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掌握利用细胞条形码技术探索癌症异质性

Mastering the use of cellular barcoding to explore cancer heterogeneity

原文发布日期:2022-08-18

DOI: 10.1038/s41568-022-00500-2

类型: Review Article

开放获取: 否

英文摘要:

摘要翻译: 

原文链接:

文章:

掌握利用细胞条形码技术探索癌症异质性

Mastering the use of cellular barcoding to explore cancer heterogeneity

原文发布日期:2022-08-18

DOI: 10.1038/s41568-022-00500-2

类型: Review Article

开放获取: 否

 

英文摘要:

Tumours are often composed of a multitude of malignant clones that are genomically unique, and only a few of them may have the ability to escape cancer therapy and grow as symptomatic lesions. As a result, tumours with a large degree of genomic diversity have a higher chance of leading to patient death. However, clonal fate can be driven by non-genomic features. In this context, new technologies are emerging not only to track the spatiotemporal fate of individual cells and their progeny but also to study their molecular features using various omics analysis. In particular, the recent development of cellular barcoding facilitates the labelling of tens to millions of cancer clones and enables the identification of the complex mechanisms associated with clonal fate in different microenvironments and in response to therapy. In this Review, we highlight the recent discoveries made using lentiviral-based cellular barcoding techniques, namely genetic and optical barcoding. We also emphasize the strengths and limitations of each of these technologies and discuss some of the key concepts that must be taken into consideration when one is designing barcoding experiments. Finally, we suggest new directions to further improve the use of these technologies in cancer research.

 

摘要翻译: 

肿瘤通常由许多基因组独特的恶性克隆组成,其中仅有少数具备逃脱癌症治疗的能力并发展成有症状的病变。因此,基因组多样性程度高的肿瘤导致患者死亡的几率更大。然而,克隆命运可能受非基因组特征驱动。在此背景下,新兴技术不仅能追踪单个细胞及其后代的时空命运,还能通过多组学分析研究其分子特征。特别是近期发展的细胞条形码技术,可实现对数以万计至数百万个癌症克隆的标记,并能识别不同微环境及治疗响应中与克隆命运相关的复杂机制。本综述重点介绍了基于慢病毒的细胞条形码技术(即遗传条形码与光学条形码)取得的最新发现,同时阐述了各项技术的优势与局限性,并讨论了设计条形码实验时需考量的关键概念。最后,我们提出了进一步优化这些技术在癌症研究中应用的新方向。

 

原文链接:

Mastering the use of cellular barcoding to explore cancer heterogeneity

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