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

• 批准号: 10188106
• 负责人: Genevieve Lauren Stein-O'Brien
• 金额: $ 11.07万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188106
• 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; 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; Institutes; Knowledge; Lead; Learning; Length; Link; Location; Machine Learning; Measurement; Methodology; Methods; Modeling; Molecular; Multiomic Data; Nature; Neuraxis; Neurodegenerative Disorders; Neuronal Plasticity; Pathway interactions; Pattern; Performance; Phase; Phenotype; Physiologic pulse; Positioning Attribute; Probability; Process; Psychological Transfer; Regulation; Research; Retina; Statistical Models; Stochastic Processes; System; Techniques; Testing; Time; Training; Transcriptional Regulation; Validation; Variant; Work; analytical tool; base; 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 circuit; neuronal circuitry; neuropsychiatric disorder; novel; nucleotide analog; pedagogy; progenitor; relating to nervous system; repository; single-cell RNA sequencing; spatiotemporal; statistics; success; supervised learning; time use; tool

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.

项目摘要 目前，中枢神经系统（CNS）中数十万细胞的高通量分析是一项重要的研究。 正在进行中然而，BRAIN计划的目标之一是建立CNS细胞类型的普查， 之前在单细胞RNA测序（scRNaseq）方面的工作已经证明了对小集合的依赖 用于细胞类型/状态/位置分类的标记基因的数量不足以解释细胞的动态性质， 细胞类别/状态的变化。我和其他人以前的研究表明， 空间方法从高维分布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，使用不受生物学约束的算法 约束或没有广泛的功能验证可以导致任意描绘细胞类/状态， “新”细胞类型的琐碎命名。由于中枢神经系统的正常发育需要精确的调节， 协调的空间和时间线索，这个应用程序的总体目标是开发分析和 将时空信息与scRNAseq整合以学习有意义的潜在信息的实验方法 空间.具体来说，我将1）生成一个全面的空间转录签名集合， 2）构建降维软件来编码空间和细胞周期信息， 解释了CNS中细胞的高度特异性组织，3）导出统计量projectionDrivers， 允许量化差异潜在空间使用的基因驱动，以及4）定义统计量， proMapR，它会告诉你一个细胞存在于大脑特定位置的概率， 从细胞的转录签名中提取时间。定义和验证生物学上有意义的潜在 spaces不仅能够通过多个数据库整合scRNA-seq数据， 大量的公共可用数据，但也为多模式数据集成奠定了基础， 必要的下一步，以表征如何个别细胞和复杂的神经回路相互作用，在时间和 空间