Decomposing cell type-specific marks in post-mortem human brain studies

在死后人脑研究中分解细胞类型特异性标记

• 批准号: 8970099
• 负责人: Andrew Ellis Jaffe
• 金额: $ 37万
• 依托单位: LIEBER INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-03 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8970099
• 关键词: Accounting; Adult; Age; Algorithms; Antibodies; Astrocytes; Autopsy; Base Sequence; Bioconductor; Biological; Biological Assay; Blood Cells; Brain; Brain Diseases; Brain region; Cell Nucleus; Cells; Cerebellum; Complement; Computer Simulation; Computer software; CpG dinucleotide; Cytosine; DNA; DNA Methylation; Data; Data Collection; Development; Dinucleoside Phosphates; Disease; Dissection; Ensure; Epigenetic Process; Failure; Gene Expression; Generations; Genetic; Genome; Genomic Segment; Glutamates; Grant; Heterogeneity; Hippocampus (Brain); Human; Individual; Interneurons; Label; Location; Maps; Mental disorders; Methylation; Microarray Analysis; Molecular; Motivation; Neuroglia; Neurons; Oligodendroglia; Outcome; Pattern; Population; Prefrontal Cortex; Process; RNA; Relative (related person); Research Personnel; Resources; Sample Size; Sampling; Seminal; Series; Signal Transduction; Site; Sorting - Cell Movement; Specificity; Statistical Methods; Tissues; United States National Institutes of Health; Variant; Whole Blood; Work; base; bisulfite sequencing; brain cell; brain research; brain tissue; caudate nucleus; cell type; design; epigenome; epigenomics; excitatory neuron; experience; gamma-Aminobutyric Acid; genome-wide; inhibitory neuron; interest; methylation pattern; neuropsychiatry; novel; open source; public health relevance; research study; tool

 DESCRIPTION (provided by applicant): Post-mortem brain studies provide important molecular information for understanding genetic- epigenetic- and expression-based determinants of human brain development and dysregulation in neuropsychiatric illness and other brain disorders. While the sample sizes of postmortem studies have increased dramatically over the past decade, the majority of large-scale human studies use homogenate brain tissue, highlighting the difficulty in obtaining specific cell types of interest in a large nmber of samples. However, we have observed large variability in the proportion of neuronal cells in over 1000 post-mortem human brain tissue homogenates, potentially due to dissection issues, effects of disease, subject age and/or individual/random variation. Failure to incorporate cellular composition, e.g. the relative proportions of functional cell types, into both epigenetic and gene expression studies in homogenate brain tissue can result in both widespread false positives and negatives. This project involves isolating four pure cellular populations (inhibitory and excitatoy neurons, oligodendrocytes, and astrocytes) across four brain regions (cerebellum, DLPFC, hippocampus and caudate nucleus) in five individuals. We will generate genome-scale DNA methylation maps in each cell type, region, and individual using whole genome bisulfite sequencing (WGBS). These data will better characterize epigenetic differences between related cell types like GABA and glutamatergic interneurons to identify what regions are differentially regulated between related classes of cells with similar functions. Perhaps more importantly, we will use these pure DNA methylation profiles to create open-source statistical software that performs in silico estimation of the relative proportion of each cell type in brain tissue homogenate provided by other researchers. This approach has been successfully applied to statistically deconvoluting whole blood into the relative proportion of blood cell types. We propose to greatly expand previous work in the brain by quantifying more functionally enriched cell types across more brain regions using fewer discriminatory markers. We hope to create an accurate assay for inexpensively estimating cellular composition of brain tissue homogenate using a series of bisulfite sequencing experiments. The easy estimation of cellular composition would have widespread use in postmortem brain research, both among epigenetic and gene expression studies. For example, during RNA extraction steps, a small amount of DNA can be concurrently extracted and used to estimate cellular composition proportions. The sample selection for the studies can utilize this composition information by excluding samples with outlying composition estimates, and sample selection for biological groups can be matched on cell composition proportions, such as ensuring a similar distribution of composition across outcome groups. This is especially important because differences in cell composition between outcome groups result in widespread false positives. We hope to introduce a powerful tool to complement important factors like tissue quality and sample characterization in the analysis of postmortem human brain data.

 描述（由申请人提供）：死后大脑研究为理解人类大脑发育和神经精神疾病和其他大脑疾病的失调的遗传、表观遗传和表达决定因素提供了重要的分子信息。尽管尸检研究的样本量在过去十年中急剧增加，但大多数大规模人类研究都使用匀浆脑组织，这凸显了从大量样本中获取感兴趣的特定细胞类型的难度。然而，我们观察到 1000 多个死后人脑组织匀浆中神经元细胞的比例存在很大差异，这可能是由于解剖问题、疾病影响、受试者年龄和/或个体/随机变化造成的。未能整合蜂窝网络 组成，例如在匀浆脑组织中进行表观遗传学和基因表达研究时，功能细胞类型的相对比例可能会导致广泛的假阳性和阴性结果。 该项目涉及在五个个体的四个大脑区域（小脑、DLPFC、海马和尾状核）中分离出四个纯细胞群（抑制性和兴奋性神经元、少突胶质细胞和星形胶质细胞）。我们将使用全基因组亚硫酸氢盐测序 (WGBS) 生成每种细胞类型、区域和个体的基因组规模 DNA 甲基化图谱。这些数据将更好地表征相关细胞类型（如 GABA 和谷氨酸中间神经元）之间的表观遗传差异，以确定具有相似功能的相关细胞类别之间哪些区域受到差异性调节。也许更重要的是，我们将使用这些纯 DNA 甲基化图谱来创建开源统计软件，该软件可以通过计算机估计其他研究人员提供的脑组织匀浆中每种细胞类型的相对比例。该方法已成功应用于将全血统计解卷积为血细胞类型的相对比例。我们建议通过使用更少的歧视性标记来量化更多大脑区域中功能更丰富的细胞类型，从而极大地扩展先前在大脑中的工作。 我们希望使用一系列亚硫酸氢盐测序实验创建一种准确的测定方法，以廉价地估计脑组织匀浆的细胞组成。细胞组成的简单估计将在死后大脑研究中广泛使用，包括表观遗传学和基因表达研究。例如，在 RNA 提取步骤中，可以同时提取少量 DNA 并用于估计细胞组成比例。研究的样本选择可以通过排除具有外围成分估计值的样本来利用该成分信息，并且生物组的样本选择可以与细胞成分比例相匹配，例如确保结果组之间的成分分布相似。这一点尤其重要，因为结果组之间细胞组成的差异会导致广泛的假阳性。我们希望引入一种强大的工具来补充死后人脑数据分析中的组织质量和样本表征等重要因素。