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Chromatin accessibility profiling methods


Chromatin accessibility, or the physical access to chromatinized DNA, is a widely studied characteristic of the eukaryotic genome. As active regulatory DNA elements are generally ‘accessible’, the genome-wide profiling of chromatin accessibility can be used to identify candidate regulatory genomic regions in a tissue or cell type. Multiple biochemical methods have been developed to profile chromatin accessibility, both in bulk and at the single-cell level. Depending on the method, enzymatic cleavage, transposition or DNA methyltransferases are used, followed by high-throughput sequencing, providing a view of genome-wide chromatin accessibility. In this Primer, we discuss these biochemical methods, as well as bioinformatics tools for analysing and interpreting the generated data, and insights into the key regulators underlying developmental, evolutionary and disease processes. We outline standards for data quality, reproducibility and deposition used by the genomics community. Although chromatin accessibility profiling is invaluable to study gene regulation, alone it provides only a partial view of this complex process. Orthogonal assays facilitate the interpretation of accessible regions with respect to enhancer–promoter proximity, functional transcription factor binding and regulatory function. We envision that technological improvements including single-molecule, multi-omics and spatial methods will bring further insight into the secrets of genome regulation.


Chromatin accessibility refers to the level of physical compaction of chromatin, a complex formed by DNA and associated proteins consisting mainly of histones, transcription factors (TFs), chromatin-modifying enzymes and chromatin-remodelling complexes1,2,3. Although eukaryotic genomes are generally packed into nucleosomes, which comprise ~147 bp of DNA wrapped around an octamer of histones4,5, nucleosome occupancy is not uniform in the genome, and varies across tissues and cell types. Nucleosomes are typically depleted at genomic locations that represent cis-regulatory elements — enhancers and promoters, among others — that interact with transcriptional regulators (for example, TFs), resulting in accessible chromatin6,7,8,9,10. Profiling chromatin accessibility on a genome-wide scale is an excellent tool to map putative regulatory elements in a cell type or cell state.

Post-translational chemical modifications of chromatin, including DNA methylation (in vertebrates) and histone methylation and acetylation, are dynamic and change between different cell states, similar to nucleosome positioning. These post-translational modifications are often correlated with chromatin accessibility and can reflect specific functionalities of genomic regions related to the regulation of gene expression11,12. Changes in these post-translational modifications, such as increased or decreased histone methylation and acetylation, are affected by a large set of chromatin-modifying enzymes that can be recruited to chromatin regions by TFs. These modifications alter the physico-chemical properties of the chromatin, which in turn can influence the formation of transcriptional condensates13,14. In addition, active chromatin remodelling impacts nucleosome occupancy; for example, the SWI/SNF complexes use ATP hydrolysis to alter histone–DNA contacts, thereby repositioning or removing nucleosomes15. Dynamic changes in the chromatin structure, chemical modifications and nucleosome positioning form a crucial interplay with the TFs that drive differentiation of cells during development16,17. Initial changes in chromatin accessibility are caused by the binding of TFs, which outcompete histones and recruit cofactors, including ATP-dependent chromatin remodellers18,19, or by TFs that preferentially bind to their recognition sequence in nucleosomal DNA20,21. The binding of these initial TFs, known as pioneer factors, can recruit other TFs to co-bind and further stabilize the nucleosome-depleted region, further contributing to the regulation of gene expression of target genes22,23,24. Consequently, the analysis of TF binding sites in regulatory regions within accessible chromatin can bring insights into cell type-specific lineage factors and gene regulatory networks.

Various changes in the chromatin landscape, as well as mutations in chromatin remodellers and in regulatory regions, are linked to a range of traits and diseases25,26,27,28. In fact, many causal genome-wide association study variants are located in accessible regulatory elements29. In order to improve our understanding of chromatin dynamics during development and in disease contexts, researchers and large consortia, including the Encyclopedia of DNA Elements (ENCODE) Consortium30, the International Human Epigenome Consortium (IHEC)31, the National Institutes of Health (NIH) Roadmap Epigenomics Mapping Consortium32 and the BLUEPRINT epigenome project33, have collected and compared chromatin landscapes across cell types and during disease development.

Over the past decades, several chromatin accessibility profiling methods have been developed and widely used34,35,36,37,38,39,40,41,42,43,44. Generally, these methods are based on the physical accessibility of the chromatin to enzymes, which mark the accessible DNA by fragmentation, tagmentation or chemical labelling (for example, methylation of GpC dinucleotides). Initial research in the 1970s showed that regions of active transcription, such as promoters and introns of expressed genes, are particularly sensitive to digestion by DNA endonucleases such as deoxyribonuclease I (DNase I), indicative of a particularly accessible form of the chromatin45. Moreover, chromatin is digested at regularly spaced sites due to nucleosome positioning2,46. DNase I is still the reagent of choice for TF footprinting, which can determine the location of TF binding sites due to the protection of the site by the TF itself47,48,49. With the advent of next-generation sequencing (NGS) techniques, DNase I hypersensitive site sequencing (DNase-seq) was one of the first adaptations to perform genome-wide profiling of accessible chromatin35,40, which was followed by a handful of other methods. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and variants36,37,38 together with DNase-seq are the two most commonly used chromatin accessibility profiling methods today50.

Importantly, as regulatory regions co-define a cell type, their chromatin accessibility is cell type-dependent10,51,52,53. When investigating heterogeneous samples, it is therefore advisable to measure chromatin accessibility in isolated subpopulations — by flow cytometry-based purification53 — or at the single-cell level to avoid averaging over heterogeneous cell populations (Fig. 1). Currently, the field of single-cell omics, including single-cell epigenomic assays such as single-cell ATAC-seq (scATAC-seq) and single-cell DNase-seq (scDNase-seq), provides exciting new opportunities to study genome regulation in complex tissues such as the brain, whole embryos and tumours54,55,56,57,58,59,60,61. Accompanied by the rise of single-cell chromatin accessibility profiling, a wide range of bioinformatics tools have been developed that allow analysis of the generated data, which is intrinsically sparse due to experimental limitations. Indeed, single-cell epigenomics is technically more challenging compared with single-cell transcriptomics because most loci in a diploid cell are present in only two copies of DNA that can be assayed.

Fig. 1: Chromatin accessibility profiling in bulk and at single-cell level reveals putative regulatory regions.

a | Representation of a chromatin landscape is shown in which transcription factor (TF)-bound enhancers and the promoter of a gene are nucleosome depleted and thus accessible. The TFs are represented as coloured circles and the arrows represent 3D looping of the enhancers to the promoter of the target gene. b | Bulk and single-cell chromatin accessibility profiles of a heterogeneous sample containing three different cell populations. When performing single-cell chromatin accessibility profiling, sparse single-cell data are used to cluster cells, often followed by aggregating the reads per cluster, thereby reconstituting pseudo-bulk profiles per cluster or cell type. H3K27ac, histone H3 acetylated at lysine 27; Pol II, polymerase II; TSS, transcription start site.

Although chromatin accessibility profiling methods may serve as an analytic foundation to identify regulatory regions, it is reported that often less than 50% of accessible regions in human DNA are active as enhancers62,63. Interestingly, however, work in both the Drosophila embryo55 and the Drosophila eye imaginal disc64 shows that when a genomic region is uniquely accessible in a specific cell type, more than 80% of the accessible enhancers are also active in the corresponding cell type55,64,65. In addition, linking active accessible regulatory regions to their target genes solely based on accessibility data remains a challenge. Therefore, additional data, including those from transcriptomics, enhancer–reporter assays and 3D chromatin conformation maps, help to determine the function of an accessible region and identify its putative target genes, especially when combined in a multi-omics way64,66,67,68,69,70,71.

This Primer provides an overview of the commonly used and most recently developed chromatin accessibility profiling methods, both in bulk and at the single-cell level (Experimentation). In addition, it provides an outline of computational analysis techniques (Results) and examples of use in diverse organisms and fields (Applications). Finally, the Primer discusses standards for data sharing (Reproducibility and data deposition), and examines currently unmet needs (Limitations and optimizations) and future opportunities for technological development (Outlook) that will increase our understanding of chromatin accessibility landscapes and their functional role in gene regulation during development, during evolution and in disease contexts.


Bulk chromatin accessibility

Chromatin accessibility is traditionally probed by assays such as digestion by nucleases or restriction enzyme digestion, typically at a few selected genomic regions each time46. However, NGS has revolutionized the way chromatin is investigated by allowing us to study its accessibility genome-wide. In this section, we will briefly describe the principles and the pros and cons of several commonly used experimental techniques to assess chromatin accessibility or nucleosome positioning in bulk, including DNase-seq, ATAC-seq and micrococcal nuclease sequencing (MNase-seq) as well as several single-molecule chromatin accessibility profiling methods (Table 1). In addition, we discuss chromatin immunoprecipitation followed by sequencing (ChIP–seq) and related methods, as these are powerful techniques to gain further insights into chromatin landscapes and TF binding. Finally, various less commonly used chromatin accessibility and nucleosome positioning methods are described in Box 1.

Table 1 Comparison of the three most commonly used chromatin accessibility profiling methods


One of the first genome-wide profiling experiments of accessible chromatin was published in 2008 by sequencing genomic DNA fragments following digestion by DNase I, an endonuclease that preferentially introduces double-stranded breaks in accessible chromatin — a technique referred to as DNase-seq35,40 (Fig. 2a). In DNase-seq, nuclei are first isolated and permeabilized using a mild detergent such as 0.1% Triton X-100, such that the DNase I enzyme can enter the nucleus efficiently. After digestion, the small DNA fragments (50–100 bp) are purified and size-selected for downstream library construction and sequencing. As DNase I digestion is an enzymatic process, the amount of the enzyme can significantly affect the digestion efficiency and, thereby, also the quality of the data. Therefore, it is necessary to titrate the amount of DNase I to achieve optimal activity when using a new type of cells, or when using DNase I from a different manufacturer or from a different batch. In addition to fresh cells, DNase-seq has also been performed on formalin-fixed paraffin-embedded samples10,35.

Fig. 2: Experimental approaches for measuring chromatin accessibility and nucleosome positioning.

a | In deoxyribonuclease I (DNase I) hypersensitive site sequencing (DNase-seq), the DNase I enzyme (represented as yellow scissors) is used to preferentially cleave accessible chromatin, generating fragments that can then be amplified into sequencing libraries. b | In Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), a hyperactive version of the Tn5 transposase (represented by the dark grey circle) is used to preferentially insert into accessible chromatin while simultaneously attaching adapters (represented by the red and blue lines on the Tn5 transposase) to the resulting fragments that can be used to directly amplify sequencing libraries. Both DNase-seq and ATAC-seq generate peaks in read coverage over accessible regions in the genome. c | In micrococcal nuclease sequencing (MNase-seq), the MNase enzyme (represented as red scissors) is used to digest DNA that is not protected by bound proteins, leaving intact fragments protected by protein occupancy (primarily nucleosomes). These fragments are then amplified, resulting in increased read coverage over positioned nucleosomes. d | DNA methyltransferase-based approaches rely on the labelling of accessible DNA with DNA methylation modifications (represented by drawing pins), which can either be sequenced using Illumina platforms following bisulfite conversion or via long-read sequencing platforms that directly read the modified bases (unmodified and modified bases are represented as light and dark blue circles, respectively). These single-molecule chromatin accessibility profiling approaches tend to provide a simultaneous read-out of both nucleosome positioning and accessible chromatin regions. Accessible chromatin regions represent themselves as higher peaks due the fact that they have more nucleotides that are accessible to the methyltransferases and are therefore more frequently methylated, compared with the internucleosomal sequences that are thus not methylated in every single-molecule read. In all four panels, bound transcription factors (TFs) are visualized via coloured circles on the accessible chromatin.

Beyond this requirement of careful enzyme titrations, major limitations of the traditional DNase-seq assay include the large number of cells (tens of millions) required as input material and its tedious and lengthy protocol that takes several days72. However, recently, a modified DNase-seq assay (scDNase-seq) has been developed to analyse single cells or a small number of cells58,73. scDNase-seq requires only hundreds to thousands of either fresh or fixed cells for a bulk cell assay and takes 1 day for library construction, without the need for fractionation of DNA fragments74. Caution must be taken when interpreting DNase-seq results because they show some intrinsic bias in cleavage sites. The DNA minor groove shows variation in width depending on the sequence, and a narrower groove is preferentially cleaved by DNase I. Also, CpG methylation enhances adjacent DNase I cleavage75,76. These factors should be considered when interpreting the footprint of a TF77. DNase-seq is the method of choice to detect TF footprints78.


ATAC-seq emerged as an alternative assay to investigate accessible chromatin profiles36. In this assay, a genetically engineered hyperactive DNA transposase (Tn5) preloaded with monovalent mosaic end adapters simultaneously cleaves and tags accessible or nucleosome-depleted chromatin regions36,79,80 (Fig. 2b). The hyperactive Tn5 mutant includes three mutations that increase the relatively low activity of the wild-type Tn5 and allows for efficient in vitro integration of the mosaic end adapters80. The target DNA fragments are purified, PCR-amplified and sequenced by NGS. As both Tn5 transposase and DNase I recognize accessible chromatin, the sequences detected by ATAC-seq have been found to be highly enriched in DNase I hypersensitive sites (DHSs)81,82,83.

A major advantage of ATAC-seq and its variants37,38 is that they are very sensitive assays that work well on low-input samples (for example, 500–50,000 cells, compared with millions of cells for DNase-seq) and use a simpler protocol due to the simultaneous chromatin fragmentation and tagging36. If fresh cells are difficult to obtain, slowly cooled cryopreserved cells can also be profiled by ATAC-seq. In addition, it is possible to generate high signal to background profiles from formaldehyde-fixed cells using an adapted method84, as well as from clinically relevant snap-frozen samples using the improved Omni-ATAC protocol38 or from nuclei collected via flow cytometry85. The Omni-ATAC protocol improves the signal to noise ratio by mainly using a combination of multiple mild detergents to improve permeabilization across a wide array of cell types and to remove mitochondria from the reaction.

Similar to the enzyme-specific cleavage bias of DNase I, the Tn5 enzyme shows steric hindrance and sequence bias in chromatin tagmentation79,86,87. Accurate prediction of TF footprints from ATAC-seq data requires Tn5 bias correction that is different from DNase-seq bias correction88. An initial limitation of the first ATAC-seq protocol was the profiling of contaminating organellar DNA, such as mitochondrial DNA and/or chloroplast DNA for plants, or Wolbachia DNA in infected Drosophila stocks36,89. Large amounts of sequencing reads can be consumed by these contaminations, meaning that deeper sequencing is necessary to reach a good signal-to-noise ratio at regions of interest in the data. However, this limitation can be significantly reduced either by improved lysis conditions (as is the case in Omni-ATAC38), by purification of nuclei via flow cytometry85 or by applying clustered regularly interspaced short palindromic repeats (CRISPR) technology to cleave mitochondrial ribosomal DNA prior to the experiment82,90. Another deficiency of the original procedure is that half of all fragments are lost as they contain two adapter sequences of the same kind. The transposome hypersensitive sites sequencing (THS-seq) version of ATAC-seq attempts to rescue the other half of fragments by using a T7 RNA polymerase linear amplification protocol91.

Given the speed (a few hours) and straightforward nature of the protocol, combined with its sensitivity and requirement for low numbers of cells, ATAC-seq and its newer variants (for example, OmniATAC-seq) are currently the most commonly used methods to generate comprehensive chromatin accessibility maps in research laboratories (~400 data sets in PubMed in 2019 compared with <100 data sets for DNase-seq, MNase-seq and FAIRE-seq (formaldehyde-assisted isolation of regulatory elements) combined)50. In addition to profiling accessible chromatin, ATAC-seq can also be used to detect TF footprints and to map nucleosome positioning.


Nucleosome positioning and occupancy in the genome play key roles in chromatin accessibility. MNase is an endo-exonuclease that cleaves the DNA regions without nucleosome protection and leaves the nucleosome core particles undigested, which can be purified, ligated to adaptors, PCR-amplified and sequenced (MNase-seq)42 (Fig. 2c). MNase-seq is thus an orthogonal assay compared with DNase-seq and ATAC-seq as it measures nucleosome-occupied regions and is the most widely used method to map nucleosome positions genome-wide. A recently developed quantitative protocol for MNase-seq involves subjecting aliquots of a sample to different levels of digestion by MNase, allowing clearer distinction of nucleosome positions and occupancy from higher-order chromatin properties, which can also be summarized in a theoretical framework92.

In MNase-seq, 10,000–100,000 either fresh or formaldehyde cross-linked cells can be used for library construction. Digestion of chromatin by MNase typically results in a nucleosome ladder consisting of mononucleosome, dinucleosome, trinucleosome and so on, depending on the concentration of MNase in the reaction. The optimal range of digestion usually leads to about 70–80% mononucleosomes and 20–30% higher nucleosome ladders42. Similar to DNase-seq, MNase-seq requires careful enzyme titrations and is time-consuming (2-day protocol). Another limitation of MNase-seq is that it requires a large number of sequencing reads, preferably 150–200 million reads for human samples93. MNase-seq also suffers from enzyme-specific cleavage biases, specifically a preferential cleavage of A/T versus G/C94.

MNase-seq has been applied to investigate the dynamics of the nucleosome landscape and their function in transcriptional regulation95. However, as nucleosome positioning and occupancy revealed by MNase-seq are based on the average profile of a large number of cells, caution should be taken when interpreting the results, particularly at inactive chromatin regions96.

Assays for single-molecule chromatin accessibility profiling

An emerging class of methods aim to map chromatin accessibility and TF binding in single molecules. The advantage of such approaches is that they do not rely on enrichment and provide information about the distribution of accessibility states within the population of chromatin fibres. The assays in this class rely on methyltransferase enzymes that preferentially modify accessible DNA (Fig. 2d). For years, the only read-out that such methods could rely on was bisulfite conversion of unmethylated cytosines followed by Sanger sequencing (for localized analysis of particular loci)97,98,99,100 and, later, NGS (for both local and genome-wide coverage). The first genome-wide assay of this kind was the methylation accessibility protocol for individual templates (MAPit101), followed by nucleosome occupancy and methylome sequencing (NOMe-seq)41,102, which both use an m5C methyltransferase that modifies cytosines in a GpC context.

As genomes of many eukaryotes contain abundant endogenous CpG methylation, and bisulfite sequencing measures methylation on cytosines, exogenous enzymes are required that methylate other dinucleotide contexts. The approach has limited spatial resolution, as it relies on GpC nucleotides that are rare in mammalian genomes, only found once every 20–30 bp, and it is common to find much larger stretches of sequence having no informative positions at all103. However, in species such as yeast and Drosophila, which lack endogenous methylation, a combination of both a GpC and a CpG methyltransferase can be used, which increases assay resolution down to 10 bp. This method is known as dual-enzyme single-molecule footprinting104. This approach has proven to be very powerful in enumerating the distinct functional states of individual promoters, down to the ability to footprint the occupancy of individual components of the basal transcriptional machinery. The approach has also been recently extended to mammalian genomes with sufficient resolution to quantify the single-molecule occupancy patterns of individual TFs at regulatory regions105. This requires knock out of endogenous methyltransferases and is limited to the fraction of regulatory regions (typically 30–50%) that contain enough informative GpC dinucleotides. Moreover, bisulfite sequencing-based methods only provide information about the state of individual molecules within, at most, 600-bp stretches of DNA, which is the current limit of combined fully sequenced paired-end read length for Illumina sequencing.

The limited length of the single-molecule read-out obtained via Illumina sequencing reads has been addressed by the advent of long-read sequencing platforms such as PacBio and Oxford Nanopore106. In addition to generating multikilobase reads (current record of 2.3 Mbp)107, these technologies are capable of reading modified bases directly within individual molecules, although with significantly decreased accuracy108,109,110. Base modification detection by long-read sequencing remains challenging, as it may require high coverage as well as training and control data sets to reduce erroneous calls111. The accuracy can be increased by using PacBio circular consensus sequencing, although this reduces the effective read length as there is trade-off between the number of sequencing passes and insert sizes112. nanoNOMe-seq and methyltransferase treatment followed by single-molecule long-read sequencing (MeSMLR–seq) assays use GpC methylation and nanopore sequencing to map accessibility on a multikilobase scale, although they are still limited in resolution by available informative positions113,114.

The limit in the number of informative positions can be overcome by taking advantage of the ability of long-read platforms to read any modification, not just methylated cytosines. For instance, non-specific methylation, such as m6A deposited via EcoGII, or other modifications (Tet-assisted pyridine borane sequencing (lrTAPS)115) can be combined with nanopore or PacBio sequencing to obtain a fine-scale read-out of chromatin accessibility at the single-molecule level. This can be done either on total genomic DNA — with the single-molecule long-read accessible chromatin mapping sequencing (SMAC-seq) assay103 or mapping chromatin fibres onto a DNA template using methyltransferases (Fiber-seq)116 — or in combination with a phasing MNase digestion step (single-molecule adenine methylated oligonucleosome sequencing assay (SAMOSA)117). The large number of informative positions allows for fine-scale footprinting almost everywhere in the genome. Although the higher error rate in base calling for long-read sequencing technologies does not yet allow nucleotide resolution for these assays, in practice, the biologically relevant scale of chromatin accessibility typically is larger than that of an individual base. Due to high error rates in the calling of the nucleotides in a read, obtaining a fully correct single-nucleotide read-out is still a challenge. For instance, if 1 in every 20 bp is wrongly identified, then multiple modified nucleotides are taken together to obtain an estimate of the accessibility of that part of the DNA, thereby compromising on the resolution. Nevertheless, the resolution is much higher compared with ATAC-seq, for instance.


ChIP–seq is used to detect the occupancy of chromatin-binding factors (such as TFs) or histone modifications at a genome-wide level34,118,119,120. The amino-terminal tails of core histones are enriched with various covalent modifications — including methylation, phosphorylation, acetylation, ubiquitylation and sumoylation — that serve as the docking sites for many chromatin-binding proteins121,122. Typical histone marks used to define regulatory elements include histone H3 acetylated at lysine 27 (H3K27ac), which correlates with DNase-seq and ATAC-seq data at transcription start sites, active promoters and distal active enhancers123,124; H3 dimethylated at lysine 4 (H3K4me2), which has a similar genomic distribution to H3K27ac; and H3 monomethylated at lysine 4 (H3K4me1), which correlates with poised or active enhancer regions in animals125,126 when it co-occurs with H3K27me3 or H2K27ac, respectively126,127,128.

For ChIP–seq analysis of chromatin modifications, chromatin can be isolated from either formaldehyde-fixed cells or non-fixed cells (native chromatin), and fragmented to 100–500 bp by sonication or through MNase digestion to profile histone modifications129,130,131. For profiling of protein-bound chromatin, for example to determine TF occupancy, the chromatin is cross-linked to stabilize protein–chromatin interactions. Through the use of specific antibodies, the target proteins or histone modifications are captured along with the associated DNA fragments by protein A/G-coupled agarose beads or magnetic beads. Chromatin is then reverse cross-linked and the DNA fragments are eluted, end-repaired, ligated to adaptors, PCR-amplified and sequenced by NGS.

Traditionally, ChIP–seq requires at least hundreds of thousands of cells for profiling histone modifications and millions of cells for profiling TFs. ChIP–seq data quality critically depends on antibody specificity, efficiency of chromatin fixation and residence time of the TF on DNA. Each antibody should therefore be screened for ChIP efficiency, and the fixation and sonication conditions need to be optimized for different cell types. The entire procedure for ChIP–seq is time-consuming (spanning multiple days) and laborious.

In the past decade, several ChIP–seq derivatives have been developed involving a lower cell input, detection of TF binding at higher resolution and/or providing a streamlined workflow. These derivatives include ULI-NChIP–seq132, μChIP–seq133, small-scale ChIP–seq134, STAR ChIP–seq135, ChIPmentation136, ACT-seq137, ChIL–seq138 and CUT&RUN139. ChIPmentation combines aspects of ChIP–seq and ATAC-seq by performing tagmentation on immunoprecipitated chromatin fragments, which reduces the input requirement and leads to a simpler, faster assay136. CUT&RUN combines antibody-tagging with MNase cleavage in a simple, robust and less expensive protocol for high-resolution profiling of chromatin binding139. Recently, some of the above-mentioned methods and other ChIP-derived techniques have even been applied at single-cell resolution, including iACT-seq137, ChIL–seq138, scCUT&Tag140, scChIC-seq141, CoBATCH142, uliCUT&RUN143, Drop-ChIP144 and scChIP–seq145.

Single-cell chromatin accessibility

Innovations in barcoding and microfluidics have recently enabled high-throughput biochemical profiling of chromatin accessibility at single-cell resolution, including scDNase-seq58, single-cell MNase-seq (scMNase-seq96) and scATAC-seq146,147,148,149,150. Of these protocols, scATAC-seq has emerged as a popular and relatively simple approach to profile chromatin accessibility across hundreds to thousands of individual cells, and we will focus on the multiple experimental implementations of this technique. Current scATAC-seq methods rely on either droplet microfluidic or fluorescence cytometrical/plate-based partitioning to uniquely label nuclei in isolation. Procedures characteristic to both types of scATAC-seq, as well as consideration for experimental design (Box 2), are described below.

Microfluidics-based scATAC-seq

Droplet-based single-cell partitioning via microfluidic devices has emerged as a powerful approach for single-cell data generation owing to its reproducibility and relative ease of use. In combination with standard sequencing library reagents and instruments, popular microfluidic approaches for scATAC-seq, such as those commercially available from 10x Genomics (Chromium Next Gem Single Cell ATAC-seq Library Kit)150 and BioRad (SureCell ATAC-seq Library Preparation Kit)149, provide all of the reagents needed to produce scATAC-seq libraries. However, these commercial applications require the acquisition of proprietary robotic sample processing devices (Chromium Controller, 10x Genomics; ddSEQ single-cell isolator, BioRad) that are non-standard in most laboratories.

Droplet microfluidic-based scATAC-seq methods generally start by performing Tn5 adapter integration on a bulk nuclei suspension, while keeping the nuclei intact, similar to traditional ATAC-seq. Transposed nuclei are then loaded onto an aqueous channel with PCR reagents and suitable buffers and mixed with gel beads containing distinct barcodes. To encapsulate individual nuclei in picolitre reaction compartments with a single gel bead, the aqueous flow is restricted to channels measuring ~55 μm in width150. Droplets are produced by exposing the aqueous flow to a continuous stream of oil. Nuclei droplet loading follows a Poisson distribution, and nuclei are loaded at low concentrations. Barcoded sequences with P5 adapters and tail sequences complementary to Tn5-inserted adapters are released from gel beads following droplet generation, enabling PCR amplification and barcoding of accessible chromatin fragments within each droplet in isolation. Finally, the droplet emulsion is broken, and the fragments are purified with magnetic beads and subjected to bulk PCR to attach sequencing indices and P7 sequences149,150.

Plate-based scATAC-seq

An alternative to the microfluidics approach is to physically separate cells into the wells of plates. Straightforward 96-well and 384-well scATAC-seq protocols have been published147, but their throughput remains limited by the low number of wells available. The adaptation of scATAC-seq to the ICELL8 Single Cell System (Takara Bio), which has 5,084 nanolitre wells, known as µATAC-seq, increased the throughput of the assay to a few thousand cells151.

Combinatorial indexing (sciATAC-seq)

Higher throughput can be achieved using a combinatorial indexing strategy, as implemented in single-cell combinatorial indexing ATAC-seq (sciATAC-seq)55,56,148. In contrast to microfluidic approaches, sciATAC-seq can be performed with access to standard instruments and reagents (for example, 96-well or 384-well plates, flow cytometry, Nextera TF buffer and so on). Whereas earlier versions of sciATAC-seq require custom-made Tn5, the latest version has been adapted to work with commercially available Tn5 (ref.152). The core idea behind combinatorial indexing is the repeated pooling and splitting of cells or nuclei coupled with labelling of DNA fragments at each step, in such a way that statistically each cell or nucleus is tagged with a unique combination of barcodes. In the simplest implementation of sciATAC-seq, nuclei are distributed into wells containing uniquely indexed Tn5 transposomes, in which tagmentation is performed. Nuclei are then pooled and distributed into the wells of a second plate at numbers sufficiently low to minimize the generation of doublets. The reactions in these wells are then subjected to indexed PCR, generating statistically unique barcode combinations for each cell. Additional rounds of barcoding are also possible, using the ligation of barcodes to transposed fragments153,154,155, vastly increasing potential throughput. Another approach for increasing throughput is to combine upstream transposition of barcoded Tn5 with a droplet-based scATAC platform such as those from 10x Genomics or BioRad, in the form of droplet combinatorial indexing or droplet-based single-cell combinatorial indexing for ATAC-seq (dsciATAC-seq)149.


In general, a chromatin accessibility analysis workflow consists of three main steps: preprocessing, peak calling and downstream analysis (Fig. 3). The latter can include differential accessibility analysis, annotation, footprinting, motif enrichment and integration with other omics data. Additional computational steps are needed for scATAC-seq data. We will discuss each of the steps in more detail and mention commonly used bioinformatics tools (Supplementary Table 1). Although there is not yet a gold standard in the field, we will mention parts of some general pipelines, such as the ENCODE ATAC-seq Data Standards and Processing Pipeline156, and we propose specific tools and a guided workflow for analysis of chromatin accessibility data. In this section, we focus on bioinformatic tools that are used for the analysis of the three most commonly used chromatin profiling methods: bulk ATAC-seq, DNase-seq and MNase-seq — we will not discuss the analysis pipeline for the methods based on single-molecule chromatin accessibility profiling as these are still in their infancy.

Fig. 3: Overview of common tasks in the analysis of bulk chromatin accessibility data.

a | Starting from raw sequencing reads. b | Bulk chromatin accessibility data generally undergo several preprocessing steps, including a pre-mapping quality control (QC) and adaptor trimming step. c | Mapping of the trimmed reads to a reference genome for the studied species. d | Mapped reads are filtered. e | An additional post-alignment QC step is recommended through several metrics and data visualizations. f | An important step in chromatin accessibility data analysis is calling peaks, as these usually form the basis of several downstream analyses. g | Differentially accessibility analysis can be performed pairwise (condition A versus B) or across multiple conditions. hm | Additional downstream analyses include annotation and enrichment analysis for the identified peaks (part h), visual inspection of chromatin accessibility data tracks (part i), motif enrichment within peaks (sets) using predefined databases or de novo (part j), transcription factor (TF) footprinting analysis (part k), mapping of nucleosome positions (part l) and integration with RNA sequencing (RNA-seq) or chromatin immunoprecipitation followed by sequencing (ChIP–seq) data to link different omics layers or to generate gene regulatory networks (part m). TSS, transcription start site.


As with most high-throughput sequencing data (Fig. 3a), pre-alignment quality control is recommended for chromatin accessibility data and can be performed using FastQC, which produces an HTML to examine sequencing quality, GC bias and over-represented sequences (Fig. 3b). The FastQC report is also produced by MultiQC157, which includes visualization of further processing steps such as the mapping percentage, alignment scores and number of reads passing filtering. Next, sequencing adaptors should be removed using tools such as cutadapt158, trimmomatic159 and fastq-mcf160, which require the input of known Illumina adaptor sequences. For certain experimental techniques or computational goals (for instance, for MNase-seq data and footprinting analysis in DNase-seq data), and given that sequencing was done in a paired-end fashion, selecting reads with a desired read length — also referred to as computational size selection — is recommended at this point. For instance, removal of multi-nucleosomal reads is advised for MNase-seq data. This is based on the size of the paired-end reads, as mononucleosomal reads should be ~147 ± 30 bp in length161. For DNase-seq, as well as removing multi-nucleosomal reads, an additional in silico filtering step for fragment inserts between 50 and 100 bp for TF binding site detection can be performed, along with the gel-based or solid-phase reversible immobilization-based experimental size selection74,77. Trimmed and filtered reads are mapped, or aligned, to an organism-specific reference genome, generating an alignment file (represented in a BAM file format). The most widely used aligners for chromatin accessibility data are Bowtie2 (ref.162) (used in the ENCODE ATAC-seq pipeline156) and bwa-mem163 (used in the Cell Ranger ATAC Algorithm) (Fig. 3c). Following alignment, some additional filtering steps are advised to discard reads with low mapping quality or multi-mapped reads, PCR-duplicated reads, ENCODE blacklisted regions164 and mitochondrial reads. This is particularly important for ATAC-seq data in which mitochondrial reads can make up as high as 75% of the total amount of mapped reads when using the original protocol36 (Fig. 3d).

A post-alignment quality control step is recommended at this point, involving visualizing accumulated read abundance around transcription start sites, which are generally highly accessible165 (Fig. 3e). Other quality control metrics include the number of reads, mapping percentages, duplication percentages and visualization of nucleosome patterning via a fragment size distribution plot50. Such diagnostic plots can, for instance, be generated using the package ATACseqQC165. In addition, visually inspecting the distribution of reads across the genome using genome browsers such as IGV166, UCSC167, Ensembl168 or JBrowse169,170 can further increase insight into the quality of the samples (Fig. 3e).

Peak calling

Following initial read processing and quality control comes one of the crucial steps in chromatin accessibility data analysis, namely defining ‘peaks’, which are genomic regions with a high accumulation of reads compared with the background (Fig. 3f). These peaks form the basis for most of the downstream analyses. The most widely used tool for peak calling is MACS2 (ref.171), which is also the default in the ENCODE ATAC-seq pipeline156. MACS2 is a model-based algorithm originally designed for ChIP–seq data analysis. It implements a dynamic Poisson distribution to capture local background biases in the genome and to effectively detect peaks171. As MACS2 was originally designed for ChIP–seq data, specific parameters (for example, --nomodel) need to be used for peak calling in ATAC-seq or DNase-seq data. The ENCODE ATAC-seq pipeline contains more detailed information on the parameters. Other general and method-specific peak callers exist, for example, ZINBA172 (general), HMMRATAC173 and Genrich174 (ATAC-seq), and F-seq175 and Hotspot176 (DNase-seq and ATAC-seq). The signal threshold, which influences the sensitivity and specificity of peak retrieval, is an important parameter to consider during the peak calling step. The default minimum false discovery rate cut-off of 0.05 for MACS2 has been shown to be optimal for a range of DNase-seq data sets177.

To ensure reproducibility in the data, ENCODE guidelines recommend that each ATAC-seq experiment should have two or more biological replicates and that replicate concordance should be checked by calculating irreproducible discovery rate (IDR) values. The IDR values can also help to define an independent threshold for peak calling178. Specifically, following a lenient peak calling with, for instance, MACS2, a core set of IDR peaks can be defined by only retaining peaks that pass a set IDR threshold, such as, for example, 5% (ref.179).

As data sets often comprise different samples, the construction of a common set of features, or genomic intervals, is crucial in order to be able to compare samples with each other in downstream steps. Usually, a consensus peak file is used for this purpose, which comprises the set of peaks that are shared between samples, and in which the start and end location of overlapping peaks are adjusted (through the so-called merging of peaks) to thus yield one consensus peak. The ENCODE pipeline provides a workflow with merge and filter steps for constructing a consensus peak file156, although other tools can serve the same purpose (for example, consensusSeekeR180). Alternatively, a predefined set of regions or a binned genome can be used as features in downstream analyses55,64. For human and mouse studies, the ENCODE SCREEN regions181 provide comprehensive sets of intervals, as well as two recently published catalogues of consensus DHS regions (926,535 for human and 339,815 for mouse). For species with more compact genomes and higher regulatory density, such as Drosophila, a set of 134,000 regions covering the entire non-coding genome may be used64. Although these compendia of accessible regions cover a large fraction of the regulatory genome, some condition-specific accessible regions could potentially be missing.

Finally, an important quality control step is the calculation of the signal to noise ratio, which can done by calculating the fraction of reads in called peaks (FRiP score). For ATAC-seq, the FRiP score should preferably be greater than 0.2–0.3 for mammalian species, and the signal proportion of tags (SPOT score) for DNase-seq should exceed 0.4 for mammalian species, which signifies that 40% of mapped reads are located within DHSs128,156. These metrics vary depending on the organism, and they can be dependent on the size and complexity of the genome.

Downstream analysis

Usually, chromatin accessibility profiling is performed on multiple samples, comparing treatment versus control, multiple tissues or cells during a differentiation process. A central question is to define the set — or signature — of peaks that is differentially accessible in each sample (Fig. 3g). For a pairwise comparison between two conditions, differential peak calling can be performed, for example using MACS2 (ref.171), in which mapped BAM files representing treatment and control samples are provided, and for which biological replicates are combined prior to differential accessibility analysis. Alternatively, statistical analyses can be performed on the count matrix, a data table containing the number of reads per feature across the samples, yielding a matrix with features as rows and the different samples as columns. As explained above, these features can be the set of consensus peaks, a predefined set of regions or a binned genome. For pairwise comparisons, several approaches have been borrowed from the RNA-seq field, including MA plots and statistical analyses based on the negative binomial distribution, which are implemented in the DESeq2 (ref.182) and edgeR183 packages or in more chromatin accessibility-specific tools such as DiffBind184, HOMER185 or DBChIP186 (Fig. 3g). Data normalization is recommended when comparing conditions or tissues, as library-specific biases or global chromatin accessibility differences can affect differential accessibility results. Multiple data normalization methods exist, for instance normalization for library size or for FRiP scores, quantile normalization and trimmed means of M value normalization187. Often, the differential accessibility tools mentioned above already include one of these normalization methods188. The choice of data normalization method can alter differential accessibility results. A typical way to visually assess the effectiveness of normalization is through an MA plot: a single cloud should be present and the MA distribution should not display an upward or downward shift188,189.

For differential accessibility analysis in multi-sample studies, several options are possible. One way is to use the normalized count matrix for dimensionality reduction and clustering, for example by hierarchical clustering and k-means clustering (Fig. 3g). Clustering allows one to group together samples with similar chromatin accessibility profiles, as well as to distinguish sets of regions that are differentially accessible across the samples and to generate groups of co-accessible regions (meaning regions that show concordant accessibility patterns across the different samples). Such clustering algorithms are, for instance, implemented in the deepTools package190 and can be visualized with a heat map (Fig. 3g). Other researchers have drawn inspiration from tools designed for clustering of regions in single-cell epigenomics data using factor analysis and unsupervised learning in order to identify differentially accessible regions. For instance, topic modelling or non-negative matrix factorization, in which a high-dimensional data set is approximated by a reduced number of representative components, can be applied directly to bulk data sets, or to a matrix with simulated single cells, created from bulk samples using a bootstrapping procedure191,192.

To gain biological insight into the sets of cell type-specific regions identified via differential accessibility analysis, region set enrichment analysis using GREAT193, ChIPseeker194, ChIPpeakAnno195, Enrichr196, cisTarget197,198, GIGGLE199 and LOLA200 is used to identify correlations of peak sets with genome annotation (for example, promoter, intronic, intergenic) or with existing ChIP–seq data sets and to couple peaks to the nearest gene, followed by Gene Ontology or pathway enrichment (Fig. 3h). In addition, chromatin segmentation approaches such as ChromHMM201, EpicSeg202 and Segway203 are used for genome-wide classification of genomic regions into chromatin states (such as ‘active promoter’ or ‘weak/poised enhancer’) based on epigenomic marks. These enrichment analyses and genome or chromatin state annotations can be useful in interpreting gained or lost accessible regions in a study. Finally, the generation of tracks — which are a way to display data per sample across the genome, specifically, here, chromatin accessibility data — and visually inspecting them together with such annotations or other public ChIP–seq or RNA-seq data can help to gain further biological insights, such as providing indications on the putative functionality of accessible regions or of the TFs bound to them (Fig. 3i).

As combinatorial binding of TFs to accessible regulatory regions forms the basis of gene regulation, one of the major downstream analysis steps is unravelling which TFs are bound to a set of cell type-specific or differentially accessible regions. As many TFs recognize and bind to TF-specific DNA sequences, we can leverage the enrichment of TF motifs in a set of sequences (Fig. 3j). Two major classes of motif analysis tools exist. The first class includes HOMER185, MEME204 and cisTarget197,198, and relies on databases of predefined TF motifs (position weight matrices205) such as JASPAR206, CIS-BP207, TRANSFAC208 and HOCOMOCO209. These approaches scan the DNA sequences of accessible regions with position weight matrices and perform an enrichment analysis compared either with a background set or with the entire genome as background. The second class of tools includes RSAT210, MEME204, Weeder211 and HOMER185, and performs de novo motif discovery, allowing unsupervised identification of enriched TF motifs. Although the identification of de novo motifs does not require a motif database, motif databases are still needed to link de novo motifs to known TFs.

Recently, machine learning methods such as the convolutional neural network models Basset212, DeepSea213, DeepLIFT214 and DeepMEL215 have shown promising results to predict TF motifs in accessible regions in a more precise and unbiased manner. Such models are trained on large sets of co-accessible peaks per cell type, can capture important TF motifs across the training regions and are able to predict their importance at single-nucleotide resolution within the regulatory sequences. Altogether, motif detection on a set of specifically accessible regulatory regions allows the decoding of genome sequences and may reveal possible master regulators that bind to these regions.

An alternative approach to identify TF binding sites from chromatin accessibility data is TF footprinting (Fig. 3k). TF footprints are small regions (8–30 bp) that display relative protection from cleavage due to binding of a TF and thus correspond to dips in the accessibility peak47,216,217. DNase I has been, and is still, the preferred footprinting reagent. Analytic genomic footprinting approaches such as the Wellington algorithm218, HINT219, DBFP220 and DNase2TF (ref.221) de novo annotate DNase I footprints, or they determine TF occupancy at a specific genomic location, such as CENTIPEDE222 and the footprint likelihood ratio223,224. TF footprinting comes with some limitations as it requires extremely deep sequencing, ideally at least 200 million uniquely mapped reads from a DNase-seq experiment for human samples224. In addition, TF footprinting is biased by the short residence times of some TFs on DNA and by intrinsic sequence preferences of the cleavage enzymes, which should be corrected for78. In general, ATAC-seq footprinting has been shown to be less accurate than DNase-seq footprinting225, which might be attributed to the large size of the Tn5 dimer and Tn5-specific cleavage biases that are not accounted for in DNase-seq-designed footprinting algorithms36,226. Nevertheless, footprinting analysis on ATAC-seq data has been performed with success by several groups, for instance in the initial ATAC-seq publication36, using DeFCoM227 or by using ATAC-seq-specific footprinting algorithms (such as HINT-ATAC226 and TOBIAS228) that consider ATAC-seq artefacts and, for instance, correct for Tn5 transposase cleavage biases.

MNase-seq is orthogonal compared with the other discussed chromatin accessibility profiling methods as it measures nucleosome-occupied regions. It is therefore the method of choice to map nucleosome positions genome-wide, for which specific tools (for example, DANPOS229) have been developed229,230 (Fig. 3l). Note that similar to TF footprinting analysis, correction for enzymatic cleavage bias should be performed in nucleosome footprinting analysis. ATAC-seq also lends itself to nucleosome positioning by partitioning paired-end reads based on their size to separate putative nucleosome free reads and mononucleosomal, dinucleosomal and trinucleosomal reads36 or by using specific tools such as NucleoATAC231. In addition, Zhong et al. have shown that DNase-seq data can also be used to infer nucleosome positions with high accuracy by using a Bayes factor-based nucleosome scoring method232. Therefore, all three commonly used chromatin accessibility profiling methods lend themselves to detect nucleosome positioning genome-wide (provided that the data are sequenced paired-end), although MNase-seq is still the most frequently method for this purpose.

Single-cell data analysis

Single-cell chromatin accessibility data require similar upstream processing steps to bulk data, including alignment, feature definition and the generation of a count matrix (Fig. 4a). However, due to the substantial scale and sparsity of the region by cell count matrix, specialized bioinformatics tools have been developed — mostly for scATAC-seq data — to handle these assay-specific challenges191,233,234,235,236,237,238,239,240,241,242. One major point in which these tools differ is the way they define genomic regions to be used as features, either as peaks from bulk or aggregated single-cell data (chromVar239, Cicero238, cisTopic191, scABC241, Scasat233, MAESTRO242), peaks from pseudo-bulk samples56 or fixed-size bins56 (SnapATAC243). Another difference between the bulk and single cell-based algorithms is what the count features represent, for example, counting reads in peaks (cisTopic56,191, scABC241, Scasat233, MAESTRO242), counting gapped k-mers under peaks or around transposase cut sites (BROCKMAN234, chromVAR239), or counting reads overlapping TF motifs in peaks or genome-wide (chromVar239, SCRAT237)244. ArchR236 uses an iterative feature definition method; it first defines a feature-by-cell count matrix of the number of reads per feature (in this case, 500-bp genomic bins) across all single cells, which then undergoes an iterative latent semantic indexing reduction to generate the cell clusters and pseudo-bulk samples on which peaks are called.

Fig. 4: Overview of common tasks in the analysis of scATAC-seq data.

a | Outline of key steps in processing single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) data sets, six of which are illustrated in panels bg. b | An important step in the analysis of scATAC-seq data is clustering the cells via dimensionality reduction of the feature by cell matrix (via UMAP, for example) to discover the different cell populations. In the given example, dots represent the single cells, and their colours and numbers represent the nine identified cell clusters or cell populations. c | Identification of marker genes and/or peaks for each of the cell clusters allows further study of the putative cell populations. d | By aggregating the accessibility profiles of all cells within a cluster, pseudo-bulk genome browser tracks can be generated for each cell population. e | Specific tools allow the identification of peak to gene links, which can reveal putative target genes of identified peaks. f | Assessing peak co-accessibility allows grouping peaks into sets of co-regulated regions. g | When analysing scATAC-seq from a time-series or differentiation experiment, trajectory analysis allows study of the dynamic changes in chromatin accessibility along a pseudo-time axis. QC, quality control.

Important follow-up steps are transformation and dimensionality reduction of the feature by cell count matrix to visualize the cells in a 2D or 3D space and performing further downstream analyses, for example clustering, to uncover the different populations in the sample and their specifically accessible regions (Fig. 4b,c). Once cell clusters are obtained, BAM files of all cells belonging to the same clusters can be aggregated to generate pseudo-bulk BAM files and tracks to visualize the data (Fig. 4d). Recently, ten computational methods for the analysis of scATAC-seq data have been benchmarked244, demonstrating that SnapATAC243, the method used in ref.56 and cisTopic191 performed best in distinguishing cell populations. Next to these, extensions of popular scRNA-seq analysis toolkits specifically designed for single-cell chromatin accessibility data, including Signac (an extension to Seurat245) and EpiScanpy246 (an extension to Scanpy247), are used in the field.

There are currently no designated tools that correct for batch effects in scATAC-seq data. Inexplicit batch correction is performed during the processing steps such as during feature selection or dimensionality reduction248. Batch correction tools designed for scRNA-seq data249,250,251,252 may be used with precautions not to remove biological variance, for instance by assessing the retainment of biological variation between easily defined cell labels and cell trajectories253. To avoid overcorrection, it is recommended to compare multiple batch correction methods to obtain the best result for a given data set. Batch correction becomes especially important when combining multiple runs into atlases or when integrating scRNA-seq data, for which BBKNN249, Scanorama250 and scVI251 performed best in a recent benchmark253. As in scRNA data, reconstruction of a pseudo-time trajectory based on scATAC-seq data can be helpful when studying a system following a cellular differentiation, for instance during embryonic development254 or haematopoiesis255 (Figs 4f,g). Tools such as Cicero238 (which implements modified aspects of the scRNA-seq trajectory inference tool Monocle256, and STREAM255) have been used to infer such trajectories from scATAC-seq data.

As the complexity of a system or disease exists across all molecular layers, computationally integrating multiple omics modalities holds great promise to achieve a systems biology view and to reconstruct gene regulatory networks. The integration of chromatin accessibility profiles with ChIP–seq and RNA-seq data is of particular interest for inferring the binding of specific TFs and for reconstruction of regulatory networks (Fig. 3m). The integration of epigenomics and transcriptomics may predict links between accessible regulatory regions and target genes (Fig. 4e). An example from the single-cell field involves the use of a least absolute shrinkage and selection operator (LASSO) model to correlate a gene’s expression level with the accessibility of all peaks within 100 kbp around its transcription start site, linking 1,260 distal regions to 321 potential target genes257. This improved the prediction of gene expression based on accessibility profiles fourfold compared with only using chromatin accessibility at promoters.


Chromatin accessibility profiling is widely useful for applications in biology and biomedicine, ranging from the analysis of gene regulation and cellular states to the dissection of healthy and diseased tissues and organs, and the investigation of populations and species. These applications benefit from the high genomic resolution of chromatin accessibility profiling, from robust and straightforward assays with low input requirements and from the ability to process many samples in a fast and reasonably cost-efficient manner.

Regulation of chromatin accessibility

As nucleosome occupancy of DNA is refractory to TF binding and transcription, regulation of chromatin accessibility is key to gene regulatory mechanisms. Multiple mechanisms for accomplishing chromatin accessibility have been proposed. Nucleosomes appear to have clear preference for certain sequences, and this bias seems to play some role in establishing nucleosome positions in yeast258,259. However, as this bias is less predictive of nucleosome positioning in metazoan genomes260,261 and accessible regions are mostly relatively large (that is, hundreds of base pairs) and associated with active cis-regulatory elements, sequence preference of nucleosomes is likely not a major contributor to the regulation of chromatin accessibility. Controlling regulatory element accessibility and activity is accomplished through the combined action of TFs, RNA polymerases, chromatin-remodelling complexes, histone chaperones and histone variants1,262.

Many developmental processes involve chromatin remodelling, especially to make previously inaccessible regions accessible. This process is most noticeable in zygotic genome activation during embryonic development, when transcription of the zygotic genome is turned on. Chromatin remodelling is also important for subsequent lineage-specifying developmental transitions, responses to many external and internal stimuli, and cellular reprogramming. Pioneer factors are particularly important in regulating these processes as they are capable of binding at previously inaccessible chromatin and they subsequently initiate the formation of an accessible state23. Well-known examples of pioneer factors include Zelda, which acts upon zygotic genome activation in Drosophila263,264,265, the Nanog/Oct/Sox pluripotency factors266,267,268, FoxA21 and numerous others269. Pioneer factors do not create and maintain an active and accessible state on their own. Rather, they recruit other TFs and chromatin remodellers, and reposition nucleosomes and chromatin modifiers that deposit histone marks characteristic of active regulatory elements23,269. Note that general TF binding can form a constraint for the reappearance of nucleosomes in accessible regions.

Cell state transitions also involve the decommissioning of previously active regulatory elements, which is accomplished by recruiting transcriptional repressors and chromatin-modifying complexes removing active chromatin histone marks and depositing repressive ones such as H3K27me3, H3K9me3 and, eventually, DNA methylation270. This process effectively remodels the chromatin to an inaccessible state.

Cell types and organs

Chromatin accessibility at gene-regulatory regions is highly dynamic during cellular differentiation and organ development17,271. Chromatin accessibility profiling has contributed to our understanding of chromatin regulation across a broad range of organs and cell types in human, mouse, Drosophila and other model organisms53,56,128. The haematopoietic lineage, in particular, has served as a blueprint for deciphering the role of chromatin accessibility and epigenetic changes in cellular differentiation33,272. Application of ATAC-seq and/or ChIP–seq to flow cytometry-purified haematopoietic cell populations has established comprehensive maps of regulatory regions and their dynamic changes in the haematopoietic lineage of human and mouse37,125,273,274. Detailed investigations of macrophages have connected the regulation of these immune cells to their tissue environment275,276, whereas analyses of CD4+ T cells36,277,278 and innate lymphoid cells279,280 have uncovered a striking degree of plasticity in these immune cell populations. Chromatin regulation in immune cells also contributes to the generation of memory T cells281 that are poised to actively respond upon re-exposure to pathogens, as well as to the more limited memory of inflammation in regulatory T cells282. Importantly, immune cell memory is not restricted to B cells and T cells but also includes monocytes and natural killer cells283, and the regulation of such trained immunity appears to involve tightly regulated changes in the epigenomes of the affected cells284,285.

Beyond the haematopoietic lineage, RNA-seq, ATAC-seq and ChIPmentation profiling in epithelial cells, endothelial cells and fibroblasts from many different organs have uncovered widespread immune gene regulation in these structural cells, and an epigenetic potential that appears to preprogramme these cells for contributing to pathogen response286. Chromatin accessibility has also been studied in neural development61,287,288,289 and in brain samples of humans57,59,290 and non-human primates291. Notable applications of chromatin accessibility profiling to other cell types and organs include the analysis of cardiac development292,293, epidermal progenitor cells in the skin294 and mammary gland development295. Finally, initial single-cell atlases of chromatin accessibility across tissues and organs are emerging55,56,60,152, which have the potential to discover new cell types and to define the chromatin states of cell types that are difficult to purify or enrich using flow cytometry. In summary, chromatin accessibility profiling has uncovered a transcription-regulatory landscape that is cell type-specific and organ-specific, and dynamically changes over the course of cellular differentiation and organ development.

Human diseases

Changes in chromatin accessibility are implicated in multiple diseases, where they reflect disease-linked changes in cell composition, gene regulation and epigenetic cell states. Alterations in gene regulation are ubiquitous in cancer296. In blood cancers, chromatin accessibility patterns are shown to reflect the cancer’s cell of origin as well as regulatory changes that appear to contribute to the process of malignant transformation and cancer evolution37,297,298,299,300. Changes in chromatin accessibility have been investigated over the course of targeted therapy in patients with chronic lymphocytic leukaemia301 and combined with chemosensitivity screening to identify promising drug combination therapies302. Chromatin accessibility landscapes have also been mapped in solid tumours, including breast cancer145, colon cancer303,304, glioblastoma305,306, gastric cancer307 and lung cancer308,309. Paediatric cancers tend to carry particularly pronounced regulatory changes, contrasting with their comparatively low rate of somatic mutations. For example, the EWS-FLI1 fusion oncogene in Ewing sarcoma has been shown to impose de novo enhancers and super-enhancers on the tumour cells310,311; and epigenome profiling has uncovered subtype-specific regulatory mechanisms in atypical teratoid rhabdoid tumours312 and in Langerhans cell histiocytosis313.

An interesting line of research has investigated the role of the tumour-associated immune cells in solid tumours. Regulatory changes are implicated in T cell exhaustion in the context of chronic inflammation and the tumour microenvironment314,315, which compromises the ability of these T cells to fight the tumour. Immunotherapy, most notably blocking of the PD1/PDL1 pathway, can revert some of the regulatory changes associated with T cell exhaustion150,316,317 and is widely useful for the treatment of those solid tumours that have a high degree of immunogenicity318. However, not all exhausted T cells are rejuvenated by immune checkpoint blockade, as some T cells appear to transition to a fixed regulatory state that renders them resistant to reprogramming314.

Beyond cancer, where chromatin accessibility has been studied most extensively, changes in chromatin accessibility have also been observed in immune diseases such as inflammatory bowel disease319 and rheumatoid arthritis320. Changes in epigenome and chromatin accessibility profiles have been observed in post-mortem brain tissue from patients with Alzheimer disease321, schizophrenia322 and autism spectrum disorder323. In summary, chromatin accessibility profiling of primary patient samples is already widely used for identifying disease-linked changes in chromatin structure and transcription regulation, and there is substantial scope for new discoveries as researchers move beyond cancer and are investigating regulatory mechanisms in many diseases that have as yet received little attention.

Variation within populations

Extension of chromatin accessibility assays to populations of diverse genetic backgrounds is valuable for advancing our understanding of how sequence variation impacts cis-regulation within a species. A striking 90% of disease-associated variants in humans, identified via genome-wide association studies, localize to non-coding loci distant from the affected gene, obfuscating functional predictions29,324,325. Mounting evidence implicates the alteration of gene regulation as a key driver of phenotypic evolution and disease proliferation. Quantitative trait loci (QTL) mapping of molecular traits, such as gene expression variation (expression QTL), provides an attractive approach for deciphering the gene regulatory potential of genetic variants within a population. Leveraging a molecular QTL framework, a large-scale DNase-seq panel of 70 lymphoblastoid cell lines from the Yoruba HapMap showed that approximately 50% of chromatin accessibility-associated variants coincide with variants associated with expression variation, with the allele conferring increased accessibility generally associated with increased gene expression326. This study also provided evidence that sequence alterations underlying cis-regulatory elements perturb TF binding affinities, leading to weakened or ablated binding.

An analysis of CD4+ T cell chromatin accessibility from 105 healthy donors revealed that only 15% of genetic variants embedded within accessible chromatin regions affect the relative accessibility of the related locus327. Thus, the majority of genetic variants located within accessible chromatin appear to lack functional consequences on gene regulation. The same study further demonstrated that pairwise correlations of accessible regions (co-accessible regions) readily recapitulate 3D higher-order chromatin interactions as defined by in situ Hi-C (high-throughput chromosome conformation capture). This similarity suggests that local chromatin accessibility among pairs of regions is coordinated with higher-order genome structure, particularly within the same topologically associated domains. In line with these findings, local chromatin accessibility in a subset of regions has been associated with variants located tens to hundreds of kilobase pairs away, reflecting putative long-distance functional interactions. Importantly, integration of population-scale accessibility data captured 10–30% of previously reported autoimmune-associated variants and explained 1–7% of disease heritability. In model organisms, chromatin accessibility can be performed across a cohort of homozygous inbred individuals, making the identification of chromatin accessibility QTL more straightforward. A critical subset of chromatin accessibility QTL could be explained by making or creating binding motifs for pioneer factors328. In an alternative approach, chromatin accessibility can also be compared between alleles, within the same individual, to identify allele-specific chromatin accessibility329.

Taken together, population-based and/or allele-specific analysis of chromatin accessibility provides a powerful approach for dissecting the regulatory potential of genetic variants associated with a trait of interest. Future studies in other tissues and disease states leveraging single-cell technologies have the potential to systematically map all chromatin accessibility-modifying variants in a cell type-specific fashion.

Evolution of chromatin accessibility

Chromatin accessibility profiling facilitates the identification of causal genetic variants underlying disease and trait variation. Similar methods and analyses have also proven useful to study the evolution of gene regulation and morphological evolution between species. For example, major morphological transitions, such as the loss of limbs in snakes and eye degeneration in subterranean mammals, are linked to loss of regulatory elements330. These regulatory regions were discovered using a combination of tissue-specific ATAC-seq and comparative genomics. In another study, chromatin accessibility data in combination with H3K27ac and H3K4me3 were used to identify promoters and enhancers in the liver tissue of 20 mammalian species259. The rate of sequence variation was much greater for enhancers in comparison with promoters. This was reflected in a lower conservation of enhancers between species, yet newly evolved enhancers are more likely to be under positive selection in a lineage-specific manner.

In plants, incorporating chromatin accessibility data into evolutionary studies helps with the identification of cis-regulatory elements, as this identification through sequence-based alignment alone is often hindered by the high proportion of DNA sequence variation in intergenic regions331,332. Chromatin accessibility profiling can help reveal important clues about the evolution of gene regulation. For instance, a comparative epigenomics study of numerous flowering plant species, ranging in genome size from ~150 to 5,000 Mbp, revealed rapid evolution of cis-regulatory sequences within accessible chromatin regions where DNA sequence conservation was detected333. The frequency of distal accessible chromatin regions was correlated with genome size and their distance from genes was mostly due to transposon and repeat expansion in these plants69,330,334.

The lack of distal regulatory regions in Capsaspora owczarzaki, a unicellular eukaryotic organism sister to other animal species, has led to the hypothesis that distal regulation is a feature of animal multicellularity335. However, with the increase in profiles of chromatin accessibility across taxa, it seems more likely that distal regulation is a consequence of genome size333. Additional comparative epigenomic studies of chromatin accessibility across diverse taxa and of species that represent key nodes in the tree of life will further unveil diverse mechanisms in the evolution of gene regulatory mechanisms.

Reproducibility and data deposition

The genomics community has been leading the way in creating standards for data information, data quality and data deposition for decades (Table 2). Many genome-wide data sets serve as community resources and, as a result, are repeatedly used and incorporated into future studies by individual laboratories. To increase the usability of epigenomics data, it is common practice to submit the data to well-funded and stable data archive facilities such as the Gene Expression Omnibus (GEO) repository336 at the National Center for Biotechnology Information (NCBI) or the ArrayExpress database337 at the European Bioinformatics Institute (EBI). These databases host records of genomics data containing not only count matrices and other useful processed output files (for example, bigWig files or BED files enriched for chromatin modification or accessibility) but also a short description of the experimental design and processing steps to reach the submitted output files, as well as a link to the archived raw sequencing data. For both bulk and single-cell chromatin accessibility data, researchers are expected to submit their FASTQ files to specific databases. In the case of scATAC-seq, specifically for data generated via the 10x Genomics platform, preferably three FASTQ files are submitted, namely the FASTQ file containing the barcode read and the two FASTQ files containing the paired-end feature reads. For non-human species and open-consent human donors, the raw sequencing data should be submitted to, for instance, the Sequence Read Archive (SRA)338, European Nucleotide Archive (ENA)339 or DNA Data Bank of Japan (DDBJ)340. For human donors where controlled access is required for adequate data protection, the raw sequencing data should be submitted, for instance, into the European Genome–phenome Archive (EGA)341 or the database of Genotypes and Phenotypes (dbGaP)342 from the NCBI.

Table 2 Commonly used databases for archiving and distributing chromatin accessibility data

To facilitate interpretation and reproducibility, the deposited data should include metadata. For example, data entry requirements that are useful to address issues associated with reproducibility could include sources of possible biological and technical variation. Sources of biological variation include genotype, sex of samples, age and tissue/organ/cell type, whereas sources of technical variation could relate to antibodies (requiring reporting of the lot number) and nucleases/integrases (requiring reporting of the lot number, sequencing library procedure, instrument used for sequencing and type of sequencing run). These possible sources of technical and biological variation are important variables that can be incorporated into data analyses as covariates or to correct for batch effects. The versions of genome assemblies and genome annotations used in data analyses should also be provided.

As well as the mentioned general epigenomic databases (GEO336 and ArrayExpress337), several secondary databases make chromatin accessibility data, or other types of omics data, easily available — often by including an experiment matrix providing a visual representation of the available data, classified based on assay type, tissue type, organism and so on. Such databases include data portals set up by large consortia that produce large numbers of epigenomes (for example, ENCODE30, Roadmap Epigenomics32, IHEC343 and BLUEPRINT33), data portals that originate from reanalysis or meta-analysis (ChIP-Atlas344 and ReMap345), UCSC track hubs346 or interactive web-based tools hosted by individual laboratories. The latter, although useful for initial exploration of the generated data, are often less sustainable and vary in their set-up, and are therefore not the preferred platform to disseminate epigenomics data in a reproducible way. Nevertheless, custom websites often provide integrated downstream analysis results that are context-specific and go beyond standard data formats56,347. There exist dedicated tools and websites to host and visualize single-cell chromatin accessibility data, including the use of SCope348, a Shiny app237 or ASAP349. Specifically for single-cell chromatin accessibility data, we envision that in the near future specific platforms will arise that provide an overview of large amounts of public data and facilitate their easy dissemination, similar to the Single Cell Expression Atlas350, a data portal hosted by the Human Cell Atlas.

Finally, the distribution of custom code and the documentation of computational methods are also paramount to reproducibility. The ENCODE Consortia has developed extensive open-source software that is accompanied with a document of best practices and descriptive details on the rationale for data processing steps, thresholds and quality metrics for data evaluation. In general, software used for data analyses should include the software version and parameter options applied. Custom code should be disseminated through public hosts such as GitHub, or can be archived in a static digital repository such as Zenodo or in more specialized repositories such as Kipoi351 for ready to use trained machine learning models for genomics. Efforts to address the biological, experimental and computational variables described above will increase reproducibility in addition to the usability of these data for years to come.

Limitations and optimizations

Although chromatin accessibility has proven a powerful and informative window into gene regulation, it is often combined with other measurements and with perturbations to build a causal or mechanistic understanding of genomic function. Whereas accessibility dynamics can be readily profiled, the specific molecular factors that drive accessibility changes may only be inferred by changes in the accessibility or footprints associated with DNA motifs. However, directly inferring the specific TF that is possibly bound based just on the observed DNA motifs is tricky, as a DNA motif may be bound by various related TFs, often within a TF family of structurally similar DNA binding domains. One way of narrowing down the specific TF of a TF family that is bound to DNA motifs enriched in regions undergoing accessibility changes is by determining which of the TFs undergoes concomitant changes in gene expression. Still, to mechanistically link the binding of a specific TF, subsequent experiments are needed, such as TF knockdown or ChIP–seq targeting the specific TF implicated.

Additionally, chromatin accessibility of a putative regulatory locus is usually necessary but not in itself sufficient for bona fide functional regulation. Other marks, such as H3K27ac or the presence of nascent transcription of enhancer RNA, appear to mark a subset of accessible elements that are more highly enriched for function352,353,354. Therefore, chromatin accessibility data should be combined with other genomic assays to build a stronger set of inferences on the functionality of specific elements.

Finally, chromatin accessibility profiling using DNase-seq and, to a lesser extent, ATAC-seq may require optimization of the reaction time, lysis protocols, cell handling and freezing or thawing, as well as library purification, to produce optimal data. For methods such as ATAC-seq, numerous quality metrics exist prior to sequencing, such as relative PCR cycles required to amplify the library or the periodicity of the length distribution of fragments generated by the transposition reaction, which allow for relatively rapid and inexpensive optimization of sequencing libraries.


The current and future challenge in the study of chromatin accessibility is to dissect the function of these regulatory regions in relation to other regulatory layers and gene expression (Fig. 5). Chromatin accessibility alone provides no information on the functional properties of the region — whether it acts as a promoter, enhancer, silencer or replication origin — or its activity state. Information on which TF factors are likely bound to the region must be inferred through sequence analysis. Many of these challenges can be overcome by a more holistic multi-omics approach, by profiling the transcriptome, histone modifications and TF occupancy from the same sample, in addition to chromatin accessibility. A common approach is to run multiple omics methods on fractions of the same sample, using protocols optimized separately for each assay, thus generating comparable data sets355,356.

Fig. 5: Schematic overview of future roads and opportunities for chromatin accessibility profiling.

In the coming years, our ability to measure chromatin accessibility concurrently with multiple regulatory layers in the same single cell will continue to expand. New insights into regulatory biology will be gained by applying these methods in the native spatial context and in systems undergoing perturbations. Development of computational tools that can dive into the complexity of the emerging data sets will be crucial for the success of these endeavours. Ultimately, these approaches will empower us to functionally dissect the role of regulatory elements and their relationship to gene expression. TF, transcription factor.

Chromatin accessibility profiling in single cells has surged dramatically in recent years, and we expect further improvements in the coming years as this trend increases. The analysis of accessibility and other regulatory features at the single-cell level is challenging. There are only two loci that can be measured simultaneously in a diploid genome by single-cell regulatory genomics-based methods. As a result, the data are mostly binary and very sparse due to the low coverage per cell. A certain degree of data aggregation across cells or features is usually required. Specialized computational tools have been developed that address the sparsity and binary nature of scATAC-seq data and facilitate more integrated analyses across groups of cells191,233,234,235,236,237,238,239,240,241. However, tools designed for scATAC-seq for specific analysis tasks, such as pseudo-time and trajectory inference, remain limited. Although comparisons of performance and applicability of scATAC-seq methods have been performed244, there are no uniform pipelines being widely used by the community, which complicates the systematic comparison and interpretation of results coming from different laboratories. In the coming years, we foresee major efforts in the standardization of comprehensive computational pipelines for the analyses of single-cell epigenomic data. In addition, it is difficult to estimate the sensitivity of scATAC-seq. Roughly, ~10–15% of known peaks are recovered per single cell146, but it is not known how many regulatory elements are accessible in any given cell at any instance in time. Technical advances have improved cell coverage, which ameliorates both issues and has led to a significant increase in assay sensitivity, allowing a sharper distinction between cell types as well as regulatory changes. Nevertheless, when homogeneous or flow cytometry-purified samples are used as input, then bulk ATAC-seq will likely remain the preferred assay.

Recent advances in single-cell methods are pushing technologies to perform multi-omics measurements simultaneously from the same single cell. Multiple methods have already been published for simultaneous scATAC-seq and transcriptome profiling. These include sci-CAR257, Paired-seq155 and SHARE-seq153, which are all based on combinatorial indexing, as well as SNARE-seq357 and 10x Genomics Chromium Single Cell Multiome358, which are droplet-based microfluidics methods. Other achievements include joint profiling of chromatin accessibility with either protein-level quantification (Pi-ATAC359) or with DNA methylation (scNOMe-seq360, COOL-seq361, EpiMethylTag362, methyl-ATAC-seq363, ATAC-Me364) and chromatin accessibility profiling with both DNA methylation and transcriptome measurements (scNMT-seq365).

Several technical challenges have so far limited the widespread application of multi-omic methods. Sample fixation, reaction conditions and other experimental parameters are often not compatible for multiple omic assays, complicating the optimization of joint protocols. Given that single-cell omic methods often suffer from sensitivity issues — meaning a low number of detected features (such as genes or regions) per single cell — running and combining two such methods could result in a very small set of overlapping features. Profiling multiple molecular layers raises the non-trivial computational challenge of integrating the data sets. Methods that can handle the harmonization of bulk and single-cell multi-omic measurements have recently been developed (MOFA366, Seurat v3 (ref.240)). A key feature required for future computational methods is flexibility; methods need to handle data sets coming from very different modalities, coming from the same cell or from the same sample, and will need to impute missing molecular layers based on the ones that were profiled. Measuring multiple parameters from the same single cell should greatly advance our ability to link regulatory properties and deconstruct regulatory connections. Having information on coordinated changes in distal open chromatin regions, such as putative enhancers, and gene transcription from the same cell, for example, would facilitate the linking of enhancers to their potential target genes. We anticipate important developments in both experimental and computational multi-omics approaches in the coming years.

The function of accessible chromatin regions can also be probed by perturbation, for example by mutating key TFs. Single-cell accessibility profiling can detect the impact of the mutations directly in the affected cell types, revealing both changes in regulation as well as alterations in cell fate decisions. Large-scale perturbation and profiling of regulatory networks has been performed in cell culture models by coupling CRISPR screening with scATAC-seq (Perturb-ATAC367). In more complex systems, where high-throughput targeted mutagenesis is not feasible, natural sequence variation can be exploited for large-scale perturbation. In this context, profiling accessibility within and between species provides insights into regulatory variation and functionality. These approaches, in combination with multi-omics measurements, may lead to more accurate and predictive models of gene expression, and to a causal understanding of enhancer and TF function.

Finally, a particularly exciting area of future development is the integration of chromatin accessibility profiling with imaging-based approaches. Current chromatin accessibility profiling protocols involve tissue dissociation to extract cells or nuclei, which leads to the loss of the native spatial context. ATAC-see84 mitigates this problem by performing the Tn5 reaction in situ on microscope slides and using fluorescent adaptors that are compatible with both imaging and sequencing. sciMAP-ATAC368 provides medium-level spatial mapping of single-cell chromatin accessibility profiles for a tissue by taking a select amount of microbiopsies of a tissue prior to the sciATAC-seq workflow. Further integration of ATAC-seq with high-throughput fluorescence in situ hybridization and other imaging-based methods will lead to new ways of interrogating the genome of complex systems in situ after stimuli and perturbations368,369. Such developments hold promise to advance discovery in multiple fields. For example, in the context of developmental biology, it would help to decode the functional impact of morphogen gradients and cellular signal transduction by measuring the regulatory response in the cells receiving the signal while maintaining information on each cell’s spatial positioning to both the source signal and their neighbouring cells. Integration with multi-omics measurements will lead to the generation of virtual models of developing embryos with enhanced resolution and predictive power. In the medical setting, this could reveal the relationship between cell growth and spatial positions within a tumour, the dependencies between the point of injury or infection, and the efficacy of drugs to elicit cellular responses depending on the cells’ position, to name but a few. Given the sensitivity of these methods and the rapid speed with which they are being developed, this will open up new, exciting avenues for diagnosis, prognosis and therapeutic intervention.


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This work was supported by the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health (NIH) (K.Z.) and the National Science Foundation (NSF; IOS-1856627) (R.J.S). R.J.S. is a Pew Scholar in the Biomedical Sciences, supported by The Pew Charitable Trusts. L.M. was supported by a PhD fellowship from the FWO (no. 1S03317N), A.P.M. by an NSF Postdoctoral Fellowship in Biology (DBI-1905869), C.B. by a European Research Council (ERC) Starting Grant (European Union’s Horizon 2020 research and innovation programme, grant agreement no. 679146), E.E.M.F. by an ERC Advanced grant (DeCRypT) and S.A. by an ERC Consolidator Grant (cis_CONTROL). W.J.G. acknowledges support as a Chan-Zuckerberg Biohub Investigator, and from NIH grants UM1-HG009436, P50-HG007735, UM1-HG009442 and U19-AI057266. The authors thank J. Demeulemeester for insights into the long-read sequencing platforms.

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