Developing Random Field based novel approaches for spatial transcriptomics

开发基于随机场的空间转录组学新方法

• 批准号: 2217515
• 负责人: Xiaobo Zhou
• 金额: $ 70.46万
• 依托单位: The University of Texas Health Science Center at Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217515&HistoricalAwards=false
• 关键词: Developing; Random; Field; based; novel

The overall goal of the project is to develop Random Field based approaches to spatially analyze and understand tissue architecture and heterogeneity as well as the communication between different cells and stromal components. The knowledge will gain insights into the potential molecular mechanism related to cell evolution, tissue development, disease progression, and drug resistance. The developed tools will significantly improve our understanding of tissue heterogeneity and development. The characterization and use of subclones and spatial architecture for function analysis will provide a completely different approach to study tissue heterogeneity. Graduate and undergraduate students will work under this project and gain experience in leading-edge research. An undergraduate summer research program on "Computational Systems Biology” will be organized. Systematic analysis methods used to analyze spatial transcriptome data are still in their infancy. Current analysis methods of spatial transcriptome data focus on transcriptional profiling. Novel methods are needed to identify genomic variants and integrate genetic and transcriptional variations. To address the challenges, a hybrid model based on Variational Graph AutoEncoder (VGAE) will be developed to characterize the spatial relationship between the spots and subclones. Hidden Markov Models (HMM) will be used to infer the copy number variations (CNVs) from spatial transcriptomic data. The new approach of identifying subclone and CNVs from spatial transcriptomics through VGAE and HMM is coined as CVAM. A Random Field based computational toolset called SPAT (spatial architectural analysis) is proposed to study the architectural heterogeneity with spatial transcriptomics and scRNA-seq data. SPAT can identify single gene-based spatial biomarkers, analyze spatial distribution patterns of signaling networks, and explore cell-cell interaction using spatial transcriptomics and scRNA-seq data. Specifically, a Gromov-Wasserstein distance and Random Field-based approach will be designed to characterize signaling between adjacent cells and stromal cells. Finally the new computational tools and results will be validated through biological experiments. Software prototypes and the variants and gene biomarkers with spatial patterns will be made publicly available to the research community via a project website at https:/ccsm.uth.edu/NSFSPA.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的总体目标是开发基于随机场的方法，以空间分析和理解组织结构和异质性以及不同细胞和基质成分之间的通信。这些知识将深入了解与细胞进化，组织发育，疾病进展和耐药性相关的潜在分子机制。所开发的工具将显着提高我们的组织异质性和发展的理解。亚克隆和空间结构的功能分析的表征和使用将提供一个完全不同的方法来研究组织异质性。研究生和本科生将在这个项目下工作，并获得前沿研究的经验。将组织一个关于“计算系统生物学”的本科生暑期研究项目。用于分析空间转录组数据的系统分析方法仍处于起步阶段。目前空间转录组数据的分析方法主要集中在转录谱分析上。需要新的方法来鉴定基因组变异并整合遗传和转录变异。为了应对这些挑战，将开发基于变分图自动编码器（VGAE）的混合模型来表征斑点和亚克隆之间的空间关系。隐马尔可夫模型（HMM）将用于从空间转录组数据推断拷贝数变异（CNV）。通过VGAE和HMM从空间转录组学中识别亚克隆和CNVs的新方法被称为CVAM。提出了一种基于随机场的计算工具集SPAT（spatial architectural analysis），用于研究空间转录组学和scRNA-seq数据的结构异质性。SPAT可以识别基于单个基因的空间生物标志物，分析信号网络的空间分布模式，并使用空间转录组学和scRNA-seq数据探索细胞间的相互作用。具体而言，将设计Gromov-Wasserstein距离和基于随机场的方法来表征相邻细胞和基质细胞之间的信号传导。最后，新的计算工具和结果将通过生物实验进行验证。软件原型和变体以及具有空间模式的基因生物标志物将通过项目网站https：/ www.example.com向研究界公开提供ccsm.uth.edu/NSFSPA.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。