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Protein Dynamics in Living Cells

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Related Experiment Video

Updated: Jul 9, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Deep learning assisted single particle tracking for automated correlation between diffusion and function.

Jacob Kæstel-Hansen^1,2,3,4, Marilina de Sautu^5,6, Anand Saminathan^7,8,9

¹Department of Chemistry University of Copenhagen.

Biorxiv : the Preprint Server for Biology

|November 28, 2023

Summary

This summary is machine-generated.

DeepSPT, a new deep learning tool, analyzes nanoscale diffusion in cells to reveal molecular and subcellular function. This framework rapidly and accurately extracts biological insights from object motion, aiding in understanding cellular processes.

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Single-Molecule Diffusion and Assembly on Polymer-Crowded Lipid Membranes

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Published on: July 19, 2022

Area of Science:

Cell Biology
Biophysics
Computational Biology

Background:

Sub-cellular diffusion is crucial for cellular processes and interactions.
Tracking nanoscale diffusion requires advanced optical microscopy but extracting functional information is challenging.
Automated, agnostic analysis of diffusion data is needed for efficient biological insight.

Purpose of the Study:

To introduce DeepSPT, a deep learning framework for analyzing sub-cellular object diffusion.
To enable rapid, efficient, and agnostic interpretation of 2D/3D diffusional behavior.
To demonstrate the framework's utility in extracting functional information from molecular motion.

Main Methods:

Development of a deep learning framework (DeepSPT) for analyzing temporal diffusion patterns.
Application of DeepSPT to track and interpret the movement of sub-cellular objects.
Validation of DeepSPT's accuracy and speed in biological contexts.

Main Results:

DeepSPT accurately maps early viral infection events, identifies endosomal organelles, and classifies clathrin-coated pits and vesicles (up to 95% accuracy).
The framework processes data in seconds, a significant improvement over traditional methods requiring weeks.
DeepSPT demonstrates that molecular motion alone encodes substantial biological function.

Conclusions:

DeepSPT provides a powerful, versatile tool for automated analysis of sub-cellular diffusion.
The framework significantly accelerates the extraction of functional biological information from diffusion data.
Molecular motion is a key determinant of function at the molecular and sub-cellular levels.