IIBR Informatics: Mining Spatial and Single-cell Transcriptomes to Understand Cell Locality and Heterogeneity in Tissues

IIBR 信息学：挖掘空间和单细胞转录组以了解组织中的细胞局部性和异质性

• 批准号: 2042159
• 负责人: Rui Kuang
• 金额: $ 80万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042159&HistoricalAwards=false
• 关键词: IIBR; Informatics; Mining; Spatial; Single

Biological tissues are composed of different structurally organized cell types which play distinct and cooperative functional roles in phenotypes. Recent spatial transcriptomics technologies have enabled spatially-resolved RNA profiling of single cells with cell identities and localizations for understanding cells’ organizations and functions. The project will develop new machine learning methods for mining RNA profiles collected from single cells and their spatial locations. The research community will benefit from the collection of tools for the analysis of spatial and single-cell genomic data in studying molecular characteristics of cellular structures in tissue. The new methods will be applied to the study of spatial cell heterogeneity of ovarian cancer and circadian rhythms in Brassica rapa. The two applications will improve understanding of cellular structure and pathology of ovarian tissues and the association of cell-specific circadian gene expression patterns with crop improvement traits. Underrepresented graduate and undergraduate students will be advised on research conduction. A summer camp for K-12 students will promote early career interest in big data, genomics, and plant science.The project will develop models to jointly analyze spatial RNA and single cell RNA profiles. The models will consider spatial structures among cells to provide interpretations of cellular mechanisms in the micro-environment of surrounding cells, macro-structures among multiple tissue regions, and spatiotemporal structures of tissue over circadian rhythms. The proposed research will lead to a class of new computational methods on integrating single-cell gene expressions with spatial and temporal structures to connect single-cell molecular profiling to tissue micro-environment and the dynamics of spatial regions in tissue. Aim 1 of the research is to develop tensor-based learning methods and graph-based neural networks to integrate spatial transcritomics data with cell images, cell spatial locations, and molecular networks for gene expression imputation, spatial gene module detection, spatial clustering to discover cell types, and co-clustering spatial locations and genes. Aim 2 will develop a multitask tensor decomposition method to integrate spatial arrangement of multiple tissue bisection regions to discover the variations of cell diversity and the trajectory of cell proliferation in large tissue (or organ). Application of the method to identify cell heterogeneity and spatial origin of single cells in ovarian cancer tissue will be carried out. Aim 3 will design a multitask joint tensor-matrix factorization method regularized by a circadian function to capture different periodical patterns for studying the dynamic characteristics of spatial gene expressions in a tissue sample. Detecting spatial variations in the circadian clock across Brassica rapa leaf cross-sections using the proposed method will be performed. The results and tools will be made available through http://compbio.cs.umn.edu/spatial-genomics/.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.

生物组织是由不同结构的细胞组成的，这些细胞在表型上发挥着不同的和合作的功能作用。最近的空间转录组学技术已经使得能够对具有细胞身份和定位的单细胞进行空间分辨的RNA分析，以了解细胞的组织和功能。该项目将开发新的机器学习方法，用于挖掘从单细胞及其空间位置收集的RNA图谱。研究界将受益于空间和单细胞基因组数据分析工具的收集，以研究组织中细胞结构的分子特征。新方法将应用于卵巢癌细胞空间异质性和芜菁昼夜节律的研究。这两项应用将提高对卵巢组织细胞结构和病理学的理解，以及细胞特异性昼夜节律基因表达模式与作物改良性状的关联。代表性不足的研究生和本科生将被告知进行研究。针对K-12学生的夏令营将促进对大数据、基因组学和植物科学的早期职业兴趣。该项目将开发模型，以联合分析空间RNA和单细胞RNA图谱。 该模型将考虑细胞之间的空间结构，以提供周围细胞的微环境中的细胞机制的解释，多个组织区域之间的宏观结构，以及昼夜节律上的组织时空结构。该研究将导致一类新的计算方法，将单细胞基因表达与空间和时间结构相结合，将单细胞分子谱与组织微环境和组织中空间区域的动态联系起来。本研究的目的1是开发基于张量的学习方法和基于图的神经网络，将空间transmitomics数据与细胞图像，细胞空间位置和分子网络整合，用于基因表达插补，空间基因模块检测，空间聚类以发现细胞类型，以及共聚类空间位置和基因。 目的二将发展一种多任务张量分解方法，整合多个组织二分区域的空间排列，以发现大组织（或器官）中细胞多样性的变化和细胞增殖的轨迹。应用该方法鉴定卵巢癌组织中单个细胞的细胞异质性和空间起源。目标3设计一种多任务联合张量矩阵分解方法，通过昼夜节律函数正则化来捕获不同的周期性模式，用于研究组织样本中空间基因表达的动态特征。将使用所提出的方法检测芜菁叶横截面上生物钟的空间变化。结果和工具将通过http://compbio.cs.umn.edu/spatial-genomics/.This奖项提供，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。

项目成果

Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network

• DOI: 10.1093/bioinformatics/btab812
• 发表时间: 2021-12
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Tianci Song;Kathleen K Markham;Zhuliu Li;K. Muller;Kathleen;Greenham;R. Kuang