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Updated: Jun 2, 2026

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Medium-throughput Screening Assays for Assessment of Effects on Ca2+-Signaling and Acrosome Reaction in Human Sperm
Published on: March 1, 2019
Label-Free Detection of Acrosome Reaction of Human Sperm Based on Diffraction Phase Microscopy
Xinyu Fan1, Peng Wang1, Ziyi He1
1School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China.
Chemical & Biomedical Imaging
|June 1, 2026
Summary
A new multimodal imaging system enables label-free assessment of sperm acrosome reaction (AR) status. This method accurately identifies fertilization potential in live sperm, offering a promising approach for clinical applications.
Area of Science:
- Reproductive Biology
- Biomedical Imaging
- Sperm Analysis
Background:
- The acrosome reaction (AR) is critical for fertilization and assessing sperm's fertilization potential in clinical settings.
- Label-free methods for determining AR status in sperm remain a significant challenge in reproductive medicine.
Purpose of the Study:
- To develop and validate a label-free multimodal imaging system for assessing sperm acrosome reaction (AR) status.
- To evaluate the efficacy of phase imaging metrics in distinguishing AR status in live sperm.
Main Methods:
- A multimodal imaging system integrating fluorescence, phase, and bright-field imaging was developed to observe the same sperm.
- Sperm phase characteristics, specifically three phase metrics (, , ), were analyzed to assess AR status.
- A classification model based on the metric was developed and validated using ROC analysis.
Main Results:
- The metric demonstrated the highest accuracy in distinguishing AR status compared to other phase metrics.
- The classification model based on achieved a high performance with an AUC of 0.907.
- Excluding sperm heads with vacuoles further improved the classification accuracy of to 95.5%.
Conclusions:
- Label-free diffractive phase microscopy provides a quantitative method for detecting AR status in live sperm.
- The developed multimodal imaging system and phase metrics show significant promise for clinical applications in fertility assessment.

