CpG Islands pp 197-208 | Cite as

Assay for Transposase Accessible Chromatin (ATAC-Seq) to Chart the Open Chromatin Landscape of Human Pancreatic Islets

  • Helena Raurell-Vila
  • Mireia Ramos-Rodríguez
  • Lorenzo Pasquali
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1766)

Abstract

The regulatory mechanisms that ensure an accurate control of gene transcription are central to cellular function, development and disease. Such mechanisms rely largely on noncoding regulatory sequences that allow the establishment and maintenance of cell identity and tissue-specific cellular functions.

The study of chromatin structure and nucleosome positioning allowed revealing transcription factor accessible genomic sites with regulatory potential, facilitating the comprehension of tissue-specific cis-regulatory networks. Recently a new technique coupled with high-throughput sequencing named Assay for Transposase Accessible Chromatin (ATAC-seq) emerged as an efficient method to chart open chromatin genome wide. The application of such technique to different cell types allowed unmasking tissue-specific regulatory elements and characterizing cis-regulatory networks. Herein we describe the implementation of the ATAC-seq method to human pancreatic islets, a tissue playing a central role in the control of glucose metabolism.

Key words

Open chromatin Pancreatic islets Gene transcription Epigenetics 

Notes

Acknowledgment

This work was supported by a grant from the Spanish Ministry of Economy and Competiveness (BFU2014-58150-R), the Spanish Diabetes Society and Fundació La Marató de TV3. LP is a recipient of a Ramon y Cajal contract from the Spanish Ministry of Economy and Competitiveness (RYC 2013-12864). Helena Raurell-Vila and Mireia Ramos-Rodríguez contributed equally to this work.

References

  1. 1.
    Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B, Garg K, John S, Sandstrom R, Bates D, Boatman L, Canfield TK, Diegel M, Dunn D, Ebersol AK, Frum T, Giste E, Johnson AK, Johnson EM, Kutyavin T, Lajoie B, Lee BK, Lee K, London D, Lotakis D, Neph S, Neri F, Nguyen ED, Qu H, Reynolds AP, Roach V, Safi A, Sanchez ME, Sanyal A, Shafer A, Simon JM, Song L, Vong S, Weaver M, Yan Y, Zhang Z, Lenhard B, Tewari M, Dorschner MO, Hansen RS, Navas PA, Stamatoyannopoulos G, Iyer VR, Lieb JD, Sunyaev SR, Akey JM, Sabo PJ, Kaul R, Furey TS, Dekker J, Crawford GE, Stamatoyannopoulos JA (2012) The accessible chromatin landscape of the human genome. Nature 489(7414):75–82.  https://doi.org/10.1038/nature11232 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Gaulton KJ, Nammo T, Pasquali L, Simon JM, Giresi PG, Fogarty MP, Panhuis TM, Mieczkowski P, Secchi A, Bosco D, Berney T, Montanya E, Mohlke KL, Lieb JD, Ferrer J (2010) A map of open chromatin in human pancreatic islets. Nat Genet 42(3):255–259.  https://doi.org/10.1038/ng.530 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Pasquali L, Gaulton KJ, Rodriguez-Segui S, Mularoni L, Miguel-Escalada I, Akerman I, Tena JJ, Moran I, Gómez-Marín C, van de Bunt M, Ponsa-Cobas J, Castro N, Nammo T, Cebola I, Garcia-Hurtado J, Maestro MA, Pattou F, Piemonti L, Berney T, Gloyn AL, Ravassard P, Muller F, McCarthy MI, Ferrer J (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk–associated variants. Nat Genet 46(2):136–143.  https://doi.org/10.1038/ng.2870 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Stitzel ML, Sethupathy P, Pearson DS, Chines PS, Song L, Erdos MR, Welch R, Parker SC, Boyle AP, Scott LJ, Margulies EH, Boehnke M, Furey TS, Crawford GE, Collins FS (2010) Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci. Cell Metab 12(5):443–455.  https://doi.org/10.1016/j.cmet.2010.09.012 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Nammo T, Rodriguez-Segui SA, Ferrer J (2011) Mapping open chromatin with formaldehyde-assisted isolation of regulatory elements. Methods Mol Biol 791:287–296.  https://doi.org/10.1007/978-1-61779-316-5_21 CrossRefPubMedGoogle Scholar
  6. 6.
    Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10(12):1213–1218.  https://doi.org/10.1038/nmeth.2688 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lavin Y, Winter D, Blecher-Gonen R, David E, Keren-Shaul H, Merad M, Jung S, Amit I (2014) Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159(6):1312–1326.  https://doi.org/10.1016/j.cell.2014.11.018 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Milani P, Escalante-Chong R, Shelley BC, Patel-Murray NL, Xin X, Adam M, Mandefro B, Sareen D, Svendsen CN, Fraenkel E (2016) Cell freezing protocol suitable for ATAC-Seq on motor neurons derived from human induced pluripotent stem cells. Sci Rep 6:25474.  https://doi.org/10.1038/srep25474 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Shapiro AM, Pokrywczynska M, Ricordi C (2016) Clinical pancreatic islet transplantation. Nat Rev Endocrinol.  https://doi.org/10.1038/nrendo.2016.178
  10. 10.
    Piemonti L, Pileggi A (2013) A 25 years of the Ricordi automated method for islet isolation. CellR 4(1):e128Google Scholar
  11. 11.
    Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc
  12. 12.
    Adey A, Morrison HG, Asan XX, Kitzman JO, Turner EH, Stackhouse B, MacKenzie AP, Caruccio NC, Zhang X, Shendure J (2010) Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol 11(12):R119.  https://doi.org/10.1186/gb-2010-11-12-r119 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Goryshin IY, Miller JA, Kil YV, Lanzov VA, Reznikoff WS (1998) Tn5/IS50 target recognition. Proc Natl Acad Sci U S A 95(18):10716–10721CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Li H, Ruan J, Durbin R (2008) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18(11):1851–1858.  https://doi.org/10.1101/gr.078212.108 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Smith AD, Chung WY, Hodges E, Kendall J, Hannon G, Hicks J, Xuan Z, Zhang MQ (2009) Updates to the RMAP short-read mapping software. Bioinformatics 25(21):2841–2842.  https://doi.org/10.1093/bioinformatics/btp533 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Schatz MC (2009) CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25(11):1363–1369.  https://doi.org/10.1093/bioinformatics/btp236 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Marco-Sola S, Sammeth M, Guigo R, Ribeca P (2012) The GEM mapper: fast, accurate and versatile alignment by filtration. Nat Methods 9(12):1185–1188.  https://doi.org/10.1038/nmeth.2221 CrossRefPubMedGoogle Scholar
  18. 18.
    Rumble SM, Lacroute P, Dalca AV, Fiume M, Sidow A, Brudno M (2009) SHRiMP: accurate mapping of short color-space reads. PLoS Comput Biol 5(5):e1000386.  https://doi.org/10.1371/journal.pcbi.1000386 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Li H, Durbin R (2009) Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 25(14):1754–1760.  https://doi.org/10.1093/bioinformatics/btp324 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25.  https://doi.org/10.1186/gb-2009-10-3-r25 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with bowtie 2. Nat Methods 9(4):357–359.  https://doi.org/10.1038/nmeth.1923 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079.  https://doi.org/10.1093/bioinformatics/btp352 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Carver T, Harris SR, Berriman M, Parkhill J, McQuillan JA (2012) Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 28(4):464–469.  https://doi.org/10.1093/bioinformatics/btr703 CrossRefPubMedGoogle Scholar
  24. 24.
    Huang W, Marth G (2008) EagleView: a genome assembly viewer for next-generation sequencing technologies. Genome Res 18(9):1538–1543.  https://doi.org/10.1101/gr.076067.108 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Wolfsberg TG (2011) Using the NCBI Map Viewer to browse genomic sequence data. Curr Protoc Hum Genet Chapter 18:Unit18 15. doi: https://doi.org/10.1002/0471142905.hg1805s69
  26. 26.
    Lee E, Helt GA, Reese JT, Munoz-Torres MC, Childers CP, Buels RM, Stein L, Holmes IH, Elsik CG, Lewis SE (2013) Web Apollo: a web-based genomic annotation editing platform. Genome Biol 14(8):R93.  https://doi.org/10.1186/gb-2013-14-8-r93 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Mularoni L, Ramos-Rodríguez M, Pasquali L (2017) The pancreatic islet regulome browser. Front Genet 8(13).  https://doi.org/10.3389/fgene.2017.00013
  28. 28.
    Speir ML, Zweig AS, Rosenbloom KR, Raney BJ, Paten B, Nejad P, Lee BT, Learned K, Karolchik D, Hinrichs AS, Heitner S, Harte RA, Haeussler M, Guruvadoo L, Fujita PA, Eisenhart C, Diekhans M, Clawson H, Casper J, Barber GP, Haussler D, Kuhn RM, Kent WJ (2016) The UCSC genome browser database: 2016 update. Nucleic Acids Res 44(D1):D717–D725.  https://doi.org/10.1093/nar/gkv1275 CrossRefPubMedGoogle Scholar
  29. 29.
    Thorvaldsdottir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14(2):178–192.  https://doi.org/10.1093/bib/bbs017 CrossRefPubMedGoogle Scholar
  30. 30.
    Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6):841–842.  https://doi.org/10.1093/bioinformatics/btq033 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Rashid NU, Giresi PG, Ibrahim JG, Sun W, Lieb JD (2011) ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions. Genome Biol 12(7):R67.  https://doi.org/10.1186/gb-2011-12-7-r67 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Boyle AP, Guinney J, Crawford GE, Furey TS (2008) F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics 24(21):2537–2538.  https://doi.org/10.1093/bioinformatics/btn480 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38(4):576–589.  https://doi.org/10.1016/j.molcel.2010.05.004 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9):R137.  https://doi.org/10.1186/gb-2008-9-9-r137 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Chen Y, Negre N, Li Q, Mieczkowska JO, Slattery M, Liu T, Zhang Y, Kim TK, He HH, Zieba J, Ruan Y, Bickel PJ, Myers RM, Wold BJ, White KP, Lieb JD, Liu XS (2012) Systematic evaluation of factors influencing ChIP-seq fidelity. Nat Methods 9(6):609–614.  https://doi.org/10.1038/nmeth.1985 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Neph S, Vierstra J, Stergachis AB, Reynolds AP, Haugen E, Vernot B, Thurman RE, John S, Sandstrom R, Johnson AK, Maurano MT, Humbert R, Rynes E, Wang H, Vong S, Lee K, Bates D, Diegel M, Roach V, Dunn D, Neri J, Schafer A, Hansen RS, Kutyavin T, Giste E, Weaver M, Canfield T, Sabo P, Zhang M, Balasundaram G, Byron R, MacCoss MJ, Akey JM, Bender MA, Groudine M, Kaul R, Stamatoyannopoulos JA (2012) An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489(7414):83–90.  https://doi.org/10.1038/nature11212 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Helena Raurell-Vila
  • Mireia Ramos-Rodríguez
  • Lorenzo Pasquali
    • 1
    • 2
    • 3
  1. 1.Program of Predictive and Personalized Medicine of Cancer (PMPPC), Endocrine Regulatory Genomics Laboratory, Department of Endocrinology and NutritionGermans Trias i Pujol University Hospital and Research InstituteBadalonaSpain
  2. 2.Josep Carreras Leukaemia Research InstituteBadalonaSpain
  3. 3.CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)BarcelonaSpain

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