从组织到细胞类型再返回:组织结构的单细胞基因表达分析

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI:10.1146/ANNUREV-BIODATASCI-080917-013452
Xi Chen, S. Teichmann, K. Meyer
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引用次数: 77

摘要

随着最近单细胞基因组学,特别是单细胞基因表达分析的革命性发展,现在可以在单细胞水平上研究组织,而不必依赖于大量测量的数据。在这里,我们回顾了单细胞RNA测序(scRNA-seq)方案的快速发展,这些方案具有对组织或生物体内所有细胞类型进行无偏鉴定和分析的潜力。此外,基因表达空间谱的新方法使我们能够将单个细胞和细胞类型映射回器官的三维背景。深入的单细胞和空间基因表达数据的结合将以前所未有的细节揭示组织结构,产生丰富的生物学知识并更好地了解许多疾病。
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From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture
With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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