单细胞病毒载量检测原理｜单细胞转录组技术应用

2020-11-16 本文已影响0人 seqyuan

作者：ahworld
链接：单细胞病毒载量检测原理｜单细胞转录组技术应用
来源：微信公众号-seqyuan
著作权归作者所有，任何形式的转载都请联系作者。

目前研究新冠病毒的单细胞转录组文献已有很多，单细胞内病毒载量的确定为定量分析病毒的细胞反应提供了基础。有些流行病研究者既做单细胞免疫研究也关注病毒单细胞载量的检测，那就有两个问题要问：

单细胞转录组技术为什么能检测每个细胞内的新冠病毒载量呢？
流行的单细胞转录组技术都能检测哪些类别的病毒在单细胞内的载量呢？

10X Genomics单细胞转录组建库原理

目前应用比较广泛的大规模平行单细胞转录组建库技术是10X Genomics公司下面的两个试剂盒。

Chromium Single Cell 3’ library preparation kit
Chromium Single Cell 5’ library preparation kit

这两个试剂盒的扩增都涉及Poly-dT引物，只有带有poly(A)尾的RNA才能够被有效扩增。

3’试剂盒的建库原理我们在《10X单细胞3'转录组建库原理》有过详细介绍。
5’试剂盒的建库原理也是需要Poly-dT引物的，如下图：

为什么10X建库试剂盒能捕获到新冠病毒reads

新冠病毒属于冠状病毒的一种，其遗传物质核酸类型为正链RNA病毒，其基因组核酸(gRNA)带有poly(A)尾，并且其转录组(Transcriptome)RNA也带有poly(A)

所以理论上10X Genomics单细胞转录组的两种试剂盒都能捕获到到单细胞内新冠病毒的genomic RNA和转录产物。只是5‘试剂盒测的是RNA的5’端，所以理论上单细胞内ORF1a的reads数从一定程度上代表了单细胞内病毒的载量。

目前已发表的单细胞技术研究新冠病毒的文章大多采用的Chromium Single Cell 5’ library preparation kit建库，这样除了单细胞转录组和病毒载量的信息外，更可以通过V(D)J分析单细胞免疫组库，例如下面这篇文献：

Liao, M., Liu, Y., Yuan, J. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med (2020). https://doi.org/10.1038/s41591-020-0901-9

采用5’建库既能得到各细胞的基因表达数据，对细胞进行分类，又能够得到每一个细胞内的病毒含量，使我们了解不同细胞类型的病毒含量。就像下面这张图一样：

病毒核酸类型

常见病毒核酸类型如下表：

这些不同核酸类型的病毒都有哪些能够用单细胞转录组的技术研究期在单个细胞中的载量呢？

如果是用10X Genomics的Chromium Single Cell 3'和5' library kit，很显然RAN病毒中有3'端有poly(A)尾的类型，和能够转录带poly(A)尾mRNA的DNA病毒能够被检测到。如果是负链RNA病毒，因其核酸大多不具有以上两种结构，所以很难被检测到。

单细胞转录组技术检测正链RNA病毒和双链DNA病毒检测示例

下面这篇文章应用Chromium Single Cell 5’ library preparation kit分别对对新冠病毒和HBV病毒前染的细胞进行了单细胞转录组建库，提供了【Viral-Track】工具对细胞中的新冠病毒和HBV病毒的转录物进行了成功的检测。

Bost, P., Giladi, A., Liu, Y., Bendjelal, Y., Xu, G., David, E., Blecher-Gonen, R., Cohen, M., Medaglia, C., Li, H., Deczkowska, A., Zhang, S., Schwikowski, B., Zhang, Z., Amit, I., Host-viral infection maps reveal signatures of severe COVID-19 patients, Cell (2020), doi: https:// doi.org/10.1016/j.cell.2020.05.006.

这篇文章也列出了不能被有效检测的其他病毒类型，我们直接摘录文章内容如下：

Viral-Track LIMITATIONS

The absence of a poly(A) tail at the end of viral RNA molecules can significantly decrease their capture rate efficiency in current scRNA-seq techniques, as shown by the LCMV example. This may hinder Viral-Track’s ability to robustly identify infected cells, or discern differential expression between infected and bystander cells in such viruses.
Other properties of the viral RNA molecules;

absence/presence of 5’ capping, nucleotide composition, or dependence on RNA binding proteins may also affect capture efficiency, and as the technology develops, further research will focus on the classification of molecular features that facilitate or prevent virus identification by scRNA-seq.

Notably, non poly(A) based scRNA-seq techniques, such as RamDA-seq (Hayashi et al., 2018) can be potentially used when profiling these datasets.

Another limiting factor for Viral-Track’s applicability is the potential scarcity of viral reads and infected cells in the sample. As shown in our analysis of SARS-CoV-2 infected samples, only a limited number of viral reads are detected in some of the samples. This may be due to the to specific stage of the disease (He et al., 2020), or sampling biases favoring mainly the lung immune populations, with lower representation of non-immune cells, which are the primary targets of the virus.

Therefore, future COVID-19 scRNA-seq studies should consider this limitation in their experimental design, and aim for a better representation of the upper respiratory tissue and the lung parenchyma.
Alternative approaches may rely on index sorting and single-cell transcriptome-trained sorting to design optimal gating strategies for capturing and enriching the stromal populations.

其他检测病毒的单细胞转录组技术

对于没有poly(A)尾结构的病毒，如果想从单个细胞内给出病毒的定量结果，可以尝试上文提到的RamDA-seq (Hayashi et al., 2018)或者SUPeR-seq，只是在检测单细胞的通量上没法和10X Genomics的kit相比。

从单细胞转录组数据中定量分析病毒的方法

需要将病毒和被侵染物种的参考基因组合并建立比对的index，再把单细胞转录组的数据比对到这个合并的基因组。例如(Liao et al., Nat Med, 2020)中写的那样

To detect SARS-CoV-2 reads, a customized reference was built by integrating human GRCh38 and SARS-CoV-2 genome (severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome, GenBank MN908947.3).

(Bost et al., cell, 2020)用了相同的方法

Fan X, Zhang X, Wu X, et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos[J]. Genome biology, 2015, 16(1): 148.