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在干草堆中找针:利用单细胞转录组学和染色质可及性分析解析肿瘤异质性。

Finding needles in a haystack: dissecting tumor heterogeneity with single-cell transcriptomic and chromatin accessibility profiling.

机构信息

Genetics Department, Stanford University School of Medicine, Stanford, CA, United States.

Genetics Department, Stanford University School of Medicine, Stanford, CA, United States.

出版信息

Curr Opin Genet Dev. 2021 Feb;66:36-40. doi: 10.1016/j.gde.2020.11.008. Epub 2021 Jan 5.

Abstract

Tumor evolution often results in a wealth of heterogeneous cancer cell types within a single tumor - heterogeneity that can include epigenetic and gene expression changes that are impossible to identify from histological features alone. The invasion of cancer cells into nearby healthy tissue, accompanied by the infiltration of responding immune cells, results in an even more complex architecture of tumor and non-tumor cells. However, bulk genomics-based methods can only assay the aggregate transcriptomic and epigenetic profiles across all of this rich cellular diversity. Such bulk averaging hides small subpopulations of tumor cells with unique phenotypes that might result in therapeutic resistance or metastatic progression. The advent of single-cell-based genomics assays for measuring transcription and chromatin accessibility - particularly scRNA-seq and scATAC-seq - has enabled the dissection of cell-types within tumors at a scale and resolution capable of unraveling the epigenetic and gene expression programs of rare and unique cellular subpopulations. This Review focuses on recent advances in scRNA-seq and scATAC-seq technologies and their application to cancer biology in the context of furthering our understanding of tumor heterogeneity.

摘要

肿瘤进化通常会导致单个肿瘤内存在大量异质性的癌细胞类型——这种异质性包括表观遗传和基因表达的变化,仅凭组织学特征是无法识别的。癌细胞侵入附近的健康组织,伴随着反应性免疫细胞的浸润,导致肿瘤和非肿瘤细胞的结构更加复杂。然而,基于基因组的批量方法只能检测所有这些丰富的细胞多样性的总转录组和表观遗传谱。这种批量平均掩盖了具有独特表型的肿瘤细胞的小亚群,这些细胞可能导致治疗耐药或转移进展。单细胞转录组和染色质可及性的基于基因组的测定方法的出现——特别是 scRNA-seq 和 scATAC-seq——使我们能够在能够揭示罕见和独特细胞亚群的表观遗传和基因表达程序的规模和分辨率内对肿瘤内的细胞类型进行剖析。这篇综述重点介绍了 scRNA-seq 和 scATAC-seq 技术的最新进展及其在癌症生物学中的应用,以进一步了解肿瘤异质性。

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