High-content screening (HCS) is defined as the integration of sample preparation, automatic microscopic imaging, and bioinformatics tools that permit experimentation and discovery with high-throughput cell images. It has potential to make large-scale cell biology a tractable approach by generating functional information through the automated measurements of the temporal and spatial activities of genes and proteins in living cells. However, there are significant computational challenges, such as accurate segmentation of the large population of cells and classification of cellular phenotypes, in high-content screening, and image informatics has become the rate-limiting factor in realizing its full potential. Therefore, we propose to develop a new generation of computational tools to fill that gap. We emphasize three key technical contributions of G-CELLIQ. First, G-CELLIQ will provide an integrated cell image processing pipeline using advanced computational algorithms to extract contents of RNAi screening images, reducing the time required in processing by manual analysis and the variability in manual analysis. Second, we will develop novel classification-controlled feedback systems to refine cell boundaries and to increase the accuracy of the scoring method that reflect the mixture of different cell phenotypes in the screening. Third, we will develop an innovative and effective scoring method based on the fuzzy set-theoretic approach. The succinct score will allow researchers to easily comprehend the significance of the results and identify the genes of interest. The hypothesis of this application is that the proposed image informatics system, G-CELLIQ (Genomic CELLular Imaging Quantitator), is critical for large scale RNAi genome screening to identify novel effectors of Rho proteins. The Rho family of small GTPases is essential for cell shape changes during normal cell migration and cancer metastasis. The goal of genome-wide RNAi screening is to identify novel effectors of Rho proteins using a cell-based assay for Rho activities. To test our hypothesis, we will evaluate the utility of the G-CELLIQ with a set of well defined, biological-driven experiments. Upon completion of the proposed project, we plan to make this package freely available to biomedical research community through a public website. More importantly, the completion of this screening project will help to answer some critical questions related to cancer metastasis. Such understanding will in turn advance our knowledge in tumor biology and open up the possibility of novel treatments in the future. This project will be a substantial contribution to the public health by understanding Rho family of small GTPases which is of fundamental relevance to developmental and cancer biology. More importantly, the completion of this screening project will help to answer some critical questions related to cancer metastasis. Such understanding will in turn advance our knowledge in tumor biology and open up the possibility of novel treatments in the future.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA121225-04
Application #
8112435
Study Section
Microscopic Imaging Study Section (MI)
Program Officer
Couch, Jennifer A
Project Start
2008-09-15
Project End
2014-07-31
Budget Start
2011-08-01
Budget End
2014-07-31
Support Year
4
Fiscal Year
2011
Total Cost
$297,002
Indirect Cost
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
185641052
City
Houston
State
TX
Country
United States
Zip Code
77030
Choi, Hyejin; Sheng, Jianting; Gao, Dingcheng et al. (2015) Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Rep 10:1187-201
Yin, Zheng; Sailem, Heba; Sero, Julia et al. (2014) How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis. Bioessays 36:1195-203
Jin, Guangxu; Wong, Stephen T C (2014) Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 19:637-44
Brandl, Miriam B; Pasquier, Eddy; Li, Fuhai et al. (2014) Computational analysis of image-based drug profiling predicts synergistic drug combinations: applications in triple-negative breast cancer. Mol Oncol 8:1548-60
Yin, Zheng; Deng, Tuo; Peterson, Leif E et al. (2014) Transcriptome analysis of human adipocytes implicates the NOD-like receptor pathway in obesity-induced adipose inflammation. Mol Cell Endocrinol 394:80-7
Yin, Zheng; Sadok, Amine; Sailem, Heba et al. (2013) A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes. Nat Cell Biol 15:860-71
Zhao, Hong; Jin, Guangxu; Cui, Kemi et al. (2013) Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases. Cancer Res 73:6149-63
Liu, Timothy; Ren, Ding; Zhu, Xiaoping et al. (2013) Transcriptional signaling pathways inversely regulated in Alzheimer's disease and glioblastoma multiform. Sci Rep 3:3467
Li, Fuhai; Yin, Zheng; Jin, Guangxu et al. (2013) Chapter 17: bioimage informatics for systems pharmacology. PLoS Comput Biol 9:e1003043
Deng, Tuo; Lyon, Christopher J; Minze, Laurie J et al. (2013) Class II major histocompatibility complex plays an essential role in obesity-induced adipose inflammation. Cell Metab 17:411-22

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