Label-free cell cycle classification using Phase Imaging with Computational Specificity

使用具有计算特异性的相位成像进行无标记细胞周期分类

基本信息

批准号：
10547446
负责人：
CATALIN CHIRITESCU
金额：
$ 22.5万
依托单位：
PHI OPTICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10547446
关键词：
3-Dimensional Agreement Antineoplastic Agents Binding Biological Assay Biological Products Biophotonics Breast Cancer Cell Businesses Capital Cell Culture Techniques Cell Cycle Cell Death Cell Proliferation Cell physiology Cells Classification Clinical Trials Colon Complex Computer software DNA DNA Repair Detection Development Disease Drug Design Drug Targeting Dyes Excision Expenditure Feasibility Studies Fluorescence Funding Goals Growth Hour Hyperplasia Illinois Image Imaging Device Individual Interphase Cell Interview Investigation Investments Label Learning Legal patent Length Letters Location MCF7 cell Malignant Neoplasms Measures Methods Microscopy Modality Morphology Nucleic Acids Optical Instrument Optics Pathway interactions Performance Pharmaceutical Preparations Pharmacologic Substance Phase Photobleaching Phototoxicity Population Preparation Privatization Procedures Process Regulation Research Resolution SW620 Sales Sampling Scientist Series Signal Transduction Small Business Innovation Research Grant Specialist Specificity Specimen Staining and Labeling Stains Stratification Stress Structure Technology Time Tissues Toxic effect United States National Institutes of Health Universities Work anti-cancer base bioimaging cancer therapy cell fixation commercialization deep neural network design verification digital drug candidate drug development drug discovery embryo cell embryo tissue experience experimental study fluorescence imaging fluorophore high throughput screening imaging modality innovation instrument lead optimization live cell imaging monolayer novel novel therapeutics optical imaging pre-clinical product development programs prototype research and development response to injury sample fixation single cell analysis success technology development tomography uptake user-friendly

项目摘要

Project Summary/Abstract Despite investments in cancer drug discovery including high-throughput screening and structure-based drug design, very few anticancer compounds pass late stages clinical trials due to lack of efficacy and unwanted toxicities. Cells are normally dormant and enter the active segments of the cell cycle when signaled. This pathway is diminished or disrupted in cancer afflictions and cancer treatments tend to target dividing cells while sparing non-dividing cells. Cell cycle classification is thus a key performance indicator during pre-clinical cancer drug candidates stratification. Current fluorescence based ID methods require long sample preparation times, and suffer from phototoxicity and photobleaching. Phi Optics Customer Discovery discussions with biopharma execs and R&D specialists revealed that faster and more accurate cell cycle phase classification is needed to for pre- clinical oncotherapy drug development (hit prioritization, hit-to-lead optimization, and shortening of the lead optimization phase). This Small Business Innovation Research Phase I project proposes to study the feasibility of developing a proof- of-concept cell cycle detection and classification instrument for cancer drug development.. The instrument will use an innovative digital staining method developed at University of Illinois at Urbana Champaign: Phase Imaging with Computational Specificity (PICS). PICS combines non-destructive Quantitative Phase Imaging (SLIM and GLIM) with the power of AI. Cell cycle assays can thus be performed with the accuracy and specificity of regular fluorescence but without the inconveniences associated with cell tagging. Once the feasibility is proven the work will continue during Phase II for the development of a lab bench optical instrument for performing high-throughput cell cycle phase identification and scoring for multiple drug candidates. The instrument will be commercialized into the research and bio-pharma market for delivery of faster (100-fold higher throughput) and more accurate stratification of cancer drug candidates in pre-clinical stages. .

项目摘要/摘要尽管对癌症药物发现进行了投资，包括高通量筛查和基于结构的药物设计，由于缺乏疗效和不必要的，很少有抗癌化合物通过后期临床试验毒性。细胞通常处于休眠状态，并在发出信号时进入细胞周期的活动段。这条路在癌症苦难中减少或破坏，癌症治疗倾向于靶向分裂细胞，而在游览中则靶向分裂非分散细胞。因此，细胞周期分类是临床前癌症药物期间的关键性能指标候选人分层。基于当前荧光的ID方法需要长时间的样品准备时间，并且患有光毒性和光漂白。 PHI光学客户发现与生物制药高管讨论研发专家显示，需要更快，更准确的细胞周期阶段分类才能进行预先临床癌症药物开发（命中优先级，命中率优化和铅缩短铅优化阶段）。这个小型企业创新研究阶段项目提议研究制定证明的可行性 - 癌症药物开发的概念细胞周期检测和分类仪器。该仪器将使用在Urbana Champaign的伊利诺伊大学开发的创新数字染色方法：阶段具有计算特异性（图片）的成像。图片结合了非破坏性定量相成像（苗条和闪闪发光）具有AI的力量。因此，可以以精度和特异性进行细胞周期测定定期荧光，但没有与细胞标记相关的不便。一旦证明可行性用于执行高通量细胞周期阶段鉴定和对多个候选药物的评分的仪器。该仪器将被商业化到研究和生物制剂市场，以更快地提供（100倍）较高的吞吐量）和临床前阶段的癌症药物候选者的更准确分层。。