Real-time computer vision tracking of stemness

实时计算机视觉跟踪干性

基本信息

批准号：
7777326
负责人：
PHIL GORDON CAMPBELL
金额：
$ 31.37万
依托单位：
CARNEGIE-MELLON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-05-01 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7777326
关键词：
Address Adult Alkaline Phosphatase Apoptosis Apoptotic Behavior Behavioral Blinded Cell Count Cell Culture Techniques Cell Differentiation process Cell Line Cell Proliferation Cell Separation Cell Therapy Cell division Cell model Cells Computer Vision Systems Dactinomycin Data Data Set Daughter Development Drops Elements Engineering Epidermal Growth Factor Excision Exhibits Extracellular Matrix Feedback Fibroblast Growth Factor 2 Future Generic Drugs Goals Growth Growth Factor Heterogeneity Human Image Imagery In Situ In Vitro Indium Individual Kinetics Life Maintenance Measurement Measures Mediating Medicine Mesenchymal Stem Cells Metric Mitotic Modeling Monitor Motion Mus Muscle Muscle Cells Muscle Fibers Muscle satellite cell Osteoblasts Osteogenesis Output Parents Pattern Phase Population Population Growth Principal Investigator Printing Process Production Proliferating Protocols documentation Quality Control Recording of previous events Regenerative Medicine Relative (related person)Research Research Personnel Robotics Sampling Serum Signal Transduction Software Tools Sorting - Cell Movement Specific qualifier value Staining method Stains Stem cells System Technology Testing Time Translations Update Validation Video Microscopy adult stem cell base bone bone morphogenetic protein 2 cell behavior cell type cellular engineering clinical application combinatorial cytochemistry detector experience human stem cells imaging Segmentation immortalized cell immunocytochemistry in vivo in vivo regeneration indexing member myogenesis novel novel strategies osteoblast differentiation osteogenic osteoprogenitor cell platelet-derived growth factor BB programs research study response self-renewal spatiotemporal stem cell biology stem cell division stemness tool vector

项目摘要

DESCRIPTION (provided by applicant): This proposal addresses the need for new stem cell engineering toolsets that can regulate adult stem cell expansion (self-renewal, without differentiation) ex vivo in order to create sufficient numbers of cells for clinical applications. The goals of this proposal are to: 1) develop a computer vision system that tracks in real-time the spatiotemporal histories of cell divisions in vitro in phase-contrast imagery; 2) use this data to automatically derive real-time metrics of symmetry, division times, confluence, and predictive population growth models; and, 3) apply this system to develop novel strategies that maximize the yield of adult stem cell expansion in growth factor-mediated conditions. 'Real-time' means that processing is completed 'on-line' in the 15 minute intervals between image acquisitions, and this information is available during cell culture as feedback for process monitoring and control. In Aim 1, robust image segmentation and tracking of dense cell populations will be achieved using multi-modal cell tracking modules that represent and reason about cell states and motion from different perspectives, and then fuse and coordinate their respective outputs to make collaborative decisions. The multi-modal modules will be formulated for fast implementation on vector-based graphics processing hardware. The system will measure: proliferation, lineage (i.e., parent-daughter relationship), quiescence, and apoptotic states of each cell in culture within developing confluent populations of muscle-derived stem cells (MDSCs), including immortalized pluripotent C2C12, primary mouse MDSCs, and primary human MDSCs. In Aim 2, the system will be applied to efficient discovery and characterization of cell expansion behaviors and determination of nominal culture conditions to maximize symmetry using bio-printed combinatorial arrays of immobilized growth factors on extracellular matrix substrates. In Aim 3, real-time monitoring and control of cell expansion will be demonstrated using an adaptive subculturing strategy; the predictive growth model will signal when to suspend cell culture, followed by fluorescent-activated cell sorting to exclude apoptotic or differentiating cells, thus selectively enriching subcultures for cells exhibiting the highest proliferation rates. Maintenance of differentiative potential will be monitored between subcultures using in vitro osteogenesis and myogenesis as paradigm differentiation indices.

描述（由申请人提供）：该提案解决了可以调节成年干细胞扩展（自我更新，无分化）的新干细胞工程工具集的需求，以便为临床应用创建足够数量的细胞。该提案的目标是：1）开发一个计算机视觉系统，该系统可以实时跟踪相对比对比图像中细胞分裂的时空历史； 2）使用此数据自动得出对称性，分裂时间，汇合和预测种群增长模型的实时指标； 3）应用该系统制定新型策略，以最大程度地提高成年干细胞在生长因子介导的条件下的产量。 “实时”意味着在图像采集之间的15分钟间隔内完成处理“在线”，并且在细胞培养过程中可用此信息作为过程监视和控制的反馈。在AIM 1中，将使用多模式的细胞跟踪模块来实现稳健的图像分割和密集细胞种群的跟踪，这些模块从不同的角度代表和理由，从不同的角度来看，然后将其相应的输出融合和协调以做出协作决策。多模式模块将被制定，以便在基于向量的图形处理硬件上快速实现。该系统将测量：增殖，谱系（即父母的关系），静止和凋亡状态在培养的每个细胞中，肌肉衍生的干细胞（MDSC）的融合种群（包括永生的多能C2C12），主要小鼠MDSC和原始人类MDSC。在AIM 2中，该系统将应用于细胞扩展行为的有效发现和表征，并确定名义培养条件，以使用对细胞外基质底物上固定生长因子的生物印刷组合阵列最大化对称性。在AIM 3中，将使用适应性亚培养策略来证明对细胞扩展的实时监测和控制；预测生长模型将何时悬浮细胞培养，然后进行荧光激活的细胞分类，以排除凋亡或区分细胞，从而选择性地富集了表现出最高增殖率的细胞的亚培养。将使用体外成骨和肌发生作为范式分化指数来监测分化潜力的维持。