Integrative cellular deconvolution of human brain RNA sequencing data

人脑 RNA 测序数据的综合细胞反卷积

• 批准号: 10359095
• 负责人: Stephanie Carinne Hicks
• 金额: $ 55.46万
• 依托单位: LIEBER INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-03 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10359095
• 关键词: Accounting; Aging; Algorithms; Astrocytes; Autopsy; Bioconductor; Bipolar Disorder; Brain; Brain Diseases; Brain region; Calibration; Cell Fraction; Cell Nucleus; Cells; Computer software; Data; Data Set; Development; Endothelial Cells; Genes; Genetic Transcription; Genetic Variation; Gold; Human; Individual; Machine Learning; Major Depressive Disorder; Microglia; Modeling; Molecular Profiling; Nuclear; Oligodendroglia; Peripheral; Population; Post-Traumatic Stress Disorders; Prefrontal Cortex; RNA; Role; Sampling; Schizophrenia; Signal Transduction; Small Nuclear RNA; Software Tools; Statistical Methods; Tissue Sample; Tissues; Validation; autism spectrum disorder; brain tissue; cell type; cognitive function; differential expression; excitatory neuron; frontal lobe; inhibitory neuron; insight; neuropsychiatry; novel; oligodendrocyte precursor; secondary analysis; single-cell RNA sequencing; statistical learning; transcriptome; transcriptome sequencing; web app

Many projects have characterized the human brain transcriptome within and across cell types to better understand changes in RNA expression associated with brain development and aging, developmental or psychiatric brain disorders, and genetic variation. Large consortia, including psychENCODE, CommonMind and BrainSeq Consortiums, have primarily focused on the molecular profiling of RNA extracted from homogenate/bulk tissue from different brain regions across thousands of individuals. However, bulk tissue like the frontal cortex contains a mixture of different important cell populations, and failing to account for the underlying composition of tissue samples can cause both false positives and missed signal in differential expression analysis. Therefore, statistical methods referred to as "cellular deconvolution" have been developed that estimate the relative fractions of different cell types in bulk RNA-seq datasets. These cell fractions can then be used to control for differences in cell composition across bulk tissue samples and can better determine the cell type(s) that drive differential expression signal in bulk tissue data. However, these approaches require reference expression profiles from the underlying cell types that will be estimated, which can be difficult to generate from human postmortem brain tissue. Recent approaches have leveraged single cell RNA sequencing (scRNA-seq) or single nuclei RNA sequencing (snRNA-seq) datasets to construct these reference profiles and perform cellular deconvolution, particularly in peripheral tissues. While many statistical or machine learning approaches have been proposed, the majority produce similar composition estimates for a given reference dataset. However, as we describe in this application, many of these existing reference datasets -regardless of the algorithm employed - are largely non- comparable to the vast majority of bulk RNA sequencing data generated from postmortem human brain tissue, and have produced incorrect estimates of cellular composition. Current algorithms estimate the relative fraction of RNA attributable to each cell type, and not the relative fraction of cell types. We therefore propose to generate a more comprehensive framework for performing cellular deconvolution in human postmortem RNA- seq data.This proposal will leverage the extensive bulk RNA sequencing performed over the past decade to better determine the relative role of cell type-specific expression in the human brain and their subsequent dysregulation in debilitating brain disorders.

许多项目都表征了细胞类型内部和跨细胞类型的人脑转录组的特征 了解与大脑发育和衰老，发育或 精神病脑疾病和遗传变异。大型财团，包括Psychencode，Commond 和Brainseq联盟，主要集中于从中提取的RNA的分子分析 来自数千个个体的不同大脑区域的匀浆/散装组织。但是，散装组织喜欢 额叶皮层包含不同重要细胞群体的混合物，无法说明 组织样品的基本组成可能会引起误报和差异信号 表达分析。因此，已经开发了称为“细胞反卷积”的统计方法 估计大量RNA-Seq数据集中不同细胞类型的相对部分。这些细胞分数可以 然后用于控制跨大块组织样品的细胞组成差异，并可以更好地确定 在散装组织数据中驱动差异表达信号的细胞类型。但是，这些方法需要 来自基础细胞类型的参考表达剖面将估计，这可能很难 由人类后脑组织产生。 最近的方法利用了单细胞RNA测序（SCRNA-SEQ）或单核RNA测序 （snRNA-seq）数据集构建这些参考曲线并执行蜂窝反卷积，特别是在 外围组织。尽管已经提出了许多统计或机器学习方法，但大多数 给定参考数据集产生类似的组成估计。但是，正如我们在此描述的那样 应用程序中的许多现有参考数据集（所采用算法的不符号）在很大程度上是非 - 与从死后人脑组织产生的绝大多数大量RNA测序数据相媲美， 并产生了细胞组成的错误估计。当前算法估计相对分数 归因于每种细胞类型的RNA，而不是细胞类型的相对分数。因此，我们建议 生成一个更全面的框架，用于在人类后RNA-中进行细胞反卷积 SEQ数据。此提案将利用过去十年进行的广泛的大量RNA测序 更好地确定细胞类型特异性表达在人脑中的相对作用及其随后的 脑疾病衰弱的失调。