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

Sperm is one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioural 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 natural...

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Published inProceedings of the Royal Society. B, Biological sciences Vol. 288; no. 1959; p. 20211553
Main Authors Hook, Kristin A, Yang, Qixin, Campanello, Leonard, Losert, Wolfgang, Fisher, Heidi S
Format Journal Article
LanguageEnglish
Published England The Royal Society 29.09.2021
Subjects
Animals
Evolution
Female
Machine Learning
Male
Mice
Sperm Head
Sperm Motility
Sperm-Ovum Interactions
Spermatozoa
automated morphometrics
collective motion
cell morphology
peromyscus
sperm aggregation
sperm conjugation
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Abstract Sperm is one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioural 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 natural variation in sperm traits observed across mice to test the hypothesis that the morphology of the sperm head influences their sperm aggregation behaviour. Using both manual and automated morphometric approaches to quantify their complex shapes, and then statistical modelling and machine learning to analyse their features, we show that the aspect ratio of the sperm head is the most distinguishing morphological trait and statistically associates with collective sperm movements obtained from observations. We then successfully use neural network analysis to predict the size of sperm aggregates from sperm head morphology and show that species with relatively wider sperm heads form larger aggregates, which is consistent with 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.
AbstractList Sperm is one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioural 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 natural variation in sperm traits observed across Peromyscus mice to test the hypothesis that the morphology of the sperm head influences their sperm aggregation behaviour. Using both manual and automated morphometric approaches to quantify their complex shapes, and then statistical modelling and machine learning to analyse their features, we show that the aspect ratio of the sperm head is the most distinguishing morphological trait and statistically associates with collective sperm movements obtained from in vitro observations. We then successfully use neural network analysis to predict the size of sperm aggregates from sperm head morphology and show that species with relatively wider sperm heads form larger aggregates, which is consistent with 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.
Sperm is one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioural 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 natural variation in sperm traits observed across mice to test the hypothesis that the morphology of the sperm head influences their sperm aggregation behaviour. Using both manual and automated morphometric approaches to quantify their complex shapes, and then statistical modelling and machine learning to analyse their features, we show that the aspect ratio of the sperm head is the most distinguishing morphological trait and statistically associates with collective sperm movements obtained from observations. We then successfully use neural network analysis to predict the size of sperm aggregates from sperm head morphology and show that species with relatively wider sperm heads form larger aggregates, which is consistent with 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.
Sperm is one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioural 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 natural variation in sperm traits observed across Peromyscus mice to test the hypothesis that the morphology of the sperm head influences their sperm aggregation behaviour. Using both manual and automated morphometric approaches to quantify their complex shapes, and then statistical modelling and machine learning to analyse their features, we show that the aspect ratio of the sperm head is the most distinguishing morphological trait and statistically associates with collective sperm movements obtained from in vitro observations. We then successfully use neural network analysis to predict the size of sperm aggregates from sperm head morphology and show that species with relatively wider sperm heads form larger aggregates, which is consistent with 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.
Author Losert, Wolfgang
Yang, Qixin
Campanello, Leonard
Hook, Kristin A
Fisher, Heidi S
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Issue 1959
Keywords automated morphometrics
collective motion
cell morphology
peromyscus
sperm aggregation
sperm conjugation
Language English
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SubjectTerms Animals
Evolution
Female
Machine Learning
Male
Mice
Sperm Head
Sperm Motility
Sperm-Ovum Interactions
Spermatozoa
Title The social shape of sperm: using an integrative machine-learning approach to examine sperm ultrastructure and collective motility
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