Integrative cellular deconvolution of human brain RNA sequencing data

许多项目已经在细胞类型内和跨细胞类型表征了人脑转录组，以更好地 了解与大脑发育和衰老、发育或衰老相关的RNA表达变化， 精神性脑疾病和遗传变异大型财团，包括psychENCODE、CommonMind 和BrainSeq Consortiums，主要集中在从DNA中提取的RNA的分子谱分析上。 来自数千个个体的不同大脑区域的匀浆/大块组织。然而，大块组织，如 额叶皮质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导致鉴别诊断中的假阳性和丢失信号。 表情分析因此，被称为“细胞去卷积”的统计方法已经被开发出来 它估计了大量RNA-seq数据集中不同细胞类型的相对分数。这些细胞片段可以 然后用于控制大量组织样品中细胞组成的差异， 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 将被估计的潜在细胞类型的参考表达谱，这可能难以 是由人类死后的脑组织产生的 最近的方法利用了单细胞RNA测序（scRNA-seq）或单核RNA测序（scRNA-seq）。 使用snRNA-seq数据集来构建这些参考谱并进行细胞去卷积，特别是在 外周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相似的成分估计。然而，正如我们在这篇文章中所描述的， 应用程序，这些现有的参考数据集中的许多-无论采用的算法-在很大程度上是非- 与绝大多数从死后人脑组织产生的大量RNA测序数据相比， 并对细胞组成做出了错误的估计。目前的算法估计相对分数 RNA的大小取决于每种细胞类型，而不是细胞类型的相对比例。因此我们建议 生成一个更全面的框架，用于在人类死后RNA中执行细胞去卷积- 该提案将利用过去十年进行的大量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 使人衰弱的大脑失调。

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.