view - Department of Reproduction, Obstetrics and Herd Health
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CHAPTER 1.1<br />
al., 2002), boars (Maes et al., 2003; Vyt et al., 2004) rams (Fukui et al., 2004), bulls (H<strong>of</strong>lack et al.,<br />
2005), <strong>and</strong> mice (Tayama et al., 2006)]. The newest version <strong>of</strong> the SQA (version V) was initially<br />
developed for human samples as well <strong>and</strong> later on adapted for different animal species, namely bulls,<br />
boars, stallions <strong>and</strong> turkeys. Specific devices for rams <strong>and</strong> roosters are in development. The SQA is<br />
ready to use after delivery <strong>and</strong> does not require (or allow) user specific settings, as such reducing a<br />
large potential source <strong>of</strong> bias (H<strong>of</strong>lack et al., 2005).<br />
14<br />
Objective motility analysis: Computer Assisted Sperm Analysis<br />
The best known way to objectively analyze sperm motility is analysis by means <strong>of</strong> a CASA<br />
system. The first CASA system was introduced over 30 years ago, <strong>and</strong> has been used ever since in<br />
human <strong>and</strong> veterinary <strong>and</strong>rology laboratories. By equipping a microscope with a camera, the sperm<br />
cells are visualized <strong>and</strong> the actual sperm tracks can be analyzed after sperm cell recognition. The<br />
latter can be achieved either based on the number <strong>of</strong> pixels <strong>and</strong> intensity or, in newer versions, by<br />
means <strong>of</strong> fluorescent dyes. This technique facilitates sperm recognition <strong>and</strong> allows CASA analysis <strong>of</strong><br />
(post thaw) samples containing egg yolk particles (Tardif et al., 1998) or debris for example after<br />
obtaining epididymal sperm in cats (Filliers, personal communication). The major advantages <strong>of</strong> CASA<br />
systems are, amongst others, the good repeatability <strong>and</strong> the absence <strong>of</strong> subjectivity. Nevertheless,<br />
st<strong>and</strong>ards for analysis need to be established first, since different technical settings <strong>and</strong> sample<br />
preparations have been shown to influence the outcome <strong>of</strong> CASA analysis (Table 1). The impact <strong>of</strong><br />
different technical settings (frame rate <strong>and</strong> number <strong>of</strong> frames analyzed) <strong>and</strong> procedures<br />
(temperature, concentration, diluents <strong>and</strong> chamber) have been studied for different species (Contri<br />
et al., 2010; Lenz et al., 2011; Rijsselaere et al., 2003). Also, the motility settings may have an<br />
influence on the CASA outcome as well. Based on low <strong>and</strong> medium average pathway velocity (VAP)<br />
cut-<strong>of</strong>f values <strong>and</strong> on straightness (STR), spermatozoa are graded. In literature, a plethora <strong>of</strong> settings<br />
is used (Table 2). Not only is there a lack <strong>of</strong> agreement in the cut-<strong>of</strong>f values used by different<br />
research groups, also the description <strong>of</strong> these values is not uniform for CASA systems from different<br />
companies.<br />
A comparable lack <strong>of</strong> st<strong>and</strong>ardization can be found in the chambers used to analyze the<br />
motility <strong>of</strong> a semen sample. Although the effect <strong>of</strong> the counting chamber was already described in<br />
2001 (Iguer-ouada <strong>and</strong> Verstegen, 2001), different br<strong>and</strong>s <strong>and</strong> types <strong>of</strong> chambers are used. The most<br />
frequently used chambers are a 20 µm deep Leja chamber (Waite et al., 2008; Ortega-Ferrusola et al.,<br />
2009; Len et al., 2010), a 20 µm deep Cell-Vu (Almeida <strong>and</strong> Ball, 2005; Glazar et al., 2009; Spirizzi et<br />
al., 2010) <strong>and</strong> the 10 µm deep reusable Makler chamber (Kavak et al., 2003; Johannisson et al., 2009).