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 基因和变体通常结合起来驱动细胞和有机表型。映射这些 功能相互作用提高了我们对生物系统的基本理解，并具有广泛的 适用于治疗性开发。基因 - 基因相互作用也可能构成相当大的 由于编码广泛的缓冲，未发现的遗传学的成分 在使许多单个基因看起来可分配的基因组中。在这方面，我们和其他人有 表明组合屏幕（例如基于CRISPR-CAS系统的屏幕）是强大的平台 绘制基因和变体之间的协同关系。但是，与基于单基因的屏幕不同 广泛使用的扰动，组合屏幕很难部署。二 组合CRISPR屏幕的基本挑战是：1）物理联系的要求 同一库元素上的多个急躁，除了使图书馆生成复杂化外， 防止不同类别的基因组和表观基因组工程工具集被容易组合；和2） 对所得组合筛选数据的分析非常复杂，尤其是在多种情况下 维表型测定。此外，因为扰动空间与 同时扰动的数量，能够计算推断相互作用以外的相互作用至关重要 实验测量。为了应对这些挑战，我们建议设计一个新的筛选平台， combinx，在RNA上表达的单个库元素，而不是DNA， 启用大量多路复用组合屏幕。因此，该平台的可伸缩性仅受细胞的限制 文化和测序能力。我们建议开发双向和多路的系统（> 2 通过应用于遗传相互作用映射和细胞重编程的组合屏幕 分别。由此产生的筛选数据将通过新的高级计算方法来解释 机器学习方法可以系统地确定遗传相互作用，并预测相互作用 远远超出了可以直接实验屏幕覆盖的屏幕。我们期待这个实验和 计算平台将在基础科学和治疗学发现中具有广泛的适用性，并且它将 生成广泛有用的试剂和数据。