Integrative cellular deconvolution of human brain RNA sequencing data

许多项目已经确定了人类大脑转录组在细胞类型内和跨细胞类型之间的特征，以更好地 了解与大脑发育和衰老相关的RNA表达变化 精神性脑部疾病和基因变异。大型财团，包括weisencode、CommonMind 和BrainSeq财团，主要专注于从 取自数千人不同脑区的匀浆/块状组织。然而，像这样的大块组织 额叶皮质包含不同重要细胞群的混合物，无法解释 组织样本的潜在成分可能会导致差异中的假阳性和遗漏信号 表情分析。因此，发展了一种称为“细胞去卷积”的统计方法 这估计了批量RNA-seq数据集中不同细胞类型的相对比例。这些细胞组分可以 然后用来控制大块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(S)。然而，这些方法需要 引用将被估计的基本细胞类型的表达配置文件，这可能很难 从人类死后的脑组织中产生。 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剖面并执行细胞去卷积，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组成估计。然而，正如我们在这篇文章中所描述的那样 应用程序中，许多现有的参考数据集--无论采用何种算法--在很大程度上是非 与从死后人脑组织产生的绝大多数批量RNA测序数据相比， 并产生了对细胞组成的错误估计。当前的算法估计相对分数 属于每种细胞类型的RNA，而不是细胞类型的相对比例。因此，我们建议 为人类死后RNA的细胞去卷积生成更全面的框架- SEQ数据。这项提议将利用过去十年执行的广泛的批量RNA测序来 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 使人衰弱的大脑紊乱中的调节失调。

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.