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.

项目摘要/摘要 我们对转录因子（TFS）如何相互作用以结合目标的知识仍然存在主要差距 顺式调节元件（CRE）并决定发育过程中的基因表达。有〜1600 tfs 脊椎动物，因此具有TF成对敲除遗传筛查的传统方法将需要 > 250万实验。即使使用高通量方法，这也不是可行的。我会建造 新颖的计算工具和深度神经网络，并使用多路复用的高通量单细胞测定 整个发育过程中，来自斑马鱼的转座酶可访问染色质（SCATAC-SEQ）数据。这些很深 神经网络将用于硅实验中，以建模CRE相互作用以学习细胞类型 发育过程中T-box蛋白的特定调节语法。 TF-TF的这些组合 然后，将使用靶向的CRISPR-CAS9诱变进行测试中的计算机实验的相互作用 与原位杂交和高通量低成本SCATAC和SCRNA-SEQ进行表型分析。 在AIM 1中，我将对细胞类型的特异性基因调控的全基因组顺序调节图 斑马鱼揭示了鼻虫信号传导在斑马鱼中胚层发育中的作用。在斑马鱼， 淋巴结突变是TGF-β受体蛋白的配体，引起异常未分化的表型 躯干中胚层并正确区分了尾部躯干中胚层。驱动 这些节点之间的差异是未知的。为了解决这个谜，我将生成单细胞时间序列 使用多路复用 高通量ScatacSeq和Scrnaseq数据。在计算上链接这些数据将代表 斑马鱼Cre和转录发展的全面参考，以及所有人的宝贵资源 斑马鱼生物学家。通过改进软件包Cicero，包括柔性泊松log normalal网络 模型，我们可以达到找到新的细胞类型特定差异所需的分辨率 在开发和扰动过程中的增强剂促销链接 在AIMS 2中，我将训练并验证深度学习神经网络模型以预测成对的转录 相互作用以激活细胞类型特定基因程序的因素。我将使用这些数据和计算 在计算机实验中执行的工具，以学习T-box TFS的细胞类型特定调节语法 在开发过程中。在使用此神经网络进行计算机实验中，我将对候选人进行排名 TF-TF相互作用使用高通量方法进行测试，用于靶向CRISPR-CAS9诱变以淘汰 TFS。我将使用此方法来揭示允许T-box家族转录因子的顺​​式调节语法 发挥其DNA基因座特异性。