Predictive engineering of cellular transcriptional state

项目概要/摘要 转录因子（TF）的特定组合在一起发挥作用时表现出涌现的特性， 从而能够产生不同的细胞类型和行为。然而，确定哪些组合调节 一个感兴趣的行为需要克服一个组合爆炸，就像人类中的~1,600个TF一样。 基因组中有大约130万对可能的配对。这种规模化的挑战迫使过去的努力， 系统地绘制这样的遗传相互作用（GI），以依赖于简单的、可并行的表型测量 例如增长率。然后，每个GI仅由单个数字表征，从而模糊了机械或 任何特定相互作用的分子基础：简而言之，细胞有很多种表现方式 “不合格”最后，许多人类细胞类型是静止的或有丝分裂后的，因此，基于生长的测量结果是不确定的。 在模式生物如酵母中非常成功相互作用不适用。 在这里，我们通过引入一种新的、大规模并行的方法来研究人类的地理信息系统， 细胞，它结合了丰富的单细胞表型与分析框架，用于预测 组合是最能提供测量信息的。我们利用最近开发的Perturb-seq筛选 该技术允许用单细胞RNA对CRISPR介导的遗传扰动进行汇总分析 测序作为表型读数。这种方法使我们能够过度表达许多程序化的 使用CRISPR激活（CRISPRa）的TF的组合，并获得其转录的直接读数。 后果由此产生的丰富的表型产生洞察地理标志的生物起源，并可以为 实施例鉴定了促进分化为不同细胞状态的TF的组合。他们还提供了一个 关键的“处理”应用现代机器学习方法。使用压缩领域的技术 感测，我们提出了一个预测的方法来搜索地理信息系统的组合空间，将太大 任何实验技术都无法进行详尽的侧写由于转录组是TF的直接读数， 功能和TF通过特定的机制相互作用，例如在靶启动子处的合作结合，这些 大规模的实验也可以用来研究地理信息系统如何机械地出现的更深层次的问题， 新形态（即全新的或不可预测的）表型是如何产生的。我们的研究提供了 一种可扩展的方法，用于在任何系统中同时查找和表征地理标志，这是一种快速 绘制在不同的发育和疾病模型中控制细胞命运的“杠杆”， 机器学习可用于设计Cas9所实现的大型组合遗传学实验。

PROJECT SUMMARY/ABSTRACT Specific combinations of transcription factors (TFs) exhibit emergent properties when functioning together, enabling the generation of diverse cell types and behaviors. However, identifying which combinations regulate a behavior of interest requires overcoming a combinatorial explosion, as among the ~1,600 TFs in the human genome there are ~1.3 million possible pairs alone. This scaling challenge has forced past efforts at systematically mapping such genetic interactions (GIs) to rely on simple, parallelizable measures of phenotype such as growth rate. Each GI is then characterized only by a single number, obscuring the mechanistic or molecular basis for any particular interaction: put simply, there are many ways for cells to appear equally “unfit.” Finally, many human cell types are quiescent or post-mitotic, so that the growth-based measures of interaction that have been highly successful in model organisms such as yeast do not apply. Here we address these challenges by introducing a new, massively parallel method for studying GIs in human cells that combines rich phenotyping of single cells with an analytical framework for predicting which combinations are most informative to measure. We leverage the recently developed Perturb-seq screening technology, which allows pooled profiling of CRISPR-mediated genetic perturbations with single-cell RNA sequencing as the phenotypic readout. This approach allows us to overexpress many programmed combinations of TFs using CRISPR activation (CRISPRa) and obtain a direct readout of their transcriptional consequences. The resulting rich phenotypes yield insight into the biological origins of GIs, and can for example identify combinations of TFs that promote differentiation to diverse cell states. They also provide a critical “handle” to apply modern machine learning methods. Using techniques from the field of compressed sensing, we propose a predictive approach for searching combinatorial spaces of GIs that would be too large to profile exhaustively by any experimental technology. Since the transcriptome is a direct readout of TF function and TFs interact via specific mechanisms such as cooperative binding at target promoters, these large-scale experiments can also be used to study deeper questions on how GIs emerge mechanistically, and how neomorphic (i.e. entirely new or unpredictable) phenotypes are generated. Our research provides the first scalable method for simultaneously finding and characterizing GIs in any system, a technique for rapidly mapping the “levers” controlling cell fate in diverse models of development and disease, and a model for how machine learning can be used to design the large combinatorial genetics experiments made possible by Cas9.