Multicolor fluorescent imaging by space-constrained computational spectral imaging.

Area of Science:

• Biophotonics and Imaging Science
• Computational Microscopy
• Fluorescence Spectroscopy

Background:

• Spectral imaging enables simultaneous study of multiple fluorophores with overlapping emissions.
• Existing methods like compressive and scanning spectral imaging face challenges with under-sampling and low throughput, respectively.

Purpose of the Study:

• To present a novel computational spectral imaging method utilizing spatial fluorescence information as a reconstruction constraint.
• To address under-sampling and low throughput issues in spectral imaging.
• To demonstrate the method's precision and applicability in multi-color biological imaging.

Main Methods:

• A computational spectral imaging approach using sample spatial fluorescence information as a reconstruction constraint.
• Validation using simulated and experimental data for two- and three-color imaging.
• Experimental application for differentiating cellular structures labeled with tdTomato and mCherry fluorescent proteins.

Main Results:

• Demonstrated high reconstruction precision in both two- and three-color imaging scenarios.
• Successfully differentiated cellular structures labeled with tdTomato and mCherry, which possess highly overlapping emission spectra.
• The method offers free wavelength selection and compatibility with conventional filter-based imaging.

Conclusions:

• The presented computational spectral imaging method effectively reconstructs multi-color images with high precision.
• This technique overcomes limitations of existing spectral imaging modalities, improving throughput and addressing under-sampling.
• The method shows significant potential for advanced biological imaging, particularly for distinguishing fluorophores with overlapping spectra and enhancing multiplexing capabilities.