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  • Mice  (1)
  • American Association for the Advancement of Science (AAAS)  (1)
  • American Chemical Society (ACS)
  • Blackwell Futura Publishing, Inc.
  • 2010-2014  (1)
  • 1
    Publication Date: 2014-10-25
    Description: 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 naive and antigen-exposed CD4(+) T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naive 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 naive 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.〈br /〉〈br /〉〈a href="" target="_blank"〉〈img src="" border="0"〉〈/a〉   〈a href="" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Krishnaswamy, Smita -- Spitzer, Matthew H -- Mingueneau, Michael -- Bendall, Sean C -- Litvin, Oren -- Stone, Erica -- Pe'er, Dana -- Nolan, Garry P -- 1K01DK095008/DK/NIDDK NIH HHS/ -- 1R01CA130826/CA/NCI NIH HHS/ -- 1U54CA121852-01A1/CA/NCI NIH HHS/ -- CA 09-011/CA/NCI NIH HHS/ -- HHSN268201000034C/HV/NHLBI NIH HHS/ -- HHSN272200700038C/PHS HHS/ -- HV-10-05/HV/NHLBI NIH HHS/ -- K01 DK095008/DK/NIDDK NIH HHS/ -- P01 CA034233/CA/NCI NIH HHS/ -- R00 GM104148/GM/NIGMS NIH HHS/ -- R01 CA130826/CA/NCI NIH HHS/ -- S10RR027582-01/RR/NCRR NIH HHS/ -- U19 AI057229/AI/NIAID NIH HHS/ -- U19 AI100627/AI/NIAID NIH HHS/ -- U54 CA149145/CA/NCI NIH HHS/ -- New York, N.Y. -- Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. Epub 2014 Oct 23.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA. ; Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA. ; Molecular Biology Section, Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA. ; Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; CD4-Positive T-Lymphocytes/*immunology ; Computer Simulation ; Image Cytometry ; Male ; Mice ; Mice, Mutant Strains ; Mitogen-Activated Protein Kinase 1/genetics ; Receptors, Antigen, T-Cell/*metabolism ; Ribosomal Protein S6/metabolism ; Signal Transduction ; Single-Cell Analysis/*methods ; Systems Biology/*methods ; eIF-2 Kinase/metabolism
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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