Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.
Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.
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DOI：
10.1093/nar/gkac333
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发表时间：
2022-08-12
影响因子：
14.9
通讯作者：
De S
中科院分区：
文献类型：
--
作者：
Ghaddar B;De S
细胞间的相互作用是组织组织和多细胞生命的基本组成部分。我们开发了Neighbor-seq，这是一种从大规模并行单细胞测序数据中的未解离细胞组分中识别和注释直接细胞-细胞相互作用和相关配体-受体信号传导的结构的方法。Neighbor-seq可准确识别不同组织类型的显微解剖学特征，如小肠上皮、终末呼吸道和脾白色髓。它还捕获了胰腺和皮肤肿瘤中癌症-免疫-基质细胞通信的不同拓扑结构，这与空间转录组学数据中观察到的模式一致。Neighbor-Seq快速且可扩展。它从常规的单细胞数据中得出推论，不需要关于样品细胞类型或多联体的先验知识。Neighbor-seq提供了一个研究健康和疾病中器官水平细胞相互作用组的框架，弥合了单细胞和空间转录组学之间的差距。相邻序列法
Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell–cell interactions and relevant ligand–receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics. Neighbor-seq method.
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