小柯机器人

在单细胞水平分析比较细胞间通讯，这一成果由美国斯坦福大学医学院Aaron J. Wilk课题组经过不懈努力而取得。该项研究成果发表在2023年5月11日出版的《自然—生物技术》上。

研究人员研发了Scriabin，这是一种灵活且可扩展的框架，用于在单细胞水平对细胞-细胞通讯进行比较分析，而无需细胞聚集或采样即可进行。研究人员使用多个已发表的图谱级数据集、遗传扰动筛选和直接实验验证来证明Scriabin能准确地重现预期的细胞-细胞通信边缘，并识别了被聚集方法掩盖的通信网络。

此外，使用空间转录组学数据研究人员表明Sciabin可以仅从解离的数据中发现相互作用的空间特征。最后，研究人员利用纵向数据集演示了应用程序，以追踪在时间点之间运行的细胞通信路径。该方法代表了一种广泛适用的策略，以揭示健康和疾病中生态位-表型关系的完整结构。

据介绍，从单细胞RNA测序数据推断细胞间通讯是揭示细胞间通讯途径的强大技术，但现有方法是在细胞类型或细胞簇水平上进行分析，从而丢弃了单细胞水平的信息。

附：英文原文

Title: Comparative analysis of cell–cell communication at single-cell resolution

Author: Wilk, Aaron J., Shalek, Alex K., Holmes, Susan, Blish, Catherine A.

Issue&Volume: 2023-05-11

Abstract: Inference of cell–cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell–cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell–cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche–phenotype relationships in health and disease.

DOI: 10.1038/s41587-023-01782-z

Source: https://www.nature.com/articles/s41587-023-01782-z

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex