PDF(Pubmed)
Abstract：
Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.
摘要：
基于测序的空间转录组学（ST）方法允许在条形码位点无偏捕获RNA分子，绘制组织中细胞类型和转录本的分布和定位图。虽然这些技术的粗略分辨率被认为是一个缺点，我们认为，在斑点上捕获的转录组的固有接近性可以用来重建细胞网络。为此,我们开发了ISCHIA(识别健康和炎症组织中的空间共存)，一个计算框架，用于分析斑点内细胞类型和转录物物种的空间共现。共现分析与差异基因表达互补，因为它不依赖于给定细胞类型的丰度或转录本表达水平，而是它们在组织中的空间关联。我们应用ISCHIA来分析细胞类型的共现，人类溃疡性结肠炎患者的Visium数据集中的配体和受体，并在匹配的基于杂交的数据上以单细胞分辨率验证了我们的发现。我们发现了涉及M细胞和成纤维细胞的炎症诱导的细胞网络，以及在发炎的人类结肠中富集的配体-受体相互作用，以及它们相关的基因特征。我们的结果突出了假设的产生能力和空间转录组学数据上的共生分析的广泛适用性。
