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Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Reconstructing and comparing signal transduction networks from single-cell protein quantification data.

Tim Stohn^1,2, Roderick van Eijl³, Klaas W Mulder³

¹Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands.

Bioinformatics (Oxford, England)

|December 24, 2025

Summary

This summary is machine-generated.

New computational methods, single-cell Modular Response Analysis (scMRA) and single-cell Comparative Network Reconstruction (scCNR), enable signal transduction network analysis from single-cell protein data. These approaches reveal cell population-specific signaling differences to guide targeted cancer treatments.

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Last Updated: Jan 8, 2026

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Published on: June 15, 2018

Area of Science:

Systems biology
Computational biology
Molecular biology

Background:

Signal transduction networks are crucial for biological processes and often dysregulated in diseases like cancer.
Understanding these networks and their variations across cell types is vital for developing effective therapies.
Traditional methods rely on perturbation experiments and bulk measurements, limiting detailed network analysis.

Purpose of the Study:

To introduce novel computational methods for reconstructing and quantifying signal transduction networks from single-cell data.
To leverage single-cell heterogeneity as a natural perturbation source for network inference.
To enable the identification of cell population-specific signaling differences.

Main Methods:

Developed single-cell Modular Response Analysis (scMRA) and single-cell Comparative Network Reconstruction (scCNR).
Utilized stochastic variation in single-cell protein abundances as endogenous perturbation signals.
Applied methods to phosphoprotein measurements from EGFR-inhibitor treated keratinocytes.

Main Results:

scMRA and scCNR successfully reconstruct signal transduction networks from single-cell proteomic data.
scCNR identifies cell population-specific network topologies and interaction strengths.
Demonstrated recovery of signaling differences downstream of EGFR in treated keratinocytes.

Conclusions:

scMRA and scCNR provide powerful tools for dissecting complex signaling networks at single-cell resolution.
These methods can uncover mechanistic differences in signaling between cell populations.
The findings will aid in designing more precise and effective therapeutic strategies for diseases like cancer.