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dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

Gabriel J. Kowalczyk, J. Agustin Cruz, Yue Guo, Qiuhong Zhang, Natalie Sauerwald, Robin E. C. Lee
doi: https://doi.org/10.1101/855213
Gabriel J. Kowalczyk
1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
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J. Agustin Cruz
1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Yue Guo
2Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Qiuhong Zhang
1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Natalie Sauerwald
3Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Robin E. C. Lee
1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
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  • For correspondence: robinlee@pitt.edu
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ABSTRACT

Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images. The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli.

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  • https://github.com/recleelab

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 25, 2019.
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dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells
Gabriel J. Kowalczyk, J. Agustin Cruz, Yue Guo, Qiuhong Zhang, Natalie Sauerwald, Robin E. C. Lee
bioRxiv 855213; doi: https://doi.org/10.1101/855213
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dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells
Gabriel J. Kowalczyk, J. Agustin Cruz, Yue Guo, Qiuhong Zhang, Natalie Sauerwald, Robin E. C. Lee
bioRxiv 855213; doi: https://doi.org/10.1101/855213

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