Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

Deconwolf 可实现宽视野荧光显微镜图像的高性能反卷积

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

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.

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