Next generation massively multiplexed combinatorial genetic screens

下一代大规模多重组合遗传筛选

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要基因和变异体经常联合作用来驱动细胞和有机体表型。映射这些功能相互作用增进了我们对生物系统的基本理解，并具有广泛的意义对治疗开发的适用性。基因与基因的相互作用也可能构成相当大的由于编码的广泛缓冲，是人类疾病的未发现遗传学的组成部分在基因组中，这使得许多个体基因显得可有可无。在这方面，我们和其他人表明组合筛选，例如基于 CRISPR-Cas 系统的组合筛选，是强大的平台绘制基因和变异之间的协同关系。然而，与基于单基因的筛选不同由于扰动被广泛使用，组合屏幕的部署要困难得多。二组合 CRISPR 筛选的基本挑战是：1) 物理连接的要求同一库元素上有多个扰动因素，这除了使库生成变得复杂之外，阻止不同类别的基因组和表观基因组工程工具集轻松组合；和 2) 对所得组合筛选数据的分析非常复杂，特别是在多因素背景下维度表型测定。此外，由于扰动空间随同时扰动的数量，能够通过计算推断出超出这些的相互作用是至关重要的通过实验测量。为了应对这些挑战，我们建议设计一个新的筛选平台， CombinX，自动连接在 RNA（而不是 DNA）水平表达的各个文库元素实现大规模多路复用组合屏幕。因此，该平台的可扩展性仅受单元限制培养和测序能力。我们建议开发双向和多路（>2 perturbagen）通过应用于遗传相互作用图谱和细胞重编程的组合筛选分别。由此产生的筛选数据将通过新的先进计算方法进行解释，机器学习方法系统地确定遗传相互作用并预测相互作用远远超出了直接实验屏幕所能覆盖的范围。我们预计这一实验性的计算平台将在基础科学和治疗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。