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

项目摘要 目前，对中枢神经系统(CNS)中数十万个细胞的高通量分析是 正在进行中。然而，大脑计划的目标之一是建立一个中枢神经系统细胞类型的普查 以前在单细胞RNA测序(ScRNAseq)方面的工作已经证明了对小样本的依赖 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 蜂窝类别/状态的变化。我和其他人之前的研究已经证明了这种潜伏期 空间方法从高维剖面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程序。然而，算法的使用不受生物的束缚 无论约束是否经过广泛的功能验证，都可能导致对单元类/状态的任意描述 “新奇”细胞类型的微不足道的名称。由于CNS的适当发展需要精确的监管和 协调空间和时间线索，这个应用程序的总体目标是开发分析性和 融合时空信息和scRNAseq学习有意义潜在信息的实验方法 空格。具体地说，我将1)为SPACE生成一个全面的转录签名集合 2)构建降维软件，将空间和细胞周期信息编码到 解释了中枢神经系统中细胞的高度特异性组织，3)推导出一个统计数据，ProjectionDivers，即 允许量化不同潜在空间使用的基因驱动因素，以及4)定义统计， ProMapR，它将告诉你一个细胞存在于大脑中特定位置的概率在给定的时间点 从细胞转录签名开始的时间。定义和验证具有生物意义的潜伏期的能力 SPACES不仅使多OMIC数据集成和scRNA-seq数据通过 大量公开可用的数据，但也为多模式数据集成奠定了基础-a 必要的下一步来描述单个细胞和复杂的神经电路如何在时间和 太空。

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.