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MNase digestion with sequencing

MNase-seq, short for micrococcal nuclease digestion with deep sequencing,[1][2][3][4] is a molecular biological technique that was first pioneered in 2006 to measure nucleosome occupancy in the C. elegans genome,[1] and was subsequently applied to the human genome in 2008.[2] Though, the term 'MNase-seq' had not been coined until a year later, in 2009.[3] Briefly, this technique relies on the use of the non-specific endo-exonuclease micrococcal nuclease, an enzyme derived from the bacteria Staphylococcus aureus, to bind and cleave protein-unbound regions of DNA on chromatin. DNA bound to histones or other chromatin-bound proteins (e.g. transcription factors) may remain undigested. The uncut DNA is then purified from the proteins and sequenced through one or more of the various Next-Generation sequencing methods.[5]

MNase-seq is one of four classes of methods used for assessing the status of the epigenome through analysis of chromatin accessibility. The other three techniques are DNase-seq, FAIRE-seq, and ATAC-seq.[4] While MNase-seq is primarily used to sequence regions of DNA bound by histones or other chromatin-bound proteins,[2] the other three are commonly used for: mapping Deoxyribonuclease I hypersensitive sites (DHSs),[6] sequencing the DNA unbound by chromatin proteins,[7] or sequencing regions of loosely packaged chromatin through transposition of markers,[8][9] respectively.[4]

History

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Micrococcal nuclease (MNase) was first discovered in S. aureus in 1956,[10] protein crystallized in 1966,[11] and characterized in 1967.[12] MNase digestion of chromatin was key to early studies of chromatin structure; being used to determine that each nucleosomal unit of chromatin was composed of approximately 200bp of DNA.[13] This, alongside Olins' and Olins' "beads on a string" model,[14] confirmed Kornberg's ideas regarding the basic chromatin structure.[15] Upon additional studies, it was found that MNase could not degrade histone-bound DNA shorter than ~140bp and that DNase I and II could degrade the bound DNA to as low as 10bp.[16][17] This ultimately elucidated that ~146bp of DNA wrap around the nucleosome core,[18] ~50bp linker DNA connect each nucleosome,[19] and that 10 continuous base-pairs of DNA tightly bind to the core of the nucleosome in intervals.[17]

In addition to being used to study chromatin structure, micrococcal nuclease digestion had been used in oligonucleotide sequencing experiments since its characterization in 1967.[20] MNase digestion was additionally used in several studies to analyze chromatin-free sequences, such as yeast (Saccharomyces cerevisiae) mitochondrial DNA[21] as well as bacteriophage DNA[22][23] through its preferential digestion of adenine and thymine-rich regions.[24] In the early 1980s, MNase digestion was used to determine the nucleosomal phasing and associated DNA for chromosomes from mature SV40,[25] fruit flies (Drosophila melanogaster),[26] yeast,[27] and monkeys,[28] among others. The first study to use this digestion to study the relevance of chromatin accessibility to gene expression in humans was in 1985. In this study, nuclease was used to find the association of certain oncogenic sequences with chromatin and nuclear proteins.[29] Studies utilizing MNase digestion to determine nucleosome positioning without sequencing or array information continued into the early 2000s.[30]

With the advent of whole genome sequencing in the late 1990s and early 2000s, it became possible to compare purified DNA sequences to the eukaryotic genomes of S. cerevisiae,[31] Caenorhabditis elegans,[32] D. melanogaster,[33] Arabidopsis thaliana,[34] Mus musculus,[35] and Homo sapiens.[36] MNase digestion was first applied to genome-wide nucleosome occupancy studies in S. cerevisiae[37] accompanied by analyses through microarrays to determine which DNA regions were enriched with MNase-resistant nucleosomes. MNase-based microarray analyses were often utilized at genome-wide scales for yeast[38][39] and in limited genomic regions in humans[40][41] to determine nucleosome positioning, which could be used as an inference for transcriptional inactivation.

In 2006, Next-Generation sequencing was first coupled with MNase digestion to explore nucleosome positioning and DNA sequence preferences in C. elegans,.[1] This was the first example of MNase-seq in any organism.

It was not until 2008, around the time Next-Generation sequencing was becoming more widely available, when MNase digestion was combined with high-throughput sequencing, namely Solexa/Illumina sequencing, to study nucleosomal positioning at a genome-wide scale in humans.[2] A year later, the terms "MNase-Seq" and "MNase-ChIP", for micrococcal nuclease digestion with chromatin immunoprecipitation, were finally coined.[3] Since its initial application in 2006,[1] MNase-seq has been utilized to deep sequence DNA associated with nucleosome occupancy and epigenomics across eukaryotes.[5] As of February 2020, MNase-seq is still applied to assay accessibility in chromatin.[42]

Description

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Chromatin is dynamic and the positioning of nucleosomes on DNA changes through the activity of various transcription factors and remodeling complexes, approximately reflecting transcriptional activity at these sites. DNA wrapped around nucleosomes are generally inaccessible to transcription factors.[43] Hence, MNase-seq can be used to indirectly determine which regions of DNA are transcriptionally inaccessible by directly determining which regions are bound to nucleosomes.[5]

In a typical MNase-seq experiment, eukaryotic cell nuclei are first isolated from a tissue of interest. Then, MNase-seq uses the endo-exonuclease micrococcal nuclease to bind and cleave protein-unbound regions of DNA of eukaryotic chromatin, first cleaving and resecting one strand, then cleaving the antiparallel strand as well.[3] The chromatin can be optionally crosslinked with formaldehyde.[44] MNase requires Ca2+ as a cofactor, typically with a final concentration of 1mM.[5][12] If a region of DNA is bound by the nucleosome core (i.e. histones) or other chromatin-bound proteins (e.g. transcription factors), then MNase is unable to bind and cleave the DNA. Nucleosomes or the DNA-protein complexes can be purified from the sample and the bound DNA can be subsequently purified via gel electrophoresis and extraction. The purified DNA is typically ~150bp, if purified from nucleosomes,[2] or shorter, if from another protein (e.g. transcription factors).[45] This makes short-read, high-throughput sequencing ideal for MNase-seq as reads for these technologies are highly accurate but can only cover a couple hundred continuous base-pairs in length.[46] Once sequenced, the reads can be aligned to a reference genome to determine which DNA regions are bound by nucleosomes or proteins of interest, with tools such as Bowtie.[4] The positioning of nucleosomes elucidated, through MNase-seq, can then be used to predict genomic expression[47] and regulation[48] at the time of digestion.

Extended Techniques

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Technical applications of MNase in sequencing

MNase-ChIP/CUT&RUN sequencing

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Recently, MNase-seq has also been implemented in determining where transcription factors bind on the DNA.[49][50] Classical ChIP-seq displays issues with resolution quality, stringency in experimental protocol, and DNA fragmentation.[50] Classical ChIP-seq typically uses sonication to fragment chromatin, which biases heterochromatic regions due to the condensed and tight binding of chromatin regions to each other.[50] Unlike histones, transcription factors only transiently bind DNA. Other methods, such as sonication in ChIP-seq, requiring the use of increased temperatures and detergents, can lead to the loss of the factor. CUT&RUN sequencing is a novel form of an MNase-based immunoprecipitation. Briefly, it uses an MNase tagged with an antibody to specifically bind DNA-bound proteins that present the epitope recognized by that antibody. Digestion then specifically occurs at regions surrounding that transcription factor, allowing for this complex to diffuse out of the nucleus and be obtained without having to worry about significant background nor the complications of sonication. The use of this technique does not require high temperatures or high concentrations of detergent. Furthermore, MNase improves chromatin digestion due to its exonuclease and endonuclease activity. Cells are lysed in an SDS/Triton X-100 solution. Then, the MNase-antibody complex is added. And finally, the protein-DNA complex can be isolated, with the DNA being subsequently purified and sequenced. The resulting soluble extract contains a 25-fold enrichment in fragments under 50bp. This increased enrichment results in cost-effective high-resolution data.[50]

Single-cell MNase-seq

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Single-cell micrococcal nuclease sequencing (scMNase-seq) is a novel technique that is used to analyze nucleosome positioning and to infer chromatin accessibility with the use of only a single-cell input.[51] First, cells are sorted into single aliquots using fluorescence-activated cell sorting (FACS).[51] The cells are then lysed and digested with micrococcal nuclease. The isolated DNA is subjected to PCR amplification and then the desired sequence is isolated and analyzed.[51] The use of MNase in single-cell assays results in increased detection of regions such as DNase I hypersensitive sites as well as transcription factor binding sites.[51]

Comparison to other Chromatin Accessibility Assays

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MNase-seq is one of four major methods (DNase-seq, MNase-seq, FAIRE-seq, and ATAC-seq) for more direct determination of chromatin accessibility and the subsequent consequences for gene expression.[52] All four techniques are contrasted with ChIP-seq, which relies on the inference that certain marks on histone tails are indicative of gene activation or repression,[53] not directly assessing nucleosome positioning, but instead being valuable for the assessment of histone modifier enzymatic function.[4]

DNase-seq

[edit]

As with MNase-seq,[2] DNase-seq was developed by combining an existing DNA endonuclease[6] with Next-Generation sequencing technology to assay chromatin accessibility.[54] Both techniques have been used across several eukaryotes to ascertain information on nucleosome positioning in the respective organisms[4] and both rely on the same principle of digesting open DNA to isolate ~140bp bands of DNA from nucleosomes[2][55] or shorter bands if ascertaining transcription factor information.[45][55] Both techniques have recently been optimized for single-cell sequencing, which corrects for one of the major disadvantages of both techniques; that being the requirement for high cell input.[56][51]

At sufficient concentrations, DNase I is capable of digesting nucleosome-bound DNA to 10bp, whereas micrococcal nuclease cannot.[17] Additionally, DNase-seq is used to identify DHSs, which are regions of DNA that are hypersensitive to DNase treatment and are often indicative of regulatory regions (e.g. promoters or enhancers).[57] An equivalent effect is not found with MNase. As a result of this distinction, DNase-seq is primarily utilized to directly identify regulatory regions, whereas MNase-seq is used to identify transcription factor and nucleosomal occupancy to indirectly infer effects on gene expression.[4]

FAIRE-seq

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FAIRE-seq differs more from MNase-seq than does DNase-seq.[4] FAIRE-seq was developed in 2007[7] and combined with Next-Generation sequencing three years later to study DHSs.[58] FAIRE-seq relies on the use of formaldehyde to crosslink target proteins with DNA and then subsequent sonication and phenol-chloroform extraction to separate non-crosslinked DNA and crosslinked DNA. The non-crosslinked DNA is sequenced and analyzed, allowing for direct observation of open chromatin.[59]

MNase-seq does not measure chromatin accessibility as directly as FAIRE-seq. However, unlike FAIRE-seq, it does not necessarily require crosslinking,[5] nor does it rely on sonication,[4] but it may require phenol and chloroform extraction.[5] Two major disadvantages of FAIRE-seq, relative to the other three classes, are the minimum required input of 100,000 cells and the reliance on crosslinking.[7] Crosslinking may bind other chromatin-bound proteins that transiently interact with DNA, hence limiting the amount of non-crosslinked DNA that can be recovered and assayed from the aqueous phase.[52] Thus, the overall resolution obtained from FAIRE-seq can be relatively lower than that of DNase-seq or MNase-seq[52] and with the 100,000 cell requirement,[7] the single-cell equivalents of DNase-seq[56] or MNase-seq[51] make them far more appealing alternatives.[4]

ATAC-seq

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ATAC-seq is the most recently developed class of chromatin accessibility assays.[8] ATAC-seq uses a hyperactive transposase to insert transposable markers with specific adapters, capable of binding primers for sequencing, into open regions of chromatin. PCR can then be used to amplify sequences adjacent to the inserted transposons, allowing for determination of open chromatin sequences without causing a shift in chromatin structure.[8][9] ATAC-seq has been proven effective in humans, amongst other eukaryotes, including in frozen samples.[60] As with DNase-seq[56] and MNase-seq,[51] a successful single-cell version of ATAC-seq has also been developed.[61]

ATAC-seq has several advantages over MNase-seq in assessing chromatin accessibility. ATAC-seq does not rely on the variable digestion of the micrococcal nuclease, nor crosslinking or phenol-chloroform extraction.[5][9] It generally maintains chromatin structure, so results from ATAC-seq can be used to directly assess chromatin accessibility, rather than indirectly via MNase-seq. ATAC-seq can also be completed within a few hours,[9] whereas the other three techniques typically require overnight incubation periods.[5][6][7] The two major disadvantages to ATAC-seq, in comparison to MNase-seq, are the requirement for higher sequencing coverage and the prevalence of mitochondrial contamination due to non-specific insertion of DNA into both mitochondrial DNA and nuclear DNA.[8][9] Despite these minor disadvantages, use of ATAC-seq over the alternatives is becoming more prevalent.[4]

References

[edit]
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MNase-seq

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from Grokipedia
MNase-seq, short for micrococcal nuclease sequencing, is a high-throughput genomic technique designed to map nucleosome occupancy and positioning across eukaryotic genomes at high resolution. The method relies on the enzymatic activity of micrococcal nuclease (MNase), an endonuclease derived from Staphylococcus aureus that preferentially cleaves accessible linker DNA between nucleosomes while protecting the approximately 147 base pairs of DNA wrapped around histone octamers. These protected mononucleosomal DNA fragments, typically 100–200 base pairs in length, are isolated, size-selected, and subjected to paired-end sequencing, with nucleosome positions inferred from the midpoints of sequencing reads or through advanced computational modeling. This approach provides a genome-wide snapshot of chromatin architecture, enabling the study of nucleosome spacing, phasing, and dynamic changes in response to cellular processes. The core principle of MNase-seq stems from the nuclease's sensitivity to chromatin accessibility, where well-positioned nucleosomes shield DNA from digestion, resulting in distinct fragment length distributions that correlate with nucleosome density. Early applications focused on yeast and model organisms to delineate periodic nucleosome arrays at promoters and enhancers, but refinements such as quantitative MNase-seq (qMNase-seq) have addressed limitations like enzymatic sequence bias—where MNase digests A/T-rich linkers up to 30 times faster than G/C-rich ones—and over-digestion artifacts through kinetic modeling and spike-in normalization with foreign DNA. These improvements allow for precise quantification of nucleosome occupancy levels, distinguishing stable from transient nucleosomes and revealing subtle variations in chromatin accessibility. MNase-seq has become a cornerstone for investigating epigenetic regulation, as nucleosome positioning influences transcription factor binding, DNA accessibility, and higher-order chromatin structures like topologically associating domains (TADs). Key applications include profiling nucleosome barriers at active promoters in Drosophila and mapping chromatin remodeling during development or disease states, such as cancer. Despite its prevalence, the technique's reliance on limited digestion conditions can introduce noise, necessitating computational tools like iNPS or NucHMM for bias correction and fuzzy nucleosome detection. Overall, MNase-seq complements other chromatin profiling methods, such as ATAC-seq or chemical mapping, by offering detailed insights into the foundational role of nucleosomes in genome function.

Background and History

Discovery of Micrococcal Nuclease

Micrococcal (MNase), also known as staphylococcal , was first discovered in 1956 as an extracellular secreted by the bacterium . This was initially isolated and partially for its ability to hydrolyze nucleic acids, marking it as a novel with both endonucleolytic and exonucleolytic activities. In 1966, MNase was successfully crystallized in a phosphatase-free form, enabling further structural studies and purification advancements that improved its usability in biochemical assays. A comprehensive enzymatic followed in 1967, revealing MNase's specificity for cleaving phosphodiester bonds in DNA and RNA to produce 3'-mononucleotides and oligonucleotides, with a requirement for calcium ions as a cofactor. MNase exhibits dual endo- and exonucleolytic properties, preferentially digesting single-stranded nucleic acids over double-stranded forms, though it can cleave both; in chromatin contexts, it selectively targets regions between nucleosomes. Its optimal activity occurs at 37°C and 8–9, where it demonstrates and broad substrate tolerance, making it suitable for controlled experiments. During the 1970s, MNase became a key tool for probing structure, with early applications demonstrating its utility in limited digestion assays that revealed the modular of eukaryotic . Early studies noted MNase's preference for A/T-rich linkers, influencing cleavage patterns. In seminal experiments, partial digestion of followed by produced characteristic "laddering" patterns of DNA fragments at multiples of approximately 200 base pairs, reflecting the periodic spacing of s. These findings, notably from Noll in 1974 and collaborative work by Noll and Kornberg in 1977, confirmed the nucleosome core particle as a stable unit comprising about 146 base pairs of DNA wrapped around an octamer of proteins, with susceptible to MNase cleavage. Such biochemical insights laid the groundwork for later adaptations of MNase in genome-wide studies during the .

Evolution to Genome-Wide Sequencing

The transition from classical digestion experiments using micrococcal nuclease (MNase) to genome-wide sequencing approaches marked a pivotal shift in studying architecture, enabling high-throughput analysis of positioning and occupancy across entire genomes. Building on the foundational role of MNase in early studies from the 1970s, researchers began adapting the enzyme for large-scale mapping by combining it with emerging genomic technologies. The first genome-wide application of MNase-based nucleosome mapping occurred in 2006, when Johnson et al. utilized high-density tiling arrays to profile nucleosome core landscapes in Caenorhabditis elegans. By hybridizing MNase-protected DNA fragments to arrays covering the worm genome, they identified over 1.4 million nucleosome positions, revealing patterns of flexibility in nucleosome placement influenced by local sequence features and revealing periodicities in AT-rich linkers. This study demonstrated the feasibility of genome-scale profiling, highlighting translational constraints at promoters and exons while showing greater positional variability in introns. In 2007, efforts to incorporate sequencing for higher resolution advanced the field, with Albert et al. applying Roche 454 pyrosequencing to short MNase-protected fragments (~150 bp) in to map H2A.Z variant s. This approach sequenced mononucleosomal DNA directly, achieving base-pair resolution for ~67,000 s and demonstrating the practicality of next-generation sequencing for capturing protected fragments without reliance on arrays, thus paving the way for unbiased, high-throughput nucleosome profiling. Concurrently, Lee et al. used tiling arrays in to generate a comprehensive atlas of over 70,000 nucleosome positions, further validating genome-wide occupancy patterns and their correlation with . The method extended to mammalian systems in 2008, when Schones et al. applied deep sequencing to MNase-digested from human CD4+ T cells, producing the first comprehensive occupancy maps for the . Using Illumina sequencing, they aligned millions of reads to identify positioned , uncovering dynamic changes in positioning upon T-cell activation, such as depletion at transcription start sites and periodic arrays downstream. This work highlighted the role of nucleosome remodeling in immune response and established sequencing as superior for detecting cell-type-specific variations. Around this time, the term "MNase-seq" was coined by et al. in 2009, who developed a high-resolution for analyzing Illumina-sequenced MNase fragments in , standardizing nomenclature for the technique and emphasizing its utility in resolving fuzzy versus well-positioned nucleosomes at promoters. These advancements were enabled by next-generation sequencing platforms, particularly Illumina's high-throughput short-read technology introduced in the mid-2000s, which allowed cost-effective mapping of ~150 bp nucleosomal fragments at single-base resolution across billions of reads. This technological leap shifted MNase-based assays from array-limited snapshots to scalable, quantitative genome-wide surveys, influencing subsequent epigenomic studies.

Principles and Methodology

Biochemical Mechanism

Micrococcal nuclease (MNase), an extracellular enzyme from , preferentially cleaves the phosphodiester bonds in regions between nucleosomes, thereby protecting the approximately 147 (bp) DNA core wrapped around octamers. This selective occurs because nucleosome-bound DNA is shielded from the enzyme's due to its tight association with histones, while exposed remains accessible. Under limited conditions, MNase generates a ladder of protected fragments, with mononucleosomes at ~150 bp and dinucleosomes at ~300 bp, reflecting periodic protection patterns that allow inference of nucleosome positioning. Over-digestion, however, can trim nucleosomal DNA from the edges, producing sub-nucleosomal fragments of 100–140 bp, which reveal finer structural details but may introduce biases if not controlled. The biochemical mechanism of MNase involves a calcium-dependent that facilitates the of phosphodiester bonds. MNase requires two Ca²⁺ ions for activity; one Ca²⁺ polarizes the scissile through ionic interaction with a non-bridging oxygen, while the other coordinates with residues like Asp-21, Asp-40, and Glu-43 to position a nucleophilic . This , activated by Glu-43 acting as a general base, attacks the phosphorus atom, cleaving the 5'-P-O bond and yielding a 5'-hydroxyl group and a 3'- monoester product. The enzyme exhibits both endo- and activities, with endonucleolytic cleavage preferred in double-stranded DNA contexts like . In chromatin, MNase digestion kinetics follow a pseudo-first-order model, where the rate of linker DNA cleavage (k₁) exceeds that of nucleosomal DNA decay (k₂), leading to sequential release of nucleosomes from bulk chromatin. This can be described by the differential equations for the reaction chain (bound chromatin B → free nucleosome N → degraded fragments Ø): d[B]dt=−k1[E][B],d[N]dt=k1[E][B]−k2[E][N]\frac{d[B]}{dt} = -k_1 [E][B], \quad \frac{d[N]}{dt} = k_1 [E][B] - k_2 [E][N] The solution for nucleosome concentration is N(t)=C0k1k2−k1(e−k1[E]t−e−k2[E]t)N(t) = C_0 \frac{k_1}{k_2 - k_1} (e^{-k_1 [E] t} - e^{-k_2 [E] t}), highlighting exponential decay of linker DNA and slower degradation of protected cores. In euchromatic regions, which are more open and A/T-rich, cleavage proceeds faster due to enhanced accessibility, resulting in quicker nucleosome release; conversely, compact heterochromatin resists digestion, producing stronger occupancy signals in sequencing reads. MNase's specificity contrasts with other endonucleases like DNase I, as it primarily maps occupied, protected nucleosomal regions rather than hypersensitive, accessible sites, enabling orthogonal profiling of chromatin structure. This protection-based approach underscores MNase's utility in revealing nucleosome occupancy without directly targeting regulatory elements.

Standard Experimental Protocol

The standard experimental protocol for bulk MNase-seq involves isolating chromatin from eukaryotic cells, digesting it with micrococcal nuclease (MNase) to generate nucleosomal DNA fragments, purifying mononucleosomal DNA, and preparing libraries for next-generation sequencing (NGS). This approach relies on MNase's preferential cleavage of linker DNA between nucleosomes, producing a characteristic laddering pattern of DNA fragments corresponding to mono-, di-, and higher-order nucleosomes. Sample preparation begins with harvesting 1–10 million cells, typically cultured mammalian cell lines such as GM12878 or K562, grown to log phase and cross-linked with 1% for 10 minutes at to stabilize and prevent displacement during handling. Cells are then pelleted by , washed in , and resuspended in hypotonic (e.g., 10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl₂, 0.5% ) to swell and rupture the plasma membrane, releasing cytoplasmic contents while preserving nuclear integrity. Nuclei are isolated by low-speed (500–1,000 × g, 5 minutes at 4°C), resuspended in a storage buffer (e.g., 50 mM Tris-HCl pH 7.5, 40 mM NaCl, 100 mM , 5 mM MgCl₂, 1 mM CaCl₂, 0.1 mM ), and quantified by DNA content using a spectrophotometer or , aiming for 5–50 μg total DNA. This step yields intact nuclei suitable for digestion, with yields scalable using low-input modifications for precious samples down to 0.5 million cells. MNase digestion is performed on isolated nuclei to fragment chromatin selectively at linker regions. Nuclei are equilibrated in digestion buffer (e.g., 50 mM Tris-HCl pH 7.9, 5 mM CaCl₂) and titrated with MNase (typically 0.05–0.5 U per μg DNA, ranging from 10–250 total units depending on cell type) for 5–15 minutes at 37°C to achieve primarily mononucleosomal fragments. Digestion is stopped by adding EDTA to 10 mM and chilling on ice, followed by reversal of formaldehyde cross-links at 65°C for 4–6 hours in the presence of proteinase K (0.2 mg/mL). Limited digestion (lower enzyme units, shorter time) preserves di- and trinucleosomes for assessing higher-order chromatin structure, while extensive digestion (higher units) enriches mononucleosomes (~147 bp protected DNA) for precise positioning maps, with cell-type optimizations such as adjusted Ca²⁺ concentrations for tissues like liver. Purification of mononucleosomal DNA follows digestion to isolate fragments of 100–200 bp for NGS. Chromatin is treated with RNase A (0.1 mg/mL, 37°C, 30 minutes) to remove , then DNA is extracted using phenol-chloroform-isoamyl alcohol (25:24:1) followed by , or alternatively via spin columns (e.g., Zymo Research DNA Clean & Concentrator) for higher throughput. The ~150 bp mononucleosomal band is size-selected using (1.5% ) or automated systems like BluePippin, yielding 100–500 ng DNA. End-repair is performed with T4 DNA polymerase and , followed by A-tailing with Klenow exo-minus, and ligation of Illumina-compatible adapters using T4 , per standard NGS kits (e.g., NEBNext Ultra II). Libraries are amplified by PCR (12–15 cycles) and quantified by qPCR. Quality control ensures optimal digestion and library integrity. Post-digestion DNA is assessed on a Bioanalyzer or TapeStation for laddering, targeting ~80% mononucleosomal DNA with minimal sub- or higher-order fragments to confirm even cleavage. Yield and purity are verified by NanoDrop (A260/A280 > 1.8), and library fragment distribution post-adapter ligation should peak at ~250–300 bp (including adapters). Over-digestion (excess sub-nucleosomal DNA <100 bp) or under-digestion (prominent higher ladders) prompts titration adjustments. Sequencing typically requires 20–50 million reads per sample for genome-wide coverage.

Data Processing and Analysis

The processing of MNase-seq data begins with and preprocessing of raw sequencing reads, typically in . Initial assessment involves tools like FastQC to evaluate read quality metrics, including per-base sequence quality, GC content, and adapter contamination, ensuring high-quality input for downstream analysis. Trimming of low-quality bases and adapters is performed using software such as Trim Galore or Cutadapt if necessary, followed by alignment to a using aligners like Bowtie2 for efficient handling of short reads or for splice-aware mapping in eukaryotic contexts. Aligned reads in BAM format are then sorted and indexed with , and duplicates—arising from PCR amplification—are marked and removed using Picard MarkDuplicates to mitigate bias in signal estimation. Post-alignment, fragment size filtering isolates mononucleosome-protected DNA, typically retaining paired-end reads with insert sizes between 140 and 180 base pairs, which correspond to the core length plus , while excluding shorter subnucleosomal or longer polynucleosomal fragments. Normalization accounts for sequencing depth and library size differences, commonly using reads per million (RPM) to scale counts, enabling comparable profiles across samples; for quantitative comparisons, spike-in controls may be incorporated to adjust for global changes. Specialized tools then perform peak calling and scoring: nucleR, an R/ package, identifies positions via non-parametric scanning of smoothed coverage, distinguishing well-positioned from fuzzy nucleosomes based on signal sharpness. Similarly, DANPOS2 quantifies dynamics by calculating as protection scores and fuzziness as the variance in dyad positions across replicates, with higher fuzzy scores indicating dynamic or delocalized nucleosomes. Visualization and interpretation leverage genome browsers like Integrative Genomics Viewer (IGV) to display aligned reads or normalized tracks, revealing periodic arrays, while heatmaps generated with tools such as deepTools highlight phasing patterns around transcription start sites (TSS) or promoters. Processed data are often converted to bigWig format for efficient storage and integration, facilitating overlay with other epigenomic tracks. Quantitative metrics include , computed as the ratio of protected reads in defined windows (e.g., 147 bp) to total genomic coverage, providing insights into density; in advanced setups, this is refined using spike-in normalization for absolute values. Comprehensive pipelines, such as nf-core/mnaseseq, automate these steps from raw reads to maps, incorporating quality filtering and reproducibility checks. Custom /Bioconductor workflows with nucleR or DANPOS2 are widely adopted for flexibility, and batch effects in multi-sample datasets can be modeled using DESeq2 to normalize for technical variation while preserving biological signals in counts. ENCODE-inspired standards emphasize uniform alignment parameters and metadata annotation to ensure interoperability across studies.

Core Applications

Nucleosome Positioning and Occupancy

MNase-seq enables the genome-wide mapping of phased arrays, particularly evident around transcription start sites (TSS), where a characteristic depletion occurs at the promoter followed by regularly spaced downstream. The +1 is typically positioned with its dyad approximately 40–100 base pairs downstream of the TSS, depending on the and promoter activity, with subsequent (+2, +3, etc.) forming periodic arrays at roughly 10-base pair resolution, reflecting the helical structure of DNA wrapped around the . This phasing is quantified through oscillatory patterns in read density from MNase-protected fragments, allowing precise delineation of boundaries and regions. Nucleosome occupancy profiles derived from MNase-seq reveal high coverage in gene bodies, where transcription-coupled positioning maintains stable arrays that facilitate processive RNA polymerase II elongation. In contrast, enhancers exhibit lower occupancy, enabling access for transcription factors and correlating positively with active histone modifications such as H3K27ac, which further destabilizes nucleosomes in these regulatory elements. Quantitative analysis of occupancy, often normalized by spike-in controls, distinguishes well-positioned nucleosomes (low fuzziness) from dynamic or depleted regions, providing insights into chromatin organization. Nucleosome positioning and occupancy are highly dynamic, varying across cell types; for instance, embryonic stem cells display more diffuse positioning at developmental genes compared to differentiated lineages, where sharpened arrays reinforce lineage-specific expression. In response to stimuli, such as T cell or hormone treatment, rapid repositioning occurs, with nucleosome eviction at inducible promoters facilitating transcriptional . These changes highlight MNase-seq's utility in capturing context-dependent . The sub-nucleosome resolution of MNase-seq, achieving approximately 10 precision, is particularly advantageous for identifying barrier nucleosomes at insulators, where positioned nucleosomes prevent ectopic interactions and maintain domain boundaries.

Chromatin Accessibility Profiling

MNase-seq indirectly profiles accessibility by exploiting the enzyme's preferential cleavage of in open regions, where nucleosomes provide less steric hindrance compared to compacted domains. Regions exhibiting rapid MNase digestion, characterized by an abundance of short fragments or reduced occupancy signals, signify accessible , while slower digestion rates in densely packed areas indicate protection. This approach reveals dynamic states, with accessible sites often corresponding to regulatory elements susceptible to enzymatic attack even at low MNase concentrations. Sub-nucleosomal fragments, typically 50-100 bp in length, arise from over-digestion in highly accessible loci and serve as hallmarks of open chromatin. These fragments, generated when MNase further trims protected nucleosomal DNA in vulnerable regions, preferentially mark active promoters and enhancers, where nucleosome instability facilitates transcription factor binding. For instance, protocols like MNase-SSP enhance detection of these short reads, improving resolution of sub-nucleosomal signals over standard MNase-seq. MNase-seq accessibility profiles integrate well with gene activity measures, such as , to link openness to transcriptional output. Nucleosome-free regions (NFRs) immediately upstream of transcription start sites (TSS) correlate with higher levels, as these depleted zones enable promoter access for . In active genes, NFRs and flanking positioned nucleosomes predict expression variance, with broader accessibility at TSS associating with elevated transcript abundance across cell types. Quantitative assessment of accessibility in MNase-seq often employs metrics like the MNase accessibility (MACC) score, derived as the slope of on fragment frequencies across digestion titrations, or the nucMACC score, which quantifies changes in mono- protection via log-transformed ratios of cleavage rates relative to nucleosome stability. These scores, validated against (eQTLs), highlight how accessibility variants influence regulatory function, with higher scores in open regions aligning with eQTL hotspots. In contexts, MNase-seq uncovers altered at regulatory elements, aiding identification of epigenetic drivers in pathologies like cancer. For example, comprehensive mapping in epithelial cells versus cancer lines reveals gain or loss of at tumor-specific enhancers, correlating with oncogenic gene dysregulation. Such profiles, using pipelines like nucMACC, detect fragile at cancer-associated motifs, such as binding sites, informing therapeutic targeting of epigenomic reprogramming.

Advanced Techniques and Variants

Antibody-Targeted Methods (CUT&RUN and CUT&Tag)

Antibody-targeted methods represent an evolution of MNase-based profiling, leveraging specific antibodies to direct enzymatic cleavage to protein-DNA interactions of interest, thereby enhancing precision and reducing off-target effects compared to unbiased approaches. These techniques build on the core principle of MNase digestion by tethering the enzyme or a related activity to antibody-bound targets within intact nuclei, allowing fragmentation that captures nearby DNA sequences for sequencing. CUT&RUN, introduced in , employs -MNase (pA-MNase), a fusion of and micrococcal , to target proteins. In this method, permeabilized nuclei are immobilized on magnetic beads and incubated with a primary specific to the target protein, such as a or modification. Secondary binding of pA-MNase tethers the near the target, and calcium activation triggers controlled cleavage, releasing ~120-150 bp fragments corresponding to protected nucleosomal DNA. The process occurs , avoiding the need for extraction or , and the released fragments are collected from the supernatant for direct library preparation and sequencing. CUT&Tag, developed in as a streamlined variant, replaces MNase with a protein A-Tn5 fusion (pA-Tn5) to combine targeting with tagmentation. Following binding in permeabilized cells or nuclei bound to Concanavalin A beads, pA-Tn5 is added and activated by magnesium, simultaneously cleaving DNA and appending sequencing adapters, which simplifies library preparation into a single-tube . This yields high-resolution profiles with fragment sizes around 80 bp for footprints, enabling efficient mapping from low cell inputs as few as 1,000 cells while minimizing background noise. Both methods offer key advantages over traditional ChIP-seq, including higher due to the absence of sonication-induced , substantially lower background signal from non-specific cleavage, and a exceeding 100-fold for detecting enrichment levels across genomic regions. CUT&RUN and CUT&Tag require fewer cells (typically 10,000-100,000) and less sequencing depth—often 10-fold lower—while providing base-pair precision for binding sites. Protocol details include a brief 10-minute calcium-activated for CUT&RUN on ice and direct tagmentation for CUT&Tag, followed by PCR amplification; analysis pipelines like CUT&RUNTools facilitate alignment, peak calling, and from the resulting short-read data. These techniques excel in applications such as mapping precise binding sites and modifications, revealing sub-nucleosomal details like protection patterns around motifs. For instance, CUT&RUN has delineated occupancy with sharp boundaries, while CUT&Tag profiles broad marks like across cell types. From 2020 to 2025, advancements include multiplexing strategies like Multi-CUT&Tag for simultaneous profiling of multiple epitopes in the same cells and integrations with single-cell via methods such as Paired-Tag, enabling multi-omic correlations of epigenetic states with at cellular resolution.00753-X)

Single-Cell MNase-seq Adaptations

Single-cell adaptations of MNase-seq address the limitations of bulk methods by enabling the resolution of heterogeneity across individual cells, revealing variations in positioning and accessibility that are masked in population averages. The foundational technique, single-cell MNase-seq (scMNase-seq), was introduced to profile genome-wide occupancy and open regions simultaneously in single mammalian cells. Developed in 2018, this method applies MNase digestion to isolate nucleosome-protected fragments and accessible DNA subfragments from individual cells, providing insights into cell-to-cell differences in architecture. The experimental workflow for scMNase-seq begins with the isolation of single cells or nuclei via fluorescence-activated cell sorting (FACS) into multi-well plates, typically 96- or 384-well formats, to ensure high viability and purity. Following sorting, cells are lysed directly in the wells, and MNase is added for controlled digestion, generating subnucleosome-sized fragments (≤80 ) from accessible and mononucleosome-sized fragments (140–180 ) from core-protected regions. DNA is then purified using column-based kits, end-repaired, and ligated to Y-shaped adapters compatible with Illumina sequencing. Linear or low-cycle PCR amplification with cell-specific indexing primers barcodes the libraries, minimizing amplification bias, and size selection recovers the relevant fragments for paired-end sequencing on platforms like HiSeq, yielding 0.5–1 million unique mapped reads per cell. This plate-based approach, detailed in a 2019 protocol, supports processing up to hundreds of cells per run and has been applied to diverse cell types including mouse embryonic stem cells and naive CD4+ T cells. Low-input adaptations of scMNase-seq are inherent to its single-cell design, requiring only 1–100 cells for library preparation, making it suitable for precious samples like sorted rare populations or dissociated tissues. While have revolutionized single-cell and , scMNase-seq primarily relies on nanowell or multi-well systems for precise control over digestion conditions, though hybrid integrations with droplet platforms for initial cell encapsulation have been explored to enhance throughput. These adaptations maintain the method's sensitivity to detect phasing and accessibility with minimal material loss, outperforming bulk MNase-seq in resolving heterogeneous states within mixed populations. Analysis of scMNase-seq data presents challenges due to the sparsity and high dimensionality of single-cell profiles, where low read coverage per cell (often <1% genome coverage) leads to dropout events in low-occupancy regions. Computational pipelines align reads to reference genomes, separate sub- and peaks using size-based filtering, and quantify occupancy via fragmentation patterns. To address sparsity, imputation algorithms such as Markov affinity-based graph imputation of cells (), adapted from scRNA-seq, denoise data by propagating signals across similar cells in a diffusion framework. Dimensionality reduction with uniform manifold approximation and projection (UMAP) facilitates clustering of landscapes, enabling identification of cell subtypes based on shared motifs. These tools reveal principles like uniform spacing in versus variable positioning at active enhancers across cells. Applications of single-cell MNase-seq emphasize dissecting variability in development and , such as detecting primed subpopulations in undifferentiated mouse embryonic stem cells with reduced nucleosome occupancy at de novo enhancers. In tissues, it profiles cell-type-specific nucleosome positioning, contributing to atlases of heterogeneity by highlighting accessibility differences in neuronal versus glial lineages. For , the method identifies rare tumor subpopulations with altered nucleosome arrays indicative of epigenetic dysregulation, aiding in the mapping of intratumor diversity. These insights underscore scMNase-seq's role in linking chromatin states to cellular identity and function. Recent advances from 2020 to 2025 have scaled scMNase-seq through combinatorial indexing strategies, inspired by sci-ATAC-seq, to profile over 10,000 cells in a single experiment by splitting and barcoding pools iteratively, reducing per-cell costs to approximately $0.1. Optimized analysis tools like scNucMap (2025) enhance -free region calling from sparse data, improving resolution for heterogeneity studies. These developments expand scMNase-seq's utility for large-scale epigenomic atlases while preserving its unbiased profiling of dynamics.

Comparisons to Other Assays

DNase-seq and ATAC-seq

DNase-seq, a genome-wide method adapted from earlier DNase I hypersensitivity assays developed in the 1970s–1980s, employs the nuclease DNase I to preferentially cleave DNA at hypersensitive sites (DHSs), generating fragments typically ranging from 10 to 500 bp that mark regions of open chromatin. This method requires approximately 10 million cells for library preparation and effectively identifies accessible genomic regions associated with regulatory elements, though it provides limited insight into nucleosome-level details due to its focus on cleavage rather than protection. In contrast, , introduced by Buenrostro et al. in 2013, utilizes hyperactive Tn5 for tagmentation, simultaneously fragmenting accessible DNA and inserting sequencing adapters, enabling rapid library preparation in just a few hours from as few as 500 to 50,000 cells. While highly efficient for low-input samples, is susceptible to biases, including overrepresentation of reads and artifacts in footprinting due to the transposase's insertion preferences. A fundamental distinction lies in their approaches to structure: MNase-seq maps -protected DNA regions, revealing occupancy and precise positioning, whereas DNase-seq and target exposed, accessible DNA, highlighting regions free of . This makes MNase-seq particularly orthogonal and advantageous for delineating arrays and barriers, offering higher specificity in compact contexts compared to the broader accessibility signals from DNase-seq and . excels in speed and minimal cell requirements but exhibits lower signal-to-noise ratios, especially in densely packed , where MNase-seq provides superior resolution for dynamics. Despite these differences, all three assays overlap in detecting regulatory elements such as promoters and enhancers, with MNase-seq complementing DNase-seq and by identifying nucleosomal barriers that constrain accessibility in those regions.

FAIRE-seq and NOMe-seq

FAIRE-seq, or Formaldehyde-Assisted Isolation of Regulatory Elements, is a chemical-based method developed in to isolate nucleosome-depleted DNA regions associated with regulatory activity. The protocol involves crosslinking with 1% to stabilize protein-DNA interactions, followed by to shear the into fragments of approximately 0.5–1 kb. The sheared is then subjected to phenol-chloroform extraction, where nucleosome-bound DNA partitions into the organic phase due to its association with crosslinked proteins, while open, nucleosome-free DNA enriches in the aqueous phase for recovery and sequencing. This approach typically requires at least 100,000 cells and provides enrichment for accessible regions, though with a resolution limited to around 1 kb owing to the shearing process. NOMe-seq, or Nucleosome Occupancy and Methylome sequencing, introduced in 2012, employs a GpC-specific methyltransferase (M.CviPI) to probe accessibility while simultaneously mapping . In the procedure, isolated nuclei from intact cells are treated with M.CviPI in the presence of , which methylates accessible GpC dinucleotides in open ; nucleosome-protected regions remain unmethylated, creating accessibility footprints. The DNA is then extracted, fragmented to ~200 bp, bisulfite-converted to distinguish endogenous CpG from GpC marks, and sequenced to generate dual maps of occupancy and at single-molecule resolution. NOMe-seq requires fewer than 1 million intact cells and excels at revealing fine-scale footprints, such as nucleosome-depleted regions at promoters, but excludes certain trinucleotides like GCG due to sequencing ambiguities. In contrast to MNase-seq's enzymatic digestion with micrococcal nuclease, which is tunable for ladder resolution, FAIRE-seq relies on chemical crosslinking that can introduce artifacts from non-specific protein associations, while NOMe-seq uses enzymatic without digestion, providing context absent in standard MNase-seq. MNase-seq offers precise positioning but may bias toward AT-rich sequences, whereas FAIRE-seq avoids enzymatic biases through physical separation, though it suffers from higher noise and lower signal-to-noise ratios due to crosslinking variability. NOMe-seq adds value by integrating with endogenous but demands intact cells and can be affected by off-target methyltransferase activity on CpGs. FAIRE-seq's strengths include its simplicity, lack of sequence bias, and ability to capture lipid-associated open regions unbiased by nucleases, making it suitable for diverse cell types; however, it is noisier and less precise for ultra-short open elements compared to MNase-seq's -level accuracy. NOMe-seq provides high-resolution footprints and dual epigenomic layers but is more complex due to processing, while MNase-seq remains simpler for pure occupancy profiling without readout. Studies in the 2010s and 2020s have explored integrating MNase-seq with NOMe-seq in multi-omics frameworks to construct comprehensive epigenomic maps, such as combining positioning data with and accessibility profiles for studying promoter states in cell lines. These approaches leverage bioinformatics to correlate datasets, enhancing insights into dynamics beyond what either method achieves alone.

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