Biological condensates form percolated networks with molecular motion properties distinctly different from dilute solutions
- Hong Kong University of Science and Technology, China
- University of Toronto, Canada
- Southern University of Science and Technology, China
Abstract
Formation of membraneless organelles or biological condensates via phase separation and related processes hugely expands the cellular organelle repertoire. Biological condensates are dense and viscoelastic soft matters instead of canonical dilute solutions. To date, numerous different biological condensates have been discovered; but mechanistic understanding of biological condensates remains scarce. In this study, we developed an adaptive single molecule imaging method that allows simultaneous tracking of individual molecules and their motion trajectories in both condensed and dilute phases of various biological condensates. The method enables quantitative measurements of concentrations, phase boundary, motion behavior and speed of molecules in both condensed and dilute phases as well as the scale and speed of molecular exchanges between the two phases. Notably, molecules in the condensed phase do not undergo uniform Brownian motion, but instead constantly switch between a (class of) confined state(s) and a random diffusion-like motion state. Transient confinement is consistent with strong interactions associated with large molecular networks (i.e., percolation) in the condensed phase. In this way, molecules in biological condensates behave distinctly different from those in dilute solutions. The methods and findings described herein should be generally applicable for deciphering the molecular mechanisms underlying the assembly, dynamics and consequently functional implications of biological condensates.
Data availability
The home-written codes and use of the codes for data analysis described in this manuscript have been uploaded in the GitHub database with unrestricted access:https://github.com/NeoLShen/Code-for-phase-simulation-and-HMM-analysis.git
Article and author information
Author details
Funding
National Natural Science Foundation of China ((82188101)
- Mingjie Zhang
Ministry of Science and Technology (2019YFA0508402)
- Mingjie Zhang
Shenzhen Bay Laboratory (S201101002)
- Mingjie Zhang
University Grants Committee (AoE-M09-12,16104518 and 16101419)
- Mingjie Zhang
Human Frontier Science Program (RGP0020/2019)
- Mingjie Zhang
Canadian Institutes of Health Research (PJT-155930)
- Hue Sun Chan
Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-04351)
- Hue Sun Chan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Shen 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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