Molecular Expression Analysis

分子表达分析

• 批准号: 10427282
• 负责人: Cecilia Ljungberg
• 金额: $ 23.11万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-22 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10427282
• 关键词: Animal Model; Area; Basic Science; Biochemical; Biological; Biological Markers; Brain; Cell model; Cells; Cellular Metabolic Process; Clinical; Clinical Research; Complementary RNA; Complex; Computational Science; Computer software; Computing Methodologies; Data; Data Analyses; Data Science; Disease; Disease model; Ensure; Experimental Designs; Faculty; Functional disorder; Funding; Future; Gene Expression; Gene Expression Profile; Genetic; Genetic Models; Genetic Transcription; Goals; Human; Image; In Situ Hybridization; Intellectual and Developmental Disabilities Research Centers; Intellectual functioning disability; Laboratories; Lead; Leadership; Liquid substance; Measurement; Measures; Medicine; Metabolism; Methods; Modality; Modeling; Molecular; Nature; Neuronal Dysfunction; Organism; Outcome; Pathogenicity; Patients; Performance; Phenotype; Post-Translational Protein Processing; Proteins; Proteome; Proteomics; RNA; RNA analysis; Regulator Genes; Reporting; Research Personnel; Resources; Sampling; Scientist; Services; Source; System; Technology; Testing; Tissue-Specific Gene Expression; Tissues; Translational Research; Variant; Work; biomarker discovery; brain tissue; college; data integration; design; experimental study; faculty research; gene regulatory network; genetic regulatory protein; gigabyte; insight; instrument; instrumentation; metabolome; metabolomics; multiple omics; novel; phenotypic data; protein complex; protein expression; protein metabolite; recruit; small molecule; synergism; tool; transcriptome; transcriptome sequencing; transcriptomics; treatment strategy

The goal of the Molecular and Expression Analysis (MEA) Core is to provide BCM IDDRC investigators with access to high-throughput methods that can identify and quantify global phenotypic differences between fluids, cells or tissues at the level of gene expression, protein expression, post-translational modification, and cell metabolism. Targeted versions of these molecular technologies are valuable for testing and verifying molecular outcomes in genetic models for disease, but the unbiased nature of many platforms makes them exciting tools for identifying the mechanistic basis for disease and for direct discovery of biomarkers. The RNA Profiling sub-core will enable IDDRC investigators to characterize transcriptomes at the single cell level using several complementary RNA-seq commercial platforms, and to visualize and validate by imaging gene expression patterns in tissues by RNA in situ hybridization (ISH) and imaging. Differential gene expression inferred from untargeted transcriptomics can help researchers confirm models, but it can also reveal unanticipated findings about gene regulatory networks; targeted RNA ISH can validate and visualize these findings in brain tissue. The Protein and Metabolite Profiling sub-core will provide services and expertise to identify and profile proteins, protein complexes, post-translational modifications, and metabolites. Proteomics can provide key insights into the states of protein regulatory networks that control cellular phenotypes, and differences in small molecule levels revealed by untargeted metabolomics of fluids from animal models or patients can identify biochemical imbalances and biomarkers for disease. Because processing and interpreting data generated by these platforms is challenging, the Data Analysis and Integration sub-core will provide computational and data science expertise to assist IDDRC investigators with analysis of RNA sequencing data, proteomics LC- MS data, metabolomics LC-HRMS data, and metabolomics NMR data. The Core will also develop new computational methods to extract information from data derived from the same biological samples but across different -omics platforms. By providing access to a suite of platforms for the molecular characterization of phenotype, and the data analysis expertise needed to make sense of these complex systems, the MEA Core will enable researchers to identify molecular changes that lead to or report on pathogenic mechanism in IDDs. Tracking differences between healthy and disease states across these different modalities may yield connections between the genetic, gene regulatory, and biochemical basis for neural dysfunction.

分子和表达分析（MEA）核心的目标是为CAPDDRC研究人员提供 获得可以识别和量化流体之间的总体表型差异的高通量方法， 在基因表达、蛋白质表达、翻译后修饰和细胞水平上， 新陈代谢.这些分子技术的靶向版本对于测试和验证分子生物学是有价值的。 疾病遗传模型的结果，但许多平台的无偏见性质使它们成为令人兴奋的工具 用于识别疾病的机制基础和用于直接发现生物标志物。RNA分析 子核心将使IDDRC研究人员能够在单细胞水平上使用几个 互补的RNA-seq商业平台，并通过成像基因表达进行可视化和验证 通过RNA原位杂交（ISH）和成像检测组织中的模式。差异基因表达推断 非靶向转录组学可以帮助研究人员确认模型，但它也可以揭示意想不到的发现 关于基因调控网络;靶向RNA ISH可以验证和可视化脑组织中的这些发现。 蛋白质和代谢物分析子核心将提供服务和专业知识， 蛋白质、蛋白质复合物、翻译后修饰和代谢物。蛋白质组学可以提供关键 深入了解控制细胞表型的蛋白质调控网络的状态，以及小细胞间的差异。 来自动物模型或患者的液体的非靶向代谢组学揭示的分子水平可以识别 生化失衡和疾病的生物标志物。因为处理和解释由 这些平台是具有挑战性的，数据分析和集成子核心将提供计算和 数据科学专业知识，协助IDDRC研究人员分析RNA测序数据，蛋白质组学LC- MS数据、代谢组学LC-HRMS数据和代谢组学NMR数据。核心还将开发新的 计算方法从来自相同生物样品但跨 不同的组学平台。通过提供一套平台， 表型，以及数据分析专业知识，需要使这些复杂的系统，MEA核心 将使研究人员能够识别导致或报告IDD致病机制的分子变化。 在这些不同的模态中跟踪健康和疾病状态之间的差异可以产生 神经功能障碍的遗传、基因调控和生化基础之间的联系。