Efficient patient-specific cell generation by image-guidance

通过图像引导高效生成患者特异性细胞

• 批准号: 8509778
• 负责人: Shih-Jong J Lee
• 金额: $ 98.35万
• 依托单位: DRVISION TECHNOLOGIES, LLC
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8509778
• 关键词: Biotechnology; Brain Diseases; Cardiac Myocytes; Cell Culture System; Cell Differentiation process; Cell Fate Control; Cells; Cellular Morphology; Computer software; Data; Development; Diagnosis; Educational process of instructing; Evaluation; Fibroblasts; Generations; Goals; Government; Harvest; Health; Healthcare; Heart; Human; Image; Imaging technology; Institutes; Kinetics; Life; Machine Learning; Medicine; Metric; Monitor; Outcome; Patients; Pattern Recognition; Performance; Persons; Pharmaceutical Preparations; Phase; Process; Production; Protocols documentation; Quality Control; Sampling; Staging; Staining method; Stains; Stem cells; Surface; System; Technology; Testing; Time; Work; alanine aminopeptidase; base; cell type; cellular imaging; cost; cost effectiveness; disease diagnosis; drug discovery; drug testing; human embryonic stem cell; improved; induced pluripotent stem cell; patient population; prototype; stem cell technology; tool; usability

DESCRIPTION (provided by applicant): This fast-track proposal applies advanced kinetic image pattern recognition (KIPR) technologies to predict induced pluripotent stem cell (iPSC) reprogramming colonies' differentiation outcomes for significantly improved yield and robustness of differentiation protocols. The objectives of the proposed tool are 1) Teaching: creation of scores for induced colony differentiation outcome prediction by machine learning; 2) Reprogramming: optimal reprogramming harvest time determination by continuous colony score monitoring; 3) Differentiation: selection of colonies with the highest prediction scores for differentiation at the reprogramming harvest time; 4) Differentiation: cell cluster quality control by continuous monitoring during differentiation. The specific aims of this fast-track proposal are Phase I: 1) Extend SVCell for the prediction of induced colony differentiation outcomes ; 2) Validate that prediction of colony differentiation outcomes can improve the yield of CM differentiation. Phase II: 1) Validate that the integrated system can be taught to be robust and high yielding for a diverse set of human fibroblast input samples and different reprogramming / differentiation protocols; 2) Integrate SVCell with a state-of-the-art continuous cell imaging and culture system to create a prototype patient-specific cell generation system; 3) Validate the integrated system as a patient-specific cell generation product. The ultimate goal of this fast-track proposal is to develop and validate an image-guided efficient patient-specific cardiomyocyte generation system. This will be achieved by integrating our established SVCell software containing advanced KIPR technologies with a live cell imaging technology to synthesize state-of-the-art cell fate control protocols against iPSC. Patient-specific cell generation systems could "personalize" medicine by reprogramming patient-specific cells and directing their differentiation to specific lineages (e.g. heart, brain) for disease diagnosis and personalized drug testing. Successful development of the patient-specific cell generation system of this proposal could catalyze personalized medicine and revolutionize health care in both diagnosis and therapy.

描述（由申请人提供）：该快速通道提案应用先进的动力学图像模式识别（KIPR）技术来预测诱导多能干细胞（iPSC）重编程集落的分化结果，以显著提高分化方案的产量和稳健性。所提出的工具的目标是1）教导：通过机器学习创建用于诱导集落分化结果预测的分数; 2）重编程：通过连续集落分数监测确定最佳重编程收获时间; 3）分化：选择在重编程收获时间具有最高预测分数的集落用于分化; 4）分化：通过在分化期间连续监测来控制细胞簇质量。该快速通道提案的具体目标是第一阶段：1）扩展SVCell用于预测诱导集落分化结果; 2）证实集落分化结果的预测可以提高CM分化的产量。第二阶段：1）证明可以教导集成系统对于不同的人成纤维细胞输入样品组和不同的重编程/分化方案是稳健和高产的; 2）将SVCell与最先进的连续细胞成像和培养系统集成以创建原型患者特异性细胞生成系统; 3）将集成系统作为患者特异性细胞生成产品。这个快速通道提案的最终目标是开发和验证一个图像引导的有效的患者特异性心肌细胞生成系统。这将通过将我们已建立的SVCell软件（包含先进的KIPR技术）与活细胞成像技术相结合来实现，以合成针对iPSC的最先进的细胞命运控制方案。患者特异性细胞生成系统可以通过重新编程患者特异性细胞并将其分化为特定谱系（例如心脏，大脑）来“个性化”医学，用于疾病诊断和个性化药物测试。该提案的患者特异性细胞生成系统的成功开发可以催化个性化医疗，并在诊断和治疗方面彻底改变医疗保健。