Integrative cellular deconvolution of human brain RNA sequencing data

人脑 RNA 测序数据的综合细胞反卷积

基本信息

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
项目类别：
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

项目摘要

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.

许多项目已经对细胞类型内和跨细胞类型的人脑转录组进行了表征，以便更好地了解与大脑发育和衰老、发育或发育相关的 RNA 表达变化精神性脑部疾病和遗传变异。大型财团，包括 psychENCODE、CommonMind 和 BrainSeq Consortiums，主要关注从提取的 RNA 进行分子分析对数千人不同大脑区域的组织进行匀浆/块状组织。然而，大块组织如额叶皮层包含不同重要细胞群的混合物，并且未能解释组织样本的潜在成分可能会导致差异检测中的误报和丢失信号表达分析。因此，开发了称为“细胞反卷积”的统计方法估计批量 RNA-seq 数据集中不同细胞类型的相对分数。这些细胞碎片可以然后用于控制大量组织样本中细胞组成的差异，并可以更好地确定在大量组织数据中驱动差异表达信号的细胞类型。然而，这些方法需要来自将要估计的基础细胞类型的参考表达谱，这可能很难由人类死后脑组织产生。最近的方法利用了单细胞 RNA 测序 (scRNA-seq) 或单核 RNA 测序 (snRNA-seq) 数据集来构建这些参考资料并执行细胞反卷积，特别是在周围组织。虽然已经提出了许多统计或机器学习方法，但大多数为给定的参考数据集生成类似的成分估计。然而，正如我们在本文中所描述的应用程序中，许多现有的参考数据集（无论采用何种算法）在很大程度上都是非与死后人类脑组织生成的绝大多数 RNA 测序数据相当，并产生了对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，用于在人类死后 RNA 中执行细胞解卷积 seq 数据。该提案将利用过去十年进行的大量 RNA 测序来更好地确定人脑中细胞类型特异性表达的相对作用及其后续作用导致衰弱性脑部疾病的调节失调。