Intelligent microfluidics: A deep learning-integrated platform for high-accuracy oocyte membrane permeability...

Area of Science:

• Biophysics
• Cell Biology
• Reproductive Science

Background:

• Oocyte cryopreservation is limited by inefficient water and cryoprotective agent (CPA) transport.
• Current methods for measuring membrane permeability lack automation and high throughput, hindering protocol optimization.
• A scalable framework is needed for robust characterization of membrane transport phenomena.

Purpose of the Study:

• To develop an automated, high-throughput platform for quantifying oocyte membrane permeability.
• To enable precise measurement of hydraulic conductivity (Lp) and CPA permeability (Ps).
• To address the limitations of manual analysis in cryopreservation research.

Main Methods:

• Development of CryoSIM, an integrated microfluidic-AI platform.
• Utilizing microfluidic chips with specialized channels and micropillar arrays for stable CPA gradients.
• Employing a deep learning model (MLSNet) for high-precision oocyte segmentation and real-time analysis.
• Implementing transport models (Two-Parameter, Kedem-Katchalsky) via a Python interface to calculate Lp and Ps.

Main Results:

• CryoSIM achieves automated, parallel quantification of oocyte membrane permeability.
• MLSNet demonstrates 98.7% pixel-level accuracy in oocyte segmentation.
• The platform enables calculation of Lp and Ps across various CPA concentrations and temperatures (4-37°C).
• CryoSIM increases analytical throughput by over 90% compared to manual methods, showing consistent performance.

Conclusions:

• CryoSIM provides a unified, high-throughput workflow integrating microfluidics, AI, and transport modeling for precise membrane permeability characterization.
• This platform advances cryopreservation analytics and offers a broadly applicable tool for biophysical studies.
• It supports fertility preservation research and bioanalytical assay development.