Combinatorial Cell State Engineering

摘要 哺乳动物细胞的全基因组筛查已成为确定细胞间关系的有力工具 单个基因与所选的生物表型相对应。然而，生物系统往往依赖于协调的 多个基因同时作用于表型。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。与此一致 观察，成功的重新编程细胞的努力，从山中开始，一般都发现同时 3-5个转录因子的表达需要引起细胞状态或类型的改变(类似于“与门”- 和其他人通过进一步干扰提高了这些转换的效率或准确性 其他因素，如表观基因重构体。考虑到这些观察，我们假设高度执行的能力 细胞状态表型的组合正向遗传筛查将在我们的能力上产生“翻天覆地的变化 设计具有高度特殊特性的细胞，改变可用于研究和细胞的细胞质量 治疗应用。为此，我们提出了一个迭代平台，该平台利用大量的 每细胞微扰(MOP)、工程微扰库的智能构建以及机器学习 识别最有可能引起特定细胞表型的扰动组合的方法，以及 设计出信息量最大的新微扰库。我们在一个简单的“玩具”上试行了这个平台 模型“，其中同时表达6种不同的蛋白质(跨越总共30个不同的宇宙 潜在因素)是引发表型所必需的。通过使每个单元具有~14个扰动的单元过载， 构建一个由大约80个扰动组合组成的库，然后确定进一步的观察结果，以提供 关于起因微扰组合的最大信息，我们能够自信地发现这六个- 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 负扰动，每个生物问题的最优MOP的识别，高级方法的完善 原代细胞的MOP，基因表达方向和机制的探索与优化 微扰，以及对状态变化的设计或选择足以持久地用于治疗用途。我们计划 初步将该平台应用于幼稚T细胞向调节性T细胞的反式分化和生成 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，我们假设， 与治疗相关的表型需要一种多基因设计语言来反映组合 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 此语言的本机实现。

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.