Current status and potential of morphometric sperm analysis.

The spermatozoon is the most diverse cell type known and this diversity is considered to reflect differences in sperm function. How the diversity in sperm morphology arose during speciation and what role the different specializations play in sperm function, however, remain incompletely characterized. This work reviews the hypotheses proposed to explain sperm morphological evolution, with a focus on some aspects of sperm morphometric evaluation; the ability of morphometrics to predict sperm cryoresistance and male fertility is also discussed. For this, the evaluation of patterns of change of sperm head morphometry throughout a process, instead of the study of the morphometric characteristics of the sperm head at different stages, allows a better identification of the males with different sperm cryoconservation ability. These new approaches, together with more studies employing a greater number of individuals, are needed to obtain novel results concerning the role of sperm morphometry on sperm function. Future studies should aim at understanding the causes of sperm design diversity and the mechanisms that generate them, giving increased attention to other sperm structures besides the sperm head. The implementation of scientific and technological advances could benefit the simultaneous examination of sperm phenotype and sperm function, demonstrating that sperm morphometry could be a useful tool for sperm assessment.

Taking advantage of the use of supervised learning methods for characterization of sperm population structure related with freezability in the Iberian red deer.

Using Iberian red deer as a model, this study presents a supervised learning method, the Support Vector Machines (SVM), to characterize sperm population structure related with freezability. Male freezability was assessed by evaluating motility, membrane status and mitochondrial membrane potential of sperm after a freezing-thawing procedure. The SVM model was generated using sperm motility information captured by computer-assisted sperm analysis (CASA) from thawed semen, belonging to six stags with marked differences on their freezability. A total of 1369 sperm tracks were recorded for seven kinematic parameters and assigned to four motility patterns based on them: weak motile, progressive, transitional and hyperactivated-like. Then, these data were split in two sets: the training set, used to train the SVM model, and the testing set, used to examine how the SVM method and three other unsupervised methods, a non-hierarchical, a hierarchical and a multistep clustering procedures, performed the sperm classification into subpopulations. The SVM was revealed as the most accurate method in the characterization of sperm subpopulations, showing all the sperm subpopulations obtained in this way high significant correlations with those sperm parameters used to characterize freezability of males. Given its superiority, the SVM method was used to characterize the sperm motile subpopulations in Iberian red deer. Sperm motile data from frozen-thawed semen belonging to 25 stags were recorded and loaded into the SVM model. The sperm population structure revealed that those males showing poor freezability were characterized by high percentages of sperm with a weak motility pattern. In opposite, males showing good freezability were characterized by higher percentages of sperm with a progressive and hyperactivated-like motility pattern and lower percentages of sperm with a weak motile pattern. We also identified a sperm subpopulation with a transitional motility pattern. This subpopulation increased as the freezability of males improved, and may be used as indicative of overall sperm motility.