Computational approaches for the analyses of spatial profiling technologies

空间剖析技术分析的计算方法

• 批准号: 10487039
• 负责人: Peng Jiang
• 金额: $ 58.74万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10487039
• 关键词: Bioinformatics; Cell Density; Cell Fraction; Cell Lineage; Cells; Clone Cells; Copy Number Polymorphism; Coupling; Data; Double-Blind Method; Gene Expression Profiling; Genomics; Geography; Hematoxylin and Eosin Staining Method; Human; Image; Immune; Knowledge; Ligands; Methods; Mining; Modeling; Pathology; Pathway interactions; Sampling; Scheme; Spottings; Stromal Cells; Technology; Tissues; Visualization; anticancer research; base; cancer cell; cell type; design; receptor; transcriptomics; tumor; tumor progression

Spatial transcriptomics (ST) technology has enabled geographical gene expression profiling in tumors. However, each tumor ST spot contains diverse immune and malignant cells, with cell densities that vary significantly across tissue regions. Therefore, the cell type decomposition in tumor ST data remains a challenge that existing methods for bulk tumors and general ST data cannot resolve. We developed Spatial Cellular Estimator (SpaCE) to infer cell identities and intercellular interactions from tumor ST data. SpaCE first estimates cancer cell clonal abundance through modeling segmental copy number variations. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCE provides higher accuracy than existing methods based on simulated samples of known composition and human tumors with double-blind pathology annotations on hematoxylin and eosin images. Moreover, coupling cell fraction results with ligand-receptor spatial expression, SpaCE reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

空间转录组学（ST）技术已使肿瘤中的地理基因表达分析。然而，每个肿瘤斑点都包含多种免疫和恶性细胞，其细胞密度在整个组织区域之间差异很大。因此，肿瘤ST数据中的细胞类型分解仍然是一个挑战，即现有的散装肿瘤方法和一般ST数据无法解决。我们开发了空间细胞估计量（空间），以从肿瘤ST数据中推断细胞身份和细胞间相互作用。空间首先通过建模分段拷贝数变化来估计癌细胞克隆丰度。然后，约束回归模型校准了局部细胞密度，并确定免疫和基质细胞谱系分数。与现有方法相比，基于苏木精和曙红图像的双盲病理学注释的模拟样本和人类肿瘤的模拟样品提供了更高的精度。此外，耦合细胞分数与配体 - 受体空间表达的结果，空间揭示了肿瘤免疫接口处的细胞间相互作用如何促进癌症的进展。