Resolving Spatiotemporal Determinants of Cell Specification in Corticogenesis with Latent Space Methods

用潜在空间方法解决皮质生成中细胞规格的时空决定因素

基本信息

批准号：
10703714
负责人：
Genevieve Lauren Stein-O'Brien
金额：
$ 24.9万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10703714
关键词：
ALS patients Algorithms Anatomy Automobile Driving BRAIN initiative Bar Codes Behavioral Biological Biological Assay Brain Brain region CCRL2 gene CRISPR/Cas technology Catalogs Cell Cycle Cell Cycle Regulation Cells Censuses Central Nervous System Classification Collection Competence Complex Computer Models Computer software Coupled Cues Data Data Set Development Dimensions Disease Doctor of Philosophy Experimental Models Gene Expression Gene Expression Profile Generations Genes Genetic Genetic Transcription Genetic Variation Genetic study Goals Human Genetics In Situ Individual Knowledge Learning Length Link Location Machine Learning Measurement Methodology Methods Modeling Molecular Multiomic Data Nature Neurodegenerative Disorders Neuronal Plasticity Pathway interactions Pattern Performance Phase Phenotype Physiologic pulse Positioning Attribute Probability Process Regulation Research Retina Specific qualifier value Statistical Models Stochastic Processes System Techniques Testing Time Training Transcriptional Regulation Validation Variant Work analytical tool biological systems cell type data integration functional plasticity genetic variant genome wide association study high dimensionality improved induced pluripotent stem cell multimodal data nervous system development neural neural circuit neuronal circuitry neuropsychiatric disorder novel nucleotide analog progenitor repository single-cell RNA sequencing spatial integration spatiotemporal statistics success supervised learning time use tool transfer learning

项目摘要

Project Summary High-throughput profiling of hundreds of thousands of cells in the central nervous system (CNS) is currently underway. One of the goals of the BRAIN initiative is to build a census of cell types in the CNS, however previous work in single cell RNA sequencing (scRNAseq) has demonstrated that reliance on small collections of marker genes for cell type/state/position classification is insufficient to account for the dynamic nature of and variation in cellular classes/states. Previous work from both myself and others has demonstrated that latent space methods identify low dimensional patterns from high dimensional profiling data can discover molecular drivers of cell types and states in scRNAseq. However, the use of algorithms untethered to biological constraints or not extensively functionally validated can lead to the arbitrary delineation of cell class/state and the trivial designation of “novel” cell types. As proper development of the CNS requires precise regulation and coordination of spatial and temporal cues, the overall objective of this application is to develop analytic and experimental methods that integrate spatiotemporal information with scRNAseq to learn meaningful latent spaces. Specifically, I will 1) generate a comprehensive collection of transcriptional signatures for spatial features of the brain, 2) build dimension reduction software to encode spatial and cell cycle information to account for the highly specific organization of cells in the CNS, 3) derive a statistic, projectionDrivers, that allows for quantification of the gene drivers of differential latent space usage, and 4) define a statistic, proMapR, that will tell you the probability of a cell existing in a particular location in the brain at a given point in time from the cell's transcriptional signature. The ability to define and validate biologically meaningful latent spaces not only enables multiOmic data integration and exploratory analysis of scRNA-seq data via the massive amount of publicly available data, but also lays the groundwork for multimodal data integration—a necessary next step to characterize how individual cells and complex neural circuits interact in both time and space.

项目概要目前正在对中枢神经系统 (CNS) 中数十万个细胞进行高通量分析然而，BRAIN 计划的目标之一是对中枢神经系统中的细胞类型进行普查。之前在单细胞 RNA 测序 (scRNAseq) 方面的工作已经证明，对小样本集合的依赖用于细胞类型/状态/位置分类的标记基因不足以解释和的动态性质我自己和其他人之前的工作已经证明了潜在的细胞/状态变化。空间方法从高维分析数据中识别低维模式可以发现分子 scRNAseq 中细胞类型和状态的驱动因素然而，算法的使用不受生物学限制。限制或主要功能未验证可能导致细胞类别/状态的任意划分和 “新”细胞类型的琐碎命名，因为中枢神经系统的正确发育需要精确的调节和控制。空间和时间线索的协调，该应用程序的总体目标是开发分析和将时空信息与 scRNAseq 相结合的实验方法，以学习有意义的潜在信息具体来说，我将 1）生成空间转录签名的全面集合。大脑的特征，2）构建降维软件来编码空间和细胞周期信息解释中枢神经系统中细胞的高度特异性组织，3) 得出一个统计数据，projectionDrivers，允许量化差异潜在空间使用的基因驱动因素，并且 4) 定义统计数据， proMapR，它将告诉您在给定点处大脑中特定位置存在细胞的概率定义和验证具有生物学意义的潜伏的能力。 space 不仅能够通过以下方式实现多组学数据集成和 scRNA-seq 数据的探索性分析海量的公开数据，也为多模式数据集成奠定了基础—— 下一步是必要的，以表征单个细胞和复杂的神经回路如何在时间和时间上相互作用空间。