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Analyzing protein-protein spatial-temporal dependencies from image sequences using fuzzy temporal random sets
María Elena Díaz1, Guillermo Ayala, Teresa León
1Departamento de Informática, Universidad de Valencia, Burjasot, Spain. elena.diaz@uv.es
Insights
This study introduces a new probabilistic model for analyzing Total Internal Reflection Fluorescence Microscopy (TIRFM) images, offering a robust method to quantify molecular dependencies in live-cell imaging. The approach overcomes limitations of traditional methods for studying protein colocalization during endocytosis.
Area of Science:
- Cell biology
- Biophysics
- Microscopy
Background:
- Total Internal Reflection Fluorescence Microscopy (TIRFM) enables high-resolution imaging of proteins near the plasma membrane.
- Existing methods for protein colocalization analysis in TIRFM data use thresholding and simple statistics, which can be imprecise.
- Accurate quantification of spatial-temporal dependencies between molecules is crucial for understanding cellular processes like endocytosis.
Purpose of the Study:
- To develop and validate a novel probabilistic model for analyzing spatial-temporal dependencies in TIRFM image sequences.
- To provide a more robust and automated method for quantifying protein-protein colocalization compared to standard thresholding techniques.
- To apply the developed method to study the interactions of proteins involved in cellular endocytosis.
Main Methods:
- Modeling image sequences of two fluorescently tagged proteins as a bivariate fuzzy temporal random set.
- Utilizing pair-correlation and K-functions to describe spatial-temporal dependencies.
- Employing Monte Carlo tests for statistical validation and assessing performance with simulated image sequences.
Main Results:
- The proposed probabilistic model effectively quantifies spatial-temporal dependencies between molecules in TIRFM data.
- Validation using simulated data demonstrated the procedure's accuracy in capturing dependencies.
- Application to endocytic proteins (Clathrin, Hip1R, Epsin, Caveolin) showed robust quantification of molecular interactions.
Conclusions:
- The developed probabilistic model offers a formal and robust approach for automated quantification of molecular dependencies in live-cell imaging.
- This method improves upon traditional colocalization analysis by avoiding arbitrary thresholding and relying on rigorous statistical testing.
- The study provides a valuable tool for biologists investigating molecular dynamics during cellular processes such as endocytosis.
Abstract:
Total Internal Reflection Fluorescence Microscopy (TIRFM) allows us to image fluorescenttagged proteins near the plasma membrane of living cells with high spatial-temporal resolution. Using TIRFM imaging of GFP-tagged clathrin endocytic proteins, areas of fluorescence are observed as overlapping spots of different sizes and durations. Standard procedures to measure protein-protein colocalization of dual labeled samples threshold the original graylevel images to segment areas covered by different proteins. This binary logic is not appropriate as it leaves a free tuning parameter which can influence the conclusions. Moreover, these procedures rely on simple statistical analysis based on correlation coefficients or visual inspection. We propose a probabilistic model to examine spatial-temporal dependencies. Image sequences of two proteins are modeled as a realization of a bivariate fuzzy temporal random set. Spatial-temporal dependencies are described by means of the pair-correlation function and the K-function and are tested using a Monte Carlo test. Five simulated image sequences were used to validate the performance of the procedure. Spatial and spatial-temporal dependencies were generated using a linked pairs model and a Poisson cluster model for the germs. To demonstrate the applicability in addressing current biological questions, we applied the procedure to fluorescent-tagged proteins involved in endocytosis (Clathrin, Hip1R, Epsin, and Caveolin). Results show that this procedure allows biologists to automatically quantify dependencies between molecules in a more formal and robust way. Image sequences and a Matlab toolbox for simulation and testing are available at http://www.uv.es/tracs/.
