Localization-based super-resolution microscopy with an sCMOS camera part III: camera embedded data processing...

Area of Science:

• Biophysics
• Microscopy
• Data Science

Background:

• Localization microscopy (LM) generates large datasets, posing challenges for data handling.
• Faster detectors like scientific complementary metal-oxide semiconductor (sCMOS) cameras exacerbate data volume issues in LM.

Purpose of the Study:

• To develop an efficient, camera-embedded data processing method for LM.
• To reduce data volume and associated challenges in LM experiments.

Main Methods:

• Utilized the field-programmable gate array (FPGA) chip within the camera.
• Implemented a method to identify and export only regions with active single molecules.
• Leveraged the natural sparsity of single-molecule images.

Main Results:

• Achieved significant data volume reduction to less than 10% of raw data.
• Maintained negligible loss of useful information (<0.2%).
• Effective at molecular densities below 0.2 molecules/μm(2).

Conclusions:

• The camera-embedded processing method effectively minimizes data transfer, storage, and analysis burdens in LM.
• This approach is particularly beneficial for high-speed imaging with advanced detectors like sCMOS cameras.