High Content Representation and Association of 3D Cell Culture Models

3D 细胞培养模型的高内涵表示和关联

基本信息

批准号：
8607905
负责人：
Bahram A. Parvin
金额：
$ 59.23万
依托单位：
UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-04-01 至 2017-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8607905
关键词：
Affect Algorithms Area Atlases Behavior Bioinformatics Biological Biological Assay Biological Markers Biological Models Biological Process Biology Breast Breast Epithelial Cells Cancer Diagnostics Cell Culture Techniques Cell Line Cells Cellular biology Clinical Commit Complex Computer software Confocal Microscopy Data Development Disease Engineering Environment Epithelial-Stromal Communication Extracellular Matrix Fluorescence Microscopy Focal Adhesions Gene Expression Generations Genes Genetic Genome Genomics Goals Graph Grouping Growth Factor Homeostasis Image Image Analysis Imagery Knowledge Laboratories Libraries Malignant - descriptor Malignant Neoplasms Mammary Neoplasms Measures Mechanics Methods Modeling Molecular Morphology Non-Malignant Oncogenic Online Systems Outcome Output Phenotype Preclinical Drug Evaluation Property Publishing Research Sampling Signal Pathway Signal Transduction Stress System Systems Integration Technology Testing Therapeutic Intervention Tissue Differentiation Tissues Validation Voting Work base computerized data processing design extracellular genetic manipulation genome wide association study improved in vivo in vivo Model indexing meetings next generation novel open source outcome forecast public health relevance research study response screening small hairpin RNA software development three-dimensional modeling tumor tumor progression two-dimensional usability

项目摘要

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：三维细胞培养模型的高内涵表示和关联我们将开发一个用于三维（3D）细胞培养模型的形态分析的平台。多细胞系统将通过共焦显微镜进行全 3D 成像；将计算细胞组织和许多其他终点；多维表型特征将与基因组数据相关联。这一举措的潜在结果是（i）对模型系统中生物过程的基本了解，该模型系统可以更好地预测体内模型，（ii）针对具有理想逆转特性的肿瘤系进行药物筛选的模板，以及（iii））通过基因组和表型数据的关联来生成和验证假设的模板。更重要的是，我们将设计涉及改变乳腺上皮细胞微环境（例如基质刚度）机械特性的实验。我们已经确定，细胞调节其对基质硬度的反应，按比例增加其收缩性，促进粘着斑组装，并增强生长因子信号传导。最终结果是癌症激活的信号通路和细胞外基质 (ECM) 硬度协同增强细胞张力，从而损害组织形态并诱发恶性行为。因此，鉴定在乳腺肿瘤中也升高的张力调节基因可以作为癌症诊断和潜在治疗的生物标志物。我们的目标是 (i) 将先进的图像分析算法与生物信息学系统相结合，以对 3D 细胞培养模型进行高内涵筛选，(ii) 开发整合表型和分子信息的新方法，以及 (iii) 测试修改基质的假设-由于基质刚度的变化，上皮相互作用通过损害细胞和组织表型来促进肿瘤行为。我们将在一组具有显着分子多样性和诱导细胞和组织形态变化的工程基质的非恶性和转化乳腺细胞系的背景下实现这些目标。三维细胞培养模型已成为研究组织分化和癌症行为的有效系统。如果癌症从根本上来说是一种异常多细胞组织的疾病，那么了解组织微环境、细胞和分子变量的影响以及对致癌表型可能的治疗干预措施需要开发和使用更复杂的模型来近似细胞-细胞和细胞-体内基质相互作用。我们将针对重要的生物学问题开发独特的技术，以开发用于 3D 细胞培养测定的下一代系统细胞生物学平台。我们提出的工作的可交付成果是（i）一个经过验证的开源平台，用于在多个端点对 3D 细胞培养模型进行常规表型表示，（ii）表型指数与相应基因组数据的无缝关联，以及（iii）开放分布带注释的原始数据和处理后的数据。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.

在乳腺 X 光密度范围内，微环境的硬度会上调 MCF10A 3D 培养物中 ERBB2 的表达。

DOI：
发表时间：
2016-07-07
期刊：
影响因子：
4.6
作者：
Cheng, Qingsu;Bilgin, Cemal Cagatay;Fontenay, Gerald;Chang, Hang;Henderson, Matthew;Han, Ju;Parvin, Bahram
通讯作者：
Parvin, Bahram

NUCLEI SEGMENTATION VIA SPARSITY CONSTRAINED CONVOLUTIONAL REGRESSION.

通过稀疏约束卷积回归进行核分割。