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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

基本信息

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
项目类别：
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

项目摘要

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.