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Related Concept Videos

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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Optimal inference of molecular interaction dynamics in FRET microscopy.

Keita Kamino1,2,3,4, Nirag Kadakia1,2,5, Fotios Avgidis6

  • 1Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511.

Proceedings of the National Academy of Sciences of the United States of America
|April 4, 2023
PubMed
Summary

We developed B-FRET, a Bayesian filtering method to analyze fluorescence resonance energy transfer (FRET) microscopy data. B-FRET significantly improves signal-to-noise ratio, revealing hidden molecular dynamics in noisy cellular imaging.

Keywords:
FRETcell signalinginformation theorylive-cell imagingstatistical inference

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Area of Science:

  • Biophysics
  • Cellular Biology
  • Microscopy

Background:

  • Fluorescence resonance energy transfer (FRET) microscopy is crucial for observing molecular interactions in cells.
  • Analyzing FRET time-series data is challenging due to noise and photobleaching, especially in single-cell studies.
  • Conventional algebraic processing methods amplify noise, limiting the signal-to-noise ratio (SNR).

Purpose of the Study:

  • To introduce a novel probabilistic approach, B-FRET, for analyzing FRET microscopy data.
  • To overcome limitations of conventional methods in inferring molecular dynamics from noisy FRET time series.
  • To enhance the SNR and uncover cellular signaling dynamics obscured by noise.

Main Methods:

  • Developed B-FRET, a method based on Bayesian filtering theory.
  • Applied B-FRET to standard 3-cube FRET-imaging data.
  • Validated the approach using both simulated and real experimental data.

Main Results:

  • B-FRET provides a statistically optimal method for inferring molecular interactions.
  • The approach significantly improves the SNR compared to conventional methods.
  • Successfully revealed hidden signaling dynamics in noisy in vivo FRET time series from bacterial cells.

Conclusions:

  • B-FRET offers a robust and generally applicable solution for analyzing noisy FRET data.
  • This probabilistic approach enhances the utility of FRET microscopy for studying cellular processes.
  • B-FRET enables the investigation of molecular dynamics previously undetectable due to low SNR.