小柯机器人

美国俄勒冈健康与科学大学Zheng Xia课题组通过整合大量和单细胞测序数据实现表型相关亚群的鉴别。2021年11月11日，《自然—生物技术》杂志在线发表了这项成果。

研究人员提出了Scissor，一种从单细胞数据中识别与特定表型相关的细胞亚群的方法。Scissor通过首先量化每个单细胞和每个批量样本之间的相似性来整合表型相关的批量表达数据和单细胞数据。然后，它优化与样本表型相关矩阵的回归模型，从而确定相关的亚群。

通过应用于肺癌单细胞RNA测序（scRNA-seq）数据集，Scissor确定了与较差的生存率和TP53突变有关的细胞亚群。在黑色素瘤中，Scissor发现了一个T细胞亚群，其PDCD1/CTLA4的低表达和TCF7的高表达与免疫疗法反应有关。除了癌症，Scissor在解释面肌萎缩症和阿尔茨海默病的数据集方面也很有效。Scissor通过利用表型和批量组学数据集，从单细胞测定中识别出生物和临床相关的细胞亚群。

据介绍，scRNA-seq可以区分异质组织中的细胞类型、状态和系谱。然而，目前的单细胞数据不能直接将细胞集群与特定的表型联系起来。

附：英文原文

Title: Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data

Author: Sun, Duanchen, Guan, Xiangnan, Moran, Amy E., Wu, Ling-Yun, Qian, David Z., Schedin, Pepper, Dai, Mu-Shui, Danilov, Alexey V., Alumkal, Joshi J., Adey, Andrew C., Spellman, Paul T., Xia, Zheng

Issue&Volume: 2021-11-11

Abstract: Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer’s disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.

DOI: 10.1038/s41587-021-01091-3

Source: https://www.nature.com/articles/s41587-021-01091-3

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