Molecular Expression Analysis

分子和表达分析(MEA)核心的目标是为BCM IDDRC调查人员提供 获得能够识别和量化流体之间的全球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的细胞或组织 新陈代谢。这些分子技术的靶向版本对于测试和验证分子 疾病遗传模型的结果，但许多平台的不偏不倚的性质使它们成为令人兴奋的工具 用于确定疾病的机制基础和直接发现生物标记物。核糖核酸图谱 亚核将使IDDRC的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，并通过成像来可视化和验证基因表达 通过RNA原位杂交(ISH)和成像检测组织中的模式。推测的差异基因表达 非定向转录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可以在脑组织中验证和可视化这些发现。 蛋白质和代谢物分析分核心将提供服务和专门知识，以确定和分析 蛋白质、蛋白质复合体、翻译后修饰和代谢物。蛋白质组学可以提供关键 对控制细胞表型的蛋白质调控网络的状态的洞察，以及小的差异 通过对动物模型或患者体液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 这些平台具有挑战性，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调查人员分析RNA测序数据，蛋白质组学LC- MS数据、代谢组学LC-HRMS数据和代谢组学核磁共振数据。核心还将开发新的 从来自相同生物样本的数据中提取信息的计算方法 不同的组学平台。通过提供对一套用于分子表征的平台的访问 表型，以及理解这些复杂系统所需的数据分析专业知识，MEA核心 将使研究人员能够识别导致IDDS的分子变化或报告其致病机制。 通过这些不同的模式跟踪健康和疾病状态之间的差异可能会产生 神经功能障碍的遗传、基因调控和生化基础之间的联系。

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.