Uncovering Nodal signaling and transcription factor interactions in somitic mesoderm development using single-cell deep learning methods

项目摘要/摘要 我们对转录因子(TF)如何相互作用以结合靶标的知识仍然存在重大空白 顺式调节元件(CRE)并决定发育过程中的基因表达。有大约1600个TF在 脊椎动物，因此传统的基因筛查方法与TF成对敲除将需要 &gt；250万次实验。即使是高通量的方法，这在实验上也是不可行的。我会建造 新的计算工具和深度神经网络，并使用多路高通量单细胞分析 斑马鱼整个发育过程中的转座酶可及染色质(scatac-seq)数据。这些深渊 神经网络将用于电子实验，以模拟Cre相互作用，以了解细胞类型 T-box蛋白在发育过程中的特定调控语法。Tf-Tf的这些组合 电子实验中的相互作用随后将通过靶向CRISPR-Cas9突变进行测试，随后 用原位杂交和高通量、低成本scATAC和scRNA-seq进行表型分析。 在目标1中，我将制作一个全基因组范围的细胞类型特异性基因调控的顺式调控图谱。 斑马鱼揭示节点信号在斑马鱼体细胞中胚层发育中的作用。在斑马鱼身上， 转化生长因子-β受体蛋白的配体Nodal的突变导致异常未分化的表型 躯干体细胞中胚层和正确分化的尾部体细胞中胚层。推动经济增长的机制 这些体节之间的差异尚不清楚。为了解开这个谜团，我将生成单元格时间序列 斑马鱼发育过程中的野生型和结节缺陷胚胎 高吞吐量scATACseq和scRNAseq数据。以计算方式链接这些数据将表示 斑马鱼Cre的全面参考和转录开发，对所有人来说都是一个宝贵的资源 斑马鱼生物学家。通过改进Cicero软件包，使其包含灵活的泊松对数正态网络 模型，我们可以达到必要的分辨率，以找到新的细胞类型的具体差异 发育和干扰过程中的增强子-启动子连接 在AIMS 2中，我将训练和验证一个深度学习神经网络模型来预测转录对 相互作用以激活细胞类型特定基因程序的因子。我将使用这些数据和计算 在电子实验中执行的工具，以了解T-box转录因子的细胞类型特定调控语法 在开发过程中。在使用此神经网络进行计算机实验后，我将对候选人进行排名 使用高通量方法测试Tf-Tf相互作用以靶向CRISPR-Cas9突变敲除 TFS。我将应用这种方法来揭示允许T-box家族转录因子的顺式调控语法 发挥其DNA基因座的特异性。

PROJECT SUMMARY/ABSTRACT Major gaps remain in our knowledge of how transcription factors (TFs) interact to bind target cis-regulatory elements (CREs) and dictate gene expression during development. There are ~1600 TFs in vertebrates, and therefore traditional approaches of genetic screens with TF pairwise knockouts would require >2.5 million experiments. Even with high throughput methods, this is not experimentally feasible. I will build novel computational tools and deep neural networks and use multiplexed high-throughput single-cell Assay for Transposase-Accessible Chromatin (scATAC-seq) data from zebrafish throughout development. These deep neural networks will be used for in silico experiments to model CRE interactions to learn the cell-type specific regulatory syntax of T-box proteins during development. These combinations of TF-TF interactions from in silico experiments will then be tested with targeted CRISPR-Cas9 mutagenesis followed by phenotype profiling with in situ hybridization and high-throughput low-cost scATAC and scRNA-seq. In Aim 1, I will make a genome-wide cis-regulatory map of cell-type specific gene regulation of zebrafish to uncover the role of Nodal signaling in zebrafish somitic mesoderm development. In zebrafish, mutations to Nodal, a ligand to TGF-Beta receptor proteins, cause a phenotype of aberrantly undifferentiated trunk somitic mesoderm and correctly differentiated tail somitic mesoderm. The mechanisms driving the differences between these somites are unknown. To resolve this mystery, I will generate single-cell time series wild-type and Nodal deficient embryos across the continuum of zebrafish development using multiplexed high-throughput scATACseq and scRNAseq data. Computationally linking these data will represent a comprehensive reference of zebrafish CRE and transcriptional development and a valuable resource for all zebrafish biologists. By improving the software package, Cicero, to include flexible Poisson lognormal network models, we can achieve the resolution necessary to find novel cell-type specific differences in enhancer-promoter links during development and perturbationc In Aims 2, I will train and validate a deep learning neural network model to predict pairs of transcription factors that interact to activate cell-type specific gene programs. I will use these data and computational tools to perform in silico experiments to learn the cell-type specific regulatory syntax of T-box TFs during development. After performing in silico experiments using this neural network, I will rank candidate TF-TF interactions to test using high-throughput methods for targeted CRISPR-Cas9 mutagenesis to knock out TFs. I will apply this method to uncover the cis-regulatory syntax that allows T-box family transcription factors to exert their DNA loci specificity.