A Multiplexed System for Quantitative Comparisons of Chromatin Landscapes.

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Mol Cell. Author manuscript; available in PMC 2017 January 07. Published in final edited form as: Mol Cell. 2016 January 7; 61(1): 170–180. doi:10.1016/j.molcel.2015.11.003.

A multiplexed system for quantitative comparisons of chromatin landscapes Peter van Galen1,3, Aaron D. Viny4, Oren Ram1,3, Russell J.H. Ryan1,3, Matthew J. Cotton1,3,5, Laura Donohue1,3,5, Cem Sievers1,3, Yotam Drier1,3, Brian B. Liau1,3, Shawn M. Gillespie1,3,5, Kaitlin M. Carroll6, Michael B. Cross6, Ross L. Levine4, and Bradley E. Bernstein1,3,5

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1

Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA

2

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

3

Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02114, USA.

4

Human Oncology and Pathogenesis Program and Leukemia Service, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA

5

Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA

6

Department of Adult Reconstruction and Joint Replacement, Hospital for Special Surgery, New York, NY 10021, USA

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Abstract

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Genome-wide profiling of histone modifications can provide systematic insight into the regulatory elements and programs engaged in a given cell type. However, conventional chromatin immunoprecipitation and sequencing (ChIP-seq) does not capture quantitative information on histone modification levels, requires large amounts of starting material, and involves tedious processing of each individual sample. Here we address these limitations with a technology that leverages DNA barcoding to profile chromatin quantitatively and in multiplexed format. We concurrently map relative levels of multiple histone modifications across multiple samples, each comprising as few as a thousand cells. We demonstrate the technology by monitoring dynamic changes following inhibition of P300, EZH2 or KDM5, by linking altered epigenetic landscapes to chromatin regulator mutations, and by mapping active and repressive marks in purified human hematopoietic stem cells. Hence, this technology enables quantitative studies of chromatin state dynamics across rare cell types, genotypes, environmental conditions and drug treatments.

Contact: Bradley E. Bernstein, M.D. Ph.D., Professor in Pathology, Massachusetts General Hospital, Harvard Medical School, Howard Hughes Medical Institute, Broad Institute of MIT and Harvard, Phone: 617-726-6906, [email protected]. Author contributions: P.v.G., O.R and B.E.B. designed the study. P.v.G., M.J.C., L.D., and S.M.G. performed experiments. R.J.H.R. and B.B.L. designed specific experiments. P.v.G., C.S. and Y.D. performed bioinformatic analysis. A.D.V., K.M.C, M.B.C. and R.L.L. obtained normal hematopoietic stem cells. P.v.G. and B.E.B. wrote the paper. Mint-ChIP data are deposited in GEO under accession number GSE74359.

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INTRODUCTION Chromatin immunoprecipitation and sequencing (ChIP-seq) can be used to map histone modifications genome-wide, enabling the identification of cell type-specific functional genomic elements and epigenetic states. However, this technology has several limitations. Recent adaptations of the method can address individual limitations, but there are tradeoffs and limitations to each of these approaches (Table S1).

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First, conventional ChIP-seq procedures involve separate immunoprecipitations that are sensitive to the amount of chromatin input and the quality of the antibody. This compromises the accuracy with which chromatin landscapes can be quantitatively compared across samples. The lack of quantitative information in ChIP-seq data is a long-standing problem and can obscure global differences in histone modification levels due to cell state transitions or genetic mutations in epigenetic regulators frequently observed in cancer (Ryan and Bernstein, 2012). Recent studies have presented strategies for quantitatively comparing ChIP-seq signal intensities by incorporating exogenous DNA or synthetic histone spike-in controls, but these protocols may not be compatible with low cell numbers and/or aneuploidy (Bonhoure et al., 2014; Grzybowski et al., 2015; Orlando et al., 2014). We reasoned that quantitative consistency might also be achieved by combining samples in the same reaction, and thus sought to develop a method for processing many samples in the same ChIP-seq assay.

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Second, conventional ChIP-seq experiments require large numbers of cells. Progress has been made towards reducing cell requirements, but the corresponding methods do not address the challenge of quantitative comparison, are low throughput and/or have only been demonstrated for certain modifications (Adli et al., 2010; Brind’Amour et al., 2015; Gilfillan et al., 2012; Lara-Astiaso et al., 2014). We sought to address this limitation by i) minimizing loss and ii) performing linear amplification of input material, which have individually been demonstrated to increase sensitivity of ChIP experiments (O’Neill et al., 2006; Shankaranarayanan et al., 2011). Third, the throughput of ChIP-seq is constrained by individual sample processing. Progress has been made towards high-throughput ChIP, but these methods have other limitations such as high input requirements (Chabbert et al., 2015; Garber et al., 2012) (Table S1). We therefore envisioned that a pool-and-split multiplexing approach could dramatically increase throughput.

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Here we combine these innovations in a procedure, termed multiplexed, indexed T7 ChIPseq (‘Mint-ChIP’), which leverages chromatin barcoding and pool-and-split multiplexing for high-throughput, quantitative profiling of chromatin states in samples containing as few as 500 cells (Fig. 1A). We demonstrate the procedure by profiling multiple marks across a panel of cell types and conditions, and thereby linking quantitative changes in chromatin landscapes to different genotypes and drug treatments.

Mol Cell. Author manuscript; available in PMC 2017 January 07.

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DESIGN Chromatin isolation and indexing First, we refined procedures for simultaneously lysing and digesting chromatin from small numbers of K562 cells (Experimental procedures). Specifically, we identified MNase digestion conditions that yield fragments of one to five nucleosomes, which is optimal for ligation and ChIP (O’Neill et al., 2006). We next designed barcoded adapters that could be ligated to the DNA ends of the fragmented nucleosomes. These double-stranded adapters contain a T7 promoter, an Illumina SBS3 PCR priming sequence and a barcode that is unique for each sample (termed index #1). They also contain a 5' C3 spacer to prevent selfligation. We ligated these T7-adapters to the nucleosomes in each sample. Pool-and-split multiplexing

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Next, we pooled up to 12 ligated chromatin samples, each containing a different barcode. We also added unlabeled carrier chromatin to further improve ChIP efficiency (O’Neill et al., 2006). We then split these pooled contents into different ChIP assays, one for each of the following chromatin epitopes: H3 lysine 4 tri-methylation (H3K4me3), H3 lysine 27 acetyl (H3K27ac), H3 lysine 27 tri-methylation (H3K27me3), and total H3. We carried out the ChIP assays using standard procedures, and isolated immunoprecipitated DNA for these modifications in four parallel reactions. The size of input- and immunoprecipitated DNA was within expected ranges (Fig. S1A). Linear amplification and library construction

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We used an optimized T7 promoter to amplify the ChIP DNA in an in vitro transcription reaction (Tang et al., 2005). This step generates multiple RNA copies per chromatin fragment while maintaining starting species representation (Hoeijmakers et al., 2011; Liu et al., 2003). Only one end of the chromatin fragment needs to be adapter-ligated for amplification, which increases efficiency for low-input samples. We identified in vitro transcription conditions for robust amplification of T7-adapter-ligated nucleosomal DNA, with a typical reaction yielding ~1350 ng of ssRNA from ~30 ng of ChIP DNA. Carrier chromatin, which lacks T7-adapters, is not retained in this procedure: addition of a 200-fold excess of mouse carrier chromatin to T7-adapter-ligated human chromatin in the ChIP assay did not lead to a detectable increase in reads aligning to the mouse genome (Fig. 1B).

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Amplified RNA is then reverse transcribed using primers that contain a random hexamer flanked by an Illumina SBS12 PCR priming sequence. The resulting single-stranded cDNA contains priming sequences on both ends. These priming sequences are used to generate a sequencing library for each ChIP assay (i.e., each histone modification). A second barcode (index #2) is added during the library construction PCR to identify the ChIP assay. We subsequently combined libraries representing different ChIP assays (index #2), each containing different starting samples (index #1 on the original T7-adapter) for a single paired-end sequencing run.


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In silico demultiplexing validates pool-and-split strategy

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We developed algorithms to process Mint-ChIP sequencing results. First, the sequencing reads are de-multiplexed by index #2 to yield separate read files for each ChIP assay. Each of these read files is then further de-multiplexed by index #1 (T7-adapter barcode) to yield separate files for each sample. All barcodes were represented in the demultiplexed data, indicating success of the individual ligation reactions. More than 90% of reads were successfully assigned to one of the T7-adapter barcodes (Fig. 1C). To check for crosscontamination between barcodes, we combined barcoded chromatin from different species in the same ChIP assay. We confirmed that 95% of the de-multiplexed reads align specifically to the expected genome sequences (Fig. 1D). An additional 4% of reads align to both species, while just 1% of reads align specifically cross-species. We also introduced a stringent procedure to ensure that each nucleosomal fragment is represented by no more than one read. We specifically removed any duplicate reads with the same genomic sequence appended to the T7-adapter index, thus alleviating amplification bias.

RESULTS Faithful mapping of activating and repressive marks using low-input samples

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We initially demonstrated the procedure using variable numbers of K562 cells, and antibodies for H3K4me3, H3K27me3, H3K27ac and total H3. We prepared barcoded chromatin from input quantities ranging from 500 to 100,000 cells at different MNase concentrations. We sequenced the resulting libraries and visualized signal tracks for each condition (Fig. 2A-B). Tracks for H3K4me3, H3K27ac and H3K27me3 revealed expected signals over promoters, distal elements, and Polycomb-repressed regions, respectively. To evaluate Mint-ChIP accuracy more precisely, we performed genome-wide comparisons against analogous data generated for the ENCODE project using conventional ChIP-seq technology (Dunham et al., 2012). Comparisons of signal intensities for each mark confirmed high genome-wide correlations between the respective datasets (H3K4me3: R = 0.87, H3K27ac: R = 0.87, H3K27me3: R = 0.91, Fig. 2C). In addition, high correlations were observed between Mint-ChIP profiles performed using different MNase concentrations, and profiles derived from 100,000 and 500 cells (R = 0.95 and R = 0.91, Fig. S1B). Thus, Mint-ChIP assays performed with as few as 500 cells yield accurate and consistent chromatin profiles.

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To further evaluate the strengths and limitations of Mint-ChIP, we examined the fraction of successfully aligned reads and the number of unique T7-adapter ligated chromatin fragments at different starting cell numbers. Regardless of input cell number, the percentage of unmapped reads was 6–14%, indicating that few artifacts were introduced during sample amplification (Fig. S1C). Working with low input samples can result in immunoprecipitation of a small number of unique chromatin fragments, and excessive amplification then leads to a high rate of duplicated reads (Brind’Amour et al., 2015; Gilfillan et al., 2012). Such low complexity libraries contain limited information. We used the PreSeq package to estimate the number of unique nucleosomal fragments immunoprecipitated from different numbers of K562 cells (Daley and Smith, 2013). For 100,000 and 5,000 K562 cells, library complexity was sufficient to generate high-resolution


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datasets (Fig. S1D). Using 1,000 and 500 cells resulted in libraries of limited complexity. Still, the chromatin profiles generated from 500 K562 cells were informative and similar to corresponding ENCODE data (Fig. 2A-C). Thus, Mint-ChIP enables analysis of less than 1000 cells, pushing the boundaries of chromatin profiling.

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To demonstrate our technology on a challenging biological sample, we attempted to map activating and repressive histone modifications in primary human hematopoietic stem cells, which make up less than 0.1% of bone marrow cells. We isolated CD34+ progenitors from the bone marrow of an individual and flow sorted CD34+CD38–CD45RA– cells (Fig. 2D). We are not aware of any prior study that mapped chromatin state in primary human hematopoietic stem cells purified to this degree. We therefore used Mint-ChIP to profile H3K27ac, H3K27me3 and H3K36me3 in this population, using 6,000 cells per mark. More than 96% of reads were successfully aligned to the human genome, and visual analysis of tracks suggested high-quality data (Fig. 2E and Fig. S1E). Integration with published mRNA expression data showed expected positive (H3K36me3) and negative (H3K27me3) correlations between histone methylation and gene expression (Fig. 2F) (Laurenti et al., 2013). For example, CD34 and MSI2 are highly expressed and marked by H3K36me3, whereas SOX2 and PAX6 are repressed by H3K27me3. These data show that Mint-ChIP can be used to investigate chromatin landscapes of rare primary human cells. Quantitative normalization to compare global modification levels

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Conventional ChIP-seq does not maintain quantitative information regarding the global abundance of a particular histone modification. Data are typically normalized based on read numbers (“reads per million” or RPM). This presents complications when comparing between samples. To address this shortcoming, exogenous spike-in controls or semisynthetic standards were recently introduced (Bonhoure et al., 2014; Grzybowski et al., 2015; Orlando et al., 2014). Here, we considered whether the consistency of pooled sample processing could allow the capture of global modification levels without spike-in controls. To this end, we included an additional IP with antibody to total H3 in our assay, which we used as a normalization factor for the modifications (Fig. 3A).

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We tested the approach for measuring global changes using small molecule inhibitors. We treated K562 cells with the p300 inhibitor C646 or the EZH2 inhibitor GSK126 (Ferrari et al., 2013; McCabe et al., 2012). Western blots confirmed that H3K27ac levels were reduced by the acetyltransferase inhibitor C646, but increased by the methyltransferase inhibitor GSK126 (Fig. 3B). Western blots did not detect significant changes in H3K27me3 levels following 48 hours of either treatment. In parallel, we barcoded chromatin samples from the various treatment and control conditions, and pooled these samples in Mint-ChIP assays for H3K27ac, H3K27me3 and total H3. After sequencing, the number of reads for H3K27ac and H3K27me3 were normalized to the number of reads for total H3. Consistent with western blots, p300 inhibitor treatment decreased the ratio between H3K27ac reads and total H3 reads, while the EZH2 inhibitor treatment increased this ratio (Fig. 3C). Mint-ChIP also detected a consistent reduction in H3K27me3 levels following EZH2 inhibitor treatment, which was not evident by western blot at this early time point.


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We next considered why Mint-ChIP analysis might reveal earlier changes in H3K27me3 levels than conventional western blots. We note that Mint-ChIP is preferential to open regions, as these are more sensitive to MNase digestion. This is evident in the relatively higher proportion of H3 Mint-ChIP reads that align to euchromatic regions (2-fold enrichment; compare to 1.2-fold enrichment in conventional ChIP-seq, Fig. S2A). These ratios are small relative to enrichments in ChIP-seq peaks, which range from 5- to 100-fold. However, they could account for increased sensitivity to early changes in H3K27me3 if these were also preferential to euchromatin. To test this, we used western blots to quantify H3K27me3 levels in MNase digested chromatin from K562 cells treated with GSK126. We observed consistent reductions in H3K27me3 after 48 hours of treatment, in contrast to conventional western blots performed on acid extracted histones, which did not reveal a significant change at this early time point (Fig. S2B). This suggests that nucleosome turnover may lead to a more rapid reduction of methylation levels in euchromatic regions following EZH2 inhibition, in contrast to more stable heterochromatic regions. It also suggests the potential of quantitative, multiplexed profiling to detect early changes following epigenetic drug treatment.

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We further demonstrated the quantitative accuracy by examining the global and locusspecific effects of the H3K4 demethylase inhibitor, KDM5-C70 (Fig. 3D) (Labelle et al., 2014). We barcoded and pooled the chromatin of K562 cells treated with DMSO or KDM5C70 for 96 hours. We then used Mint-ChIP to assay H3K4me3 and total H3. After sequencing, normalization of the number of H3K4me3 reads to the number of H3 reads reflected global modification levels, and was consistent between experiments and also with western blot analysis (Fig. 3E-F). Scaling of the H3K4me3 tracks by the inferred global levels allowed direct comparison of peak height, which clearly showed increased peak intensity following KDM5-C70 treatment. This effect was obscured when the data were subjected to conventional “reads per million” normalization (Fig. 3G). Our analyses revealed that inhibition of H3K4 demethylases increases H3K4me3 coverage and widens existing domains, while also generating new H3K4me3 peaks (Fig. 3H-J). These data illustrate the utility of Mint-ChIP for studying chromatin modifiers and epigenetic drugs. Cancer mutations drive distinct epigenetic landscapes that are resolved by quantitative normalization

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Subunits of the H3K27 methyltransferase Polycomb Repressive Complex 2 (PRC2) are subject to inactivating and gain-of-function mutations in leukemia and lymphoma, respectively (Lund et al., 2013). We therefore examined two lymphoma and one leukemia cell lines with alternative PRC2 mutations (Fig. S3). The lymphoma cell line Pfeiffer has increased PRC2 activity due to the gain-of-function mutation EZH2-A677G (Ferrari et al., 2013; McCabe et al., 2012a). The leukemia line SKM-1 has altered PRC2 activity due to an EZH2-Y641C mutation and an ASXL1 truncation (Ryan et al., 2011; Sneeringer et al., 2010; Wigle et al., 2011). The lymphoma cell line Toledo has no known PRC2 mutations, but was reported to have mutations in the H3K27 acetyltransferases EP300 and CREBBP (Andersen et al., 2012).


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We used Mint-ChIP to quantitatively compare H3K27me3 and H3K27ac landscapes in these three cell lines (Fig. S4). First, we quantified global modification levels based on the ratio of H3K27me3 (or H3K27ac) reads to H3 reads. H3K27me3 levels were 5.1-fold greater in Pfeiffer than the other cell lines, consistent with its EZH2 activating mutation (Fig. 4A). In contrast, the SKM-1 line exhibits 8.7-fold higher global levels of H3K27ac, consistent with mass spectrometry data (Fig. 4B-C) (Jaffe et al., 2013). Notably, the increased H3K27ac in SKM-1 was not restricted to punctate peaks but rather appears to be diffusely distributed across much of the genome: just 3.4% of H3K27ac reads fall within defined peaks in SKM-1 (compare to 52.4% for Toledo and 42.3% for Pfeiffer). This atypical distribution causes conventional normalization algorithms to show low-intensity H3K27ac peaks in SKM-1, whereas Mint-ChIP normalization reveals globally increased H3K27ac that was also detected by mass spectrometry (Fig. 4C-D).

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Global chromatin changes link cellular genotypes to epigenetic drug sensitivity

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DISCUSSION

We also treated the respective mutant lines with the EZH2 inhibitor GSK126, which reduced global H3K27me3 levels (Pfeiffer: 3.4-fold; SKM-1: 3.8-fold; Toledo: 1.4-fold after 72 hours, Fig. 4A). Pfeiffer cells, which had the largest absolute decrease in H3K27me3, were the most sensitive to GSK126 treatment (Pfeiffer: 56±11% cell death after 3 days; SKM-1: 15±10%, Toledo: 2±15%, Fig. 4E) (McCabe et al., 2012b). We called H3K27me3 peaks in the naïve and drug treated cell lines. Although treatment-associated changes in H3K27me3 are masked by conventional normalization, these changes are readily evident using the quantitative normalization inherent to Mint-ChIP (Fig. 4F). Thus, Mint-ChIP can quantify differences in global chromatin landscapes of cancer cells representing different genotypes or drug treatments.

Here we present Mint-ChIP, a system for global profiling of chromatin states that combines advantages of quantitative normalization, multiplexing and high sensitivity. First, parallel immunoprecipitation of total H3 allows for quantitative normalization and direct comparison of global modification levels between samples. Second, multiplexed sample processing increases throughput, cost effectiveness and consistency. Third, the addition of carrier cells and linear amplification enables low-input ChIP, and informative data can be acquired from as few as 500 cells. Limitations

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Mint-ChIP is a robust protocol that achieves high sensitivity, throughput and precision without the need for specialized instrumentation. However, there are some limitations of Mint-ChIP in its current form. First, when cell numbers are very limited, the amount of information that can be captured (library complexity) also decreases. This may limit detection and discovery of weakly marked elements. Second, our short-term MNase digestion yields fragments of several nucleosomes in length, and longer fragments are more likely to be immunoprecipitated. This could limit spatial resolution of the method for punctate modifications such as H3K4me3. Third, our current procedure requires fresh or viably frozen cell samples, and is ineffective for mapping transcription factors. We expect


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that the procedure could be adapted for fixed chromatin, which would address these limitations by extending tissue compatibility and enable analysis of various DNA-bound proteins. Applications to development, disease and drug treatments The method enabled us to map activating and repressive marks in rare hematopoietic stem cells isolated from the human bone marrow. We also quantified differences in H3K27ac and H3K27me3 landscapes and global modification levels across cancer cells, which were in agreement with the underlying genotypes. Mapping chromatin states in rare cell (sub-) populations can identify active regulatory networks and integration with genome sequencing data can unveil disease-relevant mechanisms (Farh et al., 2014; Krogan et al., 2015).

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Following epigenetic drug treatment, Mint-ChIP detected changes in H3K4me3, H3K27ac and H3K27me3 with high sensitivity. In contrast, such differences may be masked in conventional ChIP-seq data lacking quantitative normalization. Quantitative changes to histone modifications are also likely to occur during normal tissue development and differentiation (Kurimoto et al., 2015; Zhu et al., 2013). Mint-ChIP can be applied to directly quantify cell-type-specific chromatin features with global and focal precision (Handley et al., 2015). In comparison with chromatin spike-in adjustment, we expect quantitative normalization to be advantageous for analysis of variable cell numbers or aneuploid samples, whereas the two methods could be complementary in other situations.

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We expect that Mint-ChIP will be of broad use for studies of rare cell populations, developmental hierarchies and disease models, parallelized analyses of blood populations from human cohorts, and for investigating the dynamics and mechanisms of epigenetic inhibitors.

EXPERIMENTAL PROCEDURES MNase titration

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MNase (New England BioLabs M0247S) was stored as frozen aliquots at −80°C. To titrate MNase for specific cell types, we lysed and digested 10,000–100,000 cells in the same conditions as for Mint-ChIP, using 4 to 600 MNase units. MNase was inactivated with 30 mM EGTA and we performed a 2× SPRI bead cleanup to isolate DNA (Agencourt AMPure XP beads). DNA was eluted in 40 μl H2O with 0.4 μl RNase (Roche 11119915001) and 1 μl Proteinase K (Life Technologies 25530-031) and incubated for 15 minutes at 37°C and for 1 hour at 62°C. Another 2× SPRI bead cleanup was performed, and eluted DNA was run on a 2% E-gel (Life Technologies G402002) or BioAnalyzer. MNase concentrations that yielded most fragments of 1–5 nucleosomes (150–800 bp) were selected for Mint-ChIP. Multiplexed indexed T7 (Mint) ChIP 1. Lysis and digestion—Typically, we performed each experiment at 2 MNase concentrations. MNase and 10 mM sodium butyrate (histone deacetylase inhibitor) were added to 2× lysis buffer (100 mM Tris-HCL pH 8.0, 300 mM NaCl, 2% Triton X-100, 0.2% sodium deoxycholate, 10 mM CaCl2). We added 20 μl 2× lysis buffer to 20 μl cells in PBS,


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followed by lysis on ice for 20 minutes and chromatin digestion at 37°C for 10 minutes. Carrier cells (up to 400,000 for each ChIP assay) were lysed and digested at the same time.

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2. T7-adapter ligation—T7-adapters were resuspended in TE buffer at 500 μM, annealed (250 μM), and stored as frozen aliquots at −20°C (see Table S2 for sequences). To 40 μl lysed cells, we added 40 μl ligation mix: 4 μl annealed T7-adapter (12.5 μM), 30 mM EGTA to inactivate MNase, 5 mM sodium butyrate, and from the EpiCentre Fast-Link DNA ligation (LK6201H) and End-It DNA End-Repair (ER81050) kits: 1× buffer (either kit), 1.5 mM ATP, 1× NTP mix, End-Repair Enzyme mix (1.6 μl per 80 μl ligation) and Fast-Link DNA ligase (1.6 μl per 80 μl ligation). To carrier cells, instead of ligation mix, we added 40 μl of H2O with 30 mM EGTA and 5 mM sodium butyrate. Ligation was performed for 2 hours at room temperature. To end the ligation reaction, we added 80 μl lysis dilution buffer (50 mM Tris-HCl pH 8.0, 150 mM NaCl, 1% Triton X-100, 50 mM EGTA, 50 mM EDTA, 0.1% sodium deoxycholate) with 5 mM sodium butyrate to each reaction and carrier cells. 3. Pool and split—All samples from the low MNase concentration were combined (pool 1), and all samples from the high MNase concentration were combined (pool 2). Carrier chromatin was also added at this point (see Fig. S4 for graphic). Next, the 2 pools of samples were split, and we added one the following antibodies for parallel ChIP assays: 5 μl H3 (Abcam 1791), 1 μl H3K4me3 (Millipore 07-473), 2 μl H3K27ac (Active Motif 39133) or 5 μl H3K27me3 (Millipore 07-449). ChIP assays were rotated overnight at 4°C.

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4. DNA isolation—On day 2, we added 25 μl protein G Dynabeads (Life Technologies 10004D) to each ChIP assay and rotated tubes for 2–4 hours at 4°C. Beads were quickly washed five times using magnetization: 2 washes with 200 μl RIPA buffer (0.1% sodium deoxycholate, 0.1% SDS, 1% Triton X-100, 10 mM Tris-HCl pH 8.0, 1 mM EDTA, 140 mM NaCl); 1 wash with 200 μl RIPA/high salt buffer (0.1% sodium deoxycholate, 0.1% SDS, 1% Triton X-100, 10 mM Tris-HCl pH 8.0, 1 mM EDTA, 360 mM NaCl); 1 wash with 200 μl LiCl buffer (250 mM LiCl, 0.5% NP40, 0.5% sodium deoxycholate, 1 mM EDTA, 10 mM Tris-HCl pH 8.0); and 1 wash with TE buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA). Next, we added ChIP elution buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA, 0.1% SDS, 300 mM NaCl) with 0.25 mg/ml Proteinase K to the protein G Dynabeads and eluted DNA for 1 hour at 62°C. A 1× SPRI bead cleanup was performed to isolate DNA fragments of >100 bp.

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5. In vitro transcription—To the eluted DNA, we added NTP buffer mix and T7 RNA polymerase mix according to manufacturer's instructions (HiScribe T7 Quick High Yield RNA Synthesis kit, New England BioLabs E2050S). Assays were incubated at 37°C for 2– 16 hours. Next, we added 1 μl DNase per assay (from the same kit) and incubated at 37°C for 15 minutes. Silane beads were used to isolate RNA (Life Technologies 37002D). 6. Reverse transcription—To the eluted RNA, we added 50 μM RT primer (Table S2) and performed reverse transcription according to manufacturer's instructions (SuperScript III First-Strand Synthesis SuperMix, Life Technologies 18080-400). A 1× SPRI bead cleanup was performed to isolate DNA fragments of >100 bp.


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7. Library construction—For the first PCR, we added the following to the eluted cDNA: 0.2 μM forward primer 1, 0.2 μM reverse primer 1 (see Table S2 for primer sequences), and 1× PCR master mix (PfuUltra II Fusion HS DNA Polymerase, Agilent 600850). DNA was amplified for up to 12 cycles (6 cycles was usually optimal). A 1× SPRI bead cleanup was performed to isolate DNA fragments of >100 bp. For the second PCR, we added the following to 2–40 ng of eluted DNA: 0.2 μM forward primer 2, 0.2 μM reverse primer 2 with a second barcode (index #2), and 1× PCR master mix. DNA was amplified for up to 12 cycles (8 cycles was usually optimal). Finally, DNA of 200–700 bp was isolated using a double SPRI bead cleanup (0.6× and 0.95×). Libraries were quantified using the Qubit, Agilent BioAnalyzer and/or KAPA library quantification kits (KK4835), and subjected to paired-end sequencing on the Illumina NextSeq 500 or Illumina HiSeq 2500 instruments. Cell culture


Human chronic myelogenous leukemia K562 cells (ATCC CCL-243) and mouse lymphoma YAC-1 cells (ATCC TIB-160) were cultured in RPMI with 10% FCS, penicillinstreptomycin and L-glutamine. K562 and YAC-1 identity verification was performed at the Dana-Farber Cancer Institute Molecular Diagnostics Laboratory. Pfeiffer (ATCC CRL-2632), SKM-1 (DSMZ ACC 547) and Toledo (ATCC CRL-2631) were cultured in RPMI with 20% FCS, penicillin-streptomycin, L-glutamine and β-mercaptoethanol (3.5 μl in 1000 ml). To verify the identity of SKM-1, Toledo and Pfeiffer, genomic DNA was amplified using primers listed in Table S3. Quintarabio performed Sanger sequencing and alignment was done using Geneious version 7.1 created by Biomatters. For drug treatment, K562 cells were plated for 48 hours with C646 (25 μM) to inhibit p300, 48 hours with GSK126 (2 μM) to inhibit EZH2, or 96 hours with KDM5-C70 (5 μM) to inhibit KDM5A/B/C/D. For kill curves, Pfeiffer, SKM-1 and Toledo were plated in the presence of GSK126 at different concentrations for 72 hours, and viable (propidium iodide negative) cells were counted by automated flow cytometry using a BD LSRII cytometer. Histone profiles of GSK126-treated Pfeifer, SKM-1 and Toledo cells were made after 72 hours of treatment at 1 μM. Bone marrow hematopoietic stem cell isolation

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Bone marrow from healthy individuals was obtained with informed consent according to procedures approved by the institutional review boards Memorial Sloan Kettering Cancer Center and Hospital for Special Surgery. Mononuclear cells were obtained by centrifugation on Ficoll, and CD34+ cell isolation was performed using the Miltenyi Biotec CD34 Microbead Kit for magnetic separation. Viable CD34+CD38–CD45RA– hematopoietic stem cells were sorted on a BD FACSDiva cell sorter and stored in IMDM with 50% FCS and 10% DMSO at −150°C until use. Gene expression data was determined from previously published microarray data, by taking the average of CD34+CD38–CD45RA– populations (AA, BA and BB, http://jdstemcellresearch.ca/ index_files/ResearchData.htm, ExprMx.xlsx, Laurenti et al., 2013).


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Western blot


K562 cells were harvested after 48-hour treatment and histones were extracted using the Abcam histone extraction protocol. Protein concentration was measured by the Bradford Assay to ensure equal loading. Lysate was boiled with NuPage lithium dodecyl sulfate sample buffer (Invitrogen NP0008) and 0.04% β-mercaptoethanol for 10 minutes before loading on the NuPage Novex 4-12% gradient Bis-Tris Gel (Invitrogen NP0322BOX) for electrophoresis. Proteins were transferred using the iBlot system (Invitrogen IB1001) according to manufacturer’s instructions. Transferred nitrocellulose membrane was incubated with 5% BSA (Sigma A2153-50g) in TBST (Boston Bioproducts IBB-181), and blotted with respective antibodies overnight. Blots were washed three times with TBST. Secondary antibody in TBST was added and blots were incubated for 1 hour at room temperature. Blots were then washed three times with TBST. HRP substrate was added to the blots (Millipore Cat WBKLS0500) and allowed to incubate for 5 minutes at room temperature before imaging on a Kodak X-OMAT 2000A Processor. Primary antibodies include total H3 1:10,000 (Cell Signaling Technology 4499P), H3K27me3 1:15,000 (Millipore 07449) and H3K27ac 1:1,000 (Active Motif 39133). The secondary antibody was Anti-Rabbit IgG HRP Conjugate (Promega W4011) at 1:200,000 (total H3) and at 1:100,000 (H3K27me3 and H3K27ac). Data processing – demultiplexing and alignment

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All samples were sequenced by paired-end sequencing on Illumina NextSeq 500 or HiSeq 2500 machines. First, files were demultiplexed using standard bcl2fastq algorithms. Next, "read1" fastq files were demultiplexed further based on the first 6 bases of the read, which represent the T7-adapter barcode (the option to allow 1 mismatch was achieved using the package Glimpse). Next, corresponding reads were extracted from "read2" fastq files based on the read identifier and concatenated into new fastq files. The barcode was trimmed from read1. Next, the Burrows-Wheeler Aligner (BWA) was used for paired-end alignment to hg19 or mm9. Data processing – bam filters The resulting bam files underwent three stages of filtering, using Python-3.4 and the module pysam version 0.8.1: 1) Multimapped: unpaired reads that mapped to >2 locations in the genome (X0 flag > 2) were removed, as were paired reads if both mates were mapped to >2 locations.

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2) PCR duplicates were removed by running the Picard Tools version 1.802 option MarkDuplicates with REMOVE_DUPLICATES=true. 3) T7 duplicates have an identical read1 but read2 can vary due to the reverse transcription step. If multiple pairs had an identical alignment of read1, only the pair with the largest fragment size was maintained.


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Data processing – bam visualization and composite plots

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Filtered bam files were converted to tdf files for visualization in IGV using the package igvtools_2.3. Peak calling was performed using the Homer package (homer-4.5): findPeaks -style histone -size 1000 -C 0 -o auto -i Size parameter was changed to 5000 for H3K27me3 ChIP. Bed files were created using the pos2bed.pl command, and merged using bedtools merge. To create composite plots of peaks, histograms were created using the Homer annotatePeaks.pl command. To quantify histone marks using quantitative normalization, conventional normalization was disabled by adding the options -noadj -normLength 0. Quantitative normalization further required dividing the resulting histogram values by the number of H3 reads in corresponding datasets. Statistical analysis

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Mean ± SD values are given and P values are calculated by two-tailed unpaired Student's ttests. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Supplementary Material Refer to Web version on PubMed Central for supplementary material.

Acknowledgments

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We would like to thank Dr. Jacob D. Jaffe for sharing chromatin profiles of the CCLE collection, David Flowers and Irwin Bernstein for providing samples to test Mint-ChIP, Robert Nicol and Annabelle Gerard for helpful suggestions, and Antonia Kreso for critical reading of the manuscript. This work was supported by the National Human Genome Research Institute (ENCODE HG004570), the National Heart Lung and Blood Institute (U01HL100395), EMBO Fellowships (P.v.G., ALTF 1207-2014 and C.S., ALTF 654-2014), Marie Curie Actions, and a Damon Runyon Postdoctoral Fellowship (A.D.V.). Competing financial interests: B. Bernstein is founder of and consultant for HiFiBio, and a scientific advisor for Syros Pharmaceuticals. A patent application covering aspects of Mint-ChIP has been filed by Harvard University and Massachusetts General Hospital, USA.

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Author Manuscript Author Manuscript Author Manuscript Figure 1. A multiplexed, quantitative and low-input assay for profiling chromatin states

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(A) Overview of Mint-ChIP protocol. Following (1) lysis and MNase digestion, (2) a ligation mix inactivates MNase, repairs DNA ends and ligates barcoded T7-adapters to nucleosomes (index #1). (3) Indexed samples are pooled and then split for parallel ChIP assays. (4) DNA is isolated and amplified by in vitro transcription, yielding RNA, which is then (5) reverse transcribed. (6) PCR amplification yields (7) an Illumina sequencing library (index #2). (8) Sequencing data are demultiplexed in silico based on their barcodes, yielding profiles for each sample (index #1) and each mark (index #2). (B) Plot depicts proportions of Mint-ChIP reads that align to the human or mouse genomes. X-axis indicates the relative ratios of T7-adapter-ligated chromatin (human) to carrier chromatin (mouse). The mouse


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carrier lacks T7-adapters and is not amplified or sequenced. Data is shown as mean ± SD of 4 ChIP assays × 5 MNase concentrations. (C) Pie charts indicate T7-adapter barcode representations in Mint-ChIP sequencing data for total H3. These data validate the MintChIP procedures for indexing and pooling chromatin and in silico demultiplexing. (D) Four human samples (K562, T7-adapter barcode 1–4) and two mouse samples (YAC-1, T7adapter barcode 5–6) were indexed, pooled and split for three parallel Mint-ChIP assays. Plot depicts the proportions of reads for each barcode that align to the human or mouse genomes. Data is shown as mean ± SD of 3 ChIP assays. See also Fig. S1.

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Figure 2. Validation of chromatin data and sensitivity to low-input samples

Data tracks show (A) H3K4me3 and (B) H3K27me3 profiles derived by Mint-ChIP using indicated starting cell numbers. For comparison, ENCODE data generated by conventional ChIP-seq is also shown. (C) Density plots compare Mint-ChIP and ENCODE data for K562 cells. Datapoints compare the number of reads in Mint-ChIP (x-axis) vs. ENCODE (y-axis) for all promoter intervals (H3K4me3), H3K27ac peaks called from ENCODE data (H3K27ac) or all annotated transcripts (H3K27me3). R indicates Pearson correlation. (D) Workflow for hematopoietic stem cell analysis. CD34+CD38–CD45RA– cells were isolated


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from human bone marrow by flow cytometry. Mint-ChIP was used to analyze histone modifications. (E) Data tracks show H3K27ac, H3K27me3 and H3K36me3 profiles of hematopoietic stem cells at the HOXA locus. (F) Density plots depict correlation between methylation within genes (H3K36me3 and H3K27me3) and mRNA expression in human hematopoietic stem cells. Each data point corresponds to a single gene; some genes are highlighted as examples. See also Fig. S1.

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Figure 3. Mint-ChIP quantitative normalization clarifies global differences in histone modification levels

(A) Graphic for Mint-ChIP quantitative normalization. Control or drug treated cells are indexed, pooled and then split for parallel ChIP assays. The ratio between H3K27me3 reads and H3 reads is used to compare global H3K27me3 levels between samples and normalize corresponding profiles. (B) Western blot shows H3K27ac and H3K27me3 levels in K562 cells following treatment with the p300 inhibitor C646 or the EZH2 inhibitor GSK126 (compared to DMSO control). Total H3 is shown as a loading control. (C) Bar plots show global modification levels inferred from western blot (top) or Mint-ChIP (bottom). The Mol Cell. Author manuscript; available in PMC 2017 January 07.

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respective methods were applied in parallel to the same sample of K562 cells treated for 48 hours with the indicated inhibitors. Data is shown as mean ± SD of n = 3 independent experiments (symbols indicate values from independent experiments). (D) Diagram explains difference between normalization methods. Global differences in histone modification levels (e.g. by demethylase inhibition) may be masked by conventional ChIP-seq signal normalization (RPM). In contrast, quantitative normalization enables direct peak height comparisons between samples. (E) Western blot shows increased H3K4me3 levels in K562 cells following treatment with the demethylase inhibitor KDM5-C70. Total H3 is shown as a loading control, n = 3 experiments are shown. (F) Bar plots show global H3K4me3 levels inferred from Mint-ChIP. Data is shown as mean ± SD of 4 replicates; n = 3 experiments are shown. (G) Data tracks show H3K4me3 profiles, scaled by conventional or quantitative normalization. (H) Composite plot depicts average H3K4me3 signals in K562 cells treated with DMSO or KDM5-C70. Ten kb regions surrounding the centers of 36,875 peaks are shown. (I) Barplot shows the fraction of peaks within size windows. Peaks of >10 kb were classified as 10 kb such that the total area is one. (J) Venn diagram shows the number of peaks detected in K562 cells treated with DMSO or KDM5-C70. See also Fig. S2.

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Figure 4. Quantitative normalization resolves distinct chromatin landscapes resulting from cancer mutations and drug treatment

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(A-B) Heatmaps compare H3K27me3 or H3K27ac levels in different cell lines treated with GSK126, as quantified by Mint-ChIP. These experiments were performed using 2 different MNase concentrations, which are typically averaged. (C) Bar plot shows mass spectrometry quantification of H3K27ac in Pfeiffer, SKM-1 and Toledo (Jaffe et al., 2013). The mass spectrometry data match the normalized Mint-ChIP data. (D) Composite plots depict average H3K27ac signals over 20 kb regions surrounding the centers of 23,176 peaks. Values were computed by conventional normalization, wherein signal is relative to total read numbers (RPM, left) or by the quantitative normalization afforded by Mint-ChIP (right). (E) Bar plots depict viable cell counts following 72 hour GSK126 treatment of Pfeiffer, SKM-1 and Toledo. Data is shown as mean ± SD of technical triplicates × n = 2 independent experiments (* P < 0.05, ** P < 0.01, **** P < 0.0001). (F) Composite plots depict average H3K27me3 signals over 40 kb regions surrounding the centers of 2,052 peaks. Together, these data demonstrate the unique capacity of Mint-ChIP to quantitatively map and compare chromatin landscapes and modification levels between cell types and epigenetic inhibitor treatments. See also Fig. S3-4.


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