CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns

Abstract

The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile percentage and kinematic parameters at the population level, and uses kinematic gating methods to identify subpopulations such as progressive or hyperactivated sperm. The goal of this study was to develop an automated method that classifies all patterns of human sperm motility during in vitro capacitation following the removal of seminal plasma. We visually classified CASA tracks of 2817 sperm from 18 individuals and used a support vector machine-based decision tree to compute four hyperplanes that separate five classes based on their kinematic parameters. We then developed a web-based program, CASAnova, which applies these equations sequentially to assign a single classification to each motile sperm. Vigorous sperm are classified as progressive, intermediate, or hyperactivated, and nonvigorous sperm as slow or weakly motile. This program correctly classifies sperm motility into one of five classes with an overall accuracy of 89.9%. Application of CASAnova to capacitating sperm populations showed a shift from predominantly linear patterns of motility at initial time points to more vigorous patterns, including hyperactivated motility, as capacitation proceeds. Both intermediate and hyperactivated motility patterns were largely eliminated when sperm were incubated in noncapacitating medium, demonstrating the sensitivity of this method. The five CASAnova classifications are distinctive and reflect kinetic parameters of washed human sperm, providing an accurate, quantitative, and high-throughput method for monitoring alterations in motility.

Keywords: CASA; CASAnova; capacitation; hyperactivation; sperm motility; support vector machine.

Figure 1.

Time-dependent changes in sperm motility patterns during in vitro capacitation. Representative images from computer-aided sperm analysis (CASA) of human sperm from a single individual incubated for 0 h (A) or 3 h (B) in HTF complete medium. Gates corresponding to VCL ≥ 150 μm/s, LIN ≤ 50% and ALH ≥ 7 μm were applied to the sperm tracks using the CASA SORT function. These gates identifed both the cyan (arrow heads) and green tracks (arrows) as hyperactivated. The green tracks are also classified as non-progressive, based on CASA-dependent settings. Dark blue (#) tracks denote sperm that left the field before analysis was completed. Asterisks denote tracks that meet the criteria of the SORT function, but do not meet our strict criteria of hyperactivated sperm patterns by visual analysis.

Figure 2.

Examples of human sperm motility patterns identified during in vitro capacitation. Representative CASA tracks displaying vigorous motility patterns (A–E) after 3 h in capacitating medium were visually classified as progressive (A), intermediate (B), or hyperactivated (C–E).Vigorous motility tracks were enlarged by selecting the track under the software's EDIT function to highlight the difference between the angles of adjacent points. Tracks displaying nonvigorous motility were classified as either slow or weakly motile (F and G, arrows). The images of these nonvigorous motility patterns include nearby vigorous tracks to illustrate the differences in track length.

Figure 3.

Medians and distributions of kinematic parameters for tracks used in training of human CASAnova model. Kinematic parameters of VAP (A), VSL (B), VCL (C), ALH (D), and BCF (E) for each CASAnova classification are shown as Tukey box plots. Each box displays the limits of the interquartile range (IQR) with horizontal lines at the median, 25th and 75th percentiles. Whiskers indicate the highest data point within 1.5 × IQR of the upper quartile and the lowest data point with 1.5 × IQR of the lower quartile, with outliers shown as symbols above and below the whiskers.

Figure 4.

Multidimensional clustering of visually classified sperm tracks and generation of a multiclass SVM model. Sperm incubated for 3 h in HTF complete medium were visually classified according to motility pattern to generate a training set for human CASAnova. Tracks were then plotted as a function of their independent kinematic parameters (VAP, VSL, VCL, ALH, and BCF) in a multidimensional scatter plot (A). VAP, VSL, and VCL axes are shown in this plot. Progessive sperm are represented in the bottom right cluster. Intermediate sperm cluster in the center-right of the plot while hyperactivated sperm are clustered in the top center portion of the plot. Slow sperm and weakly motile sperm are clustered in the bottom center and bottom left of the plot, respectively. (B) Decision tree model demonstrating how SVM equations are sequentially applied to CASA track parameters to identify sperm motility patterns.

Figure 5.

Motility profiles of capacitating human sperm. Motility was monitored at 1-h intervals by CASA. Independent kinematic parameters generated in these analyses were then used to classify all motile sperm using the human CASAnova program. Bars represent the mean ± SEM of motile tracks from 26 individuals. Differences between motility groups at corresponding time points were determined by one-way ANOVA after arcsine transformation of percentages followed by Dunnett's post test for multiple comparisons with time 0 as a control. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Figure 6.

Vigorous motility patterns are reduced significantly when human sperm are incubated under noncapacitating conditions. Sperm samples from eight individuals were split and incubated for 5 h in either HTF (capacitating, solid lines) or mHTF (noncapacitating, dashed lines) medium. Aliquots were assessed for motility at 1-h intervals, and motility profiles were determined by CASAnova. Bars represent the mean ± SEM of motile tracks. Differences between motility groups at corresponding time points were determined by two-tailed unpaired t-test after arcsine transformation of percentages. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Figure 7.

Comparison of visual counting, CASAnova, and kinematic gates to identify hyperactivated sperm. Sperm motility tracks from 13 individuals were assessed visually to determine the percentage of hyperactivated sperm at 0 h (A) and after 3 h (B) incubation in capacitating medium. Hyperactivation levels identified via CASAnova and kinematic gates were compared to visual assessments. (C and D) Sperm identified as hyperactivated using kinematic gates (VCL ≥ 150 μm/s, LIN ≤ 50%, and ALH ≥ 7 μm) were obtained using the SORT function on the CASA software. Kinematic parameters of these gated sperm were analyzed to determine the distribution of their motility patterns as identified by CASAnova. Data are represented as mean ± SEM. Differences between visual and other estimates of hyperactivation were determined by one-way ANOVA followed by Dunnett's post test for multiple comparisons. *P < 0.05, **** P < 0.0001.