Dept of Anatomy & Histology and Institute for Biomedical Research, University of Sydney, Sydney NSW 2006 Australia
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Abstract |
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Key words: human/hyperactivation/motility/spermatozoa
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Introduction |
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Materials and methods |
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The videotape was replayed in a Panasonic NV-F66A VCR on a Panasonic TC68A61 TV monitor, giving a final magnification of x1900. The trajectories were plotted onto sheets of overhead projector film attached to the monitor's screen. Following normal practice, only trajectories which were in the central portion of the monitor screen were reconstructed to minimize track distortion caused by screen curvature. The (x, y) co-ordinates were determined by placing the sheets over mm graph paper and noting the co-ordinates to within 0.5 mm. Some of the non-hyperactivated tracks had points too close together to be able to differentiate them to 0.5 mm in each direction, so these were reconstructed at 100 images/s by placing a second overhead projector film sheet over the first and plotting every other point onto the top sheet, and at 66.7 images/s by plotting every third point onto another overhead projector film sheet. The same starting point was used each time.
To obtain the lower image sampling frequencies, the (x, y) co-ordinates entered in the spreadsheets were `culled' (Mortimer et al., 1988). For the hyperactivated trajectories, every second point of the 200 Hz trajectory was taken for 100 Hz, every third point for 66.7 Hz, every fourth point for 50 Hz, every sixth point for 33.3 Hz and every eighth point for 25 Hz. A similar procedure was used for the non-hyperactivated trajectories, but the 100 Hz trajectory was used for the 50 and 25 Hz tracks, with every second and fourth point considered, while the 33.3 Hz tracks were derived by considering every second point of the 66.7 Hz trajectories.
There were 23 hyperactivated and 24 non-hyperactivated tracks studied. All of the kinematic values were calculated for each trajectory using the Cartesian methods described previously (Mortimer and Swan, 1995a). The average path was estimated by 5-point smoothing for the 25 and 33.3 Hz tracks, 7-point smoothing for the 50 and 66.7 Hz tracks and 11-point smoothing for the 100 Hz tracks. The number of points used for track smoothing was increased with image sampling frequency to reduce the influence of individual track points upon the calculated average path. As the image sampling frequency increases, the number of track points increases, so if a low number of points are used for smoothing, the average path will be pulled towards the track peaks.
The established kinematic values: VCL, VSL, VAP, LIN, STR, WOB, ALHmean, ALHmax and BCF (Table I) were calculated for each trajectory, as well as mean angular displacement (MAD; Boyers et al., 1989
); Dancemean (DNCmean; Robertson et al., 1988
); fractal dimension (D; Mortimer et al., 1996
); and a series of new kinematic values (VINmax, VINmean, AVmax, VAM, TPAmax, TPAmean, TPAmxmn, TPAmax(f), TPAmean(f) and TPAmxmn(f); Table II
, Mortimer and Swan, 1999).
Statistical analysis
Receiveroperator characteristic (ROC) curve analyses were performed on the data for each image sampling frequency to determine the threshold levels for hyperactivation (Schoonjans et al., 1995). The effect of image sampling frequency on each kinematic measure was determined by rank correlation analysis of both the hyperactivated and non-hyperactivated values. Unpaired Wilcoxon analyses were used to compare the kinematic values for hyperactivated and non-hyperactivated tracks at each image sampling frequency. All statistical analyses were performed using MedCalc for Windows (MedCalc Software, Mariakerke, Belgium).
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Results |
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There was a marked increase in VAP of hyperactivated tracks between 50 and 66.7 Hz, presumably due to differences in the magnitude of the fixed-point running average used for its calculation. The non-hyperactivated tracks were unaffected by image sampling frequency (Table III and Figure 2
). Hyperactivated tracks had significantly higher VAP than non-hyperactivated tracks at 66.7 and 100 Hz only (both Z > 3.5, P < 0.0001). A significant threshold value for hyperactivation independent of image sampling frequency could not be established by ROC curve analysis (Table IV
).
Corresponding with the increase in VAP at 66.7 Hz, the ALHmax and ALHmean of the hyperactivated tracks dropped between 50 and 66.7 Hz (Figure 2). This effect would be expected if the average path was being pulled towards the track peaks thereby decreasing the riser height, leading to a lower ALH value. Both the ALHmax and ALHmean values were significantly higher for the hyperactivated tracks at each image sampling frequency (all Z > 5.86, P < 0.0001) and significant hyperactivation threshold values could be established across the commonly-used image sampling frequencies for each kinematic measure (ALHmax > 8.6 µm and ALHmean > 5.5 µm, both 100% sensitivity and specificity; Table IV
).
The velocity ratios LIN and WOB declined significantly with increasing image sampling frequency, while STR increased significantly (Figure 2 and Table III
). All three of the ratio values were significantly lower for hyperactivated tracks than for non-hyperactivated tracks at each image sampling frequency studied (all Z < 5.60, P < 0.0001).
BCF was highly frame rate-dependent, due to it being derived using the average and curvilinear paths, and also to it being a frequency measurement (Table III). While the BCF of the hyperactivated tracks was significantly higher than that of the non-hyperactivated tracks at 25 and 33.3 Hz (both Z > 3.30, P < 0.0001), it was significantly lower at both 66.7 Hz (Z = 3.11, P < 0.05) and 100 Hz (Z = 3.68, P < 0.0001; Figure 2
). There was no significant difference observed between the BCF values of the hyperactivated and non-hyperactivated tracks at 50 Hz. Consequently, no common hyperactivation threshold value could be determined by ROC curve analysis (Table IV
).
DNCmean increased significantly with image sampling frequency for the non-hyperactivated tracks (P < 0.0001 by rank correlation analysis) but not for the hyperactivated tracks (P = 0.050 by rank correlation analysis; Table III, Figure 2
). The DNCmean of hyperactivated tracks was significantly higher at each image sampling frequency studied (Z = 5.87, P < 0.0001 for all).
MAD was also frame rate-dependent, with the hyperactivated and non-hyperactivated values converging with increasing image sampling frequency (Figure 2 and Table III
). The hyperactivated tracks had significantly higher MADdeg values than the non-hyperactivated tracks at image sampling frequencies of 25 to 50 Hz (all Z > 4.45, P < 0.0001), with no significant difference between the 66.7 and 100 Hz values.
The fractal dimension values for the hyperactivated trajectories were not significantly affected by the image sampling frequency, but increased with increasing image sampling frequency for the non-hyperactivated tracks (Table III and Figure 2
). The fractal dimension of the hyperactivated tracks was significantly higher than for the non-hyperactivated tracks at each frame rate studied (all Z > 5.86, P < 0.0001), and a threshold value of fractal dimension >1.22 across the commonly-used frequencies was established by ROC curve analysis (Table IV
).
VINmax, AVmax and VINmean increased significantly with increasing frame rate (Figure 3 and Table III
), and all were significantly higher for the hyperactivated tracks at all image sampling frequencies (all Z > 5.70, P < 0.0001). VAM increased significantly with image sampling frequency (Figure 3
and Table III
). The VAM of the hyperactivated tracks was significantly greater than that of the non-hyperactivated tracks at each image sampling frequency studied, although the difference was not as marked at 25 Hz (Z = 3.79, P < 0.05) as it was at the other frame rates (all Z > 5.74, P < 0.0001).
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All of the kinematic values for each track obtained at each image sampling frequency (except 100 Hz) were included in ROC curve analyses to determine whether a threshold value which would be consistent across all of the commonly-used image sampling frequencies could be obtained for each kinematic measure. The only kinematic values for which a consistent hyperactivation threshold could be determined with 100% sensitivity and specificity, irrespective of frame rate (from 25 to 66.7 Hz) were ALHmax, ALHmean and DNCmean (Table IV). All of the other kinematic measures had threshold values with >90% sensitivity and specificity for the range of image sampling frequencies, except for VAM (89.1 and 85.9%), MADdeg (82.2 and 68.5%), VSL (66.0 and 43.5%), VAP (65.5 and 41.3%) and BCF (51.4 and 83.7%). Even though the results from a range of image sampling frequencies were included, it was interesting to note that the threshold values of the established kinematic measures determined by ROC curve analysis were similar to those obtained previously for the determination of hyperactivated trajectories at 60 Hz (Mortimer and Swan, 1995a
).
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Discussion |
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The method used in this study differed from that of Mortimer et al. (1988) in that all the analyses were performed by Cartesian methods, using each track's (x, y) co-ordinates, rather than by a combination of manual and semi-automated methods. However, the concept of `plucking' or `culling' track points to give trajectories equivalent to different image sampling frequencies had been introduced previously (Mortimer et al., 1988).
The shape of the trajectories changed with image sampling frequency, with much more detail observable in the 50100 Hz than in the 25 and 33.3 Hz track reconstructions (Figure 1). This observation provides further justification for the recommendation that trajectory analysis of capacitating human sperm populations be performed at image sampling frequencies of at least 50 Hz (ESHRE Andrology Special Interest Group, 1998
). As would be expected, the distance between consecutive track points was inversely proportional to the image sampling frequency, with less distance between points with increasing image sampling frequency.
Image sampling frequency exerted a significant effect on the values of most kinematic values, and this effect was not always the same for hyperactivated and non-hyperactivated tracks (Table II). The only kinematic measures which were not significantly affected by frame rate or motility pattern were TPAmax(f), TPAmxmn(f) and VSL. VSL was not affected by the changing image sampling frequency, as it is only the distance between the first and last track points, and the same starting point was used for each image sampling frequency. The observation of no effect of image sampling frequency is in contrast to a study comparing VSL measured by different CASA instruments (Morris et al., 1996
). However, in that study different sampling times, as well as frequencies, were used and this meant that different track portions were analysed. Here, the same track portion was re-analysed, so there was no effect of sampling time. The relative insensitivity of the TPA(f) values to image sampling frequency demonstrated the success of multiplication of the three-point area value by the image sampling frequency to correct for the reduction in distance between consecutive track points with increasing frame rates, as had been postulated (Mortimer and Swan, 1999
).
For other kinematic measures, i.e. ALHmean, VAP and MAD, only the non-hyperactivated tracks were not significantly affected by image sampling frequency (Table III). There was a marked increase in the VAP of hyperactivated tracks between 50 Hz and 66.7 Hz, presumably because the degree of smoothing was not sufficient for the 66.7 Hz trajectories, i.e. they were undersmoothed. Undersmoothing occurs when the number of points used for the fixed-point running average is too low, and the average path contains deviations towards the track apices. This can also result in decreased ALH values, as the riser distance, the distance between a track point and its smoothed point on the average path, is reduced. The alternative possibility was that the 50 Hz path was oversmoothed, with the smoothed average path being shorter than the `true' average path. Oversmoothing is the opposite to undersmoothing, with so many points included in the smoothing algorithm that an apex's smoothing will be influenced by the points comprising an apex on the opposite side of the track. This results in a generally straight average path, with very minor deviations to mark the presence of an apex (Davis et al., 1992
). Correspondingly, the ALH values from an oversmoothed average path are higher than the `true' ALH, as there is a greater distance between a track point and its smoothed point on the average path.
As predicted by this observation, the ALH values of the hyperactivated tracks dipped 5066.7 Hz, indicating a probable smoothing error (Figure 2). The relative insensitivity of the non-hyperactivated paths to frame rate indicated that they were probably smoothed sufficiently at each image sampling frequency. The difference between the degree of smoothing required for hyperactivated and non-hyperactivated tracks was a further indication of the differences in movement patterns between hyperactivated and non-hyperactivated spermatozoa. This observation also illustrated the difficulties encountered with the use of smoothed values, since even if the correct degree of smoothing is used for one motility type, it is not necessarily appropriate for all motility types. In any given population of capacitating spermatozoa at any given time, there would be expected to be both hyperactivated and non-hyperactivated spermatozoa, as well as some switching between motility patterns. If the kinematic values used to classify spermatozoa were influenced by the average path calculation then, depending upon the magnitude of the fixed-point running average used, the same track could be classified differently depending upon the ALH value obtained.
All of the remaining kinematic values were highly influenced by the image sampling frequency, regardless of the motility classification of the trajectory, although many still gave significantly different values for hyperactivated and non-hyperactivated tracks at all image sampling frequencies. The relationship between hyperactivated and non-hyperactivated tracks was inconsistent for both MAD and BCF (Figure 2). The convergence of the MAD values for hyperactivated and non-hyperactivated trajectories with increasing image sampling frequency reduced the potential value of this kinematic measure, as the trend of modern CASA instruments is towards increasing image sampling frequencies for kinematic analysis. Also, a theoretical study of MAD has predicted that as image sampling frequency increases, MAD would decrease, reaching zero for an image sampling frequency of infinity (Owen and Katz, 1993). It was presumed that the crossover effect observed for BCF was probably due to aliasing at the lower image sampling frequencies. Aliasing occurs when the frequency of the event being measured exceeds the Nyquist number, i.e. half the frequency of image sampling frequency (Owen and Katz, 1993; Davis and Siemers, 1995). The effect of calculation method and image sampling frequency upon the BCF of a trajectory will be explored further in another study.
In conclusion, while frame rate affected both the `established' and `new' kinematic measures, discrimination between hyperactivated and non-hyperactivated trajectories was possible at the image sampling frequencies commonly used by CASA instruments. Further, independent evaluation of the applicability of the smoothing-independent kinematic measures by application of the values in different CASA instruments is now required.
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Notes |
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References |
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Submitted on April 24, 1998; accepted on February 1, 1999.