treatments on the 95 spermatogenesis and sperm quality in this species. Since there is no prior knowledge about the 96 subpopulational structure of eel spermatozoa, we performed a previous cluster analysis on sperm samples 97 obtained following a standard protocol, at different times after activation.…”

Quantification and identification of sperm subpopulations using computer-aided sperm analysis and species-specific cut-off values for swimming speed

“…Many investigators have used CASA to demonstrate the existence of sperm subpopulations in various mammalian species including pig, gazelle, horse, red deer, dog and rabbit (Abaigar et al 1999, 2001, Quintero-Moreno et al 2003, 2004, 2007, Martinez-Pastor et al 2005, Núñez-Martínez et al 2006. It now is recommended that sperm subpopulations be evaluated rather than relying of mean values for the entire sperm population, because mean values for motility oversimplify the analysis and decrease the usefulness of the data (Abaigar et al 1999, Martinez-Pastor et al 2005.…”

“…It now is recommended that sperm subpopulations be evaluated rather than relying of mean values for the entire sperm population, because mean values for motility oversimplify the analysis and decrease the usefulness of the data (Abaigar et al 1999, Martinez-Pastor et al 2005. Overall mean values for sperm motility also can mask the effects of drug treatment on spermatozoa, especially if certain subpopulations are more responsive to the treatment in species with heterogeneous ejaculates (Núñez-Martínez et al 2006).…”