CODA transgenic RNAseq

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for these intended purposes has arisen naturally. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs from MPRA models and demonstrate the required literacy to write fit-for-purpose regulatory code.

顺式调控元件（cis-regulatory elements, CREs）可调控基因表达，协同调控组织身份、发育时序与刺激应答，共同定义了体内数千种独特的细胞类型。尽管在需要组织特异性的治疗或生物技术应用中，策略性整合顺式调控元件具有巨大潜力，但无法保证天然存在符合这些预期用途的最优顺式调控元件。本研究构建并验证了一套可用于工程化合成顺式调控元件的平台，该平台能够以程序化的细胞类型特异性驱动基因表达。我们依托针对三种细胞类型的CRE活性深度学习神经网络建模、高效的计算机虚拟优化技术，以及大规模平行报告基因检测（massively parallel reporter assays, MPRAs），设计并通过实验验证了数千个顺式调控元件。通过体外（in vitro）与体内（in vivo）验证，我们证实合成序列在驱动细胞类型特异性基因表达方面，表现优于人类基因组中的天然序列。合成序列借助独特的序列句法特征，可在靶细胞类型中增强活性，同时降低在非靶细胞中的活性。综上，本研究提供了一个可推广的框架，能够基于大规模平行报告基因检测模型前瞻性工程化构建顺式调控元件，并证明了我们具备编写适配特定用途的调控编码序列的能力。