Cell state transition dynamics at single-cell level (Hood. Ozinskv. Huang) Introduction: In metazoa, the very same genome produces a vast diversity of discrete, robust or metastable cell states, as most prosaically manifest in the various cell types of the human body. The switching between cell states is at the core of multicellular development and is also important in diseases, such as cancer, and in regeneration. In this project, we will determine how gene regulatory networks (GRN) regulate the cell state changes, and hence phenotype changes, that occur in development and cancer. Thus, ultimately we want know how these networks specify cell phenotype. This will be achieved by measuring gene expression dynamics at single cell-resolution during cell phenotype change in two clinically relevant settings: (a) cardiomyocytes reprogramming and {b) breast cancer (stem) cell epithelial-mesenchymal transition. Why are single-cell level studies critical for a systems understanding of cell state transitions? One of the key missions in biology is to decode the functional complexity of the human body beyond the traditional molecular profiling of the presumably >250 distinct

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center (P50)
Project #
5P50GM076547-07
Application #
8539499
Study Section
Special Emphasis Panel (ZGM1-CBCB-3)
Project Start
Project End
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
7
Fiscal Year
2013
Total Cost
$327,325
Indirect Cost
$145,478
Name
Institute for Systems Biology
Department
Type
DUNS #
135646524
City
Seattle
State
WA
Country
United States
Zip Code
98109
Wurtmann, Elisabeth J; Ratushny, Alexander V; Pan, Min et al. (2014) An evolutionarily conserved RNase-based mechanism for repression of transcriptional positive autoregulation. Mol Microbiol 92:369-82
Sangar, Vineet; Funk, Cory C; Kusebauch, Ulrike et al. (2014) Quantitative proteomic analysis reveals effects of epidermal growth factor receptor (EGFR) on invasion-promoting proteins secreted by glioblastoma cells. Mol Cell Proteomics 13:2618-31
Tyler, Anna L; Crawford, Dana C; Pendergrass, Sarah A (2014) Detecting and Characterizing Pleiotropy: New Methods for Uncovering the Connection Between the Complexity of Genomic Architecture and Multiple phenotypes. Pac Symp Biocomput :183-187
Mohamadlou, Hamid; Shope, Joseph C; Flann, Nicholas S (2014) Maximizing Kolmogorov Complexity for accurate and robust bright field cell segmentation. BMC Bioinformatics 15:32
Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin et al. (2014) Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction. PLoS One 9:e107863
Carpp, Lindsay N; Rogers, Richard S; Moritz, Robert L et al. (2014) Quantitative proteomic analysis of host-virus interactions reveals a role for Golgi brefeldin A resistance factor 1 (GBF1) in dengue infection. Mol Cell Proteomics 13:2836-54
Kusebauch, Ulrike; Deutsch, Eric W; Campbell, David S et al. (2014) Using PeptideAtlas, SRMAtlas, and PASSEL: Comprehensive Resources for Discovery and Targeted Proteomics. Curr Protoc Bioinformatics 46:13.25.1-13.25.28
Schoggins, John W; MacDuff, Donna A; Imanaka, Naoko et al. (2014) Pan-viral specificity of IFN-induced genes reveals new roles for cGAS in innate immunity. Nature 505:691-5
Vialas, Vital; Sun, Zhi; Loureiro y Penha, Carla Veronica et al. (2014) A Candida albicans PeptideAtlas. J Proteomics 97:62-8
Kusebauch, Ulrike; Ortega, Corrie; Ollodart, Anja et al. (2014) Mycobacterium tuberculosis supports protein tyrosine phosphorylation. Proc Natl Acad Sci U S A 111:9265-70

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