Developing high-throughput genetic perturbation strategies for single cells in cancer organoids

开发癌症类器官中单细胞的高通量遗传扰动策略

基本信息

批准号：
10212991
负责人：
BONNIE BERGER
金额：
$ 92.22万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-08 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10212991
关键词：
3-Dimensional Address Adoption Algorithms Aneuploidy Bar Codes Basic Science Bioinformatics Biopsy CRISPR interference Cancer Biology Cancer Model Cancer cell line Cell Line Cells Characteristics Clinical Oncology Collection Colon Carcinoma Color Communities Custom Data Development Doctor of Philosophy Drug Addiction Ensure Epigenetic Process Evaluation Excision Expression Library Foundations Gene Combinations Genes Genetic Genetic Recombination Genetic Screening Genetic Structures Genomics Human In Vitro Individual Institutes Investigation Libraries Machine Learning Malignant Neoplasms Malignant neoplasm of gastrointestinal tract Metabolic Modeling Nature Neoplasm Metastasis Operative Surgical Procedures Organoids Pathway interactions Patients Phenotype Population Primary Neoplasm Proteins Protocols documentation Recombinants Recurrence Recurrent tumor Reporter Reproducibility Research Research Personnel Resistance Role Scientist Screening procedure Solid Neoplasm Speed Study models Technology Therapeutic Tissue Harvesting Tumor Biology Tumor Tissue Tumor-Derived U-Series Cooperative Agreements Universities Visualization anticancer research base cancer cell cancer heterogeneity cancer recurrence cancer stem cell cancer type catalyst chemotherapy clinically relevant combinatorial data integration design epigenomics expression vector frontier heterogenous data human disease in vivo in vivo Model innovation large datasets model development mortality multidisciplinary neoplastic cell new technology next generation novel novel therapeutics promoter protein expression refractory cancer single-cell RNA sequencing stem cell biology stem cells success synthetic biology therapeutic target therapy resistant three dimensional cell culture three dimensional structure tool trait transcriptome transcriptomics tumor tumor heterogeneity

项目摘要

PROJECT SUMMARY To address the complexity of heterogeneous cancers that are resistant to chemotherapy and frequently recur or metastasize, we propose to develop a set of tools based on multidisciplinary innovations combining Synthetic Biology, Cancer Organoid Technology, and Bioinformatics. These Synthetic Tools to Annotate Reporter Organoids for Cancer Heterogeneity and Recurrence Development (StarOrchard) include: Synthetic Promoter Activated Recombination of Kaleidoscopic Organoids (SPARKO), Combinatorial Genetics En Masse (CombiGEM), and single-cell RNA sequencing panorama (Scanorama). SPARKO can annotate heterogeneous cancer populations in living cells via fluorescent protein expression libraries to make multi- colored tumor organoids. CombiGEM can rapidly identify potential therapeutic targets via large-scale, massively parallel, and unbiased combinatorial genetic screens. Scanorama can integrate the analysis of large datasets of single-cell transcriptomics via sophisticated bioinformatics algorithms. These tools focus on barcoding strategies to enable accurate tracking and analysis of individual tumor cells that harbor distinct genetic aberrations, and substantially expand the utility of the Next Generation Cancer Models (NGCMs) for cancer mechanistic investigations or therapeutic discovery. The StarOrchard tools enable targeted genetic perturbations in annotated heterogeneous tumor phenotypes without destroying cells for sequencing. These tools will be applied to a large number and variety of NGCMs to optimize experimental protocol. To ensure success, we have convened an outstanding team: PI Timothy K. Lu, MD, PhD, has made strikingly original contributions to Synthetic Biology tools that enable high-throughput genetic interrogation of cancer cell drug dependency; PI Ömer Yilmaz, MD, PhD, has extensive expertise in cancers of the gastrointestinal tract and has developed novel technologies to maintain patient-derived colon cancer organoids for in vivo modeling; and PI Bonnie Berger, PhD, will use her expertise in bioinformatics and her Scanorama algorithm to integrate data across all tumor types based on dynamic single cell RNA sequencing (scRNAseq). We are also supported by leading experts in cancer biology and various cancer types at both the basic science and clinical oncology frontiers of cancer research. The collective commitment and multidisciplinary contributions of the entire team ensure the establishment of an openly distributed investigative tool set that accelerates advancements in cancer biology and therapeutic discovery

项目概要解决对化疗耐药且经常复发的异质癌症的复杂性或转移，我们建议开发一套基于多学科创新结合合成的工具生物学、癌症类器官技术和生物信息学这些用于注释记者的合成工具。用于癌症异质性和复发发展的类器官 (StarOrchard) 包括：合成启动子万花筒类器官的激活重组 (SPARKO)，组合遗传学 En Masse（CombiGEM）和单细胞RNA测序全景图（Scanorama）都可以注释。通过荧光蛋白表达库在活细胞中形成异质癌症群体，以产生多种 CombiGEM 可以通过大规模、快速识别潜在的治疗靶点。大规模并行且无偏见的组合遗传筛选可以整合分析。通过复杂的生物信息学算法收集单细胞转录组学的大型数据集。专注于条形码策略，以实现对隐藏的单个肿瘤细胞的准确跟踪和分析独特的遗传畸变，并大大扩展下一代癌症模型的实用性 (NGCM) 用于癌症机制研究或治疗发现。StarOrchard 工具使之成为可能。在注释的异质肿瘤表型中进行有针对性的遗传扰动，而不破坏细胞这些工具将应用于大量和多种 NGCM 以优化实验。为了确保协议的成功，我们召集了一支优秀的团队：PI Timothy K. Lu，医学博士、博士，对合成生物学工具做出了惊人的原创贡献，使高通量遗传探究癌细胞药物依赖性；PI Ömer Yilmaz 医学博士、哲学博士在癌症方面拥有丰富的专业知识胃肠道，并开发了维持患者源性结肠癌的新技术用于体内建模的类器官；PI Bonnie Berger 博士将利用她在生物信息学和她的 Scanorama 算法基于动态单细胞 RNA 整合所有肿瘤类型的数据我们还得到癌症生物学和各种癌症领域领先专家的支持。癌症研究的基础科学和临床肿瘤学前沿的类型。整个团队的承诺和多学科贡献确保建立一个公开的分布式研究工具集，可加速癌症生物学和治疗发现的进步