Next generation massively multiplexed combinatorial genetic screens

下一代大规模多重组合遗传筛选

• 批准号: 10587354
• 负责人: Trey Ideker
• 金额: $ 69.9万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587354
• 关键词: Address; Bar Codes; Basic Science; Bioinformatics; Biological Assay; Buffers; CRISPR screen; Cell Culture Techniques; Cell Reprogramming; Cell model; Cells; Chromosome Mapping; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computing Methodologies; DNA; Data; Data Set; Dimensions; Elements; Enabling Factors; Engineering; Gene Expression; Generations; Genes; Genetic; Genetic Screening; Genetic Transcription; Genome; Genome engineering; Genomics; Genotype; Human Genome; Individual; Joints; Learning; Libraries; Link; Machine Learning; Maintenance; Mali; Malignant Neoplasms; Maps; Measures; Methods; MicroRNAs; Modality; Mutation; Neurons; Open Reading Frames; PF4 Gene; Pathway interactions; Phenotype; Pluripotent Stem Cells; Principal Investigator; RNA; Reagent; Recipe; Regenerative Medicine; Repression; Research; Research Personnel; Screening Result; System; Systems Biology; Systems Development; Therapeutic; Translating; Undifferentiated; Variant; biological systems; cell fate specification; cell killing; cell type; combinatorial; computational platform; computerized tools; directed differentiation; engineered stem cells; epigenome; flexibility; gene interaction; genetic analysis; human disease; insight; machine learning model; neoplastic cell; neural; neurodevelopment; next generation; novel; overexpression; pluripotency; prevent; screening; synthetic lethal interaction; technology development; therapeutic development; tool; transcription factor; transcriptome; vector

PROJECT SUMMARY/ABSTRACT Genes and variants often act in combination to drive cellular and organismal phenotypes. Mapping these functional interactions advances our fundamental understanding of biological systems and has broad applicability to therapeutics development. Gene-gene interactions also likely constitute a considerable component of the undiscovered genetics underlying human diseases, due to the extensive buffering encoded in genomes which makes many individual genes appear dispensable. In this regard, we and others have shown that combinatorial screens, such as those based on CRISPR-Cas systems, are powerful platforms for mapping synergistic relationships among genes and variants. However, unlike screens based on single-gene perturbations which are broadly utilized, combinatorial screens have been significantly harder to deploy. Two fundamental challenges underlying combinatorial CRISPR screens are: 1) the requirement to physically link multiple perturbagens on the same library element which, in addition to complicating library generation, prevents different classes of genome and epigenome engineering toolsets from being readily combined; and 2) analysis of the resulting combinatorial screening data is highly complex, especially in the context of multi- dimensional phenotypic assays. Furthermore, because the perturbation space scales exponentially with the number of simultaneous perturbagens, it is critical to be able to computationally infer interactions beyond those measured experimentally. To address these challenges, we propose to engineer a new screening platform, CombinX, that auto-tethers individual library elements expressed at the RNA, instead of the DNA, level to enable massively multiplexed combinatorial screens. Scalability of this platform is thus limited only by cell culture and sequencing power. We propose to develop the system for both two-way and multi-way (>2 perturbagen) combinatorial screens via application to genetic interaction mapping and cellular reprogramming respectively. Resulting screening data will be interpreted via new advanced computational methods and machine learning approaches to systematically determine genetic interactions, as well as to predict interactions well beyond those that can be covered by direct experimental screens. We anticipate this experimental and computational platform will have broad applicability in basic science and therapeutics discovery, and that it will generate widely useful reagents and data.

项目摘要/摘要 基因和变种常常共同作用，驱动细胞和组织的表型。绘制这些地图 功能相互作用促进了我们对生物系统的基本理解，并具有广泛的 对治疗学发展的适用性。基因-基因的相互作用也可能构成相当大的 人类疾病背后未被发现的遗传学的组成部分，这是由于编码的广泛缓冲 在基因组中，这使得许多单独的基因显得可有可无。在这方面，我们和其他国家 结果表明，组合屏幕，例如基于CRISPR-CAS系统的组合屏幕，是用于 绘制基因和变种之间的协同关系图。然而，与基于单基因的筛查不同 被广泛使用的微扰，组合屏幕的部署要困难得多。二 组合CRISPR屏幕的基本挑战是：1)物理链接的要求 在同一库元件上的多个扰动，这除了使库生成复杂化之外， 防止不同类别的基因组和表观基因组工程工具包容易地组合在一起；以及2) 对所产生的组合筛选数据的分析非常复杂，特别是在多- 空间表型分析。此外，由于微扰空间随 同时扰动的数量，关键是能够通过计算推断这些相互作用之外的相互作用 通过实验测量。为了应对这些挑战，我们提议设计一个新的筛查平台， CombinX，它自动将在RNA而不是DNA水平上表达的单个文库元素捆绑到 启用大规模多路复用组合屏幕。因此，该平台的可伸缩性仅受小区的限制 文化和定序力。我们建议开发双向和多路(&gt；2)系统 微扰)组合筛选在遗传交互作图和细胞重编程中的应用 分别进行了分析。由此产生的筛查数据将通过新的先进计算方法和 用于系统地确定遗传交互以及预测交互的机器学习方法 远远超出了直接实验屏幕所能覆盖的范围。我们期待着这一实验和 计算平台将在基础科学和治疗学发现中有广泛的适用性，而且它将 产生广泛有用的试剂和数据。