{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T19:56:17Z","timestamp":1768334177975,"version":"3.49.0"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wessels group by the Oncode Institute and Institutional"},{"name":"NKI"},{"DOI":"10.13039\/501100004622","name":"Dutch Cancer Society","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf675","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T12:53:49Z","timestamp":1766062429000},"source":"Crossref","is-referenced-by-count":0,"title":["Reconstructing and comparing signal transduction networks from single-cell protein quantification data"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4791-9199","authenticated-orcid":false,"given":"Tim","family":"Stohn","sequence":"first","affiliation":[{"name":"Department of Computer Science, Vrije Universiteit Amsterdam , Amsterdam, 1081 HV,","place":["The Netherlands"]},{"name":"Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute , Amsterdam, 1066 CX,","place":["The Netherlands"]}]},{"given":"Roderick A P M","family":"van Eijl","sequence":"additional","affiliation":[{"name":"Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University , Nijmegen, 6500 GL,","place":["The Netherlands"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8516-4458","authenticated-orcid":false,"given":"Klaas W","family":"Mulder","sequence":"additional","affiliation":[{"name":"Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University , Nijmegen, 6500 GL,","place":["The Netherlands"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1656-6995","authenticated-orcid":false,"given":"Lodewyk F A","family":"Wessels","sequence":"additional","affiliation":[{"name":"Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute , Amsterdam, 1066 CX,","place":["The Netherlands"]},{"name":"Faculty of EEMCS, Delft University of Technology , Delft, 2628 CD,","place":["The 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