Engineering cell type-specific splicing regulation

工程细胞类型特异性剪接调控

• 批准号: 10633765
• 负责人: Georg Seelig
• 金额: $ 39.57万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10633765
• 关键词: Algorithm Design; Alternative Splicing; Animals; Biological Assay; Biology; Brain; Cell Line; Cells; Code; Data; Development; Disease; Engineering; Future; Gene Expression; Genes; Genetic; High-Throughput Nucleotide Sequencing; Human Cell Line; Introns; Learning; Libraries; Machine Learning; Maps; Measures; Modeling; Molecular; Mouse Cell Line; Mutation; Neurobiology; Primary Cell Cultures; Production; Protein Isoforms; Proteins; RNA Splicing; RNA-Binding Proteins; Rat Cell Line; Rattus; Reading; Regulation; Regulatory Element; Reporter; Reporter Genes; Research; Role; Slice; Specificity; System; Testing; Tissues; Training; Variant; Work; biological systems; cancer cell; cell type; convolutional neural network; design; disease diagnosis; exon skipping; gene therapy; improved; insight; interest; machine learning model; network architecture; novel; novel strategies; predictive modeling; protein expression; recurrent neural network; side effect; single cell analysis; single-cell RNA sequencing; synthetic biology; therapeutic gene; tool; transcriptome

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.

项目概要 选择性剪接 (AS) 是蛋白质亚型多样性的主要驱动因素，并在高度细胞中受到调节 特定于类型的方式。更好地理解细胞类型特异性剪接代码不仅会提供新颖的 深入了解选择性剪接在疾病和发育中的作用，同时也将产生新的遗传工具 用于干扰和询问感兴趣的细胞类型。合成剪接构建体已被成功使用 将报告基因和治疗基因的激活靶向携带剪接因子突变的癌细胞或 使基因疗法以小分子触发为条件。现有的例子凸显了 AS 的潜力 作为一种可编程控制机制，但没有为工程接头监管提供明确的途径 可用于将基因表达靶向任何所需细胞类型或状态的序列。在此，我们建议 将合成生物学与机器学习相结合，生成高度细胞类型特异性的剪接结构。 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器来量化细胞类型特异性 AS 分析（MPRA）。我们将重点关注外显子跳跃和内含子保留，因为它们是最重要的 AS 的常见形式，并且具有高度的细胞类型特异性。对于每种类型的 AS，我们将创建库 数十万甚至数百万记者针对感兴趣的地区进行不同的报道。我们随后将 在一组细胞系和培养的原代细胞中测量这些文库的 AS（具体目标 1）。接下来，我们将 使用这些数据来训练机器学习模型，该模型可以准确预测 AS 同工型丰度 报告基因序列。我们将系统地比较不同的网络架构和方法 包括卷积神经网络和循环神经网络。然后我们将模型与序列设计结合起来 先前在实验室开发的方法，用于生成具有增强靶细胞的合成序列 特异性。我们的目标是证明我们可以生成针对我们的任何细胞类型的报告构建体 控制板。我们将通过实验验证预测并使用结果数据迭代改进模型 预测（具体目标 2）。最后，我们将把我们的方法推广到实验环境中 准确地反映了多细胞生物系统中细胞类型的多样性和复杂性。 具体来说，我们将在器官型发育的大鼠脑切片培养物中进行 MPRA 剪接。我们会优化 文库传递到切片培养物的条件，我们将类似地优化读出剪接的方法 单细胞水平的 MPRA。我们将把结果数据与特定目标的生成模型结合起来 1 设计报告基因构建体，将蛋白质表达精确靶向感兴趣的细胞类型（具体目标 3）。我们相信这项工作将为生物学研究带​​来新的遗传工具，并提供一条途径 基因疗法具有更高的特异性和更少的副作用。