A new computational approach, based on images trajectories, to identify the subjacent heterogeneity of sperm to the effects of ketanserin

• Published in: Cytometry. Part A Vol. 103; no. 8; pp. 655 - 663
• Main Authors: Rodríguez‐Martínez, Eder Alejandro, Rivas, Cindy U., Ayala, María E., Blanco‐Rodríguez, Rodolfo, Juarez, Nancy, Hernandez‐Vargas, Esteban A., Aragón, Andrés
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.08.2023; Wiley Subscription Services, Inc; Wiley
• Subjects: Algorithms; Animals; boar sperm; Clustering; Computer applications; computer assisted sperm analysis; Computer Science; Fertility; Heterogeneity; Image contrast; Image reconstruction; images of sperm trajectories; Incubation; Ketanserin; Ketanserin - pharmacology; kinematic subpopulations; Kinematics; Machine learning; Male; Motility; Parameters; patterns of motility; Semen; Semen - physiology; Semen Analysis - methods; Serotonin; Sperm; Sperm Motility; Spermatozoa - physiology; Statistical analysis; Subpopulations; Swine; Velocity; kinematic subpopulations; serotonin; boar sperm; machine learning; patterns of motility; computer assisted sperm analysis; images of sperm trajectories; Serotonin; Computer Assisted Sperm Analysis; Boar sperm; Machine learning; Patterns of motility; Kinematic subpopulations; Images of sperm trajectories
• ISSN: 1552-4922; 1552-4930; 1552-4930
• DOI: 10.1002/cyto.a.24732

The identification of kinematic subpopulations is of paramount importance to understanding the biological nature of the sperm heterogeneity. Nowadays, the data of motility parameters obtained by a computer‐assisted sperm analysis (CASA) system has been used as input to distinct algorithms to identify kinematic subpopulations. In contrast, the images of the trajectories were depicted only as examples of the patterns of motility in each subpopulation. Here, python code was written to reconstruct the images of trajectories, from their coordinates, then the images of trajectories were used as input to a machine learning clustering algorithm of classification, and the subpopulations were described statistically by the motility parameters. Finally, the images of trajectories in each subpopulation were displayed in a way we called Pollock plots. Semen samples of boar sperm were treated with distinct concentrations of ketanserin (an antagonist of the 5‐HT2 receptor of serotonin) and untreated samples were used as a control. The motility of sperm in each sample was analyzed at 0 and 30 min of incubation. Six subpopulations were found. The subpopulation 2 presented the highest values of velocities at 0 or 30 min. After 30 min of incubation, the ketanserin increased the values of the curvilinear velocity at high concentrations, whereas the linearity and the straight velocity decreased. Our computational model permits better identification of the kinematic subpopulations than the traditional approach and provides insights onto the heterogeneity of the response to ketanserin; thus, it could significantly impact the research on the relationship between sperm heterogeneity‐fertility.