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Spermatogenesis is the process by which haploid sperm cells are produced in the male testes. It starts with stem cells located close to the outer rim of seminiferous tubules. These spermatogonial stem cells divide asymmetrically to give rise to additional stem cells (meaning that these structures “self-renew”), as well as sperm progenitors, called spermatocytes. Importantly, this method of asymmetric mitotic division maintains a population of spermatogonial stem cells in the male...
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Feature-based 3D+t descriptors of hyperactivated human sperm beat patterns.

Haydee O Hernández1,2, Fernando Montoya2, Paul Hernández-Herrera2,3

  • 1Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, UNAM, Ciudad de México, Mexico.

Heliyon
|March 6, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed a new 3D method to analyze sperm motility, accurately distinguishing hyperactivated sperm crucial for fertilization. This advance aids fertility research by providing a reliable classification for sperm flagellar beating patterns.

Keywords:
3D microscopy3D+t human sperm motilityHyperactivated spermMulti-plane imagingSpatio-temporal patternsSperm flagellaUnsupervised classification

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Area of Science:

  • Reproductive Biology
  • Biophysics
  • Computational Biology

Background:

  • Sperm flagellar movement is vital for fertilization.
  • Capacitation induces changes in sperm motility, leading to hyperactivation necessary for egg fertilization.
  • Hyperactivated motility is characterized by specific asymmetric flagellar bends, but its 3D pattern remains uncharacterized.

Purpose of the Study:

  • To characterize the 3D motility pattern of hyperactivated spermatozoa.
  • To develop a method for classifying 3D sperm flagellar beating patterns.
  • To provide a ground-truth reference for future deep learning applications in sperm analysis.

Main Methods:

  • Utilized a multifocal image acquisition system for 3D+t sperm flagella tracking.
  • Developed a feature-based vector describing spatio-temporal motility patterns using an envelope of ellipses.
  • Applied the developed descriptors to classify human sperm motility patterns.

Main Results:

  • Successfully differentiated between non-hyperactivated and hyperactivated 3D sperm motility patterns.
  • The 3D feature-based descriptors provide a potential ground-truth for deep neural network training.
  • Demonstrated the effectiveness of the 3D descriptors in classifying human sperm cells.

Conclusions:

  • The study characterizes 3D hyperactivated sperm motility patterns for the first time.
  • The developed 3D descriptors offer a framework for automated sperm classification.
  • This research advances understanding in human and animal fertility.