Blair John D, Bradu Alexandra, Dalgarno Carol, Grabski Isabella N, Satija Rahul
New York Genome Center, New York, NY.
New York University, Center for Genomics and Systems Biology, New York, NY.
bioRxiv. 2025 Aug 30:2025.08.27.672635. doi: 10.1101/2025.08.27.672635.
Cell signaling plays a critical role in regulating cellular state, yet uncovering regulators of signaling pathways and understanding their molecular consequences remains challenging. Here, we present an iterative experimental and computational framework to identify and characterize regulators of signaling proteins, using the mTOR marker phosphorylated RPS6 (pRPS6) as a case study. We present a customized workflow that uses the 10x Flex assay to jointly profile intracellular protein levels, transcriptomes, and CRISPR perturbations in single cells. We use this to generate a "glossary" dataset of paired protein-RNA measurements across targeted perturbations, which we leverage to train a predictive model of pRPS6 levels based solely on transcriptomic data. Applying this model to a genome-wide Perturb-seq dataset enables screening for pRPS6 and nominates novel regulators of mTOR signaling. Experimental validation confirms these predictions and reveals mechanistic diversity among hits, including changes in signaling output driven by anabolic activity, cellular proliferation and multiple stress pathways. Our work demonstrates how integrated experimental and computational approaches provide a scalable framework for multimodal phenotyping and discovery.
细胞信号传导在调节细胞状态中起着关键作用，但揭示信号通路的调节因子并了解其分子后果仍然具有挑战性。在这里，我们提出了一个迭代的实验和计算框架，以识别和表征信号蛋白的调节因子，并以mTOR标记磷酸化RPS6（pRPS6）为例进行研究。我们展示了一个定制的工作流程，该流程使用10x Flex检测方法对单细胞中的细胞内蛋白质水平、转录组和CRISPR干扰进行联合分析。我们利用这一方法生成了一个跨靶向干扰的配对蛋白质-RNA测量的“术语表”数据集，并利用该数据集训练一个仅基于转录组数据的pRPS6水平预测模型。将该模型应用于全基因组Perturb-seq数据集能够筛选pRPS6，并提名mTOR信号传导的新调节因子。实验验证证实了这些预测，并揭示了命中靶点之间的机制多样性，包括合成代谢活性、细胞增殖和多种应激途径驱动的信号输出变化。我们的工作展示了综合实验和计算方法如何为多模态表型分析和发现提供一个可扩展的框架。
