We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.
We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.
The eukaryotic genome is packaged into chromatin via repeating nucleosome structures that consist of two copies of four histone proteins (e.g., H2A, H2B, H3, and H4) circumscribed by 146 base pairs of DNA1,2. Chromatin remodeling complexes control nucleosome position within gene promoter boundaries and participate in the regulation of gene expression by altering accessibility of the DNA to transcription factors and to the RNA polymerase machinery3,4.
Amino terminal tails of histones within the nucleosome are subjected to various covalent modifications, including acetylation, methylation, phosphorylation, ubiquitylation, sumoylation, formylation, and hydroxylation of specific amino acids5,6,7,8. Positions and degrees of these modifications dictate a chromatin state that influence chromatin structure and control access of the molecular complexes that allow activation of transcription7. Given that both nucleosome density and histone modification play a role in the local control of gene transcription, we developed a native ChIP approach that enables the simultaneous measurement of nucleosome density and histone modification9,10.
Native ChIP-seq takes advantage of the endonuclease micrococci nuclease (MNase) to digest intact chromatin in its native state within the nucleus11,12, a property that has been leveraged to map nucleosome positioning13,14,15. Nucleosome density ChIP-seq (ndChIP-seq) takes advantage of the property of preferential access of MNase to open regions of chromatin to generate measurements that combine MNase accessibility with histone modification10. ndChIP-seq is suitable for the profiling of histone modifications in rare primary cells, tissues, and cultured cells. Here, we present a detailed protocol that enables the generation of sequence datasets suitable for a previously described analytical frame work10 that integrates fragment size post immunoprecipitation, determined by paired-end read boundaries, to simultaneously investigate MNase accessibility with histone modification measurements. Previously, application of this protocol to 10,000 primary human cord blood derived CD34+ cells and human embryonic stem cells revealed unique relationships between chromatin structure and histone modifications within these cell types10. Given its ability to simultaneously measure nucleosome accessibility and histone modification, ndChIP-seq is capable of revealing epigenomic features in a cell population at a single nucleosome level, and resolving heterogeneous signatures into their constituent elements. An example of the exploration of heterogeneous cellular populations by ndChIP-seq is investigation of bivalent promoters, where both H3K4me3, an active mark, and H3K27me3, a repressive mark, are present10.
NOTE: The minimum input for this protocol is 10,000 cells per single immuno-precipitation (IP) reaction. Print out the supplied experimental worksheet and utilize as a guideline to plan out the experiment. Incubations at room temperature are assumed to be at ~22 °C. All of the buffer recipes are provided in Table 1. All of the buffers should be stored at 4 °C and kept on ice during the procedure, unless stated otherwise.
1. Cell Preparation
2. DAY 1: ndChIP-seq
3. DAY 2: ndCHIP-seq
4. DAY 3: Library Construction
Chromatin Digestion Profiles
Optimization of the MNase digestion is essential for the success of this protocol. It is crucial to generate a digestion profile dominated by single nucleosome fragment sizes, while not over-digested, to allow for recovery of higher order nucleosome fragments. An ideal digestion profile consists of a majority of single nucleosome fragments with a small fraction representing fragments smaller and larger than single nucleosomes. Figure 1 shows examples of an ideal, over-digested, and under-digested size distribution profiles. Note that sub-optimal digestion of chromatin will also be apparent in the profile of the sequencing library generated from the IP material (Figure 2).
Validation of ndChIP-seq Library Quality by qPCR
qPCR is a well-established method for assessment of the quality of ChIP18,19,20. When performing ndChIP-seq on 10,000 cells the yield of nucleic acid after IP will be below 1 ng. Therefore, it is essential to perform qPCR after library construction to assess the relative enrichment of target regions over background. To provide a background estimate, libraries constructed from the MNase digested chromatin (Input) are generated. For each IP library, two sets of primers are needed (see SupplementalTable 3 for a list of primers for commonly used histone marks). One primer set should be specific for a genomic region that is consistently associated with the histone modification of interest (positive target) and another region that is not marked with the histone modification of interest (negative target). The quality of the ChIP-seq library will be assessed as fold enrichment with respect to input library. Fold enrichment can be calculated using the following equation that assumes exponential amplification of the target genomic region: 2Ctinput– CtIP. Our custom made R statistical software package, qcQpcr_v1.2, is suitable for qPCR enrichment analysis of low input native ChIP-seq libraries (Supplemental Code Files). Figure 3 represents a qPCR result for successful and unsuccessful ChIP-seq libraries. The minimum expected fold enrichment value for good quality ndChIP-seq libraries are 16 for narrow marks, such as H3K4me3, and 7 for broad marks, for example H3K27me3.
Modeling MNase Accessibility
Computational analysis of ChIP-seq is complex and unique for each experimental setting. A set of guidelines established by International Human Epigenomic Consortium (IHEC) and The Encyclopedia of DNA Elements (ENCODE) can be used to assess the quality of the ChIP-seq libraries21. It is important to note that the sequencing depth of the libraries impacts the detection and resolution of enriched regions20. The number of peaks detected increase and approaches a plateau as read depth increases. We recommend ndChIP-seq libraries to be sequenced in accordance with the IHEC recommendations of 50 million paired-reads (25 million fragments) for narrow marks (e.g., H3K4me3) and 100 million paired-reads (50 million fragments) for broad marks (e.g., H3K27me3) and input22. These sequencing depths provide sufficient sequence alignments for detection of the most significant peaks using widely used ChIP-seq peak callers, such as MACS2 and HOMER, without reaching saturation23,24. A high quality mammalian ndChIP-seq library has a PCR duplicate rate of <10% and reference genome alignment rate of > 90% (including duplicated reads). Successful ndChIP-seq libraries will contain highly correlated replicates with a significant portion (> 40%) of aligned reads within MACS222 identified enriched peaks and inspection of aligned reads on a genome browser should reveal visually detectable enrichments compared to the input library (Figure 4). In addition, ndChIP-seq can be used to assess nucleosome density by utilizing a Gaussian mixture distribution algorithm (w1 * n(x; μ1,σ1) + w2 * n(x; μ2,σ2) = 1) at MACS2 identified enriched regions to model nucleosome density as defined by MNase accessible boundaries. In this model, w1 represents mono-nucleosome distribution weight and w2 represents di-nucleosome distribution weight. Where w1 is greater than w2, there is dominance of mono-nucleosome fragments and vice versa. This analysis requires that libraries be sequenced in a paired-end fashion so that fragment sizes can be defined. In order to apply the Gaussian mixture distribution algorithm, statistically significant enriched regions are first identified. We suggest peak calling with MACS2 using Input as a control and with default settings for narrow marks and a q value cutoff of 0.01 for broad marks. A number of statistical packages employing a Gaussian mixture distribution algorithm are available from widely used statistical software packages. Utilizing average fragment size, determined by paired-end read boundaries of the IPed samples, distributions at MACS2 identified enriched promoters, and a Gaussian mixture distribution algorithm can be applied to each promoter using the R-statistical package Mclust version 3.025 to calculate a weighted distribution. In this application, we recommend eliminating promoters containing less than 30 aligned fragments because below this threshold the resulting weight estimates become unreliable. A good quality ndChIP-seq library generates a Gaussian mixture distribution that consist of two major components with mean values corresponding to mono-, di-nucleosome fragment lengths.
Figure 1: Assessment of MNase digestion before library generation. Chip-based capillary electrophoresis analyzer profiles of an optimal MNase digested (A), under-digested (B), and over-digested (C) chromatin. Biological replicates are shown as blue, red, and green traces. Please click here to view a larger version of this figure.
Figure 2: Assessment of MNase digestion after library generation. (A) Post library construction profiles of optimally digested input (biological replicates; red, green, black) and IP (biological replicates; cyan, purple, blue) and (B) sub-optimal input (biological replicates; red, green, blue) and IP (biological replicates; cyan, purple, orange) libraries. Please click here to view a larger version of this figure.
Figure 3: Post library construction quantitative PCR can be used to assess the quality of ndChIP-seq libraries. Fold enrichment of H3K4me3 IP libraries with respect to input libraries is calculated as 2(Ct of input – Ct of IP) for positive and negative targets using qcQpcr_v1.2. Please click here to view a larger version of this figure.
Figure 4: Representative ndChIP-seq library constructed from 10,000 primary CD34+ cord blood cells. Pearson correlation of H3K4me3 signal (reads per million mapped reads) calculated in the promoters (TSS+/-2Kb) between 3 biological replicates, (A) replicate 1 and 2, (B) replicate 1 and 3, (C) replicate 2 and 3. (D) UCSC browser view of the HOXA gene cluster of cross-linked ChIP-seq generated from 1 million cells per IP, successful ndChIP-seq from 10,000 cells per IP, and unsuccessful ndChIP-seq from 10,000 cells per IP. (red: H3K27me3, green: H3K4me3, and black: Input). (E) Fraction of mapped reads within MACS2 identified enriched regions of H3K4me3 (black) and H3K27me3 (grey). Please click here to view a larger version of this figure.
Buffer Composition |
A.1. Immunoprecipitation buffer (IP) |
20 mM Tris-HCl pH 7.5 |
2 mM EDTA |
150 mM NaCl |
0.1% Triton X-100 |
0.1% Deoxycholate |
10 mM Sodium Butyrate |
A.2. Low Salt Wash buffer |
20 mM Tris-HCl pH 8.0 |
2 mM EDTA |
150 mM NaCl |
1% Triton X-100 |
0.1% SDS |
A.3. High Salt Wash buffer |
20 mM Tris-HCl pH 8.0 |
2 mM EDTA |
500 mM NaCl |
1% Triton X-100 |
0.1% SDS |
A.4. ChIP Elution buffer |
100 mM NaHCO3 |
1% SDS |
A.5. 1x Lysis buffer – 1 mL |
0.1% Triton |
0.1% Deoxycholate |
10 mM Sodium Butyrate |
A.6. Ab dilution buffer |
0.05% (w/v) Azide |
0.05% broad spectrum antimicrobial (e.g. ProClin 300) |
in PBS |
A.7. 30% PEG/1M NaCl Magnetic Bead Solution (reference16) |
30% PEG |
1 M NaCl |
10 mM Tris HCl pH 7.5 |
1 mM EDTA |
1 mL of washed super-paramagnetic beads |
A.8. 20% PEG/1M NaCl Magnetic Bead Solution (reference16) |
30% PEG |
1 M NaCl |
10 mM Tris HCl pH 7.5 |
1 mM EDTA |
1 mL of washed super-paramagnetic beads |
Table 1: ndChIP-seq buffer composition.
Histone Modification | Concentration (µg/µL) |
H3K4me3 | 0.125 |
H3K4me1 | 0.25 |
H3K27me3 | 0.125 |
H3K9me3 | 0.125 |
H3K36me3 | 0.125 |
H3K27ac | 0.125 |
Table 2: Antibody amount required for ndChIP-seq.
Reganet | Volume (µL) |
Ultra Pure Water | 478 |
1 M Tris-HCl pH 7.5 | 10 |
0.5 M EDTA | 10 |
5 M NaCl | 2 |
Glycerol | 500 |
Total Volume | 1,000 |
Table 3: Recipe for MNase dilution buffer.
Reagent | Volume (µL) |
Ultra Pure Water | 13 |
20 mM DTT | 1 |
10x MNase Buffer | 4 |
20 U/µl Mnase | 2 |
Total Volume | 20 |
Table 4: Recipe for MNase Master Mix.
Reagent | Volume (µL) |
Elution Buffer | 30 |
Buffer G2 | 8 |
Protease | 2 |
Total Volume | 40 |
Table 5: Recipe for DNA Purification Master Mix.
Reagent | Volume (µL) |
Ultra Pure Water | 3.3 |
10x Restriction Endonuclease Buffer (e.g. NEBuffer) | 5 |
25 mM ATP | 2 |
10 mM dNTPs | 2 |
T4 Polynucleotide Kinase (10 U/µl) | 1 |
T4 DNA polymerase (3 U/µl) | 1.5 |
DNA polymerase I, Large (Klenow) Fragment (5 U/µl) | 0.2 |
Total Volume | 15 |
Table 6: Recipe for End Repair Master Mix.
Reagent | Volume (µL) |
Ultra Pure Water | 8 |
10x Restriction Endonuclease Buffer (e.g. NEBuffer) | 5 |
10 mM dATP | 1 |
Klenow (3'-5' exo-) | 1 |
Total Volume | 15 |
Table 7: Recipe for A-Tailing Master Mix.
Reagent | Volume (µL) |
Ultra Pure Water | 4.3 |
5x Quick ligation buffer | 12 |
T4 DNA ligase (2000 U/µl) | 6.7 |
Total Volume | 23 |
Table 8: Recipe for Adapter Ligation Master Mix.
Reagent | Volume (µL) |
Ultra Pure Water | 7 |
25 uM PCR primer 1.0 | 2 |
5x HF buffer | 12 |
DMSO | 1.5 |
DNA Polymerase | 0.5 |
Total Volume | 23 |
Table 9: Recipe for PCR Master Mix.
Temprature (°C) | Duration (s) | Number of Cycles |
98 | 60 | 1 |
98 | 30 | |
65 | 15 | 10 |
72 | 15 | |
72 | 300 | 1 |
4 | hold | hold |
Table 10: PCR run method.
Oligo | Sequence | Modification |
PE_adapter 1 | 5’- /5Phos/GAT CGG AAG AGC GGT TCA GCA GGA ATG CCG AG -3’ | 5’ Modification: Phosphorylation |
PE_adapter 2 | 5’ – ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TC*T – 3’ | 3’Modification: *T is a phosphorothioate bond |
Supplemental Table 1: Oligo sequences for generation of PE adapter.
Primer Name | Sequence | Index | IndexRevC (To be used for sequencing) | |||||||
PCR reverse indexing primer 1 | CAAGCAGAAGACGGCATACGAGATCGTGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CGTGAT | atcacg | |||||||
PCR reverse indexing primer 2 | CAAGCAGAAGACGGCATACGAGATCTGATCCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CTGATC | gatcag | |||||||
PCR reverse indexing primer 3 | CAAGCAGAAGACGGCATACGAGATGGGGTTCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | GGGGTT | aacccc | |||||||
PCR reverse indexing primer 4 | CAAGCAGAAGACGGCATACGAGATCTGGGTCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CTGGGT | acccag | |||||||
PCR reverse indexing primer 5 | CAAGCAGAAGACGGCATACGAGATAGCGCTCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | AGCGCT | agcgct | |||||||
PCR reverse indexing primer 6 | CAAGCAGAAGACGGCATACGAGATCTTTTGCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CTTTTG | caaaag | |||||||
PCR reverse indexing primer 7 | CAAGCAGAAGACGGCATACGAGATTGTTGGCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | TGTTGG | ccaaca | |||||||
PCR reverse indexing primer 8 | CAAGCAGAAGACGGCATACGAGATAGCTAGCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | AGCTAG | ctagct | |||||||
PCR reverse indexing primer 9 | CAAGCAGAAGACGGCATACGAGATAGCATCCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | AGCATC | gatgct | |||||||
PCR reverse indexing primer 10 | CAAGCAGAAGACGGCATACGAGATCGATTACGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CGATTA | taatcg | |||||||
PCR reverse indexing primer 11 | CAAGCAGAAGACGGCATACGAGATCATTCACGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CATTCA | tgaatg | |||||||
PCR reverse indexing primer 12 | CAAGCAGAAGACGGCATACGAGATGGAACTCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | GGAACT | agttcc | |||||||
PCR reverse indexing primer 13 | CAAGCAGAAGACGGCATACGAGATACATCGCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | ACATCG | cgatgt | |||||||
PCR reverse indexing primer 14 | CAAGCAGAAGACGGCATACGAGATAAGCTACGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | AAGCTA | tagctt | |||||||
PCR reverse indexing primer 15 | CAAGCAGAAGACGGCATACGAGATCAAGTTCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CAAGTT | aacttg | |||||||
PCR reverse indexing primer 16 | CAAGCAGAAGACGGCATACGAGATGCCGGTCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | GCCGGT | accggc | |||||||
PCR reverse indexing primer 17 | CAAGCAGAAGACGGCATACGAGATCGGCCTCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CGGCCT | aggccg | |||||||
PCR reverse indexing primer 18 | CAAGCAGAAGACGGCATACGAGATTAGTTGCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | TAGTTG | caacta | |||||||
PCR reverse indexing primer 19 | CAAGCAGAAGACGGCATACGAGATGCGTGGCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | GCGTGG | ccacgc | |||||||
PCR reverse indexing primer 20 | CAAGCAGAAGACGGCATACGAGATGTATAGCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | GTATAG | ctatac | |||||||
PCR reverse indexing primer 21 | CAAGCAGAAGACGGCATACGAGATCCTTGCCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | CCTTGC | gcaagg | |||||||
PCR reverse indexing primer 22 | CAAGCAGAAGACGGCATACGAGATGCTGTACGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | GCTGTA | tacagc | |||||||
PCR reverse indexing primer 23 | CAAGCAGAAGACGGCATACGAGATATGGCACGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | ATGGCA | tgccat | |||||||
PCR reverse indexing primer 24 | CAAGCAGAAGACGGCATACGAGATTGACATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT | TGACAT | atgtca |
Supplemental Table 2: PCR reverse indexing primer sequences.
Primers | Sequence | |
ZNF333_genic_H3K9me3_F | 5'-AGCCTTCAATCAGCCATCATCCCT-3' | |
ZNF333_genic_H3K9me3_R | 5'-TCTGGTATGGGTTCGCAGATGTGT-3' | |
HOXA9-10_F | 5'-ACTGAAGTAATGAAGGCAGTGTCGT-3' | |
HOXA9-10_R | 5'-GCAGCAYCAGAACTGGTCGGTG-3' | |
GAPDH_genic_H3K36me3_F | 5'-AGGCAACTAGGATGGTGTGG-3' | |
GAPDH_genic_H3K36me3_R | 5'-TTGATTTTGGAGGGATCTCG-3' | |
GAPDH-F | 5'-TACTAGCGGTTTTACGGGCG-3' | |
GAPDH-R | 5'-TCGAACAGGAGGAGCAGAGAGCGA-3' | |
Histone modification | Positive Target | Negative Target |
H3K4me3 | GAPDH | HOXA9-10 |
H3K4me1 | GAPDH_genic | ZNF333 |
H3K27me3 | HOXA9-10 | ZNF333 |
H3K27ac | GAPDH | ZNF333 |
H3K9me3 | ZNF333 | HOXA9-10 |
H3K36me3 | GAPDH_genic | ZNF333 |
Supplemental Table 3: A list of primers for commonly used histone marks (H3K4me3, H3K4me1, H3K27me3, H3K27ac, H3K9me3, and H3K36me3).
Supplemental File 1: ndChIP-seq WorkSheet. Please click here to download this file.
Supplemental Code Files: qcQpcr_v1.2. R statistical software package for qPCR enrichment analysis of low input native ChIP-seq libraries. Please click here to download this file.
Given the combinatorial nature of chromatin modification and nucleosome positioning in transcriptional regulation, a method that enables simultaneous measurements of these features is likely to provide new insights into epigenetic regulation. The ndChIP-seq protocol presented here is a native ChIP-seq protocol optimized to enable simultaneous interrogation of histone modification and nucleosome density in rare primary cells9,10. ndChIP-seq utilizes enzymatic digestion of chromatin that, when coupled to paired-end massively parallel sequencing and a Gaussian mixture distribution model, allows for the investigation histone modifications at single nucleosome level and the deconvolution of epigenomic profiles driven by heterogeneity within a population. Using this protocol, we have previously reported a unique distribution of immuno-precipitated fragment sizes, determined by paired-end read boundaries, associated with specific chromatin states defined by ChromHMM10,24.
The quality of a ndChIP-seq library depends on multiple factors, such as antibody specificity and sensitivity, optimal MNase digestion conditions, and quality of the chromatin. The specificity of the antibodies used is crucial in producing successful ndChIP-seq library. An ideal antibody shows high affinity against the epitope of interest with little cross reactivity with other epitopes. It is equally important to choose magnetic beads with the highest affinity for the antibody of choice.
MNase digestion is a critical and time- and concentration-sensitive reaction in this protocol. Therefore, when processing multiple samples, it is important that each reaction is incubated for an equivalent amount of time (see step 2.2). The quality of the chromatin is another factor that significantly effects the outcome of ndChIP-seq. Fragmented chromatin leads to a sub-optimal MNase digestion profile and results in libraries with a low signal to noise ratio. Primary samples with low cell viability or degraded chromatin, such as formalin-fixed paraffin-embedded (FFPE) tissue are not recommended for this protocol.
The addition of a PIC during chromatin extraction reduces undesired (i.e., random) chromatin fragmentation and preserves integrity of histone tails. As such, PIC needs to be added to lysis buffer and immunoprecipitation buffer just prior to use. While selecting cells via flow cytometry, select for viable cells and ensure cells are sorted at a low flow rate to increase the accuracy of the cell number estimate and viability of cells. Avoid sorting cells directly into lysis buffer. The sheath buffer will dilute the lysis buffer and prevents effective permeabilization of the cell membrane to MNase. Depending on a type of cells or organism, titration of MNase may be required to obtain optimal digestion.
ndChIP-seq on mammalian cells requires a minimum sequencing depth of 50 million paired-reads (25 million fragments) for narrow marks and 100 million paired-reads (50 million fragments) for broad marks and input. The Gaussian mixture distribution algorithm will not perform optimally on libraries that have been sequenced to a depth significantly below this recommendation. ndChIP-seq will not classify promoters with little separation between the weighted distribution value for mono- and di-nucleosome fragment lengths into mono- or di-nucleosome dominated promoters. Therefore, these promoters must be removed in the subsequent analysis. Biological replicates can be generated to increase confidence in predicted distributions and identify technical variability in the MNase digestion and library construction.
Unlike previous iterations of native ChIP-seq protocols, ndChIP-seq provides the means to investigate combinatorial effect of chromatin structure and histone modification by utilizing fragment size post immunoprecipitation to integrate nucleosome density, determined by MNase accessibility, with histone modification measurements. Application of ndChIP-seq to primary cells and tissues will provide novel insights into the integrative nature of epigenetic regulation and permit identification of epigenetic signatures due to heterogeneity within the population.
The authors have nothing to disclose.
A.L. was supported by a Canadian Graduate Scholarship from the Canadian Institutes of Health Research. This work was supported by grants from Genome British Columbia and the Canadian Institutes of Health Research (CIHR-120589) as part of the Canadian Epigenetics, Environment and Health Research Consortium Initiative and by the Terry Fox Research Institute Program Project Grant #TFF-122869 to M.H. and a Terry Fox Research Institute New Investigator Award (Grant # 1039) to M.H.
1M Tris-HCl –pH 7.5 | Thermo Scientific | 15567-027 | |
1M Tris-HCl –pH 8 | Thermo Scientific | 15568-025 | |
0.5M EDTA | Thermo Scientific | AM9260G | |
5M NaCl | Sigma | 1001385276 | |
Triton X-100 | Sigma | 1001412354 | |
Sodium-Deoxycholate | Sigma | 1001437582 | |
SDS | Thermo Scientific | 15525-017 | |
Sodium-Bicarbonate | Fisher Scientific | S233-500 | |
100% EtOH | NA | NA | |
V-bottom 96 well plate (AB 1400) | Thermo Scientific | AB-1400-L | |
Gilson P2 pipetman | Mandel | F144801 | |
Gilson P10 pipetman | Mandel | F144802 | |
Gilson P20 pipetman | Mandel | F123600 | |
Gilson P100 pipetman | Mandel | F123615 | |
Gilson P200 pipetman | Mandel | F123601 | |
Gilson P1000 pipetman | Mandel | F123603 | |
Micrococcal Nuclease | NEB | M0247S | |
Thermo Mixer C (Heating block mixer) | Eppendorf | 5382000023 | |
Multi 12-channel Pippet P20 | Rainin | 17013803 | |
Multi 12-channel Pippet P200 | Rainin | 17013805 | |
1.5 ml LoBind tube | Eppendorf | 22431021 | |
0.5 ml LoBind tube | Eppendorf | 22431005 | |
Plastic Plate Cover | Edge Bio | 48461 | |
PCR cover | Bio Rad | MSD-1001 | |
Aluminum Plate Cover | Bio Rad | MSF-1001 | |
Elution buffer (EB buffer) | Qiagen | 19086 | |
1M DTT | Sigma | 646563 | |
Eppendorf 5810R centrifuge | Eppendorf | 22625004 | |
Ultrapure water | Thermo Scientific | 10977-015 | |
Protein G dynabeads | Thermo Scientific | 10001D | |
Protein A dynabeads | Thermo Scientific | 10001D | |
Protease inhibitor Cocktail | Calbiochem | 539134 | |
Rotating platform | Mandel | 1b109-12052010 | |
Plate magnet | Alpaqua | 2523 | |
Domed 12-cap strip | Bio Rad | TCS1201 | |
Buffer G2 | Qiagen | 1014636 | |
Protease | Qiagen | 19155 | |
Tube Magnet (DynaMag-2) | Thermo Scientific | 12321D | |
Tube Magnet (DynaMag-2) | LabCore | 730-006 | |
V shape Plastic reservoir | Mandel | S0100A | |
Vortex | Mandel | C1801 | |
Mini Fuge | Mettler Toledo | XS2035 | |
Analytical scale | Mandel | LB109-12052010 | |
Quick Ligation Reaction Buffer, 5X | NEB | B6058S | |
DNA Polymerase I, Large (Klenow) Fragment | NEB | M0210S | |
Klenow Fragment (3'-5' exo) | NEB | M0212S | |
T4 DNA Polymerase | NEB | M0203S | |
T4 Polynucleotide Kinase | NEB | M0201S | |
Deoxynucleotide Solution mix, 10mM | NEB | N0447S | |
dATP Solution 10mM | NEB | N0440S | |
Quick T4 DNA Ligase | NEB | M0202T | |
Adenosine 5'-Triphosphate (ATP), 25mM | NEB | P0756S | |
DNA Polymerase | Thermo Scientific | F549L | |
NEBuffer 2 | NEB | B7002S | |
Hank's buffered salt solution | Thermo Scientific | 14025076 | |
Fetal bovine serum | Thermo Scientific | A3160702 | |
phosphate buffer saline | Thermo Scientific | 10010023 | |
H3K4me3 | Cell Signaling | 9751S | |
H3K4me1 | Diagenode | C15410037 | |
H3K27me3 | Diagenode | pAb-069-050 | |
H3K36me3 | Abcam | ab9050 | |
H3K9me3 | Diagenode | C15410056 | |
SeraMag super-paramagnetic beads |