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

使用单细胞深度学习方法揭示体细胞中胚层发育中的节点信号传导和转录因子相互作用

• 批准号: 10749611
• 负责人: Andrew Carter Mullen
• 金额: $ 4.53万
• 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: 2023-08-16 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10749611
• 关键词: Affect; Anterior; Automobile Driving; Binding; Biology; CRISPR/Cas technology; Cells; Cellular Assay; Chromatin; Chromosome Mapping; Clustered Regularly Interspaced Short Palindromic Repeats; Communication; Computer Models; Computer software; Computing Methodologies; DNA; DNA Binding; Data; Development; Dissection; Embryo; Enhancers; Family; Gene Expression; Gene Expression Regulation; Genes; Genetic Screening; Genetic Transcription; Genomics; In Situ Hybridization; Knock-out; Knowledge; Learning; Ligands; Link; Maps; Mediating; Mesoderm; Methods; Modeling; Mutagenesis; Mutate; Mutation; Neural Network Simulation; Nodal; Phenotype; Pilot Projects; Population; Protein Family; Proteins; Regulator Genes; Regulatory Element; Research; Resolution; Resources; Role; Series; Signal Transduction; Site; Somites; Specific qualifier value; Specificity; Tail; Testing; Time; Training; Training Programs; Transforming Growth Factor beta; Transforming Growth Factor beta Receptors; Transposase; Undifferentiated; Untranslated RNA; Vertebrates; Visual; Work; Writing; Zebrafish; cell type; cofactor; computerized tools; cost; data modeling; data tools; deep learning; deep neural network; experimental study; flexibility; gene discovery; genetic approach; genome-wide; improved; in silico; learning strategy; member; mutant; network models; neural network; novel; open source; programs; promoter; receptor; recruit; single cell technology; single-cell RNA sequencing; skills; somitogenesis; success; syntax; transcription factor; zebrafish development

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.

项目总结/摘要 我们对转录因子（TF）如何相互作用以结合靶点的知识仍存在重大空白。 顺式调控元件（克雷斯），并在发育过程中决定基因表达。有~1600个TF， 脊椎动物，因此用TF成对敲除进行遗传筛选的传统方法将需要 250万次实验。即使使用高通量方法，这在实验上也是不可行的。我将建立 新的计算工具和深度神经网络，并使用多路复用高通量单细胞测定， 斑马鱼在整个发育过程中的转座酶可降解染色质（scATAC-seq）数据。这些深 神经网络将用于模拟CRE相互作用的计算机实验，以了解细胞类型 在发育过程中T-box蛋白的特定调控语法。这些TF-TF组合 然后将用靶向CRISPR-Cas9诱变，然后用靶向CRISPR-Cas9诱变来测试来自计算机实验的相互作用。 利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，我将绘制一个全基因组的顺式调控图谱，研究细胞类型特异性基因调控， Nodal信号在斑马鱼体细胞中胚层发育中的作用。在斑马鱼中， Nodal是TGF-β受体蛋白的配体，Nodal的突变导致异常未分化的表型， 躯干体节中胚层和正确分化的尾部体节中胚层。驱动这一进程的机制 这些体节之间的差异是未知的。为了解开这个谜团，我将生成单细胞时间序列 野生型和Nodal缺陷胚胎在斑马鱼发育的连续性，使用多重 高通量scATACseq和scRNAseq数据。计算连接这些数据将代表一个 全面参考斑马鱼CRE和转录发展和宝贵的资源，为所有 斑马鱼生物学家通过改进Cicero软件包，使其包含灵活的Poisson对数正态网络 模型，我们可以实现必要的解决方案，找到新的细胞类型的具体差异， 增强子-启动子连接在发育和扰动过程中c 在目标2中，我将训练和验证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异性基因程序的因子。我将使用这些数据和计算 用于进行计算机模拟实验以了解T盒TF的细胞类型特异性调控语法的工具 在发展过程中。在使用这个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相互作用以进行靶向CRISPR-Cas9诱变以敲除 TF。我将应用这种方法来揭示顺式调节语法，使T-box家族转录因子 来发挥它们的DNA位点特异性。