$$\rightleftharpoonup{xx}$$
$$\longleftharp{xx}$$,
$$\longrightharp{xx}$$,
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.