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Single Neuron Analyzer for Multi-modal, Cross-dataset (Epi)genomic Cell Type Datasets

用于多模式、跨数据集（表观）基因组细胞类型数据集的单神经元分析仪

基本信息

批准号：
9795063
负责人：
ERAN A MUKAMEL
金额：
$ 122.75万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-18 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9795063
关键词：
ATAC-seq Area Atlases BRAIN initiative Biological Biological Assay Brain Brain region Cell Nucleus Cells Censuses Characteristics Chromatin Cluster Analysis Communities Computer Analysis Computer software DNA DNA Methylation Data Data Set Databases Diffusion Dimensions Enhancers Feedback Gene Expression Gene Expression Profile Genes Genomic Segment Genomics Goals Graph Human Imagery Individual Information Systems Institutes Joints Label Laboratories Learning Link Machine Learning Measures Methods Modality Molecular Molecular Analysis Molecular Genetics Molecular Profiling Mus Neurons Neurosciences Neurosciences Research Nucleic Acid Regulatory Sequences Privatization Receiver Operating Characteristics Regulation Reporting Reproducibility Research Personnel Resolution Resources Sampling System Systems Integration Technology Validation Work base brain cell cell type cloud based computerized tools computing resources data integration epigenetic regulation epigenome epigenomics experimental study human reference genome methylome molecular scale multimodality neural circuit single cell sequencing single cell technology transcriptome transcriptome sequencing transcriptomics web-accessible

项目摘要

Project Summary/Abstract Our project will create a computational resource, the Single Neuron Analyzer, to support the neuroscience community’s efforts to build a reproducible, comprehensive, data-driven atlas of brain cell types. Laboratories in the BRAIN Initiative Cell Census Network and others are generating large-scale molecular datasets from multiple regions of the mouse and human brain using single cell sequencing technology. These datasets include single cell and single nucleus transcriptomes (RNA-Seq), as well as single nucleus DNA methylomes (mC-Seq) and chromatin accessibility (ATAC-Seq). Each data type provides complementary information about the molecular identity of brain cells: transcriptomes directly measure gene expression, while epigenomic data indicates both gene expression levels and the activity of intergenic regulatory regions such as enhancers. However, there is no computational resource for integrating these data from these multiple modalities and for statistically validating the reliability and reproducibility of the cell types defined based on each dataset. The Single Neuron Analyzer will work within the framework of the Single Cell Portal, which provides horizontally-scalable, highly performant solutions that allow researchers to efficiently scale with the growing size of datasets as the technology for single cell sequencing advances. In Aim 1, we will use machine learning and cross-validation to study the reproducibility of cell types defined by researchers based on one or more datasets. The Single Neuron Analyzer will allow users to compute a quantitative score, corresponding to the area under the receiver operating characteristic (AUROC), which quantifies the degree to which cell type labels can be predicted based on independent data such as experimental replicates or complementary molecular assays. Aim 2 will build a data integration system that can jointly analyze single cells profiled by different technologies and modalities, including transcriptomic and epigenomic data. We will take advantage of the reliable correlation of gene expression with low gene body DNA methylation and high chromatin accessibility, to link cells measured in one modality with their closest matching neighbors in the other two modalities. The resulting neighbor graphs will be used to impute the missing data, followed by joint cluster analysis and low-dimensional projection of the integrated dataset. Following joint analysis, the system will provide a variety of visualizations and downloadable reports about key markers for each cell type. By combining transcriptomic and epigenomic information, the system will predict cell type specific genes as well as putative enhancers. Single Neuron Analyzer will offer researchers across the neuroscience community a resource for rigorous multi-modal molecular analysis of neuronal cell types, helping to advance the goal of comprehensively understanding the brain’s cellular parts list.

项目总结/摘要我们的项目将创建一个计算资源，单神经元分析仪，以支持神经科学社区努力建立一个可复制的，全面的，数据驱动的脑细胞类型图谱。实验室在BRAIN Initiative Cell Census Network和其他组织中，使用单细胞测序技术对小鼠和人类大脑的多个区域进行测序。这些数据集包括单细胞和单核转录组（RNA-Seq）以及单核DNA甲基化组（mC-Seq）和染色质可及性（ATAC-Seq）。每种数据类型都提供有关以下内容的补充信息：脑细胞的分子身份：转录组直接测量基因表达，而表观基因组数据指示基因表达水平和基因间调节区如增强子的活性。然而，不存在用于整合来自这些多个模态的这些数据以及用于将这些数据与来自这些多个模态的数据进行比较的计算资源。在统计学上验证基于每个数据集定义的细胞类型的可靠性和再现性。的单神经元分析仪将在单细胞门户的框架内工作，该门户提供水平可扩展的高性能解决方案，使研究人员能够随着不断增长的随着单细胞测序技术的进步，数据集的大小也在增加。在目标1中，我们将使用机器学习和交叉验证，以研究研究人员基于一个或多个数据集。单神经元分析仪将允许用户计算定量分数，对应于受试者工作特征下面积（AUROC），用于量化细胞类型标记的程度可以基于独立的数据进行预测，例如实验重复或互补分子测定。AIM 2将建立一个数据集成系统，可以联合分析不同细胞的单个细胞。技术和模式，包括转录组学和表观基因组学数据。我们将利用基因表达与低基因体DNA甲基化和高染色质可及性的可靠相关性，以将在一种模态中测量的细胞与它们在另外两种模态中的最接近匹配的相邻细胞相关联。的将使用产生的相邻图估算缺失数据，然后进行联合聚类分析，集成数据集的低维投影。在联合分析之后，该系统将提供各种关于每种细胞类型的关键标志物的可视化和可下载的报告。通过结合转录组学和表观基因组信息，该系统将预测细胞类型特异性基因以及推定的增强子。单神经元分析仪将为神经科学界的研究人员提供一个严格的资源，神经元细胞类型的多模式分子分析，有助于全面推进了解大脑的细胞组成。