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

Abstract

MotivationSignal transduction networks regulate a multitude of essential biological processes and are frequently aberrated in diseases such as cancer. Developing a mechanistic understanding of such networks is essential to understand disease or cell population specific signaling and 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 signaling networks from single cell data. ResultsHere we introduce single cell Comparative Network Reconstruction (scCNR) to derive signal transduction networks by exploiting the heterogeneity of single cell (phospho)protein measurements. scCNR treats stochastic variation in total protein abundances as natural perturbation experiments, whose effects propagate through the network. scCNR reconstructs cell population specific networks of the same underlying topology for cells from diverse populations. We extensively validated scCNR on simulated single cell data, and we applied it to a dataset of EGFR-inhibitor treated keratinocytes to recover signaling differences downstream of EGFR and in protein interactions associated with proliferation. scCNR will help to unravel the mechanistic signaling differences between cell populations by making use of single-cell data, and will subsequently guide the development of well-informed treatment strategies. Availability and implementationscCNR is available as a python module at https://github.com/ibivu/scmra. Additionally, code to reproduce all figures is available at https://github.com/tstohn/scmra_analysis. Supplementary informationSupplementary information and data are available at Bioinformatics online.