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.
Metrics
-
- 1,826
- views
-
- 315
- downloads
-
- 21
- 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)
Biological condensates form percolated networks with molecular motion properties distinctly different from dilute solutions
eLife 12:e81907.
https://doi.org/10.7554/eLife.81907
Further reading
-
- Structural Biology and Molecular Biophysics
Segmentation is a critical data processing step in many applications of cryo-electron tomography. Downstream analyses, such as subtomogram averaging, are often based on segmentation results, and are thus critically dependent on the availability of open-source software for accurate as well as high-throughput tomogram segmentation. There is a need for more user-friendly, flexible, and comprehensive segmentation software that offers an insightful overview of all steps involved in preparing automated segmentations. Here, we present Ais: a dedicated tomogram segmentation package that is geared towards both high performance and accessibility, available on GitHub. In this report, we demonstrate two common processing steps that can be greatly accelerated with Ais: particle picking for subtomogram averaging, and generating many-feature segmentations of cellular architecture based on in situ tomography data. Featuring comprehensive annotation, segmentation, and rendering functionality, as well as an open repository for trained models at aiscryoet.org, we hope that Ais will help accelerate research and dissemination of data involving cryoET.
-
- Structural Biology and Molecular Biophysics
Agonists enhance receptor activity by providing net-favorable binding energy to active over resting conformations, with efficiency (η) linking binding energy to gating. Previously, we showed that in nicotinic receptors, η-values are grouped into five structural pairs, correlating efficacy and affinity within each class, uniting binding with allosteric activation (Indurthi and Auerbach, 2023). Here, we use molecular dynamics (MD) simulations to investigate the low-to-high affinity transition (L→H) at the Torpedo α−δ nicotinic acetylcholine receptor neurotransmitter site. Using four agonists spanning three η-classes, the simulations reveal the structural basis of the L→H transition where: the agonist pivots around its cationic center (‘flip’), loop C undergoes staged downward displacement (‘flop’), and a compact, stable high-affinity pocket forms (‘fix’). The η derived from binding energies calculated in silico matched exact values measured experimentally in vitro. Intermediate states of the orthosteric site during receptor activation are apparent only in simulations, but could potentially be observed experimentally via time-resolved structural studies.
