Combinatorial Cell State Engineering

组合细胞状态工程

• 批准号: 10702222
• 负责人: William James Greenleaf
• 金额: $ 108.08万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10702222
• 关键词: Biological; Cell Therapy; Cells; Collaborations; Complex; Dominant-Negative Mutation; Engineering; Epigenetic Process; Gene Expression; Generations; Genes; Genetic; Genetic Screening; Human; Human Biology; Individual; Intelligence; Language; Libraries; Link; Logic; Machine Learning; Mammalian Cell; Methods; Modeling; Phenotype; Property; Proteins; Regulatory T-Lymphocyte; Research; Sea; Signal Transduction; Structure; System; T-Lymphocyte; Therapeutic; Toy; Vocabulary; biological systems; cell type; cellular engineering; combinatorial; design; genome wide screen; improved; novel; programs; regenerative; tool; transcription factor; transdifferentiation

Abstract Genome-wide screens in mammalian cells have emerged as a powerful tool for determining the relationship of individual genes to a chosen biological phenotype. However, biological systems often rely on the concerted action of multiple genes at once to elicit phenotypes. Nowhere is this more evident than in cellular differentiation, where cell state transitions often involve the modulation of 5-7 master regulatory factors. Consistent with this observation, successful efforts to reprogram cells, from Yamanaka on, have generally found that simultaneous expression of 3-5 transcription factors are needed to elicit cell state or type changes (similar to an “AND-gate- like” genetic circuit), and others have improved the efficiency or accuracy of these transitions by further perturbing other factors such as epigenetic remodelers. Given these observations, we posit that the ability to carry out highly combinatorial forward genetic screens for cell state phenotypes would produce a “sea change” in our ability to engineer cells with highly specific properties, transforming the quality of cells available for research and cell therapy applications. To this end, we propose an iterative platform that leverages a large multiplicity of perturbation (MOP) per cell, intelligent structuring of engineered perturbation libraries, and machine learning approaches to both identify combinations of perturbations most likely to elicit specific cellular phenotypes, and to engineer maximally informative new perturbation libraries. We have piloted this platform on a simple “toy model” wherein the simultaneous expression of 6 different proteins (across a total universe of 30 different potential factors) are required to elicit a phenotype. By overloading cells with ~14 perturbations per cell, structuring a library of ~80 perturbation combinations, then identifying further observations that would provide maximal information about the causative perturbation combination, we were able to confidently uncover this six- input “AND-gate” underlying state logic. While this initial ability to “solve” highly polygenic phenotypes is exciting, challenges to extending our platform to primary human cells include identification and minimization of dominant negative perturbations, identification of optimal MOP for each biological question, perfection of methods for high MOP of primary cells, exploration and optimization of the direction and mechanism of gene expression perturbation, and the engineering or selection of state changes sufficiently durable for therapeutic utility. We plan to initially apply this platform to the trans-differentiation of naive T cells into regulatory T cells and the generation of inexhaustible T-cells for cell therapies, with an eye toward establishing collaborations to deploy this platform to develop diverse cell types with regenerative or therapeutic value. In short, we posit that complex, therapeutically relevant phenotypes demand a polygenic design language that reflects the combinatorial vocabulary and grammar of human biology. We anticipate that our cell engineering platform will provide the first native implementation of this language.

抽象的 哺乳动物细胞中的全基因组筛选已成为确定关系的强大工具 单个基因与选择的生物学表型。但是，生物系统通常依靠一致的 多个基因的作用立即引起表型。没有比细胞分化更多的证据 细胞状态过渡通常涉及5-7个主调节因素的调节。与此一致 观察，从Yamanaka开启的成功重新编程细胞的成功努力通常发现很简单 需要3-5个转录因子的表达以引起细胞状态或类型变化（类似于“和栅极 - 喜欢”遗传回路），而其他人则通过进一步扰动提高了这些过渡的效率或准确性 其他因素，例如表观遗传远程。鉴于这些观察结果，我们很肯定地表明能够高度执行 细胞态表型的组合前向遗传筛选将在我们的能力中产生“变化” 具有高度特定特性的工程细胞，转化可用于研究和细胞的细胞的质量 治疗应用。为此，我们提出了一个迭代平台，该平台利用了大量的多样性 每个单元格，工程摄动库的智能结构和机器学习 两者都可以识别出最有可能引起特定细胞表型的扰动组合，并且 为了设计最大信息的新扰动库。我们已经在一个简单的“玩具”上试行了这个平台 模型”其中6种不同蛋白的简单表达（跨越30个不同的总宇宙 引起表型需要潜在因素）。通过使每个细胞的扰动约为14个细胞过载， 构建约80个扰动组合的库，然后确定将提供的进一步观察结果 有关因果扰动组合的最大信息，我们能够确保发现这六个 输入“和门”基础状态逻辑。尽管这种“解决”高度多基因表型的最初能力令人兴奋，但 将我们的平台扩展到原代人细胞的挑战包括识别和最小化主要 负扰动，每个生物学问题的最佳拖把的识别，高度的方法 原代细胞的拖把，基因表达方向和机理的探索和优化 扰动，并且状态的工程或选择的变化足够耐用，可用于热用用用用率。我们计划 最初将此平台应用于幼稚T细胞的跨分化为调节性T细胞，并产生 用于细胞疗法的无尽T细胞，以建立合作以部署此平台 开发具有再生或治疗价值的潜水细胞类型。简而言之 治疗相关的表型需要一种反映组合的多基因设计语言 人类生物学的词汇和语法。我们预计我们的细胞工程平台将提供第一个 本语言的本地实施。