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背景介紹

細胞進程受大量細胞內在和外在環境的信號調控,具有高度動態性和復雜性。因此,在不破壞細胞環境的情況下跟蹤其內生環境中的多樣化和復雜過程仍然具有挑戰性。基于基因組編輯的DNA writing技術的最新進展,使活細胞內的信息編碼和連續記錄得以實現1-3。

DNA writing知多少?

這些技術有望克服研究動態生物事件(如重建細胞譜系關系、細胞內和細胞外信號提示以及基因表達動力學)方面的局限性。目前已開發出幾種DNA writing策略,包括單鏈DNA編輯4、重組酶5-6、聚合酶7、CRISPR-Cas98-11以及DNA堿基編輯器12。最近,由Cas1和Cas2介導的CRISPR spacers捕獲作為一種潛在的DNA writing技術受到越來越多的關注13-17。

技術原理

Record-seq是將轉錄事件記錄到質粒傳播的CRISPR陣列中。在細菌生長過程中,RNA驅動的spacers整合到質粒編碼的CRISPR陣列中,由融合到Cas1的RT domain逆轉錄,從而實現以質粒DNA形式永久存儲轉錄事件(圖1a)。Record-seq流程需要提取這種質粒DNA,并通過選擇性擴增、大小選擇和深度測序來檢索spacers(圖1b)。由于只有一小部分CRISPR陣列在實驗過程中獲得新的spacers,Record-seq采用了新開發的程序來選擇性地放大新獲得的protospacers,命名為SENECA(selective amplification of expanded CRISPR arrays,選擇性地放大擴展CRISPR數組)。一旦新獲取的spacers在深度測序reads中被識別,就可以將其與參考基因組進行比對、量化并提供累積轉錄表達的豐度。

技術優點

(1)Record-seq的一個主要優點是能夠記錄長時間發生的瞬態轉錄響應。例如可以用Record-seq檢測短暫暴露于細胞外刺激(如百草枯),但常規的RNA-seq則做不到。

(2)當CRISPR陣列從母細胞傳遞到子細胞時,轉錄信息反映了相同的細胞群。因此,Record-seq是一種基于種群的全局(global)轉錄,因此不用考慮單個細胞之間轉錄的變化或偶然性。

(3)FsRT-Cas1_Cas2復合體是Record-seq技術的核心,它優先從豐富的轉錄本中獲取spacers,優先選擇富含AT的區域。因此,Record-seq可以實現對積累的轉錄表達的無偏好和平行量化。因此,Record-seq可以應用于研究廣泛的轉錄反應。與以往基于大腸桿菌DNA適應Cas1_Cas2復合物的錄音技術不同,實驗設計不需要開發攜帶信號誘導啟動子的特定傳感器菌株。

(4)除了作為研究轉錄反應的有力工具外,Record-seq通過SENECA降低了測序成本。同時,Record-seq提高了可以研究間隔采集的分辨率,從而促進CRISPR-Cas生物學的機制研究。

技術局限性

當前記錄能力受到FsRT-Cas1-Cas2的 CRISPR spacers采集效率的限制。目前,每個SENECA反應需要大約1.2×109個細胞來量化至少10,000個唯一發生的間隔序列,才能產出測序深度所需的reads用于下游分析。提高間隔采集的效率可以克服這一限制并最終用較少量的細胞進行記錄。

DNA writing知多少?

圖1 | 從RNA獲取CRISPR spacers實現轉錄記錄

(a)Record-seq使用來自Fusicatenibacter saccharivorans的RNA-acquiring RT-Cas1-Cas2復合體將轉錄信息編碼到plasmid-bome CRISPR陣列中。轉錄記錄由CRISPR spacers直接從細胞內RNA采集生成,然后通過FsRT-Cas1-Cas2的RT domain對RNA protospacers進行逆轉錄。(b)提取質粒DNA,然后選擇性地擴增expanded CRISPR陣列(SENECA)和深度測序,從而實現轉錄史的重建。


參考文獻

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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