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Related Concept Videos

Protein Diffusion in the Membrane01:24

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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In column chromatography, when an analyte is introduced as a narrow band at the top of the column, the solutes begin to separate and broaden, developing a Gaussian profile. This broadening occurs due to various factors, such as longitudinal diffusion.
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Updated: Jun 4, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes.

Vishesh Kumar1, J Shepard Bryan1, Alex Rojewski1

  • 1Center for Biological Physics, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, Arizona.

Biophysical Reports
|December 19, 2024
PubMed
Summary
This summary is machine-generated.

We developed DiffMAP-GP, a Bayesian framework using Gaussian processes, to map diffusion coefficients across cellular membranes. This method reveals local membrane properties without data binning.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Membrane Biophysics

Background:

  • Diffusion coefficients vary spatially within cellular membranes, offering insights into local membrane properties like composition and stiffness.
  • Quantifying these variations is crucial for understanding membrane dynamics and function.

Purpose of the Study:

  • To develop a Bayesian framework, DiffMAP-GP, for quantifying diffusion coefficient spatial maps and their uncertainties from particle tracking data.
  • To provide an unsupervised method for continuous mapping of diffusion coefficients across membranes without data binning.

Main Methods:

  • Utilizing a Bayesian framework with Gaussian process (GP) priors on diffusion coefficient maps.
  • Employing inducing point methods for computational efficiency with nonconjugate likelihood-prior pairs.
  • Analyzing both synthetic data and live-cell single-molecule imaging data of membrane proteins.

Main Results:

  • Successfully generated spatial maps of diffusion coefficients and their uncertainties.
  • Validated the framework on synthetic data with known ground truth.
  • Demonstrated applicability to real biological data from live-cell imaging.

Conclusions:

  • DiffMAP-GP offers a rigorous, unsupervised approach to map diffusion coefficients continuously across membranes.
  • The framework provides valuable insights into local membrane properties by quantifying diffusion heterogeneity.
  • This tool advances the analysis of particle tracking data for membrane biophysics research.