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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.

项目总结