Quantitative live-cell imaging and computational modelling shed new light on endogenous WNT/CTNNB1 signaling dynamics
- Swammerdam Institute for Life Sciences, University of Amsterdam, Netherlands
- Universiteit van Amsterdam, Netherlands
Abstract
WNT/CTNNB1 signaling regulates tissue development and homeostasis in all multicellular animals, but the underlying molecular mechanism remains incompletely understood. Specifically, quantitative insight into endogenous protein behavior is missing. Here we combine CRISPR/Cas9-mediated genome editing and quantitative live-cell microscopy to measure the dynamics, diffusion characteristics and absolute concentrations of fluorescently tagged, endogenous CTNNB1 in human cells under both physiological and oncogenic conditions. State-of-the-art imaging reveals that a substantial fraction of CTNNB1 resides in slow-diffusing cytoplasmic complexes, irrespective of the activation status of the pathway. This cytoplasmic CTNNB1 complex undergoes a major reduction in size when WNT/CTNNB1 is (hyper)activated. Based on our biophysical measurements we build a computational model of WNT/CTNNB1 signaling. Our integrated experimental and computational approach reveals that WNT pathway activation regulates the dynamic distribution of free and complexed CTNNB1 across different subcellular compartments through three regulatory nodes: the destruction complex, nucleocytoplasmic shuttling and nuclear retention.
Data availability
Source data: for numerical data points in Figures 2-5,7-8 are attached to this article. In addition a comprehensive overview of all numerical data (summary statistics; median, mean and 95% CI's) for the FCS and N&B experiments depicted in Figures 5, 7, 8 plus accompanying supplements and in Tables 1, 2 and 3) is provided in summary tables as Supplementary File 1.Raw data: Original FACS data (.fcs), Western blot data (.tif), confocal images (.tif), FCS data (.ptu- and .oif reference images), N&B data (.ptu/.tif and .oif reference images) have been provided on Open Science Framework (https://osf.io/dczx8/).Source code: scripts for the following have been made publicly available on Open Science Framework (https://osf.io/dczx8/), as referenced in the materials and methods section: Cell profiler segmentation pipeline (Figure 3), R script based on PlotsOfDifference to generate Figure 3 supplement 2 and supplementary movies 4-6, ImageJ N&B analysis script (Figures 5,7 and 8), R source code for the computational model (Figure 6)
Article and author information
Author details
Renee van Amerongen
Funding
University of Amsterdam (MacGillavry fellowship)
- Renee van Amerongen
KWF Kankerbestrijding (ANW 2013-6057)
- Renee van Amerongen
KWF Kankerbestrijding (2015-8014)
- Renee van Amerongen
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (864.13.002)
- Renee van Amerongen
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (OCENW.KLEIN.169)
- Renee van Amerongen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Felix Campelo, The Barcelona Institute of Science and Technology, Spain
Version history
- Received: January 11, 2021
- Accepted: June 29, 2021
- Accepted Manuscript published: June 30, 2021 (version 1)
- Version of Record published: August 5, 2021 (version 2)
Copyright
© 2021, de Man et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Quantitative live-cell imaging and computational modelling shed new light on endogenous WNT/CTNNB1 signaling dynamics
eLife 10:e66440.
https://doi.org/10.7554/eLife.66440
Further reading
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- Cell Biology
Endogenous tags have become invaluable tools to visualize and study native proteins in live cells. However, generating human cell lines carrying endogenous tags is difficult due to the low efficiency of homology-directed repair. Recently, an engineered split mNeonGreen protein was used to generate a large-scale endogenous tag library in HEK293 cells. Using split mNeonGreen for large-scale endogenous tagging in human iPSCs would open the door to studying protein function in healthy cells and across differentiated cell types. We engineered an iPS cell line to express the large fragment of the split mNeonGreen protein (mNG21-10) and showed that it enables fast and efficient endogenous tagging of proteins with the short fragment (mNG211). We also demonstrate that neural network-based image restoration enables live imaging studies of highly dynamic cellular processes such as cytokinesis in iPSCs. This work represents the first step towards a genome-wide endogenous tag library in human stem cells.
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