单细胞基因组测序,英文文献翻译,基因蛋白

Cancer: One cell at a time

单细胞基因组测序

英文来源：nature 原文作者：Fox, E. J.和Loeb, L. A.

中文翻译：生命奥妙

作者通讯地址：

Fox, E. J. ：Department of Pathology, University of Washington, Seattle, Washington 98195-7750, USA

Loeb, L. A.：[1] Department of Pathology, University of Washington, Seattle, Washington 98195-7750, USA.

[2] Department of Biochemistry, University of Washington.

Single-cell DNA sequencing of two breast-cancer types has shown extensive mutational variation in individual tumours, confirming that generation of genetic diversity may be inherent in how tumours evolve.

对两种类型乳腺癌细胞进行的单细胞DNA测序结果发现，肿瘤细胞内包含各种类型突变，证实了遗传多样性可能会决定肿瘤进展的方向。

Next-generation DNA sequencing has revolutionized the field of cancer genomics1. Although this sequencing can identify the most frequent mutation in a population of cells, it struggles to resolve the mutational diversity and multiple genomes of the individual cells that comprise a tumour. Achieving DNA sequencing down to the resolution of a single cell has been a long-held dream for understanding the cellular heterogeneity that is inherent in many complex biological systems and, in particular, for delineating the mixture of genomes in human cancers2. On page 155 of this issue, Wang et al.3 report an innovative sequencing method, termed nuc-seq, that achieves almost complete sequencing of whole genomes in single cells.

新一代DNA测序技术已经给癌症基因组研究领域带来了革命性的进展。尽管新的测序技术可以检测出大部分经常出现的突变，但是对肿瘤细胞内存在的各类突变及多种基因组类型的分辨率并不高。一直以来，研究者都盼望着DNA测序的分辨率可以达到单个细胞，这对于研究在许多复杂生物系统内存在的细胞异质性非常重要，尤其是对人类肿瘤基因组混合物的研究而言。Wang等人建立了一种新的测序方法，称作nuc-seq，基本实现了对单个细胞完整基因组进行完全测序的目标。

As a cell prepares to divide, it replicates the DNA in its nucleus. By sorting and sequencing only the newly 'doubled' nuclei, nuc-seq takes advantage of this duplication to achieve lower rates of sequencing errors than most previous techniques4. The authors validated their method using targeted duplex sequencing, a protocol that sequences both strands of DNA to identify mutations at exceptionally high accuracy5. They suggest that the use of nuc-seq to sequence single-cell genomes, with validation by targeted deep sequencing, will be instrumental in defining the genomic heterogeneity of cancers.

当细胞准备分裂时，细胞核内DNA会进行复制。通过筛选从而仅仅对新生的“双”核进行测序，nuc-seq可以利用此类复制，获得比之前大部分的测序技术更低的测序错误率。研究者采用靶向双链测序验证了他们的方法，这是一种对DNA双链进行测序，以鉴定突变的

方法，具有超高的准确性。他们认为，使用nuc-seq对单细胞基因组进行测序，然后采用靶向深入测序进行验证，应该就可以将癌症中的基因组异质性检测出来。

To demonstrate this, Wang et al. used their technique to sequence the genomes of multiple single cells from two types of human breast cancer, and found that no two individual tumour cells were genetically identical. As well as the large numbers of mutations that are common to the majority of cells in a tumour, the authors uncovered an even greater number of subclonal and de novo mutations (those that are unique to individual cells). They also present estimates, derived from mathematical models, of mutation rates of single cells within tumours. On the basis of these models, they show that distinct types of DNA alteration seem to accumulate at different rates in different tumours, and suggest that two separate 'mutational clocks' operate in cancer. Large-scale, structural changes in DNA (such as amplification and deletion of large blocks of DNA) probably occur early in tumour development, in punctuated bursts of evolution, whereas point mutations may accumulate more gradually, generating extensive subclonal diversity. The authors' findings indicate that slower-growing 'luminal' breast-cancer cells exhibit relatively low mutation rates, whereas cells from clinically more aggressive,

'triple-negative' breast cancers have mutation rates that are 13 times greater than in normal cells.

为了阐明这一点，Wang等人对两种类型的人乳腺癌中的多个单细胞基因组进行了测序，他们并没有发现在遗传学上一模一样的两个肿瘤细胞。除了证实在肿瘤中的大部分细胞都含有大量的突变，研究者还发现肿瘤细胞中含有更大量的亚克隆及从头突变现象（这在个体细胞中是很罕见的）。他们还使用数学方法对肿瘤组织单个细胞内的突变率进行了估算。基于这些模型与方法，他们发现，不同类型的DNA突变在不同肿瘤中以不同的速度累计，而且，在癌细胞内，有两种相互独立的“突变时钟”在运行。DNA内大规模的结构改变（例如大段DNA的扩增和缺失）可能会发生于肿瘤进展的早期，而点突变则是在进程中逐渐积累的。研究结果表明，生长速度更低的luminal亚型乳腺癌细胞的突变率也相对更低，而来自侵袭性更强的三重阴性乳腺癌细胞内的突变速率，则比正常细胞高出13倍。

Nuc-seq and comparable single-cell sequencing methods6, 7, 8, 9 will allow a more detailed understanding of mutational heterogeneity in individual tumours, and will influence our understanding of how cancers evolve and our approach to their treatment. In particular, mutational diversity within a tumour is likely to be predictive of whether resistance to a particular chemotherapy will emerge during treatment, because mutations in genes that render cells resistant to specific drugs may exist before initiation of therapy. This has previously been documented for the failure of certain molecularly tailored cancer treatments10. Such findings also reinforce the fact that single, bulk sampling of a tumour — a strategy that is commonly used to select targeted therapies — is not representative of the tumour as a whole.

Nuc-seq及其它相关测序方法可以帮助我们对个体肿瘤内的突变异质性有更加透彻的了解，也会促进我们探究癌症进展及相应治疗方法。尤其是肿瘤内的突变多样性有可能被用于预测在药物治疗中，是否会出现药物抗性，因为导致药物抗性的基因突变很可能在治疗

前就已经存在了。这在一些化疗药物无法有效治疗癌症的实际应用中都有所记录。研究结果再一次说明了，单个大样品量的肿瘤组织检测——这是目前普遍用于选择靶向治疗方法的手段——并不能全面检测肿瘤的遗传学特点。

The total number of mutations that a tumour genome carries, including those present in only a small subset of cells, may in fact underlie the aggressiveness of different cancer subtypes. For example, the extent of genetic diversity within a tumour, and its divergence from normal tissue, probably influences the ability of the immune system to distinguish malignant cells from normal cells. Identifying the mechanisms by which cancer cells generate mutational heterogeneity may therefore present previously unexplored therapeutic targets.

肿瘤基因组内所包含的所有突变数目，包括那些只在少数细胞出现的突变，很有可能决定了不同肿瘤亚型侵袭性的高低。例如，肿瘤内遗传多样性的程度及肿瘤组织和正常组织的区别，可能影响着免疫系统对正常细胞和恶性细胞的区分能力。因此，找出癌细胞产生突变异质性的机制，就有可能寻找到新的治疗靶点。

An array of techniques to analyse individual cells has now been developed. It remains to be seen, however, just how robust nuc-seq and other single-cell genomics techniques, such as MALBAC6, will prove to be. For example, many cancer cells are aneuploid (they carry abnormal numbers of chromosomes), and the application of nuc-seq may be restricted to cancers that do not exhibit aneuploidy. Also, although the cost of genome sequencing continues to decline (albeit more slowly now than in the past), the cost of single-cell genomics and the complexities of the bioinformatic analyses involved are still formidable.

对个体细胞进行分析的新技术不断涌现。目前要确定的是，nuc-seq及其它单细胞基因组技术，例如MALBAC等所得到的检测结果的可信度有多高。例如，很多癌细胞是非整倍体细胞（它们的染色体数目异常），但是，nuc-seq技术可能只局限用于检测不含非整倍体细胞的癌症。此外，虽然基因组测序的成本一直在不断下降（事实上下降速度在减慢），但是，单个细胞基因组测序以及由此对复杂生物信息进行分析的成本依然是惊人的。

In our quest to decipher cancer genomes, the advent of single-cell sequencing marks a technical milestone. It crystallizes the concept that the genome of each tumour is dynamic and highly diverse, whether we are comparing cancer genomes between tumours of different patients, between anatomically distinct regions of a tumour within a patient or even between individual cells within the same tumour (Fig. 1). Single-cell sequencing will allow us to detect rare mutant subpopulations hidden within cancers that could expand and lead to drug resistance, and thus to avoid unnecessary and potentially harmful administration of ineffective, toxic therapies. Ultimately, the exceptional plasticity of the tumour genome may well prove to be a key characteristic of cancer11and a major, as yet untapped, therapeutic vulnerability.

在破译癌症基因组的道路上，单个细胞测序技术的出现无疑是一个里程碑。借助这

一技术，我们可以对不同患者的肿瘤细胞基因组进行比较，或者比较同一患者在解剖学上相互独立的器官、组织的肿瘤细胞，甚至可以比较同一肿瘤组织内的个体细胞间的差异（图1）。这些都让我们更加明确地认识到，肿瘤基因组的动态变化及高度多样性的存在。单细胞测序将帮助我们对隐藏于癌细胞内的罕见突变进行检测，这些突变可能会最终导致药物抗性的发生，从而从根本上避免给患者服用无效、甚至有毒性的药物进行治疗。最后一点需要引起注意的是，肿瘤细胞基因组的这种高度多态性，是癌症的一大特征，而且很有可能是我们尚未开始开发利用的潜在治疗靶点。

The genetic characteristics of cancers vary between patients, between primary and metastatic tumours in a single patient, and between the individual cells of a tumour. Wang et al.3 present a single-cell, whole-genome sequencing technique that will allow a better understanding of genetic heterogeneity within individual tumours.

图1 基因组多样性的类型（图片来自nature）。癌症的遗传特点在不同患者身上各不相同，例如，同一患者的原发肿瘤和转移肿瘤存在差异，又或者同一肿瘤组织内的个体细胞间也存在差异。Wang等人建立了一种单细胞全基因组测序技术，使得研究者可以更深入地了解个体肿瘤间的遗传异质性。

原文检索：Edward J.Foxand & Lawrence A.Loeb. (2014) One cell at a time. Nature, 512:143-144. 筱玥编译

原文下载：https://www.360docs.net/doc/bd3457472.html,/nature/jou ... pdf/nature13650.pdf

Cancer: One cell at a time

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