Spectrally sensitive PSF shaping for widefield single-molecule microscopy

Widefield single-molecule microscopy is a powerful technique for studying biomolecular complex formation and intramolecular structural transitions. However, the choice of fluorophores used in multicolor experiments is limited by the demands of detection: individual channels are established for fixed wavelength ranges, and the fluorophores must be chosen to avoid crosstalk between channels. This limits the flexibility of multicolor experiments and adds complexity for each additional wavelength. We propose to use deep learning to design a phase mask that produces spectrally sensitive PSFs optimized for maximum wavelength resolution in the smallest detector area. Without channel splitting in the imaging pathway, an arbitrary combination of fluorophores can be detected using the full camera chip, maximizing the field of view and SNR. In addition, the ability to detect small changes in a fluorophore's emission spectrum through changes in the PSF's shape will make widefield microscopy more suitable for biosensing applications. As a preliminary, we have used a diffraction grating to perform quantitative colocalization measurements with two fluorophores that would be challenging to distinguish in a two-channel colocalization setup. We have also evaluated different deep learning models to assess which will be most useful for PSF optimization.