Towards Robust Multiplex Genome Engineering Beyond CRISPR-Cas9

迈向 CRISPR-Cas9 之外的稳健多重基因组工程

基本信息

批准号：
10287896
负责人：
Le Cong
金额：
$ 39.36万
依托单位：
STANFORD UNIVERSITY
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：
2020-09-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10287896
关键词：
Alleles Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Award CRISPR screen CRISPR/Cas technology Cell Line Cells Clustered Regularly Interspaced Short Palindromic Repeats Collaborations Complex Computer Models Computer Simulation DNA Disease Disease model Engineering Enzymes Epigenetic Process Funding Genes Genetic Genetic Engineering Genetic Recombination Genome Genome engineering Human Knowledge Medicine Metagenomics Methods Mining Modality Modeling National Human Genome Research Institute Nerve Degeneration Neurons Neurosciences Parents Phenotype Proteins RNA Research Resource Sharing Resources Screening procedure Single Nucleotide Polymorphism Technology Testing Validation Variant Work base causal variant cohort data analysis pipeline experimental study genetic variant genome editing genome wide association study human disease human stem cells in vivo innovation machine learning algorithm novel risk variant tool

项目摘要

Supplement Application Abstract Towards Robust Multiplex Genome Engineering Beyond CRISPR-Cas9 Recent genome-wide association studies on Alzheimer’s Diseases (AD) and related dementias have provided a rich resource of AD risk genes and variants. While this bounty of information is poised to transform neurodegeneration research, we need tools to identify and validate their functions. Genome engineering tools such as CRISPR-Cas9 are valuable for such validation, allowing precise editing of AD-related genomic variants. However, current genetic engineering approaches are limited in efficiency, scalability, and have unwanted editing errors that could confound validation experiments. Moreover, we need tools with robust activities in challenging neuroscience models, beyond editing a few cell lines. Hence, building on our existing NHGRI-funded work, we will use innovative genome technologies for studying AD and related dementias, in collaboration with experts at the Stanford Alzheimer's Disease Research Center (ADRC). Firstly, we will use computational simulation with experimentation to develop precision tools to edit human risk variants in AD models. We will leverage and further develop our novel CRISPR enzymes and RNA-to-DNA editing tools that we recently established based on work from the parent award (JACS. 2019). Secondly, we are developing error- free gene-editors via mining metagenomic recombination enzymes. These error-free gene-editors are capable of engineering up to multi-kilobase sequences in human stem cells and neurons (Wang et al., under review). We will use this accurate gene-editing methods to engineer large AD risk alleles in neurodegeneration models, and, working with expert collaborators, demonstrate in vivo editing. Thirdly, we are developing Turbo-seq, a single-cell perturb-seq platform leveraging machine-learning algorithms and our multi-target CRISPR screen tool for AD studies (Hughes et al., submitted). We will apply Turbo-seq to simultaneously engineer single and multiple AD-associated variants in relevant disease models, with an initial focus on APOE alleles and related protective (or causal) variants. We will determine the functional consequences when genetically engineering these AD variants compared with healthy controls, integrating single-cell profiling of RNAs and proteins. Our multi-target, scalable CRISPR tools will significantly accelerate functional study of neurodegeneration variants when considering the large number of candidates, existing and from our collaborators’ work with the Stanford Extreme Phenotypes in AD (StEP AD) cohort, and help identify potential interactions between risk alleles. Overall, our plan is to build a gene-editing and single-cell toolkit, with an accompanying data- analysis pipeline for neurodegeneration research, thereby expanding the parent award’s tool-building and resource-sharing efforts into this new focus with the supplement.

补充应用摘要朝着CRISPR-CAS9超越强大的多重基因组工程关于阿尔茨海默氏病（AD）和相关痴呆症的最近全基因组的关联研究提供了广告风险基因和变体的丰富资源。虽然这种信息被中毒改变神经变性研究，我们需要识别和验证其功能的工具。基因组诸如CRISPR-CAS9之类的工程工具对于此类验证很有价值，可以精确编辑广告相关的基因组变体。但是，当前的基因工程方法有限效率，可扩展性，并具有可能混淆验证实验的不良编辑错误。此外，我们还需要在挑战神经科学模型中具有强大活动的工具，而不是编辑几个细胞系。因此，在我们现有的NHGRI资助的作品的基础上，我们将使用创新的基因组与斯坦福大学的专家合作，研究广告和相关痴呆症的技术阿尔茨海默氏病研究中心（ADRC）。首先，我们将使用与在广告模型中开发精确工具以编辑人类风险变体的实验。我们将利用并进一步开发了我们最近的新颖CRISPR酶和RNA到DNA编辑工具根据父母奖（JACS。2019）的工作建立。其次，我们正在开发错误 - 通过采矿宏基因组重组酶进行游离基因编辑。这些无错误的基因编辑器是能够在人类干细胞和神经元中高达多尔克洛酶序列（Wang等，，审查）。我们将使用这种准确的基因编辑方法来设计大型AD风险等位基因神经变性模型，并与专家合作者合作，展示了体内编辑。第三，我们正在开发Turbo-Seq，这是一个单细胞witturb-seq平台利用机器学习算法和我们用于广告研究的多目标CRISPR屏幕工具（Hughes等，提交）。我们将将Turbo-seq应用于相关的单一和多个广告相关的工程疾病模型，最初侧重于APOE等位基因和相关的受保护（或因果）变体。我们当基因工程这些AD变体比较时，将确定功能后果具有健康对照，整合了RNA和蛋白质的单细胞分析。我们的多目标可扩展当CRISPR工具将显着加速神经退行性变体的功能研究考虑到大量的候选人，现有以及我们与斯坦福的合作者合作 AD（步骤AD）队列中的极端表型，并有助于确定风险之间的潜在相互作用等位基因。总体而言，我们的计划是建立一个基因编辑和单细胞工具包，并随附数据 - 神经退行性研究的分析管道，从而扩大了父母奖的工具建设以及通过补充剂将资源分享的努力集中在这一新焦点中。