Measuring ligand-cell surface receptor affinities with axial line-scanning fluorescence correlation spectroscopy

Abstract

Development and homeostasis of multicellular organisms is largely controlled by complex cell-cell signaling networks that rely on specific binding of secreted ligands to cell surface receptors. The Wnt signaling network, as an example, involves multiple ligands and receptors to elicit specific cellular responses. To understand the mechanisms of such a network, ligand-receptor interactions should be characterized quantitatively, ideally in live cells or tissues. Such measurements are possible using fluorescence microscopy yet challenging due to sample movement, low signal-to-background ratio and photobleaching. Here we present a robust approach based on fluorescence correlation spectroscopy with ultra-high speed axial line scanning, yielding precise equilibrium dissociation coefficients of interactions in the Wnt signaling pathway. Using CRISPR/Cas9 editing to endogenously tag receptors with fluorescent proteins, we demonstrate that the method delivers precise results even with low, near-native amounts of receptors.

Data availability

All data reported in the paper are included in the manuscript and/or supplementary Materials. Source data files have been provided for Figures 2, 5, 6 and 7.

Article and author information

Author details

  1. Antonia Franziska Eckert

    Institute of Applied Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany
    Competing interests
    The authors declare that no competing interests exist.
  2. Peng Gao

    Institute of Applied Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5354-3944
  3. Janine Wesslowski

    Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Xianxian Wang

    Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Jasmijn Rath

    Institute of Applied Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Karin Nienhaus

    Institute of Applied Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany
    Competing interests
    The authors declare that no competing interests exist.
  7. Gary Davidson

    Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany
    For correspondence
    gary.davidson@kit.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2264-5518
  8. Gerd Ulrich Nienhaus

    Institute of Applied Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany
    For correspondence
    uli@uiuc.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5027-3192

Funding

Deutsche Forschungsgemeinschaft (SFB 1324,project A6,project Z2)

  • Gerd Ulrich Nienhaus

Deutsche Forschungsgemeinschaft (SFB 1324,project A6)

  • Gary Davidson

Helmholtz-Gemeinschaft (STN)

  • Gerd Ulrich Nienhaus

Helmholtz-Gemeinschaft (BIFTM)

  • Gary Davidson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2020, Eckert 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.

Metrics

  • 3,313
    views
  • 426
    downloads
  • 28
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Antonia Franziska Eckert
  2. Peng Gao
  3. Janine Wesslowski
  4. Xianxian Wang
  5. Jasmijn Rath
  6. Karin Nienhaus
  7. Gary Davidson
  8. Gerd Ulrich Nienhaus
(2020)
Measuring ligand-cell surface receptor affinities with axial line-scanning fluorescence correlation spectroscopy
eLife 9:e55286.
https://doi.org/10.7554/eLife.55286

Share this article

https://doi.org/10.7554/eLife.55286

Further reading

    1. Physics of Living Systems
    Emmanuel Akabuogu, Victor Carneiro da Cunha Martorelli ... Thomas A Waigh
    Research Article

    Bacterial biofilms are communities of bacteria usually attached to solid strata and often differentiated into complex structures. Communication across biofilms has been shown to involve chemical signaling and, more recently, electrical signaling in Gram-positive biofilms. We report for the first time, community-level synchronized membrane potential dynamics in three-dimensional Escherichia coli biofilms. Two hyperpolarization events are observed in response to light stress. The first requires mechanically sensitive ion channels (MscK, MscL, and MscS) and the second needs the Kch-potassium channel. The channels mediated both local spiking of single E. coli biofilms and long-range coordinated electrical signaling in E. coli biofilms. The electrical phenomena are explained using Hodgkin-Huxley and 3D fire-diffuse-fire agent-based models. These data demonstrate that electrical wavefronts based on potassium ions are a mechanism by which signaling occurs in Gram-negative biofilms and as such may represent a conserved mechanism for communication across biofilms.

    1. Cell Biology
    2. Physics of Living Systems
    Krishna Rijal, Pankaj Mehta
    Research Article

    The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (1) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct Escherichia coli promoters and (2) design nonequilibrium promoter architectures with desired input–output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.