cellSTORM-Cost-effective super-resolution on a cellphone using dSTORM.

Area of Science:

• Biophysics
• Optical Microscopy
• Consumer Electronics in Science

Background:

• High optical resolution in microscopy typically requires expensive hardware.
• Single Molecular Localization Microscopy (SMLM) offers a path to affordable super-resolution.
• Advancements in consumer-grade cameras, including smartphone sensors, present new opportunities.

Purpose of the Study:

• To demonstrate that a standard consumer cellphone can achieve optical super-resolution imaging.
• To evaluate the performance of a smartphone-based direct Stochastic Optical Reconstruction Microscopy (dSTORM) system.
• To explore the use of artificial intelligence for image reconstruction on mobile devices.

Main Methods:

• Utilized a consumer cellphone equipped with a camera for super-resolution imaging.
• Implemented direct Stochastic Optical Reconstruction Microscopy (dSTORM) protocols.
• Applied both standard reconstruction algorithms and a trained image-to-image generative adversarial network (GAN) for image processing directly on the smartphone.

Main Results:

• Achieved an optical resolution better than 80 nm using a cellphone-based dSTORM setup.
• Demonstrated the capability of a generative adversarial network to improve image reconstruction quality on the smartphone, especially in challenging conditions.
• Validated the feasibility of mobile-based super-resolution microscopy.

Conclusions:

• Consumer cellphone cameras are viable for high-resolution optical imaging.
• "cellSTORM" offers an affordable and accessible super-resolution microscopy solution.
• The ubiquity of smartphones can democratize advanced imaging techniques.