Dataset Information

Quantifying Dynamics in Phase-Separated Condensates Using Fluorescence Recovery after Photobleaching.

ABSTRACT: Cells contain numerous membraneless organelles that assemble by intracellular liquid-liquid phase separation. The viscous properties and associated biomolecular mobility within these condensed phase droplets, or condensates, are increasingly recognized as important for cellular function and also dysfunction, for example, in protein aggregation pathologies. Fluorescence recovery after photobleaching (FRAP) is widely used to assess condensate fluidity and to estimate protein diffusion coefficients. However, the models and assumptions utilized in FRAP analysis of protein condensates are often not carefully considered. Here, we combine FRAP experiments on both in vitro reconstituted droplets and intracellular condensates with systematic examination of different models that can be used to fit the data and evaluate the impact of model choice on measured values. A key finding is that model boundary conditions can give rise to widely divergent measured values. This has important implications, for example, in experiments that bleach subregions versus the entire condensate, two commonly employed experimental approaches. We suggest guidelines for determining the appropriate modeling framework and highlight emerging questions about the molecular dynamics at the droplet interface. The ability to accurately determine biomolecular mobility both in the condensate interior and at the interface is important for obtaining quantitative insights into condensate function, a key area for future research.

SUBMITTER: Taylor NO

PROVIDER: S-EPMC6818185 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantifying Dynamics in Phase-Separated Condensates Using Fluorescence Recovery after Photobleaching.

Taylor Nicole O NO Wei Ming-Tzo MT Stone Howard A HA Brangwynne Clifford P CP

Biophysical journal 20190830 7

Cells contain numerous membraneless organelles that assemble by intracellular liquid-liquid phase separation. The viscous properties and associated biomolecular mobility within these condensed phase droplets, or condensates, are increasingly recognized as important for cellular function and also dysfunction, for example, in protein aggregation pathologies. Fluorescence recovery after photobleaching (FRAP) is widely used to assess condensate fluidity and to estimate protein diffusion coefficients ...[more]

PMID: 31540706

Similar Datasets

Project description:BackgroundAcetylation of lysine residues in histone tails plays an important role in the regulation of gene transcription. Bromdomains are the readers of acetylated histone marks, and, consequently, bromodomain-containing proteins have a variety of chromatin-related functions. Moreover, they are increasingly being recognised as important mediators of a wide range of diseases. The first potent and selective bromodomain inhibitors are beginning to be described, but the diverse or unknown functions of bromodomain-containing proteins present challenges to systematically demonstrating cellular efficacy and selectivity for these inhibitors. Here we assess the viability of fluorescence recovery after photobleaching (FRAP) assays as a target agnostic method for the direct visualisation of an on-target effect of bromodomain inhibitors in living cells.ResultsMutation of a conserved asparagine crucial for binding to acetylated lysines in the bromodomains of BRD3, BRD4 and TRIM24 all resulted in reduction of FRAP recovery times, indicating loss of or significantly reduced binding to acetylated chromatin, as did the addition of known inhibitors. Significant differences between wild type and bromodomain mutants for ATAD2, BAZ2A, BRD1, BRD7, GCN5L2, SMARCA2 and ZMYND11 required the addition of the histone deacetylase inhibitor suberoylanilide hydroxamic acid (SAHA) to amplify the binding contribution of the bromodomain. Under these conditions, known inhibitors decreased FRAP recovery times back to mutant control levels. Mutation of the bromodomain did not alter FRAP recovery times for full-length CREBBP, even in the presence of SAHA, indicating that other domains are primarily responsible for anchoring CREBBP to chromatin. However, FRAP assays with multimerised CREBBP bromodomains resulted in a good assay to assess the efficacy of bromodomain inhibitors to this target. The bromodomain and extraterminal protein inhibitor PFI-1 was inactive against other bromodomain targets, demonstrating the specificity of the method.ConclusionsViable FRAP assays were established for 11 representative bromodomain-containing proteins that broadly cover the bromodomain phylogenetic tree. Addition of SAHA can overcome weak binding to chromatin, and the use of tandem bromodomain constructs can eliminate masking effects of other chromatin binding domains. Together, these results demonstrate that FRAP assays offer a potentially pan-bromodomain method for generating cell-based assays, allowing the testing of compounds with respect to cell permeability, on-target efficacy and selectivity.

Project description:Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non-linear least squares. Depending on the model, this can be time consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data are used for training different deep neural network regression models for prediction of several parameters, most importantly the diffusion coefficient. The neural networks are extremely fast and can estimate the parameters orders of magnitude faster than least squares. The performance of the neural network estimation framework is compared to conventional least squares estimation on simulated data, and found to be strikingly similar. Also, a simple experimental validation is performed, demonstrating excellent agreement between the two methods. We make the data and code used publicly available to facilitate further development of machine learning-based estimation in FRAP. LAY DESCRIPTION: Fluorescence recovery after photobleaching (FRAP) is one of the most frequently used methods for microscopy-based diffusion measurements and broadly used in materials science, pharmaceutics, food science and cell biology. In a FRAP experiment, a laser is used to photobleach fluorescent particles in a region. By analysing the recovery of the fluorescence intensity due to the diffusion of still fluorescent particles, the diffusion coefficient and other parameters can be estimated. Typically, a confocal laser scanning microscope (CLSM) is used to image the time evolution of the recovery, and a model is fit using least squares to obtain parameter estimates. In this work, we introduce a new, fast and accurate method for analysis of data from FRAP. The new method is based on using artificial neural networks to predict parameter values, such as the diffusion coefficient, effectively circumventing classical least squares fitting. This leads to a dramatic speed-up, especially noticeable when analysing large numbers of FRAP data sets, while still producing results in excellent agreement with least squares. Further, the neural network estimates can be used as very good initial guesses for least squares estimation in order to make the least squares optimization convergence much faster than it otherwise would. This provides for obtaining, for example, diffusion coefficients as soon as possible, spending minimal time on data analysis. In this fashion, the proposed method facilitates efficient use of the experimentalist's time which is the main motivation to our approach. The concept is demonstrated on pure diffusion. However, the concept can easily be extended to the diffusion and binding case. The concept is likely to be useful in all application areas of FRAP, including diffusion in cells, gels and solutions.

Dataset Information

Quantifying Dynamics in Phase-Separated Condensates Using Fluorescence Recovery after Photobleaching.

Publications

Quantifying Dynamics in Phase-Separated Condensates Using Fluorescence Recovery after Photobleaching.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets