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.

摘要 在哺乳动物细胞中进行全基因组筛选已经成为一种强有力的工具， 个体基因与所选择的生物表型相对应。然而，生物系统往往依赖于协调一致的 多个基因同时作用以引出表型。这一点在细胞分化中表现得最为明显， 其中细胞状态转换通常涉及5-7个主调节因子的调节。符合本 观察，成功的努力，重新编程细胞，从山中，一般发现，同时， 需要3-5个转录因子的表达来引发细胞状态或类型变化（类似于“AND-门”， 例如”遗传电路"），以及其他人已经通过进一步的扰动来提高这些转换的效率或准确性。 其他因素，如表观遗传重塑。鉴于这些观察结果，我们认为， 细胞状态表型的组合正向遗传筛选将使我们的能力发生“巨变”， 工程细胞具有高度特异性的特性，改变可用于研究的细胞的质量， 治疗应用。为此，我们提出了一个迭代平台，利用大量的 扰动（MOP），工程扰动库的智能结构化，以及机器学习 鉴定最有可能引起特定细胞表型的扰动组合的方法，以及 来设计最大信息量的新扰动库。我们在一个简单的“玩具”上试用了这个平台， 模型”，其中6种不同蛋白质的同时表达（跨越30种不同蛋白质的总范围）， 潜在因子）是引发表型所必需的。通过使每个细胞具有~14个扰动的细胞过载， 构建一个包含约80种扰动组合的库，然后确定进一步的观测结果， 关于因果扰动组合的最大信息，我们能够自信地揭示这六个- 输入“与门”底层状态逻辑。虽然这种“解决”高度多基因表型的初始能力令人兴奋， 将我们的平台扩展到原代人类细胞的挑战包括识别和最小化显性细胞， 负扰动，确定每个生物学问题的最佳MOP，完善高 原代细胞的MOP，基因表达方向和机制的探索和优化 扰动，以及对于治疗效用足够持久的状态变化的工程或选择。我们计划 最初将该平台应用于幼稚T细胞向调节性T细胞的转分化， 用于细胞疗法的取之不尽的T细胞，着眼于建立合作来部署这个平台 开发具有再生或治疗价值的多种细胞类型。简而言之，我们将这种复杂的， 治疗相关的表型需要多基因设计语言，其反映了治疗相关表型的组合特征。 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 本语言的本地实现。