ATAC-seq

ATAC-seq identifies accessible DNA regions by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome.{{cite book|last1=Bajic|first1=Marko|last2=Maher|first2=Kelsey A.|last3=Deal|first3=Roger B. | name-list-style = vanc |chapter=Identification of Open Chromatin Regions in Plant Genomes Using ATAC-Seq|volume=1675|year=2018|pages=183–201|issn=1064-3745|doi=10.1007/978-1-4939-7318-7_12|pmid=29052193|pmc=5693289|title=Plant Chromatin Dynamics |series=Methods in Molecular Biology|isbn=978-1-4939-7317-0}} While naturally occurring transposases have a low level of activity, ATAC-seq employs the mutated hyperactive transposase.{{cite journal | vauthors = Reznikoff WS | title = Transposon Tn5 | journal = Annual Review of Genetics | volume = 42 | issue = 1 | pages = 269–86 | year = 2008 | pmid = 18680433 | doi = 10.1146/annurev.genet.42.110807.091656 }} In a process called "tagmentation", Tn5 transposase cleaves and tags double-stranded DNA with sequencing adaptors in a single enzymatic step.{{cite journal |last1=Adey |first1=Andrew |title=Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition |journal=Genome Biology |date=December 2010 |volume=11 |issue=12 |pages=R119 |doi=10.1186/gb-2010-11-12-r119 |pmid=21143862 |pmc= 3046479 |doi-access=free }}{{cite journal | vauthors = Picelli S, Björklund AK, Reinius B, Sagasser S, Winberg G, Sandberg R | title = Tn5 transposase and tagmentation procedures for massively scaled sequencing projects | journal = Genome Research | volume = 24 | issue = 12 | pages = 2033–40 | date = December 2014 | pmid = 25079858 | pmc = 4248319 | doi = 10.1101/gr.177881.114 }} The tagged DNA fragments are then purified, PCR-amplified, and sequenced using next-generation sequencing. Sequencing reads can then be used to infer regions of increased accessibility as well as to map regions of transcription factor binding sites and nucleosome positions. The number of reads for a region correlate with how open that chromatin is, at single nucleotide resolution.

ATAC-seq requires no sonication or phenol-chloroform extraction like FAIRE-seq;{{cite journal | vauthors = Simon JM, Giresi PG, Davis IJ, Lieb JD | title = Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA | journal = Nature Protocols | volume = 7 | issue = 2 | pages = 256–67 | date = January 2012 | pmid = 22262007 | pmc = 3784247 | doi = 10.1038/nprot.2011.444 }} no antibodies like ChIP-seq;{{cite journal | vauthors = Savic D, Partridge EC, Newberry KM, Smith SB, Meadows SK, Roberts BS, Mackiewicz M, Mendenhall EM, Myers RM | display-authors = 6 | title = CETCh-seq: CRISPR epitope tagging ChIP-seq of DNA-binding proteins | journal = Genome Research | volume = 25 | issue = 10 | pages = 1581–9 | date = October 2015 | pmid = 26355004 | pmc = 4579343 | doi = 10.1101/gr.193540.115 }} and no sensitive enzymatic digestion like MNase-seq or DNase-seq.{{cite book|last1=Hoeijmakers|first1=Wieteke Anna Maria|last2=Bártfai |first2=Richárd | name-list-style = vanc |title=Chromatin Immunoprecipitation|chapter=Characterization of the Nucleosome Landscape by Micrococcal Nuclease-Sequencing (MNase-seq)|volume=1689|year=2018|pages=83–101|issn=1064-3745|doi=10.1007/978-1-4939-7380-4_8|pmid=29027167|series=Methods in Molecular Biology|isbn=978-1-4939-7379-8}} ATAC-seq preparation can be completed in under three hours.

File:ATAC-Seq application_v2.pdf

ATAC-Seq analysis is used to investigate a number of chromatin-accessibility signatures. The most common use is nucleosome mapping experiments, but it can be applied to mapping transcription factor binding sites,{{cite journal |last1=Li |first1=Zhijian |last2=Schulz |first2=Marcel H. |last3=Look |first3=Thomas |last4=Begemann |first4=Matthias |last5=Zenke |first5=Martin |last6=Costa |first6=Ivan G. |title=Identification of transcription factor binding sites using ATAC-seq |journal=Genome Biology |date=26 February 2019 |volume=20 |issue=1 |pages=45 |doi=10.1186/s13059-019-1642-2 |pmid=30808370 |pmc=6391789 |doi-access=free}} adapted to map DNA methylation sites,{{cite journal | vauthors = Spektor R, Tippens ND, Mimoso CA, Soloway PD | title = methyl-ATAC-seq measures DNA methylation at accessible chromatin | journal = Genome Research | volume = 29 | issue = 6 | pages = 969–977 | date = June 2019 | pmid = 31160376 | pmc = 6581052 | doi = 10.1101/gr.245399.118 }} or combined with sequencing techniques.{{Citation|last1=Hendrickson|first1=David G.|volume=1819|date=2018|doi=10.1007/978-1-4939-8618-7_15|pmid=30421411|series=Methods in Molecular Biology|pages=317–333|publisher=Springer New York |isbn=9781493986170 |last2=Soifer |first2=Ilya |last3=Wranik |first3=Bernd J. |last4=Botstein |first4=David |last5=Scott McIsaac |first5=R.|title=Computational Cell Biology |chapter=Simultaneous Profiling of DNA Accessibility and Gene Expression Dynamics with ATAC-Seq and RNA-Seq | name-list-style = vanc }}

The utility of high-resolution enhancer mapping ranges from studying the evolutionary divergence of enhancer usage (e.g. between chimps and humans) during development{{cite journal | vauthors = Prescott SL, Srinivasan R, Marchetto MC, Grishina I, Narvaiza I, Selleri L, Gage FH, Swigut T, Wysocka J | display-authors = 6 | title = Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest | journal = Cell | volume = 163 | issue = 1 | pages = 68–83 | date = September 2015 | pmid = 26365491 | pmc = 4848043 | doi = 10.1016/j.cell.2015.08.036 }} and uncovering a lineage-specific enhancer map used during blood cell differentiation.{{cite journal | vauthors = Lara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, Keren-Shaul H, Mildner A, Winter D, Jung S, Friedman N, Amit I | display-authors = 6 | title = Immunogenetics. Chromatin state dynamics during blood formation | journal = Science | volume = 345 | issue = 6199 | pages = 943–9 | date = August 2014 | pmid = 25103404 | pmc = 4412442 | doi = 10.1126/science.1256271 }}

ATAC-Seq has also been applied to defining the genome-wide chromatin accessibility landscape in human cancers,{{cite journal | vauthors = Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, Silva TC, Groeneveld C, Wong CK, Cho SW, Satpathy AT, Mumbach MR, Hoadley KA, Robertson AG, Sheffield NC, Felau I, Castro MA, Berman BP, Staudt LM, Zenklusen JC, Laird PW, Curtis C, Greenleaf WJ, Chang HY | display-authors = 6 | title = The chromatin accessibility landscape of primary human cancers | journal = Science | volume = 362 | issue = 6413 | pages = eaav1898 | date = October 2018 | pmid = 30361341 | pmc = 6408149 | doi = 10.1126/science.aav1898 | bibcode = 2018Sci...362.1898C }} and revealing an overall decrease in chromatin accessibility in macular degeneration.{{cite journal | vauthors = Wang J, Zibetti C, Shang P, Sripathi SR, Zhang P, Cano M, Hoang T, Xia S, Ji H, Merbs SL, Zack DJ, Handa JT, Sinha D, Blackshaw S, Qian J | display-authors = 6 | title = ATAC-Seq analysis reveals a widespread decrease of chromatin accessibility in age-related macular degeneration | journal = Nature Communications | volume = 9 | issue = 1 | pages = 1364 | date = April 2018 | pmid = 29636475 | pmc = 5893535 | doi = 10.1038/s41467-018-03856-y | bibcode = 2018NatCo...9.1364W }} Computational footprinting methods can be performed on ATAC-seq to find cell specific binding sites and transcription factors with cell specific activity.

{{See also|Single-cell sequencing}}

Modifications to the ATAC-seq protocol have been made to accommodate single-cell analysis. Microfluidics can be used to separate single nuclei and perform ATAC-seq reactions individually.{{cite journal | vauthors = Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, Chang HY, Greenleaf WJ | display-authors = 6 | title = Single-cell chromatin accessibility reveals principles of regulatory variation | journal = Nature | volume = 523 | issue = 7561 | pages = 486–90 | date = July 2015 | pmid = 26083756 | pmc = 4685948 | doi = 10.1038/nature14590 | bibcode = 2015Natur.523..486B }} With this approach, single cells are captured by either a microfluidic device or a liquid deposition system before tagmentation.{{cite journal | vauthors = Mezger A, Klemm S, Mann I, Brower K, Mir A, Bostick M, Farmer A, Fordyce P, Linnarsson S, Greenleaf W | display-authors = 6 | title = High-throughput chromatin accessibility profiling at single-cell resolution | journal = Nature Communications | volume = 9 | issue = 1 | pages = 3647 | date = September 2018 | pmid = 30194434 | pmc = 6128862 | doi = 10.1038/s41467-018-05887-x | bibcode = 2018NatCo...9.3647M }} An alternative technique that does not require single cell isolation is combinatorial cellular indexing.{{cite journal |last1=Cusanovich |first1=Darren |title=Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing |journal=Science |date=May 2015 |volume=348 |issue=6237 |pages=910–914 |doi=10.1126/science.aab1601 |pmid=25953818 |pmc=4836442|bibcode=2015Sci...348..910C }} This technique uses barcoding to measure chromatin accessibility in thousands of individual cells; it can generate epigenomic profiles from 10,000-100,000 cells per experiment.{{cite journal | vauthors = Lareau CA, Duarte FM, Chew JG, Kartha VK, Burkett ZD, Kohlway AS, Pokholok D, Aryee MJ, Steemers FJ, Lebofsky R, Buenrostro JD | display-authors = 8 |year=2019 |title=Droplet-based combinatorial indexing for massive scale single-cell epigenomics |journal=bioRxiv |doi=10.1101/612713 |doi-access=free }} But combinatorial cellular indexing requires additional, custom-engineered equipment or a large quantity of custom, modified Tn5.{{cite journal | vauthors = Chen X, Miragaia RJ, Natarajan KN, Teichmann SA | title = A rapid and robust method for single cell chromatin accessibility profiling | journal = Nature Communications | volume = 9 | issue = 1 | pages = 5345 | date = December 2018 | pmid = 30559361 | pmc = 6297232 | doi = 10.1038/s41467-018-07771-0 | bibcode = 2018NatCo...9.5345C }} Recently, a pooled barcode method called sci-CAR was developed, allowing joint profiling of chromatin accessibility and gene expression of single cells.{{Cite journal|last1=Cao|first1=Junyue|last2=Cusanovich|first2=Darren A.|last3=Ramani|first3=Vijay|last4=Aghamirzaie|first4=Delasa|last5=Pliner|first5=Hannah A.|last6=Hill|first6=Andrew J.|last7=Daza|first7=Riza M.|last8=McFaline-Figueroa|first8=Jose L.|last9=Packer|first9=Jonathan S.|last10=Christiansen|first10=Lena|last11=Steemers|first11=Frank J.|date=2018-09-28|title=Joint profiling of chromatin accessibility and gene expression in thousands of single cells|journal=Science|language=en|volume=361|issue=6409|pages=1380–1385|doi=10.1126/science.aau0730|issn=0036-8075|pmid=30166440|pmc=6571013 |bibcode=2018Sci...361.1380C |doi-access=free}}

Computational analysis of scATAC-seq is based on construction of a count matrix with number of reads per open chromatin regions. Open chromatin regions can be defined, for example, by standard peak calling of pseudo bulk ATAC-seq data. Further steps include data reduction with PCA and clustering of cells. scATAC-seq matrices can be extremely large (hundreds of thousands of regions) and is extremely sparse, i.e. less than 3% of entries are non-zero.{{cite journal |last1=Li |first1=Zhijian |last2=Kuppe |first2=Christoph |last3=Cheng |first3=Mingbo |last4=Menzel |first4=Sylvia |last5=Zenke |first5=Martin |last6=Kramann |first6=Rafael |last7=Costa |first7=Ivan G. |name-list-style = vanc | display-authors = 6 |date=2021|title=Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen|journal=Nature Communications|volume=12 |issue=1 |language=en|pages=865931|doi=10.1038/s41467-021-26530-2|pmid=34737275 |pmc=8568974 |bibcode=2021NatCo..12.6386L |doi-access=free }} Therefore, imputation of count matrix is another crucial step performed by using various methods such as non-negative matrix factorization. As with bulk ATAC-seq, scATAC-seq allows finding regulators like transcription factors controlling gene expression of cells. This can be achieved by looking at the number of reads around TF motifs{{cite journal | vauthors = Schep AN, Wu B, Buenrostro JD, Greenleaf WJ | title = chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data | journal = Nature Methods | volume = 14 | issue = 10 | pages = 975–978 | date = October 2017 | pmid = 28825706 | pmc = 5623146 | doi = 10.1038/nmeth.4401 }} or footprinting analysis.

• ScGET-seq
• Single-cell analysis

{{Reflist}}

• [https://www.nature.com/nmeth/journal/v10/n12/fig_tab/nmeth.2688_F1.html ATAC-seq probes open-chromatin state (figure)]
• [https://web.archive.org/web/20150901062145/http://greenleaf.stanford.edu/portfolio_details_buenrostro_2013_nature_methods.html ATAC-seq: Fast and sensitive epigenomic profiling]
• [https://www.regulatory-genomics.org/hint/introduction/ HINT-ATAC: Identification of Transcription Factor Binding Sites using ATAC-seq]

Category:Molecular biology techniques