Frecuencia de fotogramas óptima para evaluar la cinética espermática de verracos con un sistema CASA-Mot
DOI:
https://doi.org/10.15517/am.v32i1.41928Palabras clave:
espermatozoo, fotogramas por segundo, subpoblaciones espermáticas, cerdo, reproducción porcinaResumen
Introducción. La movilidad es el indicador de calidad seminal más utilizado, por tanto, es necesario determinar una adecuada frecuencia de fotogramas (frame rate, FR) para estandarizar los análisis cinéticos con los equipos de análisis computadorizado de semen (CASA-Mot) y disminuir la variabilidad entre laboratorios, lo que optimizará el número de dosis seminales y su calidad para la industria porcina basada en inseminación artificial (IA). Objetivo. Determinar la frecuencia óptima de fotogramas sobre la cinética espermática de verraco mediante un sistema comercial CASA-Mot y analizar la distribución de subpoblaciones espermáticas con el FR óptimo. Materiales y métodos. Se utilizaron veinte dosis seminales de diez verracos Pietrain (Sus scrofa domestica). El periodo experimental fue entre febrero y julio del año 2017. Se utilizó el sistema CASA-Mot (ISAS®v1) con cámaras de recuento ISAS®D4C20 precalentadas a 37 °C. El tiempo de captura fue de dos segundos. Resultados. La frecuencia de fotogramas afectó los parámetros de cinética espermática, exceptuando la velocidad rectilínea (VSL), debido a que no representó un comportamiento dependiente del FR. Las demás variables presentaron alteraciones significativas al cambiar su FR, y la velocidad curvilínea (VCL) y velocidad promedio (VAP) fueron las variables más sensibles al cambio. Se obtuvieron tres componentes principales (PC) definidos por patrones de movilidad, progresividad y oscilación, en donde el PC1 (velocidad-ondulación) fue el más influyente, debido a que representó el 51,34 % y 55,08 % de la varianza total explicada para 25 y 200 fps (fotogramas por segundo), respectivamente. Conclusión. La frecuencia óptima de fotogramas para analizar variables de cinética espermática en verraco con un sistema CASA-Mot fue de 200 fps. Una tasa de fotogramas inferior provoca errores en la estimación de la trayectoria de los espermatozoides. La distribución de subpoblaciones espermáticas varía según la tasa de fotogramas utilizada. Es necesario establecer un protocolo de análisis para homogenizar los resultados.
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Abaigar, T., Holt, W., Harrison, R., & del Barrio, G. (1999). Sperm subpopulations in boar (Sus scrofa) and gazelle (Gazella dama mhorr) semen as revealed by pattern analysis of computer-assisted motility assessments. Biology of Reproduction, 60(1), 32–41. https://doi.org/10.1095/biolreprod60.1.32
Amann, R., & Hammerstedt, R. (1993). In vitro evaluation of sperm quality: An opinion. Journal of Andrology, 14(6), 397–406. https://doi.org/10.1002/j.1939-4640.1993.tb03247.x
Amann, R., & Katz, D. F. (2004). Andrology Lab Corner*: Reflections on CASA After 25 Years. Journal of Andrology, 25(3), 317–325. https://doi.org/10.1002/j.1939-4640.2004.tb02793.x
Amann, R., & Waberski, D. (2014). Computer-assisted sperm analysis (CASA): Capabilities and potential developments. Theriogenology, 81(1), 5-17. https://doi.org/10.1016/J.THERIOGENOLOGY.2013.09.004
Bompart, D., García-Molina, A., Valverde, A., Caldeira, C., Yániz, J., Núñez de Murga, M., & Soler, C. (2018). CASA-Mot technology: how results are affected by the frame rate and counting chamber. Reproduction, Fertility and Development, 30(6), 810–819. https://doi.org/10.1071/RD17551
Bompart, D., Vázquez, R., Gómez, R., Valverde, A., Roldán, E., García-Molina, A., & Soler, C. (2019). Combined effects of type and depth of counting chamber, and rate of image frame capture, on bull sperm motility and kinematics. Animal Reproduction Science, 209, 106169. https://doi.org/10.1016/J.ANIREPROSCI.2019.106169
Broekhuijse, M. L. W. J., Šoštarić, E., Feitsma, H., & Gadella, B. M. (2011). Additional value of computer assisted semen analysis (CASA) compared to conventional motility assessments in pig artificial insemination. Theriogenology, 76(8), 1473-1486. https://doi.org/10.1016/j.theriogenology.2011.05.040
Broekhuijse, M. L. W. J., Šoštarić, E., Feitsma, H., & Gadella, B. M. (2012). Application of computer-assisted semen analysis to explain variations in pig fertility. Journal of Animal Science, 90(3), 779–789. https://doi.org/10.2527/jas.2011-4311
Caldeira, C., Hernández-Ibáñez, S., Valverde, A., Martin, P., Herranz-Jusdado, J. G., Gallego, V., Asturiano, J. F., Dzyuba, B., Pšenička, M., & Soler, C. (2019). Standardization of sperm motility analysis by using CASA-Mot for Atlantic salmon (Salmo salar), European eel (Anguilla anguilla) and Siberian sturgeon (Acipenser baerii). Aquaculture, 502, 223–231. https://doi.org/10.1016/j.aquaculture.2018.12.001
Castellini, C., Dal Bosco, A., Ruggeri, S., & Collodel, G. (2011). What is the best frame rate for evaluation of sperm motility in different species by computer-assisted sperm analysis? Fertility and Sterility, 96(1), 24–27. https://doi.org/10.1016/j.fertnstert.2011.04.096
Fair, S., & Romero-Aguirregomezcorta, J. (2019). Implications of boar sperm kinematics and rheotaxis for fertility after preservation. Theriogenology, 137, 15–22. https://doi.org/10.1016/j.theriogenology.2019.05.032
Flores, E., Taberner, E., Rivera, M. M., Peña, A., Rigau, T., Miró, J., & Rodríguez-Gil, J. E. (2008). Effects of freezing/thawing on motile sperm subpopulations of boar and donkey ejaculates. Theriogenology, 70(6), 936–945. https://doi.org/10.1016/j.theriogenology.2008.05.056
Gallagher, M. T., Cupples, G., Ooi, E. H., Kirkman-Brown, J. C., & Smith, D. J. (2019). Rapid sperm capture: high-throughput flagellar waveform analysis. Human Reproduction, 34(7), 1173–1185. https://doi.org/10.1093/humrep/dez056
Gallego, V., Carneiro, P., Mazzeo, I., Vílchez, M., Peñaranda, D., Soler, C., Pérez, L., & Asturiano, J. (2013). Standardization of European eel (Anguilla anguilla) sperm motility evaluation by CASA software. Theriogenology, 79(7), 1034–1040. https://doi.org/10.1016/J.THERIOGENOLOGY.2013.01.019
Gallego, V., Vílchez, M. C., Peñaranda, D. S., Pérez, L., Herráez, M. P., Asturiano, J. F., & Martínez-Pastor, F. (2015). Subpopulation pattern of eel spermatozoa is affected by post-activation time, hormonal treatment and the thermal regimen. Reproduction, Fertility and Development, 27(3), 529–543. https://doi.org/10.1071/RD13198
García-Molina, A., Valverde, A., Bompart, D., Caldeira, C., Vendrell, A., & Soler, C. (2019). Updating semen analysis: a subpopulation approach. Asian Journal of Andrology, 22(1), 118–119. https://doi.org/10.4103/aja.aja_33_19
Hirai, M., Boersma, A., Hoeflich, A., Wolf, E., Foll, J., Aumüller, T. R., & Braun, J. (2001). Objectively measured sperm motility and sperm head morphometry in boars (Sus scrofa): relation to fertility and seminal plasma growth factors. Journal of Andrology, 22(1), 104–110. http://www.ncbi.nlm.nih.gov/pubmed/11191073
Holt, C., Holt, W., & Moore, H. (1996). Choice of operating conditions to minimize sperm subpopulation sampling bias in the assessment of boar semen by computer-assisted semen analysis. Journal of Andrology, 17(5), 587–596. https://doi.org/10.1002/j.1939-4640.1996.tb01837.x
Holt, C., Holt, W., Moore, H., Reed, H., & Curnock, R. (1997). Objectively measured boar sperm motility parameters correlate with the outcomes of on-farm inseminations: results of two fertility trials. Journal of Andrology, 18, 312–323.
Ibanescu, I., Siuda, M., & Bollwein, H. (2020). Motile sperm subpopulations in bull semen using different clustering approaches – Associations with flow cytometric sperm characteristics and fertility. Animal Reproduction Science, 215, 106329. https://doi.org/10.1016/j.anireprosci.2020.106329
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200.
Katz, D. F., & Dott, H. M. (1975). Methods of measuring swimming speed of spermatozoa. Journal of Reproduction and Fertility, 45(2), 263–272. https://doi.org/10.1530/jrf.0.0450263
Kaufman, L., & Rousseuw, P. J. (1991). Finding groups in data: An introduction to cluster analysis. Biometrics, 47(2), 788. https://doi.org/10.2307/2532178
Kime, D., Van Look, K. J., McAllister, B., Huyskens, G., Rurangwa, E., & Ollevier, F. (2001). Computer-assisted sperm analysis (CASA) as a tool for monitoring sperm quality in fish. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 130(4), 425–433. https://doi.org/10.1016/S1532-0456(01)00270-8
Knox, R. V. (2016). Artificial insemination in pigs today. Theriogenology, 85(1), 83–93. https://doi.org/10.1016/j.theriogenology.2015.07.009
Kraemer, M., Fillion, C., Martin-Pont, B., & Auger, J. (1998). Factors influencing human sperm kinematic measurements by the Celltrak computer-assisted sperm analysis system. Human Reproduction, 13(3), 611–619. http://www.ncbi.nlm.nih.gov/pubmed/9572421
Lopez-Rodriguez, A., Van Soom, A., Arsenakis, I., & Maes, D. (2017). Boar management and semen handling factors affect the quality of boar extended semen. Porcine Health Management, 3(15), 7981. https://doi.org/10.1186/s40813-017-0062-5
Lu, J. C., Huang, Y. F., & Lü, N. Q. (2014). Computer-aided sperm analysis: Past, present and future. Andrologia, 46(4), 329–338. https://doi.org/10.1111/and.12093
Martínez-Pastor, F., Tizado, E., Garde, J., Anel, L., & de-Paz, P. (2011). Statistical Series: Opportunities and challenges of sperm motility subpopulation analysis. Theriogenology, 75(5), 783–795. https://doi.org/10.1016/J.THERIOGENOLOGY.2010.11.034
Morris, A. R., Coutts, J. R., & Robertson, L. (1996). A detailed study of the effect of videoframe rates of 25, 30 and 60 Hertz on human sperm movement characteristics. Human Reproduction, 11(2), 304–310. https://doi.org/10.1093/HUMREP/11.2.304
Mortimer, S. (2000). CASA - Practical Aspects. Journal of Andrology, 21(4), 515–524. https://doi.org/10.1002/J.1939-4640.2000.TB02116.X
Mortimer, D., Serres, C., Mortimer, S., & Jouannet, P. (1988). Influence of image sampling frequency on the perceived movement characteristics of progressively motile human spermatozoa. Gamete Research, 20(3), 313–327. https://doi.org/10.1002/mrd.1120200307
Mortimer, S., & Swan, M. (1999). Effect of image sampling frequency on established and smoothing-independent kinematic values of capacitating human spermatozoa. Human Reproduction, 14(4), 997–1004. https://doi.org/10.1093/humrep/14.4.997
Murtagh, F., & Legendre, P. (2014). Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? Journal of Classification, 31(3), 274–295. https://doi.org/10.1007/s00357-014-9161-z
Nöthling, J. O., & dos Santos, I. P. (2012). Which fields under a coverslip should one assess to estimate sperm motility? Theriogenology, 77(8), 1686–1697. https://doi.org/10.1016/j.theriogenology.2011.12.014
Parrilla, I., Perez-Patiño, C., Li, J., Barranco, I., Padilla, L., Rodriguez-Martinez, H., Martinez, E. A., & Roca, J. (2019). Boar semen proteomics and sperm preservation. Theriogenology, 137, 23–29. https://doi.org/10.1016/j.theriogenology.2019.05.033
Peña, A., Adán, S., Quintela, L., Becerra, J., & Herradón, P. (2018). Relationship between motile sperm subpopulations identified in frozen-thawed dog semen samples and their ability to bind to the zona pellucida of canine oocytes. Reproduction in Domestic Animals, 53, 14–22. https://doi.org/10.1111/rda.13349
Ramió, L., Rivera, M. M., Ramírez, A., Concha, I. I., Peña, A., Rigau, T., & Rodríguez-Gil, J. E. (2008). Dynamics of motile-sperm subpopulation structure in boar ejaculates subjected to “in vitro” capacitation and further “in vitro” acrosome reaction. Theriogenology, 69(4), 501–512. https://doi.org/10.1016/j.theriogenology.2007.10.021
Ravinder, K., Nasaruddin, K., Majumdar, K. C., & Shivaji, S. (1997). Computerized analysis of motility, motility patterns and motility parameters of spermatozoa of carp following short-term storage of semen. Journal of Fish Biology, 50(6), 1309–1328. https://doi.org/10.1111/j.1095-8649.1997.tb01655.x
Rijsselaere, T., Van Soom, A., Maes, D., & de Kruif, A. (2004). Effect of technical settings on canine semen motility parameters measured by the Hamilton-Thorne analyzer. Theriogenology, 60(8), 1553–1568. https://doi.org/10.1016/S0093-691X(03)00171-7
Saravia, F., Núñez-Martínez, I., Morán, J., Soler, C., Muriel, A., Rodríguez-Martínez, H., & Peña, F. (2007). Differences in boar sperm head shape and dimensions recorded by computer-assisted sperm morphometry are not related to chromatin integrity. Theriogenology, 68(2), 196–203. https://doi.org/10.1016/j.theriogenology.2007.04.052
Schulze, M., Ruediger, K., Mueller, K., Jung, M., Well, C., & Reissmann, M. (2013). Development of an in vitro index to characterize fertilizing capacity of boar ejaculates. Animal Reproduction Science, 140(1–2), 70-76. https://doi.org/10.1016/j.anireprosci.2013.05.012
Sellés, E., Gadea, J., Romar, R., Matás, C., & Ruiz, S. (2003). Analysis of in vitro fertilizing capacity to evaluate the freezing procedures of boar semen and to predict the subsequent fertility. Reproduction in Domestic Animals = Zuchthygiene, 38(1), 66–72. http://www.ncbi.nlm.nih.gov/pubmed/12535333
Soler, C., Contell, J., Bori, L., Sancho, M., García-Molina, A., Valverde, A., & Segarvall, J. (2017). Sperm kinematic, head morphometric and kinetic-morphometric subpopulations in the blue fox (Alopex lagopus). Asian Journal of Andrology, 19(2), 154–159. https://doi.org/10.4103/1008-682X.188445
Soler, C., Fuentes, M., Sancho, M., García, A., Núñez de Murga, M., & Núñez de Murga, J. (2012). Effect of counting chamber on seminal parameters, analyzing with the ISASv1®. Revista Internacional de Andrología, 10(4), 132–138. https://doi.org/10.1016/S1698-031X(12)70069-9
Soler, C., García, A., Contell, J., Segervall, J., & Sancho, M. (2014). Kinematics and Subpopulations’ Structure Definition of Blue Fox (Alopex lagopus) Sperm Motility using the ISAS® V1 CASA System. Reproduction in Domestic Animals, 49(4), 560–567. https://doi.org/10.1111/rda.12310
Spencer, N. H. (2013). Essentials of Multivariate Data Analysis. Chapman and Hall/CRC Press. https://doi.org/10.1201/b16344
Thurston, L., Watson, P., Mileham, A., & Holt, W. (2001). Morphologically distinct sperm subpopulations defined by Fourier shape descriptors in fresh ejaculates correlate with variation in boar semen quality following cryopreservation. Journal of Andrology, 22(3), 382–394. http://www.ncbi.nlm.nih.gov/pubmed/11330638
Tremoen, N. H., Gaustad, A. H., Andersen-Ranberg, I., van Son, M., Zeremichael, T. T., Frydenlund, K., Grindflek, E., Våge, D. I., & Myromslien, F. D. (2018). Relationship between sperm motility characteristics and ATP concentrations, and association with fertility in two different pig breeds. Animal Reproduction Science, 193, 226–234. https://doi.org/10.1016/J.ANIREPROSCI.2018.04.075
Valverde, A., Arenán, H., Sancho, M., Contell, J., Yániz, J., Fernández, A., & Soler, C. (2016). Morphometry and subpopulation structure of Holstein bull spermatozoa: variations in ejaculates and cryopreservation straws. Asian Journal of Andrology, 18(6), 851–857. https://doi.org/10.4103/1008-682X.187579
Valverde, A., Barquero, V., & Soler, C. (2020). The application of computer-assisted semen analysis (CASA) technology to optimise semen evaluation. A review. Journal of Animal and Feed Sciences, 29(3), 189–198. https://doi.org/10.22358/jafs/127691/2020
Valverde, A., & Madrigal-Valverde, M. (2018). Sistemas de análisis computadorizado de semen en la reproducción animal. Agronomía Mesoamericana, 29(2), 469–484. https://doi.org/10.15517/ma.v29i2.30613
Valverde, A., & Madrigal-Valverde, M. (2019). Evaluación de cámaras de recuento sobre parámetros espermáticos de verracos analizados con un sistema CASA-Mot. Agronomía Mesoamericana, 30(2), 447–458. https://doi.org/10.15517/am.v30i1.34145
Valverde, A., Madrigal-Valverde, M., Caldeira, C., Bompart, D., Núñez de Murga, J., Arnau, S., & Soler, C. (2019a). Effect of frame rate capture frequency on sperm kinematic parameters and subpopulation structure definition in boars, analyzed with a CASA-Mot system. Reproduction in Domestic Animals, 54(2), 167–175. https://doi.org/10.1111/rda.13320
Valverde, A., Madrigal-Valverde, M., Camacho-Calvo, M., Zambrana-Jiménez, A., & López, L. (2018). Efecto de la composición racial sobre la calidad espermática de verracos. Agronomía Mesoamericana, 29(3), 485. https://doi.org/10.15517/ma.v29i3.32445
Valverde, A., Madrigal-Valverde, M., Castro-Morales, O., Gadea-Rivas, A., Johnston, S., & Soler, C. (2019b). Kinematic and head morphometric characterisation of spermatozoa from the Brown Caiman (Caiman crocodilus fuscus). Animal Reproduction Science, 207, 9–20. https://doi.org/10.1016/J.ANIREPROSCI.2019.06.011
Valverde, A., Madrigal-Valverde, M., Lotz, J., Bompart, D., & Soler, C. (2019c). Effect of video capture time on sperm kinematic parameters in breeding boars. Livestock Science, 220, 52–56. https://doi.org/10.1016/j.livsci.2018.12.008
van der Horst, G., Maree, L., & du Plessis, S. (2018). Current perspectives of CASA applications in diverse mammalian spermatozoa. Reproduction, Fertility and Development, 30(6), 875–888. https://doi.org/10.1071/RD17468
Vásquez, F., Soler, C., Camps, P., Valverde, A., & García-Molina, A. (2016). Spermiogram and sperm head morphometry assessed by multivariate cluster analysis results during adolescence (12-18 years) and the effect of varicocele. Asian Journal of Andrology, 18(6), 824–830. https://doi.org/10.4103/1008-682X.186873
Verstegen, J., Iguer-Ouada, M., & Onclin, K. (2002). Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology, 57(1), 149–179. https://doi.org/10.1016/S0093-691X(01)00664-1
Víquez, L., Barquero, V., Soler, C., Roldan, ERS., & Valverde, A. (2020). Kinematic sub-populations in bull spermatozoa: A comparison of classical and bayesian approaches. Biology, 9(6), 138. https://doi.org/10.3390/biology9060138
Vyt, P., Maes, D., Quinten, C., Rijsselaere, T., Deley, W., Aarts, M., De Kruif, A., & Van-Soom, A. (2008). Detailed motility evaluation of boar semen and its predictive value for reproductive performance in sows. Vlaams Diergeneeskundig Tijdschrift, 77(77), 291-298. https://doclib.uhasselt.be/dspace/bitstream/1942/9075/1/sows.pdf
Wilson-Leedy, J. G., & Ingermann, R. L. (2007). Development of a novel CASA system based on open source software for characterization of zebrafish sperm motility parameters. Theriogenology, 67(3), 661–672. https://doi.org/10.1016/j.theriogenology.2006.10.003
Winters, R. A., Hamilton, D. N., Bhatnagar, A. S., Fitzgerald, R., Bovin, N., & Miller, D. J. (2018). Porcine sperm binding to oviduct cells and glycans as supplements to traditional laboratory semen analysis. Journal of Animal Science, 96(12), 5265–5275. https://doi.org/10.1093/jas/sky372
Yániz, J., Palacín, I., Caycho, K., Soler, C., Silvestre, M., & Santolaria, P. (2018). Determining the relationship between bull sperm kinematic subpopulations and fluorescence groups using an integrated sperm quality analysis technique. Reproduction, Fertility and Development, 30(6), 919–923. https://doi.org/10.1071/RD17441
Yániz, J., Soler, C., Alquézar-Baeta, C., & Santolaria, P. (2017). Toward an integrative and predictive sperm quality analysis in Bos taurus. Animal Reproduction Science, 181, 108–114. https://doi.org/10.1016/j.anireprosci.2017.03.022
Yániz, J., Vicente-Fiel, S., Soler, C., Recreo, P., Carretero, T., Bono, A., Berné, J., & Santolaria, P. (2016). Comparison of different statistical approaches to evaluate morphometric sperm subpopulations in men. Asian Journal of Andrology, 18(6), 819–823. https://doi.org/10.4103/1008-682X.186872
Zhu, J. J., Pacey, A. A., Barratt, C. L. R., & Cooke, I. D. (1994). Computer-assisted measurement of hyperactivation in human spermatozoa: Differences between european and american versions of the hamilton-thorn motility analyser. Human Reproduction, 9(3), 456–462. https://doi.org/10.1093/oxfordjournals.humrep.a138527
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