Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

Area of Science:

• Bioimaging
• Molecular Biology
• Computational Biology

Background:

• Microscopy-based spatially resolved omic methods are revolutionizing life sciences.
• Current methods struggle with resolving crowded molecular targets using high numerical aperture objectives, limiting data acquisition.

Purpose of the Study:

• To develop an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images.
• To overcome limitations in resolving crowded molecular targets and enhance biological information extraction.

Main Methods:

• Development of Deconwolf, an open-source software for image deconvolution.
• Implementation of high-performance deconvolution algorithms for widefield fluorescence microscopy.
• Software designed for efficient operation on standard laptop computers.

Main Results:

• Deconwolf enables accurate quantification of diffraction-limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images.
• Robust detection of individual transcripts in tissue sections using standard air objectives.
• More than threefold increase in identified transcripts for in situ spatial transcriptomics.
• Drastic improvement in chromosome tracing for fluorescence in situ sequencing.

Conclusions:

• Deconwolf significantly enhances the capability of widefield fluorescence microscopy for analyzing crowded molecular targets.
• The software facilitates the application of deconvolution techniques across diverse bioimaging applications.
• Deconwolf democratizes advanced image analysis for spatial omics research.