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Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

Authors :
Wernersson, Erik
Gelali, Eleni
Girelli, Gabriele
Wang, Su
Castillo, David
Mattsson Langseth, Christoffer
Verron, Quentin
Nguyen, Huy Q.
Chattoraj, Shyamtanu
Martinez Casals, Anna
Blom, Hans
Lundberg, Emma
Nilsson, Mats
Marti-Renom, Marc A.
Wu, Chao-ting
Crosetto, Nicola
Bienko, Magda
Wernersson, Erik
Gelali, Eleni
Girelli, Gabriele
Wang, Su
Castillo, David
Mattsson Langseth, Christoffer
Verron, Quentin
Nguyen, Huy Q.
Chattoraj, Shyamtanu
Martinez Casals, Anna
Blom, Hans
Lundberg, Emma
Nilsson, Mats
Marti-Renom, Marc A.
Wu, Chao-ting
Crosetto, Nicola
Bienko, Magda
Publication Year :
2024

Abstract

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1457483347
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1038.s41592-024-02294-7