Engineering cell type-specific splicing regulation

项目总结 选择性剪接(AS)是蛋白质异构体多样性的主要驱动因素，在高度细胞中受到调控 特定于类型的方式。更好地理解细胞类型特定的剪接密码不仅将提供新的 对选择性剪接在疾病和发育中的作用的洞察，但也将导致新的遗传工具 用于干扰和询问感兴趣的细胞类型。合成剪接结构已被成功地使用 将报告和治疗基因的激活靶向携带剪接因子突变的癌细胞或 使基因治疗以小分子触发为条件。现有的例子突出了AS的潜力 作为一种可编程控制机制，但没有提供一条通向工程拼接监管的明确途径 可用于针对任何所需细胞类型或状态的基因表达的序列。在此，我们建议 将合成生物学与机器学习相结合，生成高度特定于细胞类型的剪接结构。 在我们早期工作的基础上，我们将首先使用剪接大规模并行报告来量化特定细胞类型 检测(MPRAs)。我们将重点关注外显子跳过和内含子保留，因为它们是 常见的强直性脊柱炎和强直性脊柱炎具有高度的细胞特异性。对于每种类型的AS，我们将使用 针对感兴趣地区的数十万甚至数百万名不同的记者。到时候我们会的 在一组细胞系和培养的原代细胞中测量这些文库(特定目标1)。接下来，我们将 使用这些数据来训练机器学习模型，这些模型可以准确地预测来自 报告基因序列。我们将系统地比较不同的网络体系结构和方法 包括卷积神经网络和递归神经网络。然后，我们将把模型与序列设计结合起来 以前在实验室开发的生成具有增强靶细胞的合成序列的方法 专一性。我们的目标是展示我们可以生成特定于我们的 面板。我们将通过实验验证预测，并使用生成的数据反复改进模型 预测(具体目标2)。最后，我们将把我们的方法推广到一个实验环境， 准确地反映了多细胞生物系统中遇到的细胞类型的多样性和复杂性。 具体地说，我们将在器官发育中的大鼠脑片培养中进行MPRA的剪接。我们将优化 为图书馆提供切片培养的条件，我们将同样优化读出剪接的方法 在单细胞水平上的MPRAs。我们将把产生的数据与特定目标的生成模型结合起来 1设计准确针对感兴趣细胞类型的蛋白质表达的报告构建体(特定目标 3)。我们相信，这项工作将为生物学研究带来新的基因工具，并提供一条通往 基因治疗具有更高的特异性和更少的副作用。

PROJECT SUMMARY Alternative splicing (AS) is a major driver of protein isoform diversity and is regulated in a highly cell type-specific manner. A better understanding of the cell type-specific splicing code will not only provide novel insights into the role of alternative splicing in disease and development but will also result in novel genetic tools for perturbing and interrogating cell types of interest. Synthetic splicing constructs have been successfully used to target activation of reporter and therapeutic genes to cancer cells carrying mutations in splice factors or to make gene therapies conditional on a small molecular trigger. Existing examples highlight the potential of AS as a programmable control mechanism but do not provide a clear path towards engineering splice regulatory sequences that can be used to target gene expression to any desired cell type or state. Here, we propose to combine synthetic biology with machine learning to generate highly cell type-specific splicing constructs. Building on our earlier work, we will first quantify cell type-specific AS using splicing massively parallel reporter assays (MPRAs). We will focus on exon skipping and intron retention because they are among the most common forms of AS and can be highly cell type-specific. For each type of AS, we will create libraries with hundreds of thousands or even millions of reporters with variation targeted to regions of interest. We will then measure AS for these libraries in a panel of cell lines and cultured primary cells (Specific Aim 1). Next, we will use these data to train machine learning models that can accurately predict AS isoform abundance from reporter gene sequence. We will systematically compare different network architectures and approaches including convolutional and recurrent neural networks. We will then combine models with sequence design approaches previously developed in the lab to generate synthetic sequences with enhanced target cell specificity. We aim to show that we can generate reporter constructs that are specific to any cell type in our panel. We will validate predictions experimentally and use resulting data to iteratively improve model predictions (Specific Aim 2). Finally, we will generalize our approach to an experimental setting that more accurately reflects the diversity and complexity of cell types encountered in multi-cellular biological systems. Specifically, we will perform splicing MPRAs in organotypic developing rat brain slice culture. We will optimize conditions for library delivery to slice culture and we will similarly optimize approaches for reading out splicing MPRAs at the single cell level. We will combine the resulting data with the generative models from Specific Aim 1 to design reporter constructs that precisely target protein expression to cell types of interest (Specific Aim 3). We believe that this work will result in novel genetic tools for biology research and provide a path towards gene therapies with increased specificity and reduced side effects.