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Stohn, T.

Publications and source records attributed to Stohn, T..

2 recordsLinked to original sources

Cell-state specific drug-responses are associated with differences in signaling network wiring.

Intracellular signaling pathways form networks through which information is transmitted, often in the form of kinase-mediated phosphorylation events, to interpret extracellular signals and elicit appropriate cellular responses. Yet, even isogenic cells in a homogenous environment show heterogeneity in their intracellular "cell-state", as well as their response to extracellular signals. Here, we aimed to better understand this relation between these phenomena by investigating how information flows through the EGF-receptor centered network upon targeted drug treatment, and how this is affected by cell-to-cell-state differences. Using single-cell ID-seq, we profiled the cell-state and signaling activity of primary human epidermal stem cells by measuring 69 (phospho-)proteins upon inhibition of the Erk/MAPK (p90RSK) and Akt/mTOR (p70S6K) routes downstream of the EGF pathway. We found that the effects of drug treatment propagated from the EGF-signaling pathway to other connected parts of the cellular signaling network, indicating altered signaling flow. We identified nine distinct cell-states that show pervasive state-dependent drug-responses for many (phospho-)proteins. Computational modeling of the signaling network using single-cell Comparative Network Reconstruction showed that many interactions between phospho-proteins (i.e. network wiring) were quantitatively different between cell-states. Furthermore, (phospho-)proteins with a cell-state dependent drug response, were more likely to be involved in interactions that showed a cell-state dependent strength. Overall, our results indicate that drug treatment response and signaling interactions between proteins are closely related and modulated by cell-state.

systems biology↗

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

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.

bioinformatics↗