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Reproduction, Fertility and Development Reproduction, Fertility and Development Society
Vertebrate reproductive science and technology
RESEARCH ARTICLE

9 CAN ONE PREDICT THE RESISTANCE OF BULL SPERM TO CRYOPRESERVATION BY ANALYZING MOTILITY PARAMETERS BEFORE FREEZING?

L. Defoin, A. Granados and I. Donnay

Reproduction, Fertility and Development 19(1) 123 - 123
Published: 12 December 2006

Abstract

Batches of straws often need to be thrown away after freezing due to a too-few-number of motile or progressive sperm cells (spz), whereas the quality of the fresh sperm was considered as acceptable. Our objective was to evaluate whether variables related to velocity or linearity for fresh spz could help to predict the resistance to freezing and allow the discard of poor-quality batches before freezing. Motility traits of 20 ejaculates from 20 Belgian Blue bulls collected at an AI center were evaluated for motile spz both before and after freezing using Computer-Assisted Sperm Analysis (CASA, Spermvision; MinitÜb, Tiefenbach, Germany). Only six traits of motility showed a normal distribution in the population of motile spz and were kept for further analysis together with the proportion of motile (%mot) and progressive (%prog) spz: velocity on the curved line (VCL), velocity on the straight line (VSL), velocity on the average path (VAP), linearity (LIN = VSL/VCL), beat cross frequency (BCF), and average orientation change of the head (AOC). Significant variation between bulls was observed both before and after freezing for all of the analyzed traits (ANOVA2; P < 0.001). Moreover, freezing significantly altered the motility measures (ANOVA2; P < 0.001). For each variable, a significant correlation was observed between the values (mean or percentage) obtained for each bull before and after freezing (Pearson coefficient: R = 0.43 to 0.72; P < 0.05). However, the impact of freezing on the quality of motility differed between bulls, with low impact for some bulls and major impact for others. Three motility traits measured before freezing were highly correlated with %mot or %prog after freezing: VAP, VSL, and %prog (R = 0.75 to 0.82; P < 0.001). When we evaluated the prediction of rejection or acceptance of a batch of straws after freezing (based on a threshold of 15% progressive spz) by using motility measures recorded before freezing, five traits allowed us to discriminate low-quality batches: %mot, %prog, VAP, VSL, and LIN. Applying to fresh sperm a threshold of 92 µm s-1 for VAP or 84 µm s-1 for VSL allowed us to predict, respectively, 6 and 7 out of the 9 batches that would be rejected after freezing, without discarding batches of acceptable quality. Moreover, using the %mot or %prog before freezing caused us to discard only 3 and 4 batches, respectively. Combining different traits did not add to the precision. In conclusion, analysis of velocity traits for fresh sperm seems more efficient than analysis of %mot or %prog to discard batches that will be of poor quality after freezing. Such analysis could prevent useless work and expense related to straw filling and freezing. However, the definition of thresholds needs further analysis with a larger number of batches of semen and will vary from one AI center to another, depending, for example, on the breed characteristics, the number of spz per straw, the CASA system, or the freezing procedure.

This work was supported by the programme FIRST Objective 3 of the European Commission and the Ministery of the Région wallonne de Belgique.

https://doi.org/10.1071/RDv19n1Ab9

© CSIRO 2006

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