Predictive engineering of cellular transcriptional state

项目摘要/摘要 转录因子(TF)的特定组合在一起发挥作用时表现出新的特性， 能够产生不同的细胞类型和行为。然而，确定哪些组合可以监管 感兴趣的行为需要克服组合爆炸，就像人类的~1600个TF中的组合爆炸一样 仅基因组就有大约130万对可能的配对。这种扩展挑战迫使过去的努力 依靠简单的、可并行的表型测量来系统地定位这种遗传交互作用(GIs) 比如增长率。然后，每个GI的特征只有一个数字，模糊了机械或 任何特定相互作用的分子基础：简单地说，有许多方法可以让细胞看起来一样 “不适合。”最后，许多人类细胞类型处于静止或有丝分裂后，因此基于生长的指标 在酵母菌等模式生物中非常成功的相互作用并不适用。 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来解决这些挑战 将单个细胞的丰富表型与预测哪个细胞的分析框架相结合 组合是最容易衡量的信息量。我们利用最近开发的扰动序列筛查 允许用单细胞RNA对CRISPR介导的遗传扰动进行汇集分析的技术 测序作为表型读数。这种方法允许我们过度表达许多编程的 使用CRISPR激活的TF的组合(CRISPRA)并获得其转录的直接读出 后果。由此产生的丰富的表型有助于深入了解美国退伍军人的生物学起源，并可以 例如，确定促进分化为不同细胞状态的转录因子的组合。它们还提供了一个 关键的“处理”，以应用现代机器学习方法。使用来自压缩领域的技术 感知，我们提出了一种预测方法来搜索过大的地理信息系统的组合空间 通过任何实验技术详尽地描述。由于转录组是Tf的直接读出 功能和转录因子通过特定的机制相互作用，例如在目标启动子上的协同结合，这些 大规模实验也可以用来研究更深层次的问题，即地理信息系统是如何机械地出现的，以及 如何产生新的(即全新的或不可预测的)表型。我们的研究提供了第一个 在任何系统中同时发现和表征地理信息系统的可扩展方法，一种快速 在不同的发育和疾病模型中绘制控制细胞命运的“杠杆”，并为如何 机器学习可以用来设计CAS9使之成为可能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.