High-content imaging and network modeling maps drug-induced regulation of cardiac fibroblast phenotype

Anders R. Nelson; Steven L. Christiansen; Jeffrey J. Saucerman

doi:10.1016/j.ifacol.2023.01.008

High-content imaging and network modeling maps drug-induced regulation of cardiac fibroblast phenotype

Anders R. Nelson, Steven L. Christiansen, Jeffrey J. Saucerman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Cardiac fibroblast activation is necessary for wound healing following myocardial infarction. To better understand how candidate fibrosis therapeutics affect fibroblast signaling, we treated human cardiac fibroblasts with combinations of drugs and pathologically relevant cytokines. Using high-content microscopy, we measured 137 image features at the single cell level. Using dimensionality reduction and clustering on these features, we identified fibroblast phenotypic responses that deviate from the classical axis of fibroblast quiescence and activation. We then employed a regression-coupled network modeling approach to predict regulators of cell features that were affected by candidate drugs.

Original language	English (US)
Title of host publication	IFAC-PapersOnLine
Editors	Maria Klapa, Daniel P. Howsmon, Ioannis P. Androulakis
Publisher	Elsevier B.V.
Pages	25-26
Number of pages	2
Edition	23
ISBN (Electronic)	9781713867890
DOIs	https://doi.org/10.1016/j.ifacol.2023.01.008
State	Published - Sep 1 2022
Externally published	Yes
Event	9th IFAC Conference on Foundations of Systems Biology in Engineering, FOSBE 2022 - Cambridge, United States Duration: Aug 28 2022 → Aug 31 2022

Publication series

Name	IFAC-PapersOnLine
Number	23
Volume	55
ISSN (Electronic)	2405-8963

Conference

Conference	9th IFAC Conference on Foundations of Systems Biology in Engineering, FOSBE 2022
Country/Territory	United States
City	Cambridge
Period	8/28/22 → 8/31/22

Keywords

Modelling of complex biological systems
Network inference and modeling (signaling, regulation, metabolic)

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.1016/j.ifacol.2023.01.008

Cite this

High-content imaging and network modeling maps drug-induced regulation of cardiac fibroblast phenotype. / Nelson, Anders R.; Christiansen, Steven L.; Saucerman, Jeffrey J.
IFAC-PapersOnLine. ed. / Maria Klapa; Daniel P. Howsmon; Ioannis P. Androulakis. 23. ed. Elsevier B.V., 2022. p. 25-26 (IFAC-PapersOnLine; Vol. 55, No. 23).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nelson, AR, Christiansen, SL & Saucerman, JJ 2022, High-content imaging and network modeling maps drug-induced regulation of cardiac fibroblast phenotype. in M Klapa, DP Howsmon & IP Androulakis (eds), IFAC-PapersOnLine. 23 edn, IFAC-PapersOnLine, no. 23, vol. 55, Elsevier B.V., pp. 25-26, 9th IFAC Conference on Foundations of Systems Biology in Engineering, FOSBE 2022, Cambridge, United States, 8/28/22. https://doi.org/10.1016/j.ifacol.2023.01.008

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abstract = "Cardiac fibroblast activation is necessary for wound healing following myocardial infarction. To better understand how candidate fibrosis therapeutics affect fibroblast signaling, we treated human cardiac fibroblasts with combinations of drugs and pathologically relevant cytokines. Using high-content microscopy, we measured 137 image features at the single cell level. Using dimensionality reduction and clustering on these features, we identified fibroblast phenotypic responses that deviate from the classical axis of fibroblast quiescence and activation. We then employed a regression-coupled network modeling approach to predict regulators of cell features that were affected by candidate drugs.",

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AB - Cardiac fibroblast activation is necessary for wound healing following myocardial infarction. To better understand how candidate fibrosis therapeutics affect fibroblast signaling, we treated human cardiac fibroblasts with combinations of drugs and pathologically relevant cytokines. Using high-content microscopy, we measured 137 image features at the single cell level. Using dimensionality reduction and clustering on these features, we identified fibroblast phenotypic responses that deviate from the classical axis of fibroblast quiescence and activation. We then employed a regression-coupled network modeling approach to predict regulators of cell features that were affected by candidate drugs.

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ER -

High-content imaging and network modeling maps drug-induced regulation of cardiac fibroblast phenotype

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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