High-Content Representation and Association of Three-Dimensional Cell Culture Models We will develop a platform for morphometric profiling of three-dimensional (3D) cell culture models. Multicellular systems will be imaged with confocal microscopy in full 3D;cellular organization and a number of other end points will be computed;and multidimensional phenotypic signatures will be associated with genomic data. The potential results of this initiative are (i) a basic understanding of the biological processes in a model system that is a better predictor of in vivo models, (ii) a template for drug screening against tumor lines with desirable reversion properties, and (iii) a template for hypothesis generation and validation through associations of genomic and phenotypic data. More importantly, we will design experiments that involve the alteration of mechanical properties of the microenvironment (e.g., matrix stiffness) of mammary epithelial cells. We have established that cells tune their response to matrix stiffness, proportionally increase their contractibility, promote focal adhesion assembly, and enhance growth factor signaling. The end result is that cancer-activated signaling pathways and extracellular matrix (ECM) stiffness collaborate to enhance cell tension, which compromises tissue morphology and induces malignant behavior. Therefore, identification of tension-regulated genes that are also elevated in breast tumors can serve as biomarkers for cancer diagnostic and potential therapy. Our goal is to (i) couple advanced image analysis algorithms with a bioinformatics system for high-content screening of 3D cell culture models, (ii) develop novel ways to integrate phenotypic and molecular information, and (iii) test the hypothesis that modified stromal-epithelial interactions promote tumor behavior by compromising cell and tissue phenotypes as a result of changes in the matrix stiffness. We will meet these goals in the context of a set of nonmalignant and transformed breast cell lines with significant molecular diversity and engineered matrices that induce diverse changes in cell and tissue morphology. Three-dimensional cell culture models have emerged as effective systems to study tissue differentiation and cancer behavior. If cancer is fundamentally a disease of aberrant multicellular organization, then understanding the effects of the tissue microenvironment, cellular and molecular variables, and possible therapeutic interventions on the oncogenic phenotype requires the development and use of more sophisticated models that can approximate cell-cell and cell-matrix interactions in vivo. We will develop unique technologies with important biological questions to develop the next generation of systems cell biology platforms for use with 3D cell culture assays. The deliverables of our proposed efforts are (i) a validated open source platform for routine phenotypic representation of 3D cell culture models at multiple endpoints, (ii) a seamless association of phenotypic indices with the corresponding genomic data, and (iii) an open distribution of annotated raw and processed data.

Public Health Relevance

There are inherent barriers in the quantitative profiling of 3D cell culture models imaged through confocal microscopy and the association of their morphometric properties with genomic data. This proposal aims to develop technologies for next-generation high-content screening systems. In addition, the technology pipeline will be applied to a high-impact problem of identifying and validating tension-regulated genes that are elevated in breast tumors and to provide a novel means for cancer diagnostic and potential therapy.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-IMST-L (90))
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Li, Jerry
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Lawrence Berkeley National Laboratory
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Khoshdeli, Mina; Winkelmaier, Garrett; Parvin, Bahram (2018) Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes. BMC Bioinformatics 19:294
Khoshdeli, Mina; Parvin, Bahram (2018) Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network. IEEE Trans Biomed Eng 65:625-634
Khoshdeli, Mina; Cong, Richard; Parvin, Bahram (2017) Detection of Nuclei in H&E Stained Sections Using Convolutional Neural Networks. IEEE EMBS Int Conf Biomed Health Inform 2017:105-108
Cheng, Qingsu; Bilgin, Cemal Cagatay; Fontenay, Gerald et al. (2016) Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density. Sci Rep 6:28987
Bilgin, Cemal Cagatay; Fontenay, Gerald; Cheng, Qingsu et al. (2016) BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models. PLoS One 11:e0148379
Hines, William C; Kuhn, Irene; Thi, Kate et al. (2016) 184AA3: a xenograft model of ER+ breast adenocarcinoma. Breast Cancer Res Treat 155:37-52
Chang, Hang; Zhou, Yin; Borowsky, Alexander et al. (2015) Stacked Predictive Sparse Decomposition for Classification of Histology Sections. Int J Comput Vis 113:3-18
Chang, Hang; Wen, Quan; Parvin, Bahram (2015) Coupled Segmentation of Nuclear and Membrane-bound Macromolecules through Voting and Multiphase Level Set. Pattern Recognit 48:882-893
Hines, William C; Yaswen, Paul; Bissell, Mina J (2015) Modelling breast cancer requires identification and correction of a critical cell lineage-dependent transduction bias. Nat Commun 6:6927
Chang, Hang; Parvin, Bahram (2015) Classification of 3D Multicellular Organization in Phase Microscopy for High Throughput Screening of Therapeutic Targets. Proc IEEE Workshop Appl Comput Vis 2015:436-441

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