Single-cell sequencing analysis within biologically relevant dimensions

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2024-01-09 DOI:10.1016/j.cels.2023.12.005
Robert Kousnetsov, Jessica Bourque, Alexey Surnov, Ian Fallahee, Daniel Hawiger
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Abstract

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer’s disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

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在生物相关维度内进行单细胞测序分析
目前,转录组学和表观基因组学单细胞分析的主要方法依赖于受限于缩减维度和算法推导及注释集群的僵化视角。在这里,我们开发了 Seqtometry(测序到测量),这是一种基于生物相关维度的单细胞分析策略,通过使用多个基因组(特征)进行高级评分来检查各器官系统的基因表达和可及性。通过只利用特定特征形式的信息,Sequtometry 避开了无监督聚类和聚类的个体注释。相反,Sequtometry 将定性和定量细胞类型鉴定与实验或疾病条件下各种生物过程的具体特征描述相结合。通过 Seqtometry 对各种免疫细胞以及来自不同器官和疾病诱发状态(包括多发性骨髓瘤和阿尔茨海默病)的其他细胞进行综合分析,超越了相应的基于聚类的分析结果。我们建议将 Seqtometry 作为一种适用于基础和临床研究的单细胞测序分析方法。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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