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Related Experiment Video

Updated: Jul 20, 2025

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Published on: June 6, 2019

Deep learning-based method for analyzing the optically trapped sperm rotation.

Jiangcheng Zhao¹, Chuanbiao Bai¹, Zhiguo Zhang²

¹CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, 230027, China.

Scientific Reports

|August 3, 2023

Summary

This summary is machine-generated.

We developed a fast deep-learning method to track cell rotation using optical tweezers. This technique accurately analyzes sperm cell head rotation, offering potential for clinical sperm quality assessment.

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Last Updated: Jul 20, 2025

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Published on: October 13, 2023

Area of Science:

Biophysics
Cell Biology
Machine Learning

Background:

Optical tweezers are vital for cell manipulation and movement analysis.
Cellular rotation, particularly in sperm, significantly impacts motility and function.
Accurate methods for analyzing cell rotation are needed for biological and clinical applications.

Purpose of the Study:

To introduce a rapid, deep-learning-based method for automated cell projection orientation determination.
To apply this method for analyzing the planar rotation of optically trapped sperm cells.
To investigate sperm cell activity and quality by examining rotation rates under varying conditions.

Main Methods:

Development of a fast deep-learning algorithm for cell orientation analysis.
Utilizing optical tweezers to trap and manipulate sperm cells.
Analyzing planar rotation of the sperm head and correlating rotation rates with environmental factors.

Main Results:

The deep-learning method successfully determined the projection orientation of ellipsoidal cells without specialized optics.
Feasibility demonstrated in extracting the rotation of sperm cell heads trapped by optical tweezers.
Sperm rotation rates varied significantly under different temperatures and laser power, indicating sensitivity to environmental changes.

Conclusions:

The proposed deep-learning method is effective for analyzing cell rotation, specifically sperm cells.
The rotation analysis technique shows promise for evaluating sperm motility and quality in clinical settings.
This approach offers a non-invasive, efficient tool for understanding cell dynamics and potential diagnostic applications.