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. 2011 Jun;84(6):1207-15.
doi: 10.1095/biolreprod.110.088989. Epub 2011 Feb 23.

Classification of mouse sperm motility patterns using an automated multiclass support vector machines model

Summer G Goodson  1 , Zhaojun Zhang, James K Tsuruta, Wei Wang, Deborah A O'Brien
Affiliations

Classification of mouse sperm motility patterns using an automated multiclass support vector machines model

Summer G Goodson et al. Biol Reprod. 2011 Jun.

Abstract

Vigorous sperm motility, including the transition from progressive to hyperactivated motility that occurs in the female reproductive tract, is required for normal fertilization in mammals. We developed an automated, quantitative method that objectively classifies five distinct motility patterns of mouse sperm using Support Vector Machines (SVM), a common method in supervised machine learning. This multiclass SVM model is based on more than 2000 sperm tracks that were captured by computer-assisted sperm analysis (CASA) during in vitro capacitation and visually classified as progressive, intermediate, hyperactivated, slow, or weakly motile. Parameters associated with the classified tracks were incorporated into established SVM algorithms to generate a series of equations. These equations were integrated into a binary decision tree that sequentially sorts uncharacterized tracks into distinct categories. The first equation sorts CASA tracks into vigorous and nonvigorous categories. Additional equations classify vigorous tracks as progressive, intermediate, or hyperactivated and nonvigorous tracks as slow or weakly motile. Our CASAnova software uses these SVM equations to classify individual sperm motility patterns automatically. Comparisons of motility profiles from sperm incubated with and without bicarbonate confirmed the ability of the model to distinguish hyperactivated patterns of motility that develop during in vitro capacitation. The model accurately classifies motility profiles of sperm from a mutant mouse model with severe motility defects. Application of the model to sperm from multiple inbred strains reveals strain-dependent differences in sperm motility profiles. CASAnova provides a rapid and reproducible platform for quantitative comparisons of motility in large, heterogeneous populations of mouse sperm.

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Figures

FIG. 1.
FIG. 1.
Changes in sperm motility patterns during in vitro capacitation. Representative CASA fields showing sperm tracks immediately after isolation (0 min; A) and after 90 min (B) of incubation in HTF complete medium. Representative tracks corresponding to five distinct motility groups used to generate the multiclass SVM model are shown: progressive (a), intermediate (b), hyperactivated (c and d), slow (e), and weakly motile (f).
FIG. 2.
FIG. 2.
Generation of a multiclass SVM model to identify sperm motility patterns. A) In a multidimensional scatter plot of CASA velocity parameters (VCL, VSL, and VAP) associated with sperm tracks in the training set, the tracks are clustered based on their visual classification. B) This decision tree diagrams how the multiclass SVM equations (Table 2) are used to classify sperm motility patterns.
FIG. 3.
FIG. 3.
Time-dependent changes in the distribution of sperm motility patterns during in vitro capacitation. Motility of sperm from six CD1 mice was assessed at 30-min intervals during incubation in HTF complete medium with (A) or without (B) 25 mM sodium bicarbonate, and CASA parameters were subjected to analysis by CASAnova. The red box highlights significant differences in the appearance of intermediate and hyperactivated tracks under capacitating conditions. Bars represent the percentage (mean ± SEM) of motile tracks identified in each group at each time point. Differences between motility groups in A and B at corresponding time points were analyzed using one-way ANOVA. *P < 0.05, **P < 0.01, ***P < 0.001.
FIG. 4.
FIG. 4.
Distribution of sperm motility patterns in wild-type (WT) and Gapdhs−/− mice. CASAnova analysis of CASA parameters was used to compare motility patterns of sperm from wild-type (black bars, n = 3) and Gapdhs−/− mice (white bars, n = 3). Bars represent the percentage (mean ± SEM) of motile tracks identified in each group immediately after isolation in HTF complete medium (time zero). Significance was determined using two-tailed unpaired t-test. *P < 0.05, **P < 0.01, ***P < 0.001.
FIG. 5.
FIG. 5.
Differences in motility profiles between inbred and outbred mouse strains. A) Sperm from BL6, 129, PWK, and CD1 mice (n = 4 mice/strain) were incubated in HTF complete medium and assayed by CASA over a 90-min time course, followed by analysis by CASAnova. Bars represent the percentage (mean ± SEM) of motile tracks identified in each group at each time point. B) Percentage (mean ± SEM) of hyperactivated sperm at 90 min was determined as a function of the total number of sperm analyzed by CASA (motile plus immotile). Differences between motility groups at corresponding time points were analyzed using one-way ANOVA followed by Dunnett posttest to determine significance relative to the outbred CD1 strain. *P < 0.05, **P < 0.01, ***P < 0.001.
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