用測序檢測轉座酶可及的染色質ATAC-seq,英文全稱為Assay for Transposase-Accessible Chromatin using sequencing)是用於檢測基因組染色質可及性的分子生物學手段 [1]。此方法使用高活性突變型Tn5轉座酶將擴增引物插入開放的染色質,之後對這些區域進行擴增,以知道哪些區域是開放(可及)的。此方法是MNase-seqFAIRE-SeqDNase-Seq的替代方法,於2013年發表[2][3][4]。對與表觀基因組的分析,ATAC-seq比DNase-seq和MNase-seq都更快、更靈敏[2][3][4]

描述

ATAC-seq通過使用高活性突變型Tn5轉座酶探測開放染色質來識別可訪問的DNA區域。DNA測序時需要進行PCR擴增,酶需先與引物結合才能進行擴增。Tn5轉座酶可將引物插入基因組的開放區域[2][5],以擴增這些區域。天然存在的轉座酶活性較低,ATAC-seq使用突變型轉座酶具有高活性[6]

在稱為「標記化」的過程中,Tn5轉座酶使用測序接頭切割和標記雙鏈DNA[7][8]。然後對標記的DNA片段進行純化、PCR擴增,並使用下一代測序進行測序[8]。然後可以使用測序讀取片段來推斷可訪問性增加的區域以及繪製轉錄因子結合位點和核小體位置的區域[2]。 在單核苷酸分辨率下,一個區域的讀取片段個數與染色質的開放程度相關[2]。ATAC-seq不需要像FAIRE-seq那樣需要使用超聲處理或苯酚-氯仿提純[9];不需要像ChIP-seq那樣使用抗體[10];也不需要像MNase-seq或DNase-seq那樣進行敏感的酶消化[11]。可以在三個小時內完成ATAC-seq的準備[12]

應用

 
ATAC-Seq的應用

ATAC-Seq分析可以用於研究許多染色質可及性特徵。最常見的用途是核小體定位實驗[3],也可以可用於定位轉錄因子結合位點[13],適用於定位DNA甲基化位點[14],或與其他測序技術相結合[15]

高分辨率增強子映射的用途包括研究發育過程中增強子使用的進化差異(例如黑猩猩和人類之間的比較)[16]和揭示血細胞分化過程中使用的譜系特異性增強子圖[17]

ATAC-Seq也被應用於定義人類癌症中全基因組染色質可及性情況[18],揭示了黃斑變性中染色質可及性的整體下降[19]。可以在 ATAC-seq 上運用計算足跡方法,以找到具有細胞特異性活性的細胞特異性結合位點和轉錄因子[13]

單細胞ATAC-seq

為了進行單細胞分析,有人已對ATAC-seq步驟進行了修改,以進行scATAC-seq(sc代表「單細胞」)。微流控技術可用於分離單個核並單獨執行ATAC-seq反應[12]。通過這種方法,單個細胞在標記之前被微流體裝置或液體沉積系統捕獲[12][20]。一種不需要單細胞分離的替代技術是組合細胞索引[21]。該技術使用條形碼來測量數千個單個細胞中染色質的可及性;它可以在每個實驗中生成10,000-100,000個細胞的表觀基因組圖譜[22]。但是組合細胞索引需要額外的、定製設計的設備或大量定製的、修改過的Tn5[23]。最近,有人開發了稱為sci-CAR的混合條形碼方法,允許對單細胞的染色可及性和基因表達進行聯合分析[24]

在進行scATAC-seq的計算分析時,可以以每個開放染色質區域的讀取片段數建立計數矩陣。可通過偽多細胞的ATAC-seq數據的標準峰值來定義開放染色質區域。隨後,可使用PCA進行數據降維,對細胞進行聚類[20]。scATAC-seq矩陣可能非常大(數十萬個區域)並且非常稀疏,即不到3%的條目是非零的[25]。 因此,計數矩陣的填補是另一個關鍵步驟。與多細胞ATAC-seq一樣,scATAC-seq可以找到調節因子,如控制細胞基因表達的轉錄因子。這可以通過查看圍繞轉錄因子模式序列的讀取片段個數[26]或足跡分析[25]來實現。

參考資料

  1. ^ Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature Methods. December 2013, 10 (12): 1213–8. PMC 3959825 . PMID 24097267. doi:10.1038/nmeth.2688. 
  2. ^ 2.0 2.1 2.2 2.3 2.4 Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Current Protocols in Molecular Biology. January 2015, 109: 21.29.1–21.29.9. PMC 4374986 . PMID 25559105. doi:10.1002/0471142727.mb2129s109. 
  3. ^ 3.0 3.1 3.2 Schep AN, Buenrostro JD, Denny SK, Schwartz K, Sherlock G, Greenleaf WJ. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Research. November 2015, 25 (11): 1757–70. PMC 4617971 . PMID 26314830. doi:10.1101/gr.192294.115. 
  4. ^ 4.0 4.1 Song L, Crawford GE. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harbor Protocols. February 2010, 2010 (2): pdb.prot5384. PMC 3627383 . PMID 20150147. doi:10.1101/pdb.prot5384. 
  5. ^ Bajic, Marko; Maher, Kelsey A.; Deal, Roger B. Identification of Open Chromatin Regions in Plant Genomes Using ATAC-Seq. Plant Chromatin Dynamics. Methods in Molecular Biology 1675. 2018: 183–201. ISBN 978-1-4939-7317-0. ISSN 1064-3745. PMC 5693289 . PMID 29052193. doi:10.1007/978-1-4939-7318-7_12. 
  6. ^ Reznikoff WS. Transposon Tn5. Annual Review of Genetics. 2008, 42 (1): 269–86. PMID 18680433. doi:10.1146/annurev.genet.42.110807.091656. 
  7. ^ Adey, Andrew. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biology. December 2010, 11 (12): R119. PMC 3046479 . PMID 21143862. doi:10.1186/gb-2010-11-12-r119. 
  8. ^ 8.0 8.1 Picelli S, Björklund AK, Reinius B, Sagasser S, Winberg G, Sandberg R. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Research. December 2014, 24 (12): 2033–40. PMC 4248319 . PMID 25079858. doi:10.1101/gr.177881.114. 
  9. ^ Simon JM, Giresi PG, Davis IJ, Lieb JD. Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA. Nature Protocols. January 2012, 7 (2): 256–67. PMC 3784247 . PMID 22262007. doi:10.1038/nprot.2011.444. 
  10. ^ Savic D, Partridge EC, Newberry KM, Smith SB, Meadows SK, Roberts BS, et al. CETCh-seq: CRISPR epitope tagging ChIP-seq of DNA-binding proteins. Genome Research. October 2015, 25 (10): 1581–9. PMC 4579343 . PMID 26355004. doi:10.1101/gr.193540.115. 
  11. ^ Hoeijmakers, Wieteke Anna Maria; Bártfai, Richárd. Characterization of the Nucleosome Landscape by Micrococcal Nuclease-Sequencing (MNase-seq). Chromatin Immunoprecipitation. Methods in Molecular Biology 1689. 2018: 83–101. ISBN 978-1-4939-7379-8. ISSN 1064-3745. PMID 29027167. doi:10.1007/978-1-4939-7380-4_8. 
  12. ^ 12.0 12.1 12.2 Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. July 2015, 523 (7561): 486–90. Bibcode:2015Natur.523..486B. PMC 4685948 . PMID 26083756. doi:10.1038/nature14590. 
  13. ^ 13.0 13.1 Li, Zhijian; Schulz, Marcel H.; Look, Thomas; Begemann, Matthias; Zenke, Martin; Costa, Ivan G. Identification of transcription factor binding sites using ATAC-seq. Genome Biology. 26 February 2019, 20 (1): 45. doi:10.1186/s13059-019-1642-2 . 
  14. ^ Spektor R, Tippens ND, Mimoso CA, Soloway PD. methyl-ATAC-seq measures DNA methylation at accessible chromatin. Genome Research. June 2019, 29 (6): 969–977. PMC 6581052 . PMID 31160376. doi:10.1101/gr.245399.118. 
  15. ^ Hendrickson, David G.; Soifer, Ilya; Wranik, Bernd J.; Botstein, David; Scott McIsaac, R., Simultaneous Profiling of DNA Accessibility and Gene Expression Dynamics with ATAC-Seq and RNA-Seq, Methods in Molecular Biology 1819, Springer New York: 317–333, 2018, ISBN 9781493986170, PMID 30421411, doi:10.1007/978-1-4939-8618-7_15 
  16. ^ Prescott SL, Srinivasan R, Marchetto MC, Grishina I, Narvaiza I, Selleri L, et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell. September 2015, 163 (1): 68–83. PMC 4848043 . PMID 26365491. doi:10.1016/j.cell.2015.08.036. 
  17. ^ Lara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, et al. Immunogenetics. Chromatin state dynamics during blood formation. Science. August 2014, 345 (6199): 943–9. PMC 4412442 . PMID 25103404. doi:10.1126/science.1256271. 
  18. ^ Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, et al. The chromatin accessibility landscape of primary human cancers. Science. October 2018, 362 (6413): eaav1898. Bibcode:2018Sci...362.1898C. PMC 6408149 . PMID 30361341. doi:10.1126/science.aav1898. 
  19. ^ Wang J, Zibetti C, Shang P, Sripathi SR, Zhang P, Cano M, et al. ATAC-Seq analysis reveals a widespread decrease of chromatin accessibility in age-related macular degeneration. Nature Communications. April 2018, 9 (1): 1364. Bibcode:2018NatCo...9.1364W. PMC 5893535 . PMID 29636475. doi:10.1038/s41467-018-03856-y. 
  20. ^ 20.0 20.1 Mezger A, Klemm S, Mann I, Brower K, Mir A, Bostick M, et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nature Communications. September 2018, 9 (1): 3647. Bibcode:2018NatCo...9.3647M. PMC 6128862 . PMID 30194434. doi:10.1038/s41467-018-05887-x. 
  21. ^ Cusanovich, Darren. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. May 2015, 348 (6237): 910–914. Bibcode:2015Sci...348..910C. PMC 4836442 . PMID 25953818. doi:10.1126/science.aab1601. 
  22. ^ Lareau CA, Duarte FM, Chew JG, Kartha VK, Burkett ZD, Kohlway AS, Pokholok D, Aryee MJ, et al. Droplet-based combinatorial indexing for massive scale single-cell epigenomics. bioRxiv. 2019. doi:10.1101/612713 . 
  23. ^ Chen X, Miragaia RJ, Natarajan KN, Teichmann SA. A rapid and robust method for single cell chromatin accessibility profiling. Nature Communications. December 2018, 9 (1): 5345. Bibcode:2018NatCo...9.5345C. PMC 6297232 . PMID 30559361. doi:10.1038/s41467-018-07771-0. 
  24. ^ Cao, Junyue; Cusanovich, Darren A.; Ramani, Vijay; Aghamirzaie, Delasa; Pliner, Hannah A.; Hill, Andrew J.; Daza, Riza M.; McFaline-Figueroa, Jose L.; Packer, Jonathan S.; Christiansen, Lena; Steemers, Frank J. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science. 2018-09-28, 361 (6409): 1380–1385 [2021-08-30]. ISSN 0036-8075. PMID 30166440. doi:10.1126/science.aau0730 . (原始內容存檔於2021-08-30) (英語). 
  25. ^ 25.0 25.1 Li, Zhijian; Kuppe, Christoph; Cheng, Mingbo; Menzel, Sylvia; Zenke, Martin; Kramann, Rafael; et al. scOpen: chromatin-accessibility estimation of single-cell ATAC data. bioRxiv. 2019-12-05: 865931 [2021-08-30]. doi:10.1101/865931 . (原始內容存檔於2021-08-30) (英語). 
  26. ^ Schep AN, Wu B, Buenrostro JD, Greenleaf WJ. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nature Methods. October 2017, 14 (10): 975–978. PMC 5623146 . PMID 28825706. doi:10.1038/nmeth.4401. 

外部連結