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Profiling Cell Signaling Networks at Single-cell Resolution


Lun, Xiao-Kang; Bodenmiller, Bernd (2020). Profiling Cell Signaling Networks at Single-cell Resolution. Molecular & Cellular Proteomics, 19(5):744-756.

Abstract

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

Abstract

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Analytical Chemistry
Life Sciences > Biochemistry
Life Sciences > Molecular Biology
Language:English
Date:May 2020
Deposited On:15 Jan 2021 10:36
Last Modified:01 Feb 2021 16:22
Publisher:American Society for Biochemistry and Molecular Biology
ISSN:1535-9476
OA Status:Hybrid
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1074/mcp.R119.001790
PubMed ID:32132232

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