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
Peterson, Eliza J R; Ma, Shuyi; Sherman, David R et al. (2016) Network analysis identifies Rv0324 and Rv0880 as regulators of bedaquiline tolerance in Mycobacterium tuberculosis. Nat Microbiol 1:16078
Cromie, Gareth A; Tan, Zhihao; Hays, Michelle et al. (2016) Dissecting Gene Expression Changes Accompanying a Ploidy-Based Phenotypic Switch. G3 (Bethesda) :
Gatto, Laurent; Hansen, Kasper D; Hoopmann, Michael R et al. (2016) Testing and Validation of Computational Methods for Mass Spectrometry. J Proteome Res 15:809-14
Plaisier, Christopher L; O'Brien, Sofie; Bernard, Brady et al. (2016) Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis. Cell Syst 3:172-86
Vialas, Vital; Sun, Zhi; Reales-Calderón, Jose A et al. (2016) A comprehensive Candida albicans PeptideAtlas build enables deep proteome coverage. J Proteomics 131:122-30
Deutsch, Eric W; Sun, Zhi; Campbell, David S et al. (2016) Tiered Human Integrated Sequence Search Databases for Shotgun Proteomics. J Proteome Res 15:4091-4100
Stittrich, Anna B; Ashworth, Justin; Shi, Mude et al. (2016) Genomic architecture of inflammatory bowel disease in five families with multiple affected individuals. Hum Genome Var 3:15060
Zhou, Joseph Xu; Samal, Areejit; d'Hérouël, Aymeric Fouquier et al. (2016) Relative stability of network states in Boolean network models of gene regulation in development. Biosystems 142-143:15-24
Xue, Ting; Liu, Ping; Zhou, Yong et al. (2016) Interleukin-6 Induced ""Acute"" Phenotypic Microenvironment Promotes Th1 Anti-Tumor Immunity in Cryo-Thermal Therapy Revealed By Shotgun and Parallel Reaction Monitoring Proteomics. Theranostics 6:773-94
McDermott, Suzanne M; Luo, Jie; Carnes, Jason et al. (2016) The Architecture of Trypanosoma brucei editosomes. Proc Natl Acad Sci U S A 113:E6476-E6485

Showing the most recent 10 out of 300 publications