Saravia-Ramos, Fernando
- Department of Clinical Sciences, Swedish University of Agricultural Sciences
Research article2005Peer reviewedOpen access
Pena, FJ; Saravia, F; Garcia-Herreros, M; Nunez-Martinez, I; Tapia, JA; Johannisson, A; Wallgren, M; Rodriguez-Martinez, H
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 hew information on the biological characteristics of boar ejaculates that is not given by conventional sperm analysis
Journal of Andrology
2005, volume: 26, number: 6, pages: 716-723
Publisher: AMER SOC ANDROLOGY, INC
Clinical Science
https://res.slu.se/id/publ/6974