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Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. Epub 2014 Oct 23.

Systems biology. Conditional density-based analysis of T cell signaling in single-cell data.

Author information

1
Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA.
2
Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
3
Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA.
4
Molecular Biology Section, Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
5
Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA. dpeer@biology.columbia.edu.

Abstract

Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4(+) T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.

PMID:
25342659
PMCID:
PMC4334155
DOI:
10.1126/science.1250689
[Indexed for MEDLINE]
Free PMC Article

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