Depth-enhanced high-throughput microscopy by compact PSF engineering

Area of Science:

• Optical microscopy
• Biotechnology
• Computational imaging

Background:

• High-throughput microscopy is essential for screening applications using 3D cellular models.
• Current methods suffer from defocus susceptibility, necessitating axial scanning that reduces throughput and increases photobleaching.
• Point spread function (PSF) engineering offers 3D imaging but is typically hindered by bulky optical setups, limiting its use in high-throughput systems.

Purpose of the Study:

• To develop a compact PSF engineering method for high-throughput microscopy.
• To enhance imaging depth of field and enable 3D information recovery from single snapshots.
• To demonstrate the utility of high-throughput microscopy for generating deep learning training data.

Main Methods:

• Implemented compact PSF engineering directly within the objective lens.
• Combined engineered PSF imaging with deep learning algorithms.
• Utilized high-throughput microscopy to capture training datasets.

Main Results:

• Achieved enhanced imaging depth of field using compact PSF engineering.
• Successfully recovered 3D information from single-snapshot images via deep learning.
• Demonstrated the feasibility of integrating PSF engineering into high-throughput microscopy.

Conclusions:

• Compact PSF engineering offers a viable solution for 3D imaging in high-throughput microscopy.
• This approach mitigates limitations of current 3D high-throughput techniques, improving efficiency and reducing photodamage.
• The study highlights the synergy between advanced microscopy and deep learning for biological imaging challenges.