title: Systems biology. Conditional density-based analysis of T cell signaling in single-cell data.
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.
authors.affiliation: Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA.
authors.forename: Smita
authors.initials: S
authors.lastname: Krishnaswamy
authors.affiliation: Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Spitzer
authors.affiliation: Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Mingueneau
authors.affiliation: Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
authors.forename: Sean C
authors.initials: SC
authors.lastname: Bendall
authors.affiliation: Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA.
authors.forename: Oren
authors.initials: O
authors.lastname: Litvin
authors.affiliation: Molecular Biology Section, Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
authors.forename: Erica
authors.initials: E
authors.lastname: Stone
authors.affiliation: Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA. dpeer@biology.columbia.edu.
authors.forename: Dana
authors.initials: D
authors.lastname: Pe'er
authors.affiliation: Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
authors.forename: Garry P
authors.initials: GP
authors.lastname: Nolan
mesh_terms: D000818:Animals; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology*; D003198:Computer Simulation; D019044:Image Cytometry; D008297:Male; D051379:Mice; D008817:Mice, Mutant Strains; D019950:Mitogen-Activated Protein Kinase 1 / Q000235:genetics; D011948:Receptors, Antigen, T-Cell / Q000378:metabolism*; D038601:Ribosomal Protein S6 / Q000378:metabolism; D015398:Signal Transduction; D059010:Single-Cell Analysis / Q000379:methods*; D049490:Systems Biology / Q000379:methods*; D019892:eIF-2 Kinase / Q000378:metabolism
chemical_list: D019950:Mitogen-Activated Protein Kinase 1; D011948:Receptors, Antigen, T-Cell; D038601:Ribosomal Protein S6; D019892:eIF-2 Kinase; C448579:ribosomal protein S6, mouse; C535148:Mapk1 protein, mouse