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.

项目总结/摘要 基因和变异体通常联合作用，以驱动细胞和生物体的表型。映射这些 功能性相互作用推进了我们对生物系统的基本理解， 适用于治疗发展。基因-基因相互作用也可能构成一个相当大的 由于编码的广泛缓冲， 使许多单个基因看起来是重叠的。在这方面，我们和其他国家 显示组合筛选，例如基于CRISPR-Cas系统的那些，是用于 绘制基因和变体之间的协同关系。然而，与基于单基因的筛选不同， 由于受到广泛使用的扰动，组合筛已经明显难以部署。两 组合CRISPR筛选的基本挑战是：1）需要物理连接 在同一库元素上的多个扰动，除了使库生成复杂化之外， 阻止不同类别的基因组和表观基因组工程工具集容易地组合;以及2） 对所得到的组合筛选数据的分析是高度复杂的，尤其是在多- 三维表型分析。此外，由于扰动空间与 同时扰动的数量，关键是要能够计算推断出这些以外的相互作用 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，其自动连接在RNA而不是DNA水平表达的单个文库元件， 能够实现大规模多路复用的组合筛选。因此，此平台的可扩展性仅受单元的限制 文化和排序能力。我们建议开发双向和多路（>2 通过应用于遗传相互作用作图和细胞重编程的组合筛选 分别将通过新的高级计算方法解释筛选数据， 机器学习方法系统地确定遗传相互作用，以及预测相互作用 远远超出了直接实验屏幕所能覆盖的范围。我们期待这个实验性的， 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 产生广泛有用的试剂和数据。