- Email: [email protected]
Assessment of the Sperm Quality Analyzer*†‡
Vol. 63, No.5, May 1995
FERTILITY AND STERILITY Copyright
©
Printed on acid·free paper in U. S. A.
1995 American Society for Reproductive Medicine
Assessment of the Sperm Quality Analyzer*t:t: Robyn C. Johnston, B.Sc.§ Gary N. Clarke, M.Sc.11 De Yi Liu, Ph.D.~ H. W. Gordon Baker, M.D.,
Ph.D.§~
Prince Henry's Institute of Medical Research, Clayton, University of Melbourne and Royal Women's Hospital, Carlton, Victoria, Australia
Objective: To assess the relationship between the results of the Sperm Quality Analyzer (United Medical Systems Inc., Santa Ana, CA), which measures motile sperm concentration by light scattering, conventional manual semen analysis characteristics, and computer-assisted sperm motility analyses. Design: Sperm Quality Analyzer measurements and manual and computer-assisted semen analyses were performed on 150 (50, 62, and 38) samples in three laboratories and the results were compared. Setting: The study was performed in the Andrology Laboratory of Prince Henry's Institute of Medical Research, Monash Medical Centre, and Andrology Laboratory and Reproductive Biology Unit at the Royal Women's Hospital, Melbourne, Victoria, Australia. Patients: Patients presented to the laboratories for routine fertility evaluation in the male and were selected at random to reflect the range of normal and abnormal samples seen in the laboratories. Interventions: None. Main Outcome Measures: Sperm count, motility (percent motility, motility index, velocity, and amplitude of lateral head displacement [ALH]), morphology, and normal acrosomes were evaluated by manual and computer-assisted semen analysis and sperm quality analyzer motility index. Results: Spearman nonparametric univariate analysis showed strong correlations between sperm motility index and manual sperm concentration, motility, abnormal morphology, and normal acrosomes by Pisum sativum agglutinin; and computer-assisted sperm motility analysis sperm concentration, motile concentration, and percent static. Curvilinear velocity, straightline velocity (VSL), and linearity also were related significantly to sperm motility index values. By multiple regression analysis, the significant covariates of the sperm motility index were motile sperm concentration, abnormal morphology, ALH, and straight-line velocity and these accounted for 85.5% of the variance of the sperm motility index. Conclusions: The Sperm Quality Analyzer is easy to use. The good correlation between the sperm motility index, motile sperm concentration, and, in addition, a number of other semen parameters supports the use of the Sperm Quality Analyzer for screening patients and in situations that warrant a rapid verification of semen quality, such as in the IVF or artificial insemination clinic. Further investigation of the Sperm Quality Analyzer in the management of male infertility is warranted. Fertil Steril 1995;63:1071-6 Key Words: Male infertility, semen analysis, sperm motility, automated sperm motility assessment
The relationship of semen characteristics with fertility is known to be poor unless an absolute defect such as azoospermia is present, however, convenReceived May 3,1994; revised and accepted December 7,1994. * United Medical Systems Inc., Santa Ana, California, Patent Pending, U.S. Patent No. 4176953. t Presented in part at the 12th Annual meeting ofthe Fertility Society of Australia, Sydney, New South Wales, Australia, November 3 to 6, 1993. Vol. 63, No.5, May 1995
tional and automated semen analysis methods continue to serve as a guide to the evaluation offertility. The automation of semen assessment has the poten-
:j: Reprints not available.
§ Prince Henry's Institute of Medical Research.
II Andrology Laboratory, Royal Women's Hospital. ~
Department of Obstetrics and Gynaecology, University of Melbourne.
Johnston et a1. Assessment of the Sperm Quality Analyzer
1071
tial to provide objective and quantitative information, however, currently available systems not only are expensive but tend merely to replace old problems with new ones, hence the search for new and better methods continues. Sperm motility and the concentration of motile sperm are recognized to be important indicators of fertility (1-4). Motility assessment in particular is difficult, with considerable subjectivity and between technician variability. Over the years a wide variety of approaches have been examined to improve sperm motility assessment. These are based either on physical principles such as turbidimetry and spectrophotometry or on photomicrographical methods. The complex cell-tracking systems (computer-assisted sperm motility analysis) that are used commonly today are based on the latter principle (5). These systems allow the calculation of a number of sperm motion characteristics, such as velocities, lateral head displacement, and linearity. Although the systems provide useful information for research in specialized laboratories, accuracy and reproducibility are suboptimal and time and cost factors remain prohibitive in clinical situations. Most methods based on physical principles have been considered less useful because the data they produce are related to the population of cells rather than any individual cell (6). Authors claimed early turbidimetric methods were able to assess the rapidly motile fraction of an ejaculate by monitoring the ability of a sample to swim up into a clear medium as a time-dependent increase in turbidity (7). Laser Doppler spectroscopy, which is based on the analysis ofthe optical Doppler effect with a low power laser as light source, allows the determination of a number of population characteristics, including the sperm concentration, the characteristic velocity, the distribution of velocities, and the percentage of motile sperm (8, 9). Although this information potentially was useful, the systems did not provide the required accuracy (10) and financial considerations made them inappropriate for routine applications. These methods now have been overshadowed by computerassisted sperm motility analysis. The Sperm Quality Analyzer (United Medical Systems Inc., Santa Ana, CA) is a simple and inexpensive commercial unit. The device uses light passed through a small sample of the semen to detect variations in optical density (OD) that result from moving particles. Fluctuations in OD are registered by a photometric cell and the frequency of the analog wave-like electrical signal is converted digitally to provide a numerical output called the sperm motility index. The higher the frequency of the pulses in the analog readings the higher the sperm motility index value. Bartoov et al. (11) have reported a significant 1072
correlation between the sperm motility index and the motile cell concentration in a large patient population (11). The aim of this study was to examine the relationship between the results of the Sperm Quality Analyzer and conventional manual methods and computer-assisted sperm motility analyses. MATERIALS AND METHODS
One hundred fifty patients were selected at random from men attending three Melbourne Andrology Laboratories (50, 62, and 38 samples) for routine infertility investigations. Initial studies revealed consistently low readings for azoospermic samples and the number of azoospermic samples included in the study therefore was limited to seven. The remainder of the sample population (n = 143) represents the variety of nonazoospermic patients, both fertile and nonfertile, seen at the laboratories. Ejaculates were collected after at least 2 days abstinence and were examined within 2 hours of collection. Each ejaculate was examined by routine manual analysis according to the methods of the World Health Organization (12), noting ejaculate pH and volume. Manual analysis included assessment of sperm count, motility divided into four grades (including percent motile and motility index, a quantitative index of the quality of sperm motility), percent live cells, and normal morphology. The motile sperm concentration was calculated as the product of the manual sperm concentration and percent motility. The intraobserver and interobserver variation for manual analysis as determined by repeated analysis of the same sample was :=;;10% in each laboratory, although there are occasions where the variation will be larger than this, particularly in motility and morphology estimations, which are more subjective and particularly reliant on the laboratory's procedures. A double-blind study looking at the variability in the morphology between the laboratories revealed coefficients of variation ranging from 11% to 51%. The sperm concentration, motility characteristics (curvilinear velocity [VCL] , straight-line velocity [VSL], linearity [LIN], amplitude oflateral head displacement [ALH], and average path velocity [VAPD, motile concentration, and percent static for each ejaculate were assessed also using computer assisted methods: Hamilton-Thorn Motility Analyzer (n = 50) (HTM-2030, software version 7.2; Hamilton Thorn Research, Beverly, MA; parameter settings: frames 10, frame rate 25 frames/s, minimum contrast 8, minimum size 6, size gates 0.6 to 1.5, intensity gates 0.6 to 1.5, nonmotile head size 9, nonmotile intensity 250, medium VAP value 30, low VAP value 10, threshold straightness 90; coefficient of variation determined by repeated analysis of the same sample
Johnston et al. Assessment of the Sperm Quality Analyzer
Fertility and Sterility
Table 1 Semen Characteristics and Sperm Motility Index Readings as Determined for 150 Patients in Three Laboratories* Sperm Quality Analyzer readings Sperm motility index (1) (n = 150) Sperm motility index (2) (n = 150) Sperm motility index (3) (n = 150) Mean sperm motility index (n = 150) Semen characteristics (manual) Volume (n = 149) Concentration (l06/mL) (n = 150) Motility (%) (n = 150) Motility index (n = 97) Motile concentration (l06) (n = 150) Motile concentration (curt) (n = 150) Live cells (%) (n = 93) Abnormal forms (%) (n = 150) Normal acrosomes (%) (n = 37) Computer-assisted semen analysis Concentration (l06/mL) (n = 104) Motile concentrationt (n = 99) Motile concentration (curt) (n = 99) Static (%) (n = 99) VAP (n = 45) VCL (n = 99) VSL (n = 99) LIN (n = 99) Straightness (n = 45) ALH (n = 96) Beat cross frequency (n = 45)
111.72 112.03 114.12 112.62
± ± ± ±
89.09 89.72 91.52 89.83
[0 to [0 to [0 to [0 to
3.85 75.51 47.76 92.84 41.39 2.87 78.80 82.86 60.81
± ± ± ± ± ± ± ± ±
1.93 80.19 19.79 35.15 49.62 1.43 14.38 13.16 21.93
(-0.026) [0.6 to 12.0] (0.900) [0 to 436] (0.529) [0 to 85] (0.577) [5 to 170] (0.921) [0 to 270.00] (0.921) [0 to 6.46] (0.174) [1 to 97] (-0.577) [37 to 100] (0.535) [4 to 90]
78.89 42.82 3.00 59.21 42.82 50.16 28.20 54.82 82.73 3.27 10.40
± ± ± ± ± ± ± ± ± ± ±
72.92 (0.857) [0.1 to 436] 52.38 (0.849) [0.88 to 274.56] 1.30 (0.849) [0.96 to 6.50] 22.57 (-0.689) [12.0 to 96.0] 7.67 (0.304) [26.0 to 59.0] 11.21 (0.338) [28.0 to 77.0] 11.28 (0.416) [2.9 to 52.0] 16.81 (0.315) [9.0 to 89.0] 6.19 (-0.271) [65.0 to 96.0] 1.11 (0.116) [1.0 to 5.9] 1.69 (-0.329) [3.7 to 13.0]
312] 308] 332] 317.33]
* Values are means ± SD with range in brackets. The correlation coefficient (p as calculated by Spearman p nonparametric analysis) relative to the mean sperm motility index value is in parentheses.
t Calculated as the concentration multiplied by 100% static.
(n = 5) previously reported: sperm count 7.5%, VSL 5.7%, ALH 9.1% [13]) at the Andrology Laboratory Royal Women's Hospital or by the CellSoft computer automated semen analyzer (n = 62) (Cryo Resources Ltd., Montgomery, NY; parameter settings: image sampling frequency 25 frames/s, number of frames analyzed 20, minimum sampling for motile 2 frames, minimum sampling for velocity 10 frames, minimum sampling for ALH 10 frames, threshold velocity 8 J.Lrn/s, maximum velocity 200 J.Lrn/s, minimum velocity 3 J.Lrn/s, minimum linearity for ALH 25, cell size range 5 to 25 pixels, magnification calibration 0.688 J.Lrn/pixel; coefficient of variation for repeated analysis of the same sample (n = 5) previously reported: VCL 12%, LIN 10%, ALH 9% [14]) at Prince Henry's Institute. The acrosomal status was examined by the Pisum sativum agglutinin method (n = 38) (15). In all laboratories the sperm motility index was determined in three sequential readings by introducing semen into a thin glass capillary tube (internal dimensions: depth 0.3 mm, width 3 mm, length 50 mm), which was housed in a plastic casing with a 2-mm diameter optical aperture. The sample was processed by the Sperm Quality Analyzer according to the manufacturer's instructions. Using this method each sperm motility index reading represents the mean of four 10-second measurements of the analog signal. The number of sperm measured in
each reading is concentration dependent and would vary from 940 to 282 x 103 sperm for motile sperm counts of 1 to 300 X lOs/mL, respectively. The differences between the three Sperm Quality Analyzer measurements were analyzed by two-way analysis of variance. Data were analyzed by Spearman nonparametric and multiple linear regression analysis and the difference between grouped data was analyzed by Kruskal-Wallis and Wilcoxon rank sum tests with the use of the Statistical Package for Interactive Data Analysis (SPIDA) (Macquarie University, Sydney, New South Wales, Australia).
Vol. 63, No.5, May 1995
RESULTS The means of the three replicate measurements of each sample revealed a small but significant increase in sperm motility index units with time (first reading, 111.720; second reading, 112.033; third reading, 114.120; P < 0.05). The mean of the three Sperm Quality Analyzer readings was subsequently used in all calculations. Table 1 shows the sperm motility index readings and the mean and ranges and correlation coefficient (p) relative to the mean sperm motility index value for the semen variables; manual sperm concentration, percent motility, motility index, motile sperm concentration, percentage live cells, abnormal morphology, normal acrosomes
Johnston et al. Assessment of the Sperm Quality Analyzer
1073
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Figure 1 Relationship between the sperm motility index (mean) and motile sperm concentration (manual): untransformed (A) and cube root transformation (B) of the motile sperm concentration. 8MI, sperm motility index.
by Pisum sativum agglutinin, computer-assisted sperm motility analysis estimates of concentration, motile concentration, percent static, VAP, VCL, VSL, LIN, straightness, ALH, and beat cross frequency. There was a curvilinear relationship between sperm motility index and motile sperm concentration. This could be linearized by cube root transformation of the motile sperm concentration (Fig. 1). Spearman nonparametric univariate analysis showed strong correlations (as described by p; Table 1) between sperm motility index and manual sperm concentration, percentage motility, motility index, motile sperm concentration, abnormal morphology and normal acrosomes, computer-assisted sperm motility analysis sperm concentration, motile concentration, and percent static. Straight-line velocity, VCL, and LIN also were significant. Linear regression analysis of the relationship between the sperm motility index reading (mean) and manual motile sperm concentration (cube root transformed) indicated that 82.1% of the variance of the sperm motility index could be accounted for by the motile sperm concentration alone (cube root transformed) (coefficient 59.157; SE 2.803; P = 0.000; r2 = 0.821). Linear regression analysis of the relationship between the sperm motility index reading 1074
(mean) and computer-assisted sperm motility analysis assessed motile sperm concentration (cube root transformed) indicated that 69.4% of the variance could be accounted for by the computer-assisted sperm motility analysis assessed motile sperm concentration (cube root transformed) (coefficient 56.056; SE 3.783; P = 0.000; r2 = 0.694) (n = 98). Abnormal morphology, VSL, and ALH were also additional independently significant covariates of sperm motility index and together with motile sperm concentration accounted for 85.5% of the variance of the sperm motility index (motile concentration [cube root transformed], coefficient 53.805; SE 2.982; P = 0.000; abnormal morphology, coefficient 0.680; SE 0.287; P = 0.020; ALH, coefficient 8.695; SE 3.116; P = 0.006; VSL, coefficient 1.123; SE 0.340; P = 0.001). Figure 2 shows the relationship between the manually determined and computer-assisted sperm motility analysis determined motile concentrations (cube root transformed). By Pearson's correlation and linear regression analysis, the manually determined motile concentration (cube root transformed) was shown to correlate better with the mean sperm motility index reading (r = 0.821) than with the motile concentration (cube root transformed) determined by computer assisted means (r2 = 0.694). Potential outliers in the data were detected by plotting the residuals versus predicted values. A single outlier with inconsistent sperm motility index readings (158; 82; 102) was detected and was disregarded from the analyses. All other samples appeared to fit the predicted model. Table 2 shows the separation of semen analysis results into the sperm motility index ranges recommended by the manufacturer (abnormal sperm motility index < 80; doubtful sperm motility index 80 to 160; normal sperm motility index >160). Kruskal Wallis and Wilcoxon rank sum tests revealed significant differences in the manual count, motility, motile concentration, and
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Figure 2 Relationship between manually determined and computer-assisted sperm motility analysis determined motile sperm concentration (cube root transformed).
Johnston et al. Assessment of the Sperm Quality Analyzer
Fertility and Sterility
Table 2
Semen Analysis Variables for Samples Divided According to Sperm Motility Index* Sperm motility index <80
Concentration (manual)t Motility (%)t Motile concentrationt Abnormal morphologyt Motile concentration (computer-assisted sperm motility analysis) Static (%)t ALH LIN:j:
80 to 160 (n = 33)
(n = 59)
Semen variable 16.5 36.3 6.2 89.9
(0 to 118) (0 to 72) (0 to 38.9) (54 to 100)
58.6 49.3 28.2 81.5
(12 to 138) (24 to 76) (4.9 to 66.0) (37 to 99)
6.0 79.0 3.13 45.6
(0.9 to 20.8) (37 to 96) (1.00 to 5.60) (12.0 to 89.0)
23.9 59.3 3.11 57.5
(1.6 to 59.8) (31 to 90) (1.90 to 5.90) (9.0 to 79.0)
>160 (n = 58)
145.2 58.6 84.7 76.4 75.1 46.5 3.43 59.4
(31 to 436) (10 to 85) (11.9 to 270.0) (42 to 100) (6.7 to 274.6) (12 to 88) (2.0 to 5.6) (30.0 to 81.0)
* The manufacturer designates semen samples with sperm motility index <80 as abnormal, <80 to 160 as doubtful, and> 160 as normal. t Sperm motility index <80, sperm motility index 80 to 160, and sperm motility index > 160 are significantly different from each other, P < 0.05.
:j: Sperm motility index <80 is significantly different from sperm motility index 80 to 160 and sperm motility index> 160, P < 0.05. Sperm motility index 80 to 160 and sperm motility index> 160 are not significantly different.
abnormal morphology and computer-assisted sperm motility analysis motile concentration, percent static, and LIN of samples (P < 0.05) when the sperm motility index was used to group the data into these sperm motility index ranges.
The Sperm Quality Analyzer has good reproducibility particularly in comparison to conventional semen analysis, which is highly subjective, and to computer-assisted sperm motility analysis techniques, which have problems associated with the distinction between debris and sperm. As the Sperm Quality Analyzer only recognizes motile particles, problems with debris have been largely overcome. Although the Sperm Quality Analyzer output appeared to be consistent when three replicates were made from the same sample, there was a significant overall trend to an increase in sperm motility index with time, perhaps indicating a change in the temperature of a sample when left in the chamber over time. Only 1 of 151 samples appeared to have been loaded incorrectly by the technician. This error was readily detectable. The sperm motility index was shown to be a reflection of a combination of semen characteristics, notably the motile sperm concentration (both manual and computer-assisted sperm motility analysis and including a number of individual motility characteristics), but also interestingly of the morphology and acrosomal status of the sample. It is possible that a spermatozoon's movement is influenced by the morphology and the correlation of sperm motility index with morphology in this study may reflect this intimate relationship. Alternately, sperm with abnormal morphology may scatter light differently. Although the more complex computer-assisted sperm motility analysis systems produce a number of variables to describe the sperm motility of individual cells, which is then pooled (including VeL, VSL, LIN, and ALH), methods such as the Sperm Quality Analyzer produce a single output that is dependent predominantly on both the average rate of movement of
DISCUSSION
Modern semen analysis methodology depends on adequately qualified staff and often on expensive and technically complicated equipment. There remain potential advantages in many areas offertility and infertility for the development of a simple reliable method of performing semen examination. In particular, the current interest in male contraceptive procedures would provide a potentially large market for such a device. This study has shown that the Sperm Quality Analyzer, a system based on the scattering of a light beam as it is passed through a sample in a flat (0.3mm depth) capillary tube, provides the user with a numerical value (sperm motility index) that reflects a variety of aspects of semen quality. The mean sperm motility index values were found to correlate well with the motile sperm concentration of 150 patients' samples as determined by standard manual methods. Furthermore, the sperm motility index correlation with manual methods was better than that achieved using current computer-assisted sperm motility analysis technology. In comparison with the alternative methods of semen analysis, the Sperm Quality Analyzer is simple to use, requiring a single piece of equipment and capillary inserts. Ease oftransportation would facilitate use of the machine in satellite laboratories or on site in IVF or donor insemination units. Vol. 63, No.5, May 1995
Johnston et al. Assessment of the Sperm Quality Analyzer
1075
the cell population and on the concentration of cells. Although a single output has the disadvantage that poor characteristics such as a poor motility may be concealed by other good characteristics such as a high sperm count, making it impossible to determine the type of semen defect and in some cases the fact that there is a defect at all, the simplicity of this test may make it useful in instances where an immediate indication of overall semen quality is required in the absence of qualified laboratory staff. It also may have a place in infertility screening; the testing of semen before insemination would be a good example of this. Furthermore, it also may be useful for repeated measurements of semen quality in large studies of the relationship between semen analysis results and fertility in vivo and in vitro. The Sperm Quality Analyzer also has the potential to facilitate and standardize post-thaw recovery assessments of donor cryopreserved or patients' semen. Another potential use would be for the verification or quality control of conventional semen characteristics. For example, it should be possible to estimate the motility of a sample given the sperm count if the sperm motility index is correlated highly to the motile sperm concentration as has been shown in this paper. Studies in these aspects have commenced. The Sperm Quality Analyzer was designed as a screening instrument to define normal and abnormal semen and is more sensitive in the range of 80 to 160 units, which corresponds to motile sperm concentrations between 4.9 and 148.0 X 106 sperm. At low motile sperm concentrations, the Sperm Quality Analyzer was more variable, for example a zero sperm motility index reading in at least two of the three replicates corresponded to motile sperm concentrations in the range 0.7 to 9.45 X 106 sperm (mean 2.8). This would make the current machine inadequate for use with severe male factor patients (i.e., sperm counts < 10 X 106/mL). Improved sensitivity in the low range should render the equipment more useful for assessment of sperm suspensions for insemination in vitro and for testing for male contraception in which suppression of motile sperm concentration below a certain low value is the goal. The evaluation of an improved model, purportedly with greater sensitivity in the lower range, currently is underway. Acknowledgments. The authors thank Mr. Rick Held from Cabras Business Ltd. for loaning us the equipment for this study.
1076
We also thank Mrs. Anne Bell, Mrs. MingLi Liu, and Mr. Peter Elliott for their excellent technical assistance.
REFERENCES 1. Aitken RJ, Best FSM, Richardson DW, Djahanbakhch 0, Lees MM. The correlates of fertilizing capacity in normal fertile men. Fertil Steril 1982; 38:68-76. 2. David G, Czyglick F, Mayaux MJ, Schwartz D. The success of A.LD. and semen characteristics: study on 1489 cycles and 192 ejaculates. Int J Androl 1980:3;613-9. 3. Francavilla F, Romano R, Santucci R, Poccia G. Effect of sperm morphology and motile sperm count on outcome of intrauterine insemination in oligozoospermia and/or asthenozoospermia. Fertil Steril 1990;53:892-7. 4. Johnston RC, Kovacs GT, Lording DH, Baker HWG. Correlation of semen variables and pregnancy rates for donor insemination: a 15-year retrospective. Fertil Steril 1994:61;355-9. 5. Mortimer D. Objective analysis of sperm motility and kinematics In: Keel BA, Webster BW, editors. Handbook of the laboratory diagnosis and treatment of infertility. Boca Raton (FL): CRC Press, 1990:97 -133. 6. Boyers SP, Davis RO, Katz DF. Automated semen analysis. Curr Probl Obstet Gynecol Fertil 1989:12; 165-200. 7. Sokoloski JE, Blasco L, Storey BT, Wolf DP. Turbidimetric analysis of human sperm motility. Fertil Steril 1977;28: 1337-41. 8. Dubois M, Jouannet P, Berge P, Volochine B, Serres C, David G. Methode et appareillage de mesure objective de la motilite des spermatoizoides humains. Ann Phys BioI Med 1975;9: 19-41. 9. Jouannet P, Volochine B, Deguent P, Serres C, David G. Light scattering determination of various characteristic parameters of spermatozoa motility in a series of human sperm. Andrologia 1977;9:36-49. 10. Brotherton J. Determination of human sperm count and sperm motility using a laser beam and doppler effect (LAZYMOT Machine). Andrologia 1988;20:33-43. 11. Bartoov B, Ben-Barak J, Mayevsky A, Sneider M, Yogev L, Lightman A. Sperm motility index: a new parameter for human sperm evaluation. Fertil Steril 1991;56:108-12. 12. World Health Organization. WHO laboratory manual for the examination of human semen and semen-cervical mucus interaction. 3rd ed. New York: Cambridge University Press, 1993. 13. Liu DY, Clarke GN, Baker HWG. Relationship between sperm motility assessed with the Hamilton-Thorn motility analyzer and fertilization rates in vitro. J Androl 1991; 12:231-9. 14. Mbizvo MT, Johnston RC, Baker HWG. The effect of the motility stimulants, caffeine, pentoxifYlline, and 2-deoxyadenosine on hyperactivation of cryopreserved human sperm. Fertil Steril 1993;59:1112-7. 15. Liu DY, Baker HWG. The proportion of human sperm with poor morphology but normal intact acrosomes detected with Pisum sativum agglutinin correlates with fertilization in vitro. Fertil Steril 1988;50:288-93.
Johnston et al. Assessment of the Sperm Quality Analyzer
Fertility and Sterility
FERTILITY AND STERILITY Copyright
©
Printed on acid·free paper in U. S. A.
1995 American Society for Reproductive Medicine
Assessment of the Sperm Quality Analyzer*t:t: Robyn C. Johnston, B.Sc.§ Gary N. Clarke, M.Sc.11 De Yi Liu, Ph.D.~ H. W. Gordon Baker, M.D.,
Ph.D.§~
Prince Henry's Institute of Medical Research, Clayton, University of Melbourne and Royal Women's Hospital, Carlton, Victoria, Australia
Objective: To assess the relationship between the results of the Sperm Quality Analyzer (United Medical Systems Inc., Santa Ana, CA), which measures motile sperm concentration by light scattering, conventional manual semen analysis characteristics, and computer-assisted sperm motility analyses. Design: Sperm Quality Analyzer measurements and manual and computer-assisted semen analyses were performed on 150 (50, 62, and 38) samples in three laboratories and the results were compared. Setting: The study was performed in the Andrology Laboratory of Prince Henry's Institute of Medical Research, Monash Medical Centre, and Andrology Laboratory and Reproductive Biology Unit at the Royal Women's Hospital, Melbourne, Victoria, Australia. Patients: Patients presented to the laboratories for routine fertility evaluation in the male and were selected at random to reflect the range of normal and abnormal samples seen in the laboratories. Interventions: None. Main Outcome Measures: Sperm count, motility (percent motility, motility index, velocity, and amplitude of lateral head displacement [ALH]), morphology, and normal acrosomes were evaluated by manual and computer-assisted semen analysis and sperm quality analyzer motility index. Results: Spearman nonparametric univariate analysis showed strong correlations between sperm motility index and manual sperm concentration, motility, abnormal morphology, and normal acrosomes by Pisum sativum agglutinin; and computer-assisted sperm motility analysis sperm concentration, motile concentration, and percent static. Curvilinear velocity, straightline velocity (VSL), and linearity also were related significantly to sperm motility index values. By multiple regression analysis, the significant covariates of the sperm motility index were motile sperm concentration, abnormal morphology, ALH, and straight-line velocity and these accounted for 85.5% of the variance of the sperm motility index. Conclusions: The Sperm Quality Analyzer is easy to use. The good correlation between the sperm motility index, motile sperm concentration, and, in addition, a number of other semen parameters supports the use of the Sperm Quality Analyzer for screening patients and in situations that warrant a rapid verification of semen quality, such as in the IVF or artificial insemination clinic. Further investigation of the Sperm Quality Analyzer in the management of male infertility is warranted. Fertil Steril 1995;63:1071-6 Key Words: Male infertility, semen analysis, sperm motility, automated sperm motility assessment
The relationship of semen characteristics with fertility is known to be poor unless an absolute defect such as azoospermia is present, however, convenReceived May 3,1994; revised and accepted December 7,1994. * United Medical Systems Inc., Santa Ana, California, Patent Pending, U.S. Patent No. 4176953. t Presented in part at the 12th Annual meeting ofthe Fertility Society of Australia, Sydney, New South Wales, Australia, November 3 to 6, 1993. Vol. 63, No.5, May 1995
tional and automated semen analysis methods continue to serve as a guide to the evaluation offertility. The automation of semen assessment has the poten-
:j: Reprints not available.
§ Prince Henry's Institute of Medical Research.
II Andrology Laboratory, Royal Women's Hospital. ~
Department of Obstetrics and Gynaecology, University of Melbourne.
Johnston et a1. Assessment of the Sperm Quality Analyzer
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tial to provide objective and quantitative information, however, currently available systems not only are expensive but tend merely to replace old problems with new ones, hence the search for new and better methods continues. Sperm motility and the concentration of motile sperm are recognized to be important indicators of fertility (1-4). Motility assessment in particular is difficult, with considerable subjectivity and between technician variability. Over the years a wide variety of approaches have been examined to improve sperm motility assessment. These are based either on physical principles such as turbidimetry and spectrophotometry or on photomicrographical methods. The complex cell-tracking systems (computer-assisted sperm motility analysis) that are used commonly today are based on the latter principle (5). These systems allow the calculation of a number of sperm motion characteristics, such as velocities, lateral head displacement, and linearity. Although the systems provide useful information for research in specialized laboratories, accuracy and reproducibility are suboptimal and time and cost factors remain prohibitive in clinical situations. Most methods based on physical principles have been considered less useful because the data they produce are related to the population of cells rather than any individual cell (6). Authors claimed early turbidimetric methods were able to assess the rapidly motile fraction of an ejaculate by monitoring the ability of a sample to swim up into a clear medium as a time-dependent increase in turbidity (7). Laser Doppler spectroscopy, which is based on the analysis ofthe optical Doppler effect with a low power laser as light source, allows the determination of a number of population characteristics, including the sperm concentration, the characteristic velocity, the distribution of velocities, and the percentage of motile sperm (8, 9). Although this information potentially was useful, the systems did not provide the required accuracy (10) and financial considerations made them inappropriate for routine applications. These methods now have been overshadowed by computerassisted sperm motility analysis. The Sperm Quality Analyzer (United Medical Systems Inc., Santa Ana, CA) is a simple and inexpensive commercial unit. The device uses light passed through a small sample of the semen to detect variations in optical density (OD) that result from moving particles. Fluctuations in OD are registered by a photometric cell and the frequency of the analog wave-like electrical signal is converted digitally to provide a numerical output called the sperm motility index. The higher the frequency of the pulses in the analog readings the higher the sperm motility index value. Bartoov et al. (11) have reported a significant 1072
correlation between the sperm motility index and the motile cell concentration in a large patient population (11). The aim of this study was to examine the relationship between the results of the Sperm Quality Analyzer and conventional manual methods and computer-assisted sperm motility analyses. MATERIALS AND METHODS
One hundred fifty patients were selected at random from men attending three Melbourne Andrology Laboratories (50, 62, and 38 samples) for routine infertility investigations. Initial studies revealed consistently low readings for azoospermic samples and the number of azoospermic samples included in the study therefore was limited to seven. The remainder of the sample population (n = 143) represents the variety of nonazoospermic patients, both fertile and nonfertile, seen at the laboratories. Ejaculates were collected after at least 2 days abstinence and were examined within 2 hours of collection. Each ejaculate was examined by routine manual analysis according to the methods of the World Health Organization (12), noting ejaculate pH and volume. Manual analysis included assessment of sperm count, motility divided into four grades (including percent motile and motility index, a quantitative index of the quality of sperm motility), percent live cells, and normal morphology. The motile sperm concentration was calculated as the product of the manual sperm concentration and percent motility. The intraobserver and interobserver variation for manual analysis as determined by repeated analysis of the same sample was :=;;10% in each laboratory, although there are occasions where the variation will be larger than this, particularly in motility and morphology estimations, which are more subjective and particularly reliant on the laboratory's procedures. A double-blind study looking at the variability in the morphology between the laboratories revealed coefficients of variation ranging from 11% to 51%. The sperm concentration, motility characteristics (curvilinear velocity [VCL] , straight-line velocity [VSL], linearity [LIN], amplitude oflateral head displacement [ALH], and average path velocity [VAPD, motile concentration, and percent static for each ejaculate were assessed also using computer assisted methods: Hamilton-Thorn Motility Analyzer (n = 50) (HTM-2030, software version 7.2; Hamilton Thorn Research, Beverly, MA; parameter settings: frames 10, frame rate 25 frames/s, minimum contrast 8, minimum size 6, size gates 0.6 to 1.5, intensity gates 0.6 to 1.5, nonmotile head size 9, nonmotile intensity 250, medium VAP value 30, low VAP value 10, threshold straightness 90; coefficient of variation determined by repeated analysis of the same sample
Johnston et al. Assessment of the Sperm Quality Analyzer
Fertility and Sterility
Table 1 Semen Characteristics and Sperm Motility Index Readings as Determined for 150 Patients in Three Laboratories* Sperm Quality Analyzer readings Sperm motility index (1) (n = 150) Sperm motility index (2) (n = 150) Sperm motility index (3) (n = 150) Mean sperm motility index (n = 150) Semen characteristics (manual) Volume (n = 149) Concentration (l06/mL) (n = 150) Motility (%) (n = 150) Motility index (n = 97) Motile concentration (l06) (n = 150) Motile concentration (curt) (n = 150) Live cells (%) (n = 93) Abnormal forms (%) (n = 150) Normal acrosomes (%) (n = 37) Computer-assisted semen analysis Concentration (l06/mL) (n = 104) Motile concentrationt (n = 99) Motile concentration (curt) (n = 99) Static (%) (n = 99) VAP (n = 45) VCL (n = 99) VSL (n = 99) LIN (n = 99) Straightness (n = 45) ALH (n = 96) Beat cross frequency (n = 45)
111.72 112.03 114.12 112.62
± ± ± ±
89.09 89.72 91.52 89.83
[0 to [0 to [0 to [0 to
3.85 75.51 47.76 92.84 41.39 2.87 78.80 82.86 60.81
± ± ± ± ± ± ± ± ±
1.93 80.19 19.79 35.15 49.62 1.43 14.38 13.16 21.93
(-0.026) [0.6 to 12.0] (0.900) [0 to 436] (0.529) [0 to 85] (0.577) [5 to 170] (0.921) [0 to 270.00] (0.921) [0 to 6.46] (0.174) [1 to 97] (-0.577) [37 to 100] (0.535) [4 to 90]
78.89 42.82 3.00 59.21 42.82 50.16 28.20 54.82 82.73 3.27 10.40
± ± ± ± ± ± ± ± ± ± ±
72.92 (0.857) [0.1 to 436] 52.38 (0.849) [0.88 to 274.56] 1.30 (0.849) [0.96 to 6.50] 22.57 (-0.689) [12.0 to 96.0] 7.67 (0.304) [26.0 to 59.0] 11.21 (0.338) [28.0 to 77.0] 11.28 (0.416) [2.9 to 52.0] 16.81 (0.315) [9.0 to 89.0] 6.19 (-0.271) [65.0 to 96.0] 1.11 (0.116) [1.0 to 5.9] 1.69 (-0.329) [3.7 to 13.0]
312] 308] 332] 317.33]
* Values are means ± SD with range in brackets. The correlation coefficient (p as calculated by Spearman p nonparametric analysis) relative to the mean sperm motility index value is in parentheses.
t Calculated as the concentration multiplied by 100% static.
(n = 5) previously reported: sperm count 7.5%, VSL 5.7%, ALH 9.1% [13]) at the Andrology Laboratory Royal Women's Hospital or by the CellSoft computer automated semen analyzer (n = 62) (Cryo Resources Ltd., Montgomery, NY; parameter settings: image sampling frequency 25 frames/s, number of frames analyzed 20, minimum sampling for motile 2 frames, minimum sampling for velocity 10 frames, minimum sampling for ALH 10 frames, threshold velocity 8 J.Lrn/s, maximum velocity 200 J.Lrn/s, minimum velocity 3 J.Lrn/s, minimum linearity for ALH 25, cell size range 5 to 25 pixels, magnification calibration 0.688 J.Lrn/pixel; coefficient of variation for repeated analysis of the same sample (n = 5) previously reported: VCL 12%, LIN 10%, ALH 9% [14]) at Prince Henry's Institute. The acrosomal status was examined by the Pisum sativum agglutinin method (n = 38) (15). In all laboratories the sperm motility index was determined in three sequential readings by introducing semen into a thin glass capillary tube (internal dimensions: depth 0.3 mm, width 3 mm, length 50 mm), which was housed in a plastic casing with a 2-mm diameter optical aperture. The sample was processed by the Sperm Quality Analyzer according to the manufacturer's instructions. Using this method each sperm motility index reading represents the mean of four 10-second measurements of the analog signal. The number of sperm measured in
each reading is concentration dependent and would vary from 940 to 282 x 103 sperm for motile sperm counts of 1 to 300 X lOs/mL, respectively. The differences between the three Sperm Quality Analyzer measurements were analyzed by two-way analysis of variance. Data were analyzed by Spearman nonparametric and multiple linear regression analysis and the difference between grouped data was analyzed by Kruskal-Wallis and Wilcoxon rank sum tests with the use of the Statistical Package for Interactive Data Analysis (SPIDA) (Macquarie University, Sydney, New South Wales, Australia).
Vol. 63, No.5, May 1995
RESULTS The means of the three replicate measurements of each sample revealed a small but significant increase in sperm motility index units with time (first reading, 111.720; second reading, 112.033; third reading, 114.120; P < 0.05). The mean of the three Sperm Quality Analyzer readings was subsequently used in all calculations. Table 1 shows the sperm motility index readings and the mean and ranges and correlation coefficient (p) relative to the mean sperm motility index value for the semen variables; manual sperm concentration, percent motility, motility index, motile sperm concentration, percentage live cells, abnormal morphology, normal acrosomes
Johnston et al. Assessment of the Sperm Quality Analyzer
1073
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Figure 1 Relationship between the sperm motility index (mean) and motile sperm concentration (manual): untransformed (A) and cube root transformation (B) of the motile sperm concentration. 8MI, sperm motility index.
by Pisum sativum agglutinin, computer-assisted sperm motility analysis estimates of concentration, motile concentration, percent static, VAP, VCL, VSL, LIN, straightness, ALH, and beat cross frequency. There was a curvilinear relationship between sperm motility index and motile sperm concentration. This could be linearized by cube root transformation of the motile sperm concentration (Fig. 1). Spearman nonparametric univariate analysis showed strong correlations (as described by p; Table 1) between sperm motility index and manual sperm concentration, percentage motility, motility index, motile sperm concentration, abnormal morphology and normal acrosomes, computer-assisted sperm motility analysis sperm concentration, motile concentration, and percent static. Straight-line velocity, VCL, and LIN also were significant. Linear regression analysis of the relationship between the sperm motility index reading (mean) and manual motile sperm concentration (cube root transformed) indicated that 82.1% of the variance of the sperm motility index could be accounted for by the motile sperm concentration alone (cube root transformed) (coefficient 59.157; SE 2.803; P = 0.000; r2 = 0.821). Linear regression analysis of the relationship between the sperm motility index reading 1074
(mean) and computer-assisted sperm motility analysis assessed motile sperm concentration (cube root transformed) indicated that 69.4% of the variance could be accounted for by the computer-assisted sperm motility analysis assessed motile sperm concentration (cube root transformed) (coefficient 56.056; SE 3.783; P = 0.000; r2 = 0.694) (n = 98). Abnormal morphology, VSL, and ALH were also additional independently significant covariates of sperm motility index and together with motile sperm concentration accounted for 85.5% of the variance of the sperm motility index (motile concentration [cube root transformed], coefficient 53.805; SE 2.982; P = 0.000; abnormal morphology, coefficient 0.680; SE 0.287; P = 0.020; ALH, coefficient 8.695; SE 3.116; P = 0.006; VSL, coefficient 1.123; SE 0.340; P = 0.001). Figure 2 shows the relationship between the manually determined and computer-assisted sperm motility analysis determined motile concentrations (cube root transformed). By Pearson's correlation and linear regression analysis, the manually determined motile concentration (cube root transformed) was shown to correlate better with the mean sperm motility index reading (r = 0.821) than with the motile concentration (cube root transformed) determined by computer assisted means (r2 = 0.694). Potential outliers in the data were detected by plotting the residuals versus predicted values. A single outlier with inconsistent sperm motility index readings (158; 82; 102) was detected and was disregarded from the analyses. All other samples appeared to fit the predicted model. Table 2 shows the separation of semen analysis results into the sperm motility index ranges recommended by the manufacturer (abnormal sperm motility index < 80; doubtful sperm motility index 80 to 160; normal sperm motility index >160). Kruskal Wallis and Wilcoxon rank sum tests revealed significant differences in the manual count, motility, motile concentration, and
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Figure 2 Relationship between manually determined and computer-assisted sperm motility analysis determined motile sperm concentration (cube root transformed).
Johnston et al. Assessment of the Sperm Quality Analyzer
Fertility and Sterility
Table 2
Semen Analysis Variables for Samples Divided According to Sperm Motility Index* Sperm motility index <80
Concentration (manual)t Motility (%)t Motile concentrationt Abnormal morphologyt Motile concentration (computer-assisted sperm motility analysis) Static (%)t ALH LIN:j:
80 to 160 (n = 33)
(n = 59)
Semen variable 16.5 36.3 6.2 89.9
(0 to 118) (0 to 72) (0 to 38.9) (54 to 100)
58.6 49.3 28.2 81.5
(12 to 138) (24 to 76) (4.9 to 66.0) (37 to 99)
6.0 79.0 3.13 45.6
(0.9 to 20.8) (37 to 96) (1.00 to 5.60) (12.0 to 89.0)
23.9 59.3 3.11 57.5
(1.6 to 59.8) (31 to 90) (1.90 to 5.90) (9.0 to 79.0)
>160 (n = 58)
145.2 58.6 84.7 76.4 75.1 46.5 3.43 59.4
(31 to 436) (10 to 85) (11.9 to 270.0) (42 to 100) (6.7 to 274.6) (12 to 88) (2.0 to 5.6) (30.0 to 81.0)
* The manufacturer designates semen samples with sperm motility index <80 as abnormal, <80 to 160 as doubtful, and> 160 as normal. t Sperm motility index <80, sperm motility index 80 to 160, and sperm motility index > 160 are significantly different from each other, P < 0.05.
:j: Sperm motility index <80 is significantly different from sperm motility index 80 to 160 and sperm motility index> 160, P < 0.05. Sperm motility index 80 to 160 and sperm motility index> 160 are not significantly different.
abnormal morphology and computer-assisted sperm motility analysis motile concentration, percent static, and LIN of samples (P < 0.05) when the sperm motility index was used to group the data into these sperm motility index ranges.
The Sperm Quality Analyzer has good reproducibility particularly in comparison to conventional semen analysis, which is highly subjective, and to computer-assisted sperm motility analysis techniques, which have problems associated with the distinction between debris and sperm. As the Sperm Quality Analyzer only recognizes motile particles, problems with debris have been largely overcome. Although the Sperm Quality Analyzer output appeared to be consistent when three replicates were made from the same sample, there was a significant overall trend to an increase in sperm motility index with time, perhaps indicating a change in the temperature of a sample when left in the chamber over time. Only 1 of 151 samples appeared to have been loaded incorrectly by the technician. This error was readily detectable. The sperm motility index was shown to be a reflection of a combination of semen characteristics, notably the motile sperm concentration (both manual and computer-assisted sperm motility analysis and including a number of individual motility characteristics), but also interestingly of the morphology and acrosomal status of the sample. It is possible that a spermatozoon's movement is influenced by the morphology and the correlation of sperm motility index with morphology in this study may reflect this intimate relationship. Alternately, sperm with abnormal morphology may scatter light differently. Although the more complex computer-assisted sperm motility analysis systems produce a number of variables to describe the sperm motility of individual cells, which is then pooled (including VeL, VSL, LIN, and ALH), methods such as the Sperm Quality Analyzer produce a single output that is dependent predominantly on both the average rate of movement of
DISCUSSION
Modern semen analysis methodology depends on adequately qualified staff and often on expensive and technically complicated equipment. There remain potential advantages in many areas offertility and infertility for the development of a simple reliable method of performing semen examination. In particular, the current interest in male contraceptive procedures would provide a potentially large market for such a device. This study has shown that the Sperm Quality Analyzer, a system based on the scattering of a light beam as it is passed through a sample in a flat (0.3mm depth) capillary tube, provides the user with a numerical value (sperm motility index) that reflects a variety of aspects of semen quality. The mean sperm motility index values were found to correlate well with the motile sperm concentration of 150 patients' samples as determined by standard manual methods. Furthermore, the sperm motility index correlation with manual methods was better than that achieved using current computer-assisted sperm motility analysis technology. In comparison with the alternative methods of semen analysis, the Sperm Quality Analyzer is simple to use, requiring a single piece of equipment and capillary inserts. Ease oftransportation would facilitate use of the machine in satellite laboratories or on site in IVF or donor insemination units. Vol. 63, No.5, May 1995
Johnston et al. Assessment of the Sperm Quality Analyzer
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the cell population and on the concentration of cells. Although a single output has the disadvantage that poor characteristics such as a poor motility may be concealed by other good characteristics such as a high sperm count, making it impossible to determine the type of semen defect and in some cases the fact that there is a defect at all, the simplicity of this test may make it useful in instances where an immediate indication of overall semen quality is required in the absence of qualified laboratory staff. It also may have a place in infertility screening; the testing of semen before insemination would be a good example of this. Furthermore, it also may be useful for repeated measurements of semen quality in large studies of the relationship between semen analysis results and fertility in vivo and in vitro. The Sperm Quality Analyzer also has the potential to facilitate and standardize post-thaw recovery assessments of donor cryopreserved or patients' semen. Another potential use would be for the verification or quality control of conventional semen characteristics. For example, it should be possible to estimate the motility of a sample given the sperm count if the sperm motility index is correlated highly to the motile sperm concentration as has been shown in this paper. Studies in these aspects have commenced. The Sperm Quality Analyzer was designed as a screening instrument to define normal and abnormal semen and is more sensitive in the range of 80 to 160 units, which corresponds to motile sperm concentrations between 4.9 and 148.0 X 106 sperm. At low motile sperm concentrations, the Sperm Quality Analyzer was more variable, for example a zero sperm motility index reading in at least two of the three replicates corresponded to motile sperm concentrations in the range 0.7 to 9.45 X 106 sperm (mean 2.8). This would make the current machine inadequate for use with severe male factor patients (i.e., sperm counts < 10 X 106/mL). Improved sensitivity in the low range should render the equipment more useful for assessment of sperm suspensions for insemination in vitro and for testing for male contraception in which suppression of motile sperm concentration below a certain low value is the goal. The evaluation of an improved model, purportedly with greater sensitivity in the lower range, currently is underway. Acknowledgments. The authors thank Mr. Rick Held from Cabras Business Ltd. for loaning us the equipment for this study.
1076
We also thank Mrs. Anne Bell, Mrs. MingLi Liu, and Mr. Peter Elliott for their excellent technical assistance.
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Johnston et al. Assessment of the Sperm Quality Analyzer
Fertility and Sterility