Tim Stohn’s research while affiliated with Hamburg University and other places

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Publications (3)


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

January 2025

Niels Kramer

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Roderick van Eijl

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Tim Stohn

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[...]

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Klaas W. Mulder

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-states, as well as in 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.


Fig. 1: Schematic overview of scMRA and scCNR. (A) The methods exploit natural heterogeneity of phospho-and total protein abundances between cells to infer the network topology and quantify the interaction strengths between network nodes. (B) The methods take phospho-and total protein abundances from single cells as input, with additional cell population annotations for scCNR. Optionally the methods can be enriched with perturbation data and a prior network topology. The algorithms exploit deviations of total protein from the population mean (R tot ) as 'natural perturbations'. They fit the data to describe single-cell deviations of phosphoprotein from its population mean (R) for a single population (scMRA) or several populations in parallel (scCNR) to derive (cell populationspecific) interaction strengths (r). The algorithms penalize the number of edges in the network. scCNR further penalizes the number of population-specific interaction strengths.
Reconstructing and comparing signal transduction networks from single cell protein quantification data
  • Preprint
  • File available

April 2024

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54 Reads

Motivation Signal 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. Results Here 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 implementation scCNR 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 information Supplementary information and data are available at Bioinformatics online.

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Modeling with Alternate Locations in X-ray Protein Structures

April 2023

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22 Reads

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5 Citations

Journal of Chemical Information and Modeling

In many molecular modeling applications, the standard procedure is still to handle proteins as single, rigid structures. While the importance of conformational flexibility is widely known, handling it remains challenging. Even the crystal structure of a protein usually contains variability exemplified in alternate side chain orientations or backbone segments. This conformational variability is encoded in PDB structure files by so-called alternate locations (AltLocs). Most modeling approaches either ignore AltLocs or resolve them with simple heuristics early on during structure import. We analyzed the occurrence and usage of AltLocs in the PDB and developed an algorithm to automatically handle AltLocs in PDB files enabling all structure-based methods using rigid structures to take the alternative protein conformations described by AltLocs into consideration. A respective software tool named AltLocEnumerator can be used as a structure preprocessor to easily exploit AltLocs. While the amount of data makes it difficult to show impact on a statistical level, handling AltLocs has a substantial impact on a case-by-case basis. We believe that the inspection and consideration of AltLocs is a very valuable approach in many modeling scenarios.

Citations (1)


... However, where detectable, crystallographers model atoms in multiple alternate locations (commonly termed, altlocs). Alternately located segments of the protein backbone have remained under-recognised, since most visualisation platforms (e.g., pymol and chimeraX) and programs using structural models as inputs (e.g., gromacs) ignore altlocs altogether or resolve them with simple heuristics [4]. A recent work [11] created a comprehensive catalogue of altlocs extracted from PDB structures, suggesting that this dataset should find use in efforts towards predicting multiple structures from a single sequence. ...

Reference:

Generative modeling of protein ensembles guided by crystallographic electron densities
Modeling with Alternate Locations in X-ray Protein Structures
  • Citing Article
  • April 2023

Journal of Chemical Information and Modeling