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背景介绍

细胞进程受大量细胞内在和外在环境的信号调控,具有高度动态性和复杂性。因此,在不破坏细胞环境的情况下跟踪其内生环境中的多样化和复杂过程仍然具有挑战性。基于基因组编辑的DNA writing技术的最新进展,使活细胞内的信息编码和连续记录得以实现1-3。

DNA writing知多少?

这些技术有望克服研究动态生物事件(如重建细胞谱系关系、细胞内和细胞外信号提示以及基因表达动力学)方面的局限性。目前已开发出几种DNA writing策略,包括单链DNA编辑4、重组酶5-6、聚合酶7、CRISPR-Cas98-11以及DNA碱基编辑器12。最近,由Cas1和Cas2介导的CRISPR spacers捕获作为一种潜在的DNA writing技术受到越来越多的关注13-17。

技术原理

Record-seq是将转录事件记录到质粒传播的CRISPR阵列中。在细菌生长过程中,RNA驱动的spacers整合到质粒编码的CRISPR阵列中,由融合到Cas1的RT domain逆转录,从而实现以质粒DNA形式永久存储转录事件(图1a)。Record-seq流程需要提取这种质粒DNA,并通过选择性扩增、大小选择和深度测序来检索spacers(图1b)。由于只有一小部分CRISPR阵列在实验过程中获得新的spacers,Record-seq采用了新开发的程序来选择性地放大新获得的protospacers,命名为SENECA(selective amplification of expanded CRISPR arrays,选择性地放大扩展CRISPR数组)。一旦新获取的spacers在深度测序reads中被识别,就可以将其与参考基因组进行比对、量化并提供累积转录表达的丰度。

技术优点

(1)Record-seq的一个主要优点是能够记录长时间发生的瞬态转录响应。例如可以用Record-seq检测短暂暴露于细胞外刺激(如百草枯),但常规的RNA-seq则做不到。

(2)当CRISPR阵列从母细胞传递到子细胞时,转录信息反映了相同的细胞群。因此,Record-seq是一种基于种群的全局(global)转录,因此不用考虑单个细胞之间转录的变化或偶然性。

(3)FsRT-Cas1_Cas2复合体是Record-seq技术的核心,它优先从丰富的转录本中获取spacers,优先选择富含AT的区域。因此,Record-seq可以实现对积累的转录表达的无偏好和平行量化。因此,Record-seq可以应用于研究广泛的转录反应。与以往基于大肠杆菌DNA适应Cas1_Cas2复合物的录音技术不同,实验设计不需要开发携带信号诱导启动子的特定传感器菌株。

(4)除了作为研究转录反应的有力工具外,Record-seq通过SENECA降低了测序成本。同时,Record-seq提高了可以研究间隔采集的分辨率,从而促进CRISPR-Cas生物学的机制研究。

技术局限性

当前记录能力受到FsRT-Cas1-Cas2的 CRISPR spacers采集效率的限制。目前,每个SENECA反应需要大约1.2×109个细胞来量化至少10,000个唯一发生的间隔序列,才能产出测序深度所需的reads用于下游分析。提高间隔采集的效率可以克服这一限制并最终用较少量的细胞进行记录。

DNA writing知多少?

图1 | 从RNA获取CRISPR spacers实现转录记录

(a)Record-seq使用来自Fusicatenibacter saccharivorans的RNA-acquiring RT-Cas1-Cas2复合体将转录信息编码到plasmid-bome CRISPR阵列中。转录记录由CRISPR spacers直接从细胞内RNA采集生成,然后通过FsRT-Cas1-Cas2的RT domain对RNA protospacers进行逆转录。(b)提取质粒DNA,然后选择性地扩增expanded CRISPR阵列(SENECA)和深度测序,从而实现转录史的重建。


参考文献

1. Schmidt, F. & Platt, R. J. Applications of CRISPR-Cas for synthetic biology and genetic recording. Curr. Opin. Syst. Biol. 5, 9–15 (2017).

2. Farzadfard, F. & Lu, T. K. Emerging applications for DNA writers and molecular recorders. Science 361, 870–875 (2018).

3. Esvelt, K. M. & Wang, H. H. Genome-scale engineering for systems and synthetic biology. Mol. Syst. Biol. 9, 641 (2013).

4. Farzadfard, F. & Lu, T. K. Synthetic biology. Genomically encoded analog memory with precise in vivo DNA writing in living cell populations. Science 346, 1256272 (2014).

5. Roquet, N., Soleimany, A. P., Ferris, A. C., Aaronson, S. & Lu, T. K. Synthetic recombinase-based state machines in living cells. Science 353, aad8559 (2016).

6. Weinberg, B. H. et al. Large-scale design of robust genetic circuits with multiple inputs and outputs for mammalian cells. Nat. Biotechnol. 35, 453–462 (2017).

7. Zamft, B. M. et al. Measuring cation dependent DNA polymerase fidelity landscapes by deep sequencing. PloS ONE 7, e43876 (2012).

8. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

9. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).

10. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

11. Perli, S. D., Cui, C. H. & Lu, T. K. Continuous genetic recording with self-targeting CRISPR-Cas in human cells. Science 353, https://doi.org/10.1126/science.aag0511 (2016).

12. Tang, W. & Liu, D. R. Rewritable multi-event analog recording in bacterial and mammalian cells. Science 360, https://doi.org/10.1126/science.aap8992 (2018).

13. Sheth, R. U., Yim, S. S., Wu, F. L. & Wang, H. H. Multiplex recording of cellular events over time on CRISPR biological tape. Science 358, 1457–1461 (2017).

14. Silas, S. et al. Direct CRISPR spacer acquisition from RNA by a natural reverse transcriptase-Cas1 fusion protein. Science 351, aad4234 (2016).

15. Shipman, S. L., Nivala, J., Macklis, J. D. & Church, G. M. Molecular recordings by directed CRISPR spacer acquisition. Science 353, aaf1175 (2016).

16. Shipman, S. L., Nivala, J., Macklis, J. D. & Church, G. M. CRISPR-Cas encoding of a digital movie into the genomes of a population of living bacteria. Nature 547, 345–349 (2017).

17. Schmidt, F., Cherepkova, M. Y. & Platt, R. J. Transcriptional recording by CRISPR spacer acquisition from RNA. Nature 562, 380–385 (2018).

18. Tanna, T., Schmidt, F., Cherepkova, M.Y., Okoniewski, M. & Platt, R. J. Recording transcriptional histories using Record-seq. Nature Protocol 15, 513-539


原文摘要

It is difficult to elucidate the transcriptional history of a cell using current experimental approaches, as they are destructive in nature and therefore describe only a moment in time. To overcome these limitations, we recently established Record-seq, a technology that enables transcriptional recording by CRISPR spacer acquisition from RNA. The recorded transcriptomes are recovered by SENECA, a method that selectively amplifies expanded CRISPR arrays, followed by deep sequencing. The resulting CRISPR spacers are aligned to the host genome, thereby enabling transcript quantification and associated analyses. Here, we describe the experimental procedures of the Record-seq workflow as well as subsequent data analysis. Beginning with the experimental design, Record-seq data can be obtained and analyzed within 1–2 weeks.


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