Computational approaches for the analyses of spatial profiling technologies

空间转录组学（ST）技术使肿瘤中的地理基因表达谱。然而，每个肿瘤ST点包含不同的免疫和恶性细胞，细胞密度在组织区域之间差异显著。因此，肿瘤ST数据中的细胞类型分解仍然是现有的用于大块肿瘤和一般ST数据的方法无法解决的挑战。我们开发了空间细胞估计（SpaCE），从肿瘤ST数据推断细胞身份和细胞间相互作用。SpaCE首先通过模拟片段拷贝数变异来估计癌细胞克隆丰度。然后，约束回归模型校准局部细胞密度并确定免疫和基质细胞谱系分数。SpaCE提供了比现有方法更高的准确性，基于已知成分的模拟样本和人类肿瘤，在苏木精和伊红图像上进行双盲病理学注释。此外，将细胞分数与配体-受体空间表达相结合，SpaCE揭示了肿瘤-免疫界面的细胞间相互作用如何促进癌症进展。

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.