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Related Concept Videos

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.1K
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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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

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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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Related Experiment Video

Updated: Jul 3, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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Deep learning assisted single particle tracking for automated correlation between diffusion and function.

Nikos Hatzakis, Jacob Kaestel-Hansen, Marilina de Sautu

    Research Square
    |February 14, 2024
    PubMed
    Summary
    This summary is machine-generated.

    DeepSPT, a deep learning framework, analyzes nanoscale diffusion to reveal cellular functions. This tool rapidly interprets molecular and organelle motion, unlocking insights into biological processes.

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    Single-Molecule Diffusion and Assembly on Polymer-Crowded Lipid Membranes
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    Area of Science:

    • Cell Biology
    • Biophysics
    • Computational Biology

    Background:

    • Sub-cellular diffusion is crucial for cellular processes.
    • Tracking nanoscale diffusion offers insights into molecular and organelle behavior.
    • Automated extraction of functional information from diffusion data is challenging.

    Purpose of the Study:

    • To introduce DeepSPT, a deep learning framework for analyzing sub-cellular diffusion.
    • To provide an agnostic and efficient method for interpreting molecular and organelle motion.
    • To demonstrate the utility of diffusion analysis for understanding cellular function.

    Main Methods:

    • Development of a deep learning framework (DeepSPT).
    • Application of DeepSPT to analyze 2D or 3D diffusional temporal behavior.
    • Utilizing microscopy data for object tracking and diffusion analysis.

    Main Results:

    • DeepSPT accurately maps early viral infection events.
    • Identified distinct endosomal organelles, clathrin-coated pits, and vesicles with up to 95% accuracy.
    • Analysis completed in seconds, significantly reducing processing time.

    Conclusions:

    • DeepSPT effectively extracts biological information from diffusion patterns alone.
    • Molecular and subcellular motion, in addition to structure, encodes vital cellular function.
    • This approach offers a rapid and versatile tool for sub-cellular analysis.