Protein signaling networks from single cell fluctuations and information theory profiling |
| |
| Authors: | Shin Young Shik Remacle F Fan Rong Hwang Kiwook Wei Wei Ahmad Habib Levine R D Heath James R |
| |
| Affiliation: | †Nanosystems Biology Cancer Center and Kavli Nanoscience Institute, California Institute of Technology, Pasadena, California;‡Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California;§Division of Engineering and Applied Science, Bioengineering, California Institute of Technology, Pasadena, California;¶Material Science, California Institute of Technology, Pasadena, California;‖Département de Chimie, Université de Liège, Liège, Belgium;∗∗The Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel;††Crump Institute for Molecular Imaging and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, California;‡‡Department of Biomedical Engineering, Yale University, New Haven, Connecticut |
| |
| Abstract: | ![]()
Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect PubMed 等数据库收录！ |
|
