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3D Bioprinted Omentum tissue to model ovarian cancer metastasis

3D Bioprinted Omentum tissue to model ovarian cancer metastasis
3D 生物打印网膜组织用于模拟卵巢癌转移
批准号：
10683668
负责人：
Marc Ferrer
金额：
$90.89万
依托单位：
NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
--
资助国家：
美国
项目状态：
未结题
起止时间：
至
来源：
https://reporter.nih.gov/project-details/10683668
关键词：
3-DimensionalAnimal ModelAntineoplastic AgentsBiological AssayBlood VesselsCancer Cell GrowthCell LineCell modelCell-Matrix JunctionCellsDevelopmentEndothelial CellsFibroblastsFluorescence MicroscopyFunctional disorderGenetically Engineered MouseGoalsGrowthHumanImmunodeficient MouseIn VitroLabelLocationMalignant NeoplasmsMalignant neoplasm of ovaryMesothelial CellMetastatic Malignant Neoplasm to the OvaryModelingMonitorMorphologyNeoplasm MetastasisOmentumPatientsPericytesPhysiologicalPhysiologyPreclinical TestingProductionProtocols documentationSiteSurfaceSuspensionsThree-Dimensional ImagingTissuesToxicity TestsXenograft procedurebiofabricationbioprintingcancer cellcell growthcellular imagingclinical efficacydrug developmentdrug discoveryefficacy testingmonolayerneoplastic cellnovel therapeuticsovarian neoplasmpredictive testpreventscreeningtumor

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中文摘要
翻译
我们已经开发了一种生物制造方案，用于生产包括患者来源的成纤维细胞、内皮细胞和周细胞以及一层患者间皮细胞的血管化大网膜模型。患者细胞来自我们的合作者Ernst Lengyel博士和Hilary Kenny博士。将荧光标记的卵巢肿瘤细胞悬液添加到组织上的间皮细胞层的顶部，并使用荧光显微镜监测组织内的肿瘤形成。3D成像分析已经被开发出来，以量化肿瘤的大小和数量以及它们的位置。这些分析将被用来筛选一组有针对性的化合物，以防止组织中的肿瘤形成。
英文摘要
We have developed biofabrication protocols for the production of a vascularized omentum model that includes patient derived fibroblasts, endothelial cells and pericytes, and a layer of patient mesothelial cells. Patient cells were obtained from our collaborators Dr. Ernst Lengyel and Hilary Kenny. Addition of a suspension of fluorescently labeled ovarian tumor cells to the top of mesothelial cell layer on the tissue and used fluorescence microscopy to monitor tumor formation inside the tissue. 3D imaging analysis have been developed to quantiate size and number of tumors, and their location. These analytics will be used to screen a focused set of compounds for compounds that prevent tumor formation in the tissue.
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