? Imaging Management and Analysis Core The Imaging Management and Analysis Core (Imaging Core) will meet the requirements of the three Measuring, Modeling, and Controlling Heterogeneity-Cancer Systems Biology Consortium (M2CH-CSBC) Projects and the Outreach Core by developing and integrating state-of-the-art resources for a complete imaging workflow. Specifically, we will 1) use high-content imaging to measure baseline and drug and microenvironment response phenotypes separately and in combination in a panel of cell lines, 2) optimize and deploy novel, highly multiparameter cyclic multiplexed immunofluorescence (cmIF) imaging workflows to measure microenvironment and drug responses in heterogeneous tissues in 2D and 3D, and 3) deploy correlative light/electron microscopic analysis based on super-resolution light microscopy and scanning electron microscopy to provide the ultrastructural cellular context. To execute these tasks in close collaboration with the Projects, the Imaging Core will expand on existing resources and build a customized imaging environment to a) acquire image data in automated ways from the multiple imaging instruments, following precise protocols and maintaining complete metadata, b) store images and metadata and all derived and associated measurement data in highly robust databases and repositories with powerful query, visualization, and programmer access tools, c) quantitatively analyze images by deploying and developing state-of-the-art image reconstruction methods, image segmentation algorithms, feature extraction and classification routines, normalization procedures, and machine learning approaches to classify image features at multiple resolution levels, and d) develop novel visualization methods for interacting with images and extracted features and models in combination with experimental metadata and data from external databases.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA209988-04
Application #
9964676
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2020-05-01
Budget End
2021-04-30
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Oregon Health and Science University
Department
Type
DUNS #
096997515
City
Portland
State
OR
Country
United States
Zip Code
97239
Gray, Elliot; Mitchell, Elizabeth; Jindal, Sonali et al. (2018) A METHOD FOR QUANTIFICATION OF CALPONIN EXPRESSION IN MYOEPITHELIAL CELLS IN IMMUNOHISTOCHEMICAL IMAGES OF DUCTAL CARCINOMA IN SITU. Proc IEEE Int Symp Biomed Imaging 2018:796-799
Davis, Ryan J; Gönen, Mehmet; Margineantu, Daciana H et al. (2018) Pan-cancer transcriptional signatures predictive of oncogenic mutations reveal that Fbw7 regulates cancer cell oxidative metabolism. Proc Natl Acad Sci U S A 115:5462-5467
Burlingame, Erik A; Margolin, Adam A; Gray, Joe W et al. (2018) SHIFT: speedy histopathological-to-immunofluorescent translation of whole slide images using conditional generative adversarial networks. Proc SPIE Int Soc Opt Eng 10581:
Chang, Young Hwan; Heo, You Jeong; Cho, Junhun et al. (2018) Computational measurement of tumor immune microenvironment in gastric adenocarcinomas. Sci Rep 8:13887
Risom, Tyler; Langer, Ellen M; Chapman, Margaret P et al. (2018) Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat Commun 9:3815
Su, Yulong; Pelz, Carl; Huang, Tao et al. (2018) Post-translational modification localizes MYC to the nuclear pore basket to regulate a subset of target genes involved in cellular responses to environmental signals. Genes Dev 32:1398-1419
Archer, Tenley C; Ehrenberger, Tobias; Mundt, Filip et al. (2018) Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups. Cancer Cell 34:396-410.e8
Chang, Young Hwan; Thibault, Guillaume; Johnson, Brett et al. (2017) Integrative Analysis on Histopathological Image for Identifying Cellular Heterogeneity. Proc SPIE Int Soc Opt Eng 10140:
Azimi, Vahid; Chang, Young Hwan; Thibault, Guillaume et al. (2017) BREAST CANCER HISTOPATHOLOGY IMAGE ANALYSIS PIPELINE FOR TUMOR PURITY ESTIMATION. Proc IEEE Int Symp Biomed Imaging 2017:1137-1140
Tsujikawa, Takahiro; Kumar, Sushil; Borkar, Rohan N et al. (2017) Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis. Cell Rep 19:203-217