FISH-quant v2: a scalable and modular tool for smFISH image analysis
- Arthur Imbert1,2,3,
- Wei Ouyang4,
- Adham Safieddine5,
- Emeline Coleno6,
- Christophe Zimmer7,
- Edouard Bertrand6,
- Thomas Walter1,2,3 and
- Florian Mueller7
- 1Centre for Computational Biology (CBIO), MINES ParisTech, PSL University, 75272 Paris Cedex 06, France
- 2Institut Curie, 75248 Paris Cedex, France
- 3INSERM, U900, 75248 Paris Cedex, France
- 4Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH—Royal Institute of Technology, 17165 Solna, Sweden
- 5Sorbonne Université, CNRS, Institut de Biologie Paris-Seine (IBPS), Laboratoire de Biologie du Développement, F-75005 Paris, France
- 6IGH, University of Montpellier, CNRS, 34090 Montpellier, France
- 7Imaging and Modeling Unit, Institut Pasteur, UMR 3691 CNRS, C3BI USR 3756 IP CNRS, 75015 Paris, France
- Corresponding authors: Thomas.Walter{at}mines-paristech.fr, fmueller{at}pasteur.fr
Abstract
Regulation of RNA abundance and localization is a key step in gene expression control. Single-molecule RNA fluorescence in situ hybridization (smFISH) is a widely used single-cell-single-molecule imaging technique enabling quantitative studies of gene expression and its regulatory mechanisms. Today, these methods are applicable at a large scale, which in turn come with a need for adequate tools for data analysis and exploration. Here, we present FISH-quant v2, a highly modular tool accessible for both experts and non-experts. Our user-friendly package allows the user to segment nuclei and cells, detect isolated RNAs, decompose dense RNA clusters, quantify RNA localization patterns and visualize these results both at the single-cell level and variations within the cell population. This tool was validated and applied on large-scale smFISH image data sets, revealing diverse subcellular RNA localization patterns and a surprisingly high degree of cell-to-cell heterogeneity.
Keywords
- Received December 2, 2021.
- Accepted February 19, 2022.
This article, published in RNA, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
