美国哈佛大学Fei Chen和Rafael A. Irizarry团队共同合作近期取得重要研究进展，他们报道了空间转录组差异表达的细胞类型特异性推断方法。相关论文于2022年9月1日在线发表于《自然—方法学》杂志上。

研究人员介绍了一种统计方法，即差异表达的细胞类型特异性推断法（C-SIDE），它在空间转录组学中识别细胞类型特异性差异表达（DE），从而解释其他细胞类型的定位。研究人员将基因表达建模为跨细胞类型的对数线性细胞类型特定表达函数的混合。C-SIDE的框架适用于许多情况：由于病理学、解剖区域、细胞间相互作用和细胞微环境导致的DE。

此外，C-SIDE能够进行跨多个/重复的统计推断。Slide-seq、MERFISH和Visium数据集的模拟和验证实验表明，C-SIDE通过有效的不确定性量化准确地识别DE。最后，研究人员应用C-SIDE来识别阿尔茨海默病中斑块依赖性免疫活性，以及肿瘤和免疫细胞之间的细胞相互作用。研究人员在R包中发布了C-SIDE，https://github.com/dmcable/spacexr。

据介绍，空间转录组学的一个核心问题是跨组织检测细胞类型中DE的基因。学习DE的挑战包括跨越空间改变细胞类型组成和检测来自多种细胞类型的转录本的测量像素。

附：英文原文

Title: Cell type-specific inference of differential expression in spatial transcriptomics

Author: Cable, Dylan M., Murray, Evan, Shanmugam, Vignesh, Zhang, Simon, Zou, Luli S., Diao, Michael, Chen, Haiqi, Macosko, Evan Z., Irizarry, Rafael A., Chen, Fei

Issue&Volume: 2022-09-01

Abstract: A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE’s framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer’s disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr.

DOI: 10.1038/s41592-022-01575-3

Source: https://www.nature.com/articles/s41592-022-01575-3