Predicting fertility from sperm motility landscapes.

Area of Science:

• Animal reproduction
• Sperm biology
• Bioinformatics

Background:

• Understanding sperm motility is crucial for evolutionary and applied reproductive science.
• Computer-aided sperm analysis promises improved quantification but clarity on sperm variability and fertility is lacking.
• Existing methods struggle to fully capture the complex dynamics of sperm behavior.

Purpose of the Study:

• To characterize pig sperm motility using t-SNE for behavioral variability analysis.
• To establish a link between sperm motility patterns and fertility outcomes.
• To develop a predictive model for fertility based on sperm motion characteristics.

Main Methods:

• Utilized t-SNE (t-distributed Stochastic Neighbor Embedding) for visualizing and analyzing sperm motility data.
• Employed Bayesian logistic regression for fertility prediction based on identified motility features.
• Analyzed sperm ejaculates from individuals to identify hierarchical organization.

Main Results:

• t-SNE revealed a hierarchical organization in sperm motility across ejaculates and individuals.
• Sperm motility features, specifically high-speed and straight-lined motion, showed a positive correlation with fertility.
• The developed model enabled accurate fertility predictions.

Conclusions:

• Sperm motility exhibits a hierarchical structure that can be uncovered using embedding methods like t-SNE.
• Specific motility parameters are more indicative of fertility than general variability.
• Combining embedding methods with Bayesian inference offers a powerful framework for understanding animal and human fertility.