The social shape of sperm: Using an integrative machine-learning approach to examine sperm ultrastructure and collective motility

Sperm are one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioral variation, including the formation of collective groups of cells that swim together for motility or transport through the female reproductive tract. Here we take advantage of the natural variation in sperm traits observed across Peromyscus mice to test the hypothesis that the morphology of the rodent sperm head influences their sperm aggregation behavior. Using machine learning and traditional morphometric approaches to quantify and analyze their complex shapes, we show that the elongation of the sperm head is the most distinguishing morphological trait in these rodents and, as predicted, significantly associates with collective sperm movements obtained from in vitro observations. We then successfully use neural network analysis to predict the size and proportion of sperm aggregates from sperm head morphology and show that species whose sperm feature relatively wider heads aggregate more often and form larger groups, providing support for the theoretical prediction that an adhesive region around the equatorial region of the sperm head mediates these unique gametic interactions. Together these findings advance our understanding of how even subtle variation in sperm design can drive differences in sperm function and performance.