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.

项目摘要目前，中枢神经系统（CNS）中数十万个细胞的高通量分析是进行。大脑计划的目标之一是在中枢神经系统中建立细胞类型的普查，但是单细胞RNA测序（SCRNASEQ）的先前工作证明了对小型集合的缓解细胞类型/状态/位置分类的标记基因不足以说明和细胞类别/状态的变化。我和他人的先前工作都证明了潜在空间方法从高维分析数据中识别出低维模式可以发现分子 scrnaseq中细胞类型和状态的驱动因素。但是，使用算法不受限制地与生物学约束或未在功能验证方面进行的约束是否可以导致单元类/状态的任意描述，并且 “新颖”细胞类型的琐碎设计。由于中枢神经系统的适当发展需要精确的监管，并且空间和临时提示协调，本应用的总体目的是开发分析和将时空信息与scrnaseq集成以学习有意义的潜在的实验方法空间。具体来说，我将1）生成全面的空间转录签名大脑的特征，2）构建缩小维度软件以编码空间和细胞周期信息说明中枢神经系统中细胞的高度特定组织，3）得出一个统计量，投影驱动程序，即允许量化差分潜在空间使用的基因驱动器，4）定义一个统计量， Promapr，这将告诉您在特定位置在大脑中特定位置存在的细胞的可能性来自单元的转录签名的时间。定义和验证生物学有意义的潜在能力空间不仅可以通过该数据集成和探索性分析SCRNA-SEQ数据通过大量公开数据，但也为多模式数据集成奠定了基础必要的下一步来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。