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Updated: Jan 8, 2026
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 Stohn1,2, Roderick van Eijl3, Klaas W Mulder3
1Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands.
Motivation:
Signal transduction networks regulate many essential biological processes and are frequently aberrated in diseases such as cancer. A mechanistic understanding of such networks, and how they differ between cell populations, is essential to design effective treatment strategies. Typically, such networks are computationally reconstructed based on systematic perturbation experiments, followed by quantification of signaling protein activity. Recent technological advances now allow for the quantification of the activity of many (signaling) proteins simultaneously in single cells. This makes it feasible to reconstruct or quantify signaling networks without performing systematic perturbations.
Results:
Here, we introduce single-cell modular response analysis (scMRA) and single-cell comparative network reconstruction (scCNR) to derive signal transduction networks by exploiting the heterogeneity of single-cell (phospho-)protein measurements. The methods treat stochastic variation in total protein abundances as natural perturbation experiments, whose effects propagate through the network and hence facilitate the reconstruction and quantification of the underlying signaling network. scCNR reconstructs cell population-specific networks, where cells from different populations have the same underlying topology, but the interaction strengths can differ between populations. We extensively validated scMRA and scCNR on simulated data, and applied it to unpublished data of (phospho-)protein measurements of EGFR-inhibitor-treated keratinocytes to recover signaling differences downstream of EGFR. scCNR will help to unravel the mechanistic signaling differences between cell populations, and will subsequently guide the development of well-informed treatment strategies.
Availability And Implementation:
The code used for scCNR in this study has been deposited on Zenodo https://doi.org/10.5281/zenodo.17600937 and is also available as a Python module at https://github.com/ibivu/scmra. Additionally, data and code to reproduce all figures is available at https://github.com/tstohn/scmra_analysis.
Insights
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.
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.
