Integrative analysis of spatial transcriptomics with histology images and single cells

空间转录组学与组织学图像和单细胞的综合分析

• 批准号: 10733815
• 负责人: Mingyao Li
• 金额: $ 54.66万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10733815
• 关键词: Address; Back; Bar Codes; Basic Science; Behavior; Benchmarking; Cell Communication; Cells; Cellular Morphology; Clinical; Clinical Research; Collaborations; Companions; Computer software; Data; Data Analyses; Data Set; Detection; Disease; Environment; Eye diseases; Frontotemporal Dementia; Gene Expression; Gene Expression Profiling; Genes; Genomics; Hematoxylin and Eosin Staining Method; Histologic; Histology; Image; Joints; Kidney Diseases; Knowledge; Learning; Location; Malignant neoplasm of lung; Maps; Measures; Methodology; Methods; Modeling; Motivation; Pathology; Performance; Research Personnel; Resolution; Spottings; Stains; Stroke; Structure; Technology; Tissue imaging; Tissues; Training; Translations; Vision; cell type; cost; disease diagnosis; human disease; machine learning algorithm; machine learning method; malignant stomach neoplasm; method development; multimodal data; next generation sequencing; novel; open source; single-cell RNA sequencing; spatial integration; transcriptome; transcriptome sequencing; transcriptomics; ultra high resolution; virtual

PROJECT SUMMARY The function. relative disease tissues in our body consist of diverse cell t ypes with each cell type specialized to carry out a particular The behavior of a cell is influenced by its surrounding environment within a tissue. Knowledge of the locations of different cells in a tissue is critical for understanding the spatial organization of cell types and pathology.Although single-cell RNA sequencing (scRNA-seq) has made it possible to characterize cell types and states at an unprecedented resolution, the lack of physical relationships among cells has hindered the study of cell-cell communications within tissue context. Recent technology advances in spatial transcriptomics (ST) have enabled gene expression profiling while retaining location information in tissues. A popular ST technology is based on spatial barcoding followed by next-generation sequencing in which transcriptome-wide gene expression is measured in spatially barcoded spots. Data from such ST technologies often include a high- resolution hematoxylin and eosin (H&E)-stained histology image of the tissue section from which the gene expression data are obtained. Although ST is powerful, such data are still expensive to generate. On the other hand, it is relatively cheaper to generate H&E-stained histology images and scRNA-seq data. The main motivation of this project is to leverage information in ST to gain additional knowledge from the relatively easy- to-obtain histology images and scRNA-seq data. Building upon our expertise in statistical genomics, we propose to develop novel machine learning methods to address key computational challenges when performing integrative analysis of ST, histology images, and single cells. Our methods will jointly model gene expression and histology to characterize the spatial organization of tissues and predict spatial gene expression from histology images. The resulting spatial map from these analyses will further enable the spatial mapping of single cells back to tissues. The proposed methods will be applied to public data and data generated from ongoing collaborations in various diseases to evaluate their performance. The successful completion of this project will allow researchers to take advantage of advanced machine learning algorithms to integrate ST, histology, and single-cell data to gain a holistic view of the spatial organization of tissues.

项目总结 这个 功能。 相对的 疾病 我们身体中的组织由不同的细胞类型组成，每种细胞类型专门执行特定的 细胞的行为受到组织内周围环境的影响。了解以下内容 组织中不同细胞的位置对于理解细胞类型的空间组织和 病理学。尽管单细胞RNA测序(scRNA-seq)已经使鉴定细胞成为可能 类型和状态以前所未有的分辨率，细胞之间缺乏物理关系阻碍了 研究组织环境中的细胞-细胞通讯。空间转录组学的最新技术进展 (ST)实现了基因表达谱分析，同时保留了组织中的位置信息。一条受欢迎的街 技术是基于空间条形码，然后是下一代测序，其中转录组范围 基因的表达是在空间条形码的点上测量的。来自这类ST技术的数据通常包括高度的 组织切片的分辨率苏木精-伊红(H&E)染色的组织学图像 获得表情数据。尽管ST功能强大，但生成此类数据的成本仍然很高。另一方面 另一方面，生成H&E染色的组织学图像和scRNA-seq数据相对便宜。主 该项目的动机是利用ST中的信息从相对容易的- 获取组织学图像和scRNA-seq数据。基于我们在统计基因组学方面的专业知识，我们建议 开发新的机器学习方法，以解决执行以下任务时的关键计算挑战 ST、组织学图像和单细胞的综合分析。我们的方法将联合模拟基因表达 和组织学来描述组织的空间组织并预测空间基因表达 组织学图像。通过这些分析得到的空间地图将进一步支持单个 细胞回到组织中。拟议的方法将适用于公共数据和正在进行的 在各种疾病方面的合作，以评估其表现。这一项目的成功完成将 允许研究人员利用高级机器学习算法将ST、组织学和 单细胞数据，以获得组织的空间组织的整体视图。