Multi-omic phenotyping of human transcriptional regulators

人类转录调节因子的多组学表型分析

• 批准号: 10733155
• 负责人: Sheng Li
• 金额: $ 57.61万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-11 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10733155
• 关键词: ATAC-seq; Address; Affect; Alleles; Auxins; Biological; Biological Assay; Biological Models; Biomedical Research; CRISPR/Cas technology; Catalogs; Cell model; Cells; ChIP-seq; Characteristics; Chromatin; Collaborations; Collecting Cell; Communities; Computing Methodologies; Data; Data Analyses; Databases; Drops; Ensure; Epigenetic Process; Gene Expression Regulation; Gene Targeting; Genes; Genetic Transcription; Genomic medicine; Goals; Healthcare; Human; Human Genome; Individual; Knockout Mice; Knowledge; Messenger RNA; Methods; Molecular; Molecular Profiling; Molecular Target; National Human Genome Research Institute; Noise; Outcome; Participant; Penetrance; Performance; Phase; Phenotype; Positioning Attribute; Production; Proteins; Protocols documentation; Publishing; Recommendation; Regulation; Regulon; Reporter; Role; Source; System; The Jackson Laboratory; Time; Tissues; Transcriptional Regulation; Validation; Vision; Work; XCL1 gene; biological heterogeneity; cell behavior; cell type; crowdsourcing; data mining; differential expression; diverse data; dynamic system; experimental study; gene function; gene network; gene regulatory network; genomic data; genomic variation; heterogenous data; improved; in silico; knock-down; knockout gene; molecular phenotype; multimodal data; multiple omics; novel; novel strategies; phenotypic data; power analysis; programs; single cell sequencing; single-cell RNA sequencing; success; tissue/cell culture; tool; transcription factor; transcriptome sequencing; transcriptomics

PROJECT SUMMARY The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) program is using multiple perturbation strategies to realize the NHGRI's vision of assigning function to every human gene. Strategies include pooled and individual gene knockouts and knockdowns (KDs), generated using CRISPR technologies and auxin-inducible degrons. Following application of such assays, molecular phenotypes of the cells are profiled longitudinally and at individual time points using bulk and single-cell (sc)RNA-seq. Perturbation strategies have intrinsic sources of variability, e.g., KD penetrance, while the single-cell sequencing approaches contribute technical noise, e.g., `drop out.' Quantifying and controlling this variability are crucial to ensure reliable phenotypic assessment and fulfill MorPhiC's goal to accurately catalog gene function. Given the critical role of transcription factors (TFs) in regulating cell state, all four MorPhiC Data Production Centers (DPCs) will perturb TFs and then profile cells using bulk or sc-RNA-seq. A wide range of other `regulatory phenotyping' data, including (bulk or single-cell) ATAC-seq, are being generated within MorPhiC and TF ChIP-seq, HiC, and massively parallel reporter assay (MPRA) data are available in the ENCODE and Impact of Genomic Variation on Function (IGVF) consortia. To robustly define the regulatory impact of TF perturbation, we propose a JAX MorPhiC Data Analysis and Validation Center (DAV) to analyze these multi-modal data. Our team is uniquely positioned to establish this TF-focused DAV: we are co-located with the JAX MorPhiC DPC and have consortium-level collaborations with its PI, while our own work focuses on elucidating transcriptional regulation of genes and on developing robust computational methods through community efforts. In Aim 1, we will quantify and control variability in perturbation-based regulatory phenotyping by using heterogeneous data generated within MorPhiC to isolate their technical noise characteristics and to derive a set of TF-gene target pairs (TF-GTs). We will then computationally simulate large- scale perturbation screens, through which we will perform power analysis to quantify data variability and make recommendations that ameliorate it. In Aim 2, we will evaluate published gene regulatory network (GRN) inference methods. We will also conduct two “crowd-sourced” DREAM Challenges, in which community participants will develop GRN inference methods that we will objectively evaluate with MorPhiC data. Using top- performing methods, as well as a novel approach we are developing based on dynamical systems, we will perform in silico TF perturbation within the GRNs to prioritize TFs for experimental validation in MorPhiC. In Aim 3, we will further improve robustness of inferred TF-GTs by integrating them with TF ChIP-seq, HiC, and MPRA data, knockout mouse phenotyping data (KOMP2), and spatial transcriptomics data from JAX and MorPhiC. We will validate published methods for defining tissue-specific GRNs by overlapping them with relevant MorPhiC model systems and will then use them to predict TF-GTs in systems yet to be profiled by MorPhiC. Our Aims will bolster the field's ability to decipher the regulatory function of the ~ 1600 TF genes within the human genome.

项目摘要 细胞中无效等位基因的分子表型（形态）程序正在使用多种扰动策略 实现NHGRI将功能分配给每个人类基因的愿景。策略包括合并和个人 基因敲除和敲除（KDS），使用CRISPR技术和可生长素诱导的降解产生。 应用此类评估后，细胞的分子表型将纵向和在 使用散装和单细胞（SC）RNA-seq的单个时间点。扰动策略具有内在的来源 可变性，例如KD渗透率，而单细胞测序方法则有助于技术噪声，例如 “退学。”量化和控制这种可变性对于确保可靠的表型评估和 实现形态准确分类基因功能的目标。鉴于转录因子（TFS）在 调节细胞状态，所有四个形态数据生产中心（DPC）都将扰动TF，然后配置单元格细胞 使用散装或sc-rna-seq。其他广泛的“调节表型”数据，包括（散装或单细胞） ATAC-SEQ正在形态和TF芯片seq，HIC和大量平行的记者测定中生成 （MPRA）数据在基因组变异对功能（IGVF）联盟的编码和影响中可用。到 我们提出了JAX形态数据分析和验证的稳健定义TF扰动的调节影响 中心（DAV）分析这些多模式数据。我们的团队独特地确定了这一以TF为中心的 DAV：我们与JAX Morphic DPC共同分享，并与PI进行了财团级的合作，而 我们自己的工作着重于阐明基因的转录调节，并开发可靠的计算 通过社区努力的方法。在AIM 1中，我们将量化和控制基于扰动的变异性 通过使用形态中产生的异质数据来隔离其技术噪声，调节表型 特征并得出一组TF基因目标对（TF-GTS）。然后，我们将在计算上模拟大型 扩展扰动屏幕，通过该屏幕，我们将执行功率分析以量化数据可变性并使 改善它的建议。在AIM 2中，我们将评估已发布的基因调节网络（GRN） 推理方法。我们还将采取两个“众包”的梦想挑战，在这个挑战中 参与者将开发GRN推断方法，我们显然将使用形态数据进行评估。使用顶部 执行方法以及我们基于动态系统开发的一种新颖方法，我们将 在GRN中执行硅TF扰动，以优先考虑TFS在形态上实验验证。目标 3，我们将通过将TF-GT与TF芯片，HIC和MPRA集成来进一步提高推断的TF-GT的鲁棒性 数据，基因敲除小鼠表型数据（KOMP2）以及JAX和MOMPHIC的空间转录组学数据。我们 将通过与相关形态重叠来验证已发布的方法来定义组织特异性GRN 建模系统，然后使用它们来预测尚未由形态介绍的系统中的TF-GT。我们的目标会 增强了该领域破译人类基因组中〜1600 TF基因的调节功能的能力。