WO2012047726A1 - Procédés d'immunoprécipitation de la chromatine - Google Patents

Procédés d'immunoprécipitation de la chromatine Download PDF

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WO2012047726A1
WO2012047726A1 PCT/US2011/054072 US2011054072W WO2012047726A1 WO 2012047726 A1 WO2012047726 A1 WO 2012047726A1 US 2011054072 W US2011054072 W US 2011054072W WO 2012047726 A1 WO2012047726 A1 WO 2012047726A1
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chromatin
affinity
dna
binding
antibody
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PCT/US2011/054072
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English (en)
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Ido Amit
Bradley Bernstein
Manuel Garber
Alon Goren
Oren Ram
Aviv Regev
Noam Shoresh
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The Broad Institute, Inc.
The General Hospital Corporation D/B/A Massachusetts General Hospital
Massachusetts Institute Of Technology
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Publication of WO2012047726A1 publication Critical patent/WO2012047726A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6804—Nucleic acid analysis using immunogens
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6806—Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6875—Nucleoproteins

Definitions

  • Chromatin immuno-precipitation is a powerful tool for evaluating interaction of proteins with specific genomic DNA regions in vivo, to provide a better understanding of the mechanisms of gene regulation, DNA replication, and DNA repair.
  • the ChIP technique involves fixative treatment of live cells with formaldehyde to chemically cross-link DNA- bound proteins. The cells are then lysed, and the chromatin is sheared mechanically or enzymatically, in order to reduce fragment size and increase resolution. The resultant sheared complexes are then immuno-precipitated with antibodies specific to the protein of interest, and the DNA fragments are analyzed, e.g., using real time PCR, sequencing, or microarray hybridization.
  • the ChIP protocol introduced in 1988 (Solomon MJ et al. Cell. (1988) 53(6):937-47), is widely used.
  • iChlP Chromatin Immuno- Precipitation
  • iChlP can be applied to reconstruct the physical regulatory landscape of a mammalian cell, by building genome-wide binding maps for, e.g., transcription factors (TFs) and chromatin marks at specific time points following stimulation of, e.g., primary dendritic cells (DCs) with pathogen components.
  • TFs transcription factors
  • DCs primary dendritic cells
  • the methods described herein may provide a foundation for future
  • iChlP methods that, in some embodiments, when performed give high quality immuno-precipitated material with yields superior to (e.g. , greater amounts than and/or higher purity than) existing protocols, including when processing multiple samples in parallel.
  • iChlP methods that are adapted for semi- or full automation.
  • the entire iChlP process can be performed, for example, in a single nanodroplet, using microfluidic technology.
  • the iChlP methods described herein, in some embodiments may be practiced by a person of average or limited skill in the art of chromatin immuno-precipitation or even an unskilled person.
  • the iChlP methods provided herein reduce the time required to carry out the methods and/or reduce the sample to sample variability (e.g. , improve reproducibility) in quality and/or yield, when compared to existing ChIP methods.
  • chromatin immuno-precipitation methods for parallel processing of multiple samples (e.g., in a multi-well format), the methods include: a) cross-linking a chromatin-associated factor to chromatin,
  • first affinity molecule and/or second affinity molecule optionally is coupled to a substrate suitable for parallel processing of multiple samples.
  • contacting of the nucleic acid fragments in (e) is carried out using an affinity interaction between the nucleic acid fragment and the second affinity molecule, optionally wherein the nucleic acid is suitably modified for this interaction.
  • the modification of the nucleic acid is addition of poly-A tails or biotinylation.
  • the second affinity molecule is a poly-T
  • the second affinity molecule is silica.
  • the substrate is a surface of a bead or a well, optionally, the bead is a magnetic bead (e.g., a bead coated in streptavidin).
  • steps (e) and (f) of the aforementioned methods are not carried out using a purification column or using phenol/chloroform extraction and ethanol precipitation. In certain embodiments, steps (e) and (f) are not carried out using a purification column comprising silica.
  • the aforementioned methods are carried out in a multiwall format that is a 6-well plate, a 12-well plate, a 24-well plate, a 96-well plate, a 384-well plate or a 1536- well plate.
  • the first affinity molecule in step (c) is an antibody that specifically binds a chromatin-associated factor cross-linked to the nucleic acid fragment.
  • the antibody is coupled to a substrate and optionally, the substrate is a surface of a bead or a well.
  • the substrate comprises protein A or protein G.
  • the chromatin-associated factor binds to the afore- mentioned antibody before the antibody is coupled to the substrate.
  • the chromatin-associated factor comprises an affinity tag.
  • the affinity tag is FLAG-tag, myc-tag, biotin or DHFR.
  • the affinity molecule is an antibody that specifically binds the affinity tag, avidin or streptavidin.
  • the antibody is an anti-FLAG antibody, or an anti-myc antibody.
  • the aforementioned methods include a shearing in step (b) that is carried out by sonication or micrococcal nuclease digestion.
  • the aforementioned methods include a step of analyzing the isolated nucleic acid fragments.
  • analyzing the isolated nucleic acid fragments includes determining the nucleotide sequence.
  • the nucleotide sequence is determined using sequencing or hybridization techniques with or without amplification, optionally such techniques are ChlP-Seq, real-time polymerase chain reaction (PCR), DNA microarray, or NANOSTRING ® array.
  • kits for parallel processing of multiple samples in a multi-well format, wherein the kits include: a) a multi-well plate having wells coated on an inside surface of the wells with a first affinity molecule that binds to a chromatin-associated factor, or is coated with a molecule that binds to the first affinity molecule, to form a first affinity surface, and
  • kits optionally further include a protein inhibitor, a cross-linking solution, a cell lysis buffer, a wash buffer, an elution buffer, and/or user instructions or directions to obtain user instructions (e.g., via an internet website).
  • the aforementioned chromatin immuno-precipitation kits have a single multi-well plate that has different wells for first and second affinity surfaces or the kits have a single multi-well plate that has wells that have both first and second affinity surfaces.
  • kits for parallel processing of multiple samples, wherein the kits include:
  • kits further include a multi- well plate, a protein inhibitor, a cross-linking solution, a cell lysis buffer, a wash buffer, an elution buffer, and/or user instructions.
  • the second affinity molecule comprises silica, poly-T oligonucleotide, a poly-A oligonucleotide, avidin, streptavidin, or biotin.
  • the aforementioned kits have multi-well plates that are 6- well plates, 12- well plates, 24-well plates, 96-well plates, 384-well plates, or 1536-well plates.
  • the molecule that binds to the first affinity molecule includes protein A or protein G.
  • the first affinity molecule includes an antibody that specifically binds to a chromatin-associated factor, an antibody that specifically binds to an affinity tag, avidin, streptavidin, or biotin.
  • the affinity tag is FLAG-tag, myc-tag, biotin, or DHFR.
  • the antibody is an anti- FLAG antibody, an anti-myc antibody, or an anti-DHFR antibody.
  • immuno-precipitated chromatin may be prepared from about 5 to about 20 million cells, or more.
  • a method described herein may further comprise collecting (e.g., harvesting) a sample of about 100 cells, 1000 cells, 10,000 cells, or 100,000 cells.
  • the sample comprises less than 100 cells, while in other embodiments, the sample comprises more than 100,000 cells.
  • the sample comprises about 1 million to about 20 million cells, or more.
  • any one of the methods described herein may further comprise providing or obtaining chromatin and associated proteins prepared from a single sample of about 50 cells, about 100 cells, about 150 cells, about 200 cells, about 300 cells, about 400 cells, about 500 cells, about 1000 cells, about 2000 cells, about 3000 cells, about 4000 cells, about 5000 cells, about 10,000 cells, about 20,000 cells, about 30,000 cells, about 40,000 cells, about 50,000 cells, about 100,000 cells, about 200,000 cells, about 300,000 cells, about 400,000 cells, about 500,000 cells, or about 1,000,000 cells.
  • chromatin and associated proteins are prepared from a single sample of about 100 cells to about 10,000 cells, or about 10,000 cells to about 100,000 cells, or more.
  • kits for preparing indexed sequence libraries comprising: (a) purifying or obtaining purified ChIP DNA processed using any one of the methods described herein; (b) adding unique sequence identifiers to the purified ChIP DNA; and (c) selecting the ChIP DNA in (b) based on size.
  • the methods further comprise assessing the ChIP DNA in (c) for enriched molecular binding sites.
  • the methods further comprising sequencing the ChIP DNA.
  • a method can be performed in a multi-well format or a microfluidic chamber/channel.
  • an indexed sequence library may be constructed on magnetic particles.
  • the indexed sequence libraries can be used to screen and evaluate functional properties of nucleic acids and/or binding factors.
  • the libraries may be used to identify or isolate nucleic acids and/or binding proteins of interest (e.g., promoters, enhancers, transcription factors/regulators).
  • an indexed sequence library may comprise a plurality of nucleic acid fragments having unique sequence identifiers (e.g., each fragment of a selected nucleic acid may be associated with a sequence identifier, for example, a unique sequence identifier).
  • kits for preparing an indexed sequence library comprising any one or more of the reagents described in any one or more of the foregoing embodiments.
  • Figure 1 shows a high throughput indexed chromatin immuno-precipitation (iChIP) pipeline.
  • Figure 1A shows a blueprint of the iChIP pipeline. Top: Protein-DNA fragments are precipitated using antibody coupled magnetic beads in 96- well plates. Middle:
  • Precipitated DNA is purified using magnetic beads, indexed adapters are ligated and DNA is size selected to generate sequencing libraries. Bottom: Samples are validated using ChlP- String; successful samples are pooled and sequenced. Figure IB shows iChlP-String validation. Nanostring probes target active regulatory regions of a signature gene set.
  • FIG. 1C shows a strategy for ab initio transcription factor (TF)-DNA binding maps. The strategy consists of four steps: (1) Expression analysis using RNA-Seq, (2) Selection of expressed TFs, (3) Screening for all potential ChlP-Seq antibodies, and (4) ChlP'ing in appropriate time points all validated TF targets.
  • Figure 2 shows an Epigenetic and transcription factor binding landscape.
  • Figure 2A shows Integrative Genomics Viewer (IGV) tracks, including enhancer and promoter calls of the Illa-b loci showing "compressed" alignments for various TFs. Call-out boxes show time course data for selected factors.
  • Figure 2B shows the total number of high scoring peaks (enrichment score > 20) of Ctcf, Pol-II, the 29 TFs and the three chromatin marks. Each pie chart shows the distribution of peaks across promoter, 3 'UTR, exonic, intronic, enhancer and unannotated, regions. The total number of peaks is shown in parenthesis and the most significant motif found by de novo motif discovery at these peaks is shown below whenever available. Factors in italics indicate that the motif shown is not the canonical binding motif for the factor and factors in light grey indicate they are one of the highest scoring Pol-II runners.
  • Figure 3 shows Runxl 3' end binding.
  • Figure 3A shows an overview of the Cxcl2 inflammatory gene loci showing time course RNA-Seq and ChlP-Seq data for selected factors, including Runxl.
  • Figure 3B shows a comparison of the enrichment of promoter and 3' end bound Runxl targets in inflammatory (dark gray) vs. anti-viral genes (light gray).
  • Figure 3C shows cumulative distribution plots of Runxl peak scores at promoters and 3' end.
  • Figure 3D shows cumulative distribution plots of the expression (in RPKM) of genes bound by Runxl at the promoter and 3' end.
  • Figure 3E shows the median score of Runxl peaks at the promoter, 3' end and enhancer across the LPS response time course.
  • Figure 3F shows the expression fold change in Runxl knockdown dendritic cells (DC) 6 hr post stimulation (compared to nontargeting short hairpin (sh)RNA (Amit, et al. Science, 326: 257-263, 2009) of significantly down and up regulated genes. Blue and red starts indicate genes that are bound by Runxl at the 3' end and promoter respectively.
  • Figure 4 shows co-binding of TFs in regulatory regions.
  • Figure 4A The degree of a regulatory region is defined as the number of TF bound to it.
  • the heatmaps show for every TF a distribution of the degrees associated with its bound regions.
  • the left heatmap shows the original data while the right plot is obtained from a random process in which the degree of every region is proportional to its length.
  • Figure 4B shows TF co-binding at similar regions. Significant TF pairs (p ⁇ 10-3) are shade-coded by their respective fold enrichment. Selected overlaps are highlighted.
  • Figure 5 shows dynamics of TF binding.
  • Figure 5 A shows Ifit locus showing our "compressed" alignments for various TFs. Call-out boxes showing time course data for selected factors.
  • Figure 5B shows a bar plot showing the fraction of TF peaks gained (>3 fold increase compared to the unstimulated state; left plot) or lost (>3 fold decrease; right plot) during the response to LPS. Each bar is subdivided and colored to represent the fraction of peaks that are gained (lost) at each time point.
  • Figure 6 shows connecting TF binding with gene expression.
  • Figure 6A shows a schematic example of region annotation and association of regions to genes. Top: two typical genes (in black and white), gene 2 has a previously unannotated alternative start site discovered through RNA-Seq. Middle: Promoters were defined as H3K4me3 rich regions (H3K4me3+) that either overlap an existing annotation or a reconstructed transcript.
  • Enhancers were associated with TF-bound H3K4mel rich regions (H3K4mel+). Bottom: Both gene 1 and 2 are within 150 kb away from the annotated enhancer, however, the enhancer is associated with gene 2 since its promoter shares a common TF with the enhancer. Bottom right: A cartoon model of looping between the annotated enhancer and the promoter of gene 2.
  • Figure 6B shows binding of TF (x-axis) at regulatory elements of genes (y-axis); black cells indicate no change in binding over time; red cells are increased binding and blue cells are decreased binding. Genes were clustered into 8 groups based on their binding profile.
  • Figure 6C shows enrichment of factor binding at inflammatory (dark gray) or antiviral (light gray) genes. Displayed values are -log 10 of the hypergeometric p-value.
  • Figure 6D shows a bar plot showing the percentage of induced genes (>2 fold change compared to basal state) and average transcription levels (taking the maximum across time) in sets of genes with similar numbers of bound TFs (using the compressed binding data).
  • Figure 6E shows a matrix showing percent of gain (red) or loss (blue) of binding on late (> two hours, top row) intermediate (between the first and second hours, middle row) and early (up to one hour, bottom row) induced genes. Gain or loss events that occurred after the induction of the gene were ignored. Only significant enrichments are shown.
  • Figure 7 shows feedbacks and robustness in the DC transcriptional network.
  • Figure 7B Top: Binary matrix indicating TF binding at the signature immune genes (Amit et al., 2009); Bottom: Heat map showing expression in DCs infected with TF targeting shRNAs (labels of the TF are shown below) compared to DC infected with control shRNA 6 hours post stimulation (Amit et al., 2009).
  • Figure 7C Top: Percent of functional binding: for each TF presented are the number of bound genes that are affected by its knockdown divided by the total number of bound genes; Bottom: percent of indirect effect. For each TF, presented the number of non-bound genes that are affected by its knockdown divided by the total number of affected genes. The analysis is limited to the signature set of genes (Amit et al., 2009).
  • Figure 8 shows diversity of binding properties suggests a layered TF architecture
  • Figure 8A shows principle components analysis performed with a binding characteristic matrix which includes: the number of bound regions, Turnover score, ratio of enhancer to promoter binding, Pol-II runner score and the fraction of high scoring motif matches that are bound by the TF.
  • the plot depicts the projections of the TFs and the loading of the different covariates for the first three principle components.
  • Figure 8B shows a model depicting the layered TF network architecture: pioneer factors initially bind and initiate remodeling of the epigenome, strong binders prime targets for expression and specific TFs control expression of smaller subsets of genes.
  • Figure 9 compares traditional ChIP and iChlP.
  • Figure 9A shows that Pol-II precipitated DNA (1 ng) was split through the traditional ChlP-Seq and iChlP (HT-ChIP- Seq) library production process. Matrix showing correlation of the two-library production process.
  • Figure 9B shows IGV tracks showing the Zfp36 locus for the traditional ChlP-Seq (red) and iChlP library production process.
  • FIG 10 shows a Western blot for the ChIP TFs.
  • Cell lysates from DC activated for 2 hours with LPS where subjected to Western blotting (WB) using the indicated antibodies.
  • WB Western blotting
  • Figure 11 shows percentage of transcriptional changes (induction/ repression) during the first two hours post stimulation.
  • a gene is defined to be induced (repressed) if it increased (decreased) by at least 2-fold change.
  • the cumulative plots depict, for every time point (x-axis) the percentage of induced genes that already showed at least 2-fold change (y- axis).
  • Panel A was computed with the RNA-Seq data, panel B with the 4SU-seq and panel C with Pol-II binding data.
  • Figure 12 shows an example of previously unannotated promoter at the Ncoa6 and Lhx2 loci. Combining RNA-Seq reconstruction with iChlP of chromatin marks reveals start sites and promoter regions. Figure 12A shows the Ncoa6 gene.
  • the tracks in top-down order show: 1) Annotations in the RefSeq database (black), 2) Reconstructed transcripts using total RNA-Seq data (blue), promoters called by our annotation pipeline (gray box), the arrow points to the annotated promoter for Ncoa6, 3,4) RNA-Seq read density plots obtained from DCs before LPS stimulation and four hours post stimulation (blue), 5,6,7) iChlP read density plots for "compressed" data for H3K4mel, H3K4m3 and K4k27Ac (gray).
  • Figure 12B shows the Lhx2 locus.
  • enhancer annotations and iChlP binding data for Statl and PU.l were included.
  • Figure 13F also includes scatter plots for Statl biological replicates at 2 hours post stimulation. The scatter plots shown in Figure 13F compare two biological replicates against the library used in the main analysis.
  • Figure 14 shows the most significant motifs found. A summary of all motifs found by applying MEME to the set of high scoring peaks (enrichment > 30) for each factor.
  • Figure 15 shows TF "running" with Pol2.
  • Figure 15A shows IGV browser tracks of the IL1-1B locus for RNA-Seq, Pol-II, Chromatin marks, E2fl, and PU.l at indicated time points.
  • Figure 15B shows a bar plot showing the running score for each TF and for Pol-II (brown) and Ctcf (black), included respectively as positive and negative controls of association with Pol-II. Error bars were computed using the standard error.
  • Figure 14C shows a Western blot of co-immuno precipitations of Statl and PU. l with Pol-II and IgG (control), respectively.
  • Figure 16 shows the distribution of TF binding in regulatory regions.
  • Figure 16A shows the average degree (#bound TF) of bound regions in the original data (red bars) and in the randomized data (blue bars.
  • Figure 16B shows the percentage of bound
  • FIG. 16C shows that for each degree value (x-axis), the figure depicts the expected ratio of regions with this degree (y-axis). Plots are shown for the original data (red), and the randomized data (blue).
  • Figure 17 shows turnover score with different fold cutoffs. The percentage of dynamic changes in binding (gain or loss) with different fold cutoffs. For each TF, f, and each cutoff level, x, the figure depicts the percentage of genes bound by f that have more than x-fold change (up/down) in their binding enrichment score, in at least one time point, compared to the basal state.
  • Figure 18 shows TF binding in enhancers and promoters.
  • Figure 18A show a heatmap of TF binding (columns) in promoters (rows).
  • Figure 18B shows the number of promoters bound by every TF.
  • Figure 18C shows a heatmap of TF binding (columns) in enhancers (rows).
  • Figure 18D shows the number of enhancers bound by every TF.
  • Figure 19 shows an example of our enhancer and promoter annotation: the cis regulatory landscape of the Tnfaip3 locus. IGV browser tracks for 150 Kb regulatory region of the Tnfap3 gene showing iChIP compressed data for Pol-II, histone modifications and 24 transcription factors. The example highlights the shared enrichment for TF ChIP data at enhancers and promoters.
  • Figure 20 shows preference for binding at induced genes or at highly expressed genes at basal state.
  • Figure 20A shows percent genes bound by each TF (x-axis) at groups of genes with different expression levels (1 to 5th quantile of fold-change using the 4SU-Seq data). The average level of expression in each bin is depicted on the left. Non-significant entries are depicted in gray.
  • Figure 20B shows a similar analysis as in Figure 20A, where the genes are grouped by their post-stimulation induction level (using the 4SU-Seq data).
  • Figure 20C shows a similar analysis as in panel Figure 20A, where the genes are grouped by their induction level, only considering genes with low basal transcription level (lower than the value of the 3-rd quantile (out of 10)).
  • Figure 21 shows Irf2 knockdown and binding. Heat map of genes affected by Irf2 knockdown at the basal state. The cells were compared to DCs infected with non-targeting shRNA at the basal state. Colors represent effect of knockdown on expression levels measured with nanostring nCounter ® . The second column indicates in black genes that are directly bound by Irf2 at the basal state.
  • Figure 22 shows enriched TF binding at different clusters of co- transcribed genes mRNA transcription (using 4SU-Seq) and Pol-II binding are shown for clusters of genes with a similar temporal profile. Significant binding of TFs to the different clusters are shown across 4 time points of LPS stimulation (0, 0.5 1, 2, hours). The colors in the heat maps represent the percentage of bound genes at the respective time point.
  • Figure 23 shows the Histone gene clusters are repressed following LPS stimulus.
  • IGV browser tracks for the Histone gene cluster for the indicated sequencing libraries (RNA- Seq, Pol-II, Chromatin marks, and various TFs as indicated) and time points.
  • Figure 24 is a schematic that provides an overview of the iChIP method and antibody validation.
  • Figure 25 is a schematic providing an overview of the NANOSTRING ® platform and quantitative analysis using fluorescently labeled reporter probes and non-labeled capture probes.
  • Figure 26A shows plots of data from iChlP DNA that was run on ChlP-string and was compared to ChlP-seq data of the same cells.
  • Figure 26B shows ChlP-string probes that were designed for specific chromatin states according to an HMM tool (Ernst, et al. , Nature biotechnology (2010) 28:817-825).
  • the left-most column is a representation of 198 informative probes of five different chromatin states, as follows: blue - bivalent: H3K27me3, H3K4me2, H3K4me3; -initiation: H3K4me2, H3K4me3, H3K9Ac, H3K27Ac; gold - enhancers: H3K4mel, H3K4me2, H3K27Ac; orange - heterochromatin: H3K9me3; purple - silencing: H3K27me3.
  • Circles - Clusters Al-3 (Initiation): H3K4me3 / H3K9ac / RBbP5 / CHD1 / SIRT6 / KAT3A / Ashl / Plul / JHDM1D / Sap30 / RNApolII(5s) / MLL4; Purple circle - Cluster B (Silencing): H3K27me3 / EZH2 / SUZ12 / HDAC1; Gold circle - Cluster C (Enhancers): H3K79me2 / KMT4 / Ring IB / KMT4 / NSD2.
  • FIG. 26C depicts genomic viewer examples of these data (top - HG19, chrl3:91,986,894-92,018,706, bottom chr9: 133,556,867- 134,180,979).
  • Figure 27 shows antibody correlation within the SCN and with the relevant 20 million cell ChlP-string data.
  • iChlP high-throughput indexed
  • the iChlP method is automated.
  • iChlP uses, e.g., magnetic beads for chromatin immunoprecipitation and DNA purifications, thus, in some instances, eliminating the need for laborious manual washes, DNA purification and gel extraction steps.
  • the entire iChlP process can performed in the same well (or chamber, channel, droplet, or the like), in some instances, reducing sample loss due to, for example, transfer of material.
  • iChlP further leverages the yield of current next-generation sequencing by multiplexing an arbitrary number of different indexed sequencing adapters to combine samples in, for example, a single flow cell.
  • iChIP methods that produce high quality immuno-precipitated material with yields superior to (e.g., greater than) existing protocols. It was found that a second purification step introduced in the iChIP protocol significantly improves the quality and/or yield of the immuno-precipitated material to an unexpected degree when compared to existing protocols, allowing, for example, subsequent analyses of low abundance chromatin- associated factors using methods such as ChlP-Seq and ChlP-on-chip.
  • the second purification step involves the purification of nucleic acid fragments after they have been released from the specific chromatin-associated factor and/or antibody with which or to which the nucleic acid fragments were bound.
  • the purified nucleic acids can provide a starting material for subsequent analyses that is unexpectedly superior (e.g. , higher quality material, greater yield of material) to the starting material obtained when using existing ChIP methods.
  • the material is suitable for the detection of binding sites or regions on the chromatin of low abundance chromatin-associated factors using methods such as ChlP-Seq and ChlP-on-chip.
  • the purified nucleic acids can provide starting material for generating sequencing libraries, described below.
  • the purification steps comprise immobilizing the nucleic acid fragments on a substrate, such as a bead, membrane, or surface (e.g. , of a well or tube) that is coated with an affinity molecule suitable for immobilizing the nucleic acid fragments.
  • a substrate such as a bead, membrane, or surface (e.g. , of a well or tube) that is coated with an affinity molecule suitable for immobilizing the nucleic acid fragments.
  • the affinity molecule is silica or carboxyl-coated magnetic beads (SPRI beads).
  • the library e.g. , for next generation sequencing
  • a further advantage of providing an affinity surface in a well or as a bead, e.g. , a magnetic bead is that the iChIP protocol may be adapted for parallel processing of multiple samples, such as in a 96-well format or microfluidic platform, from starting chromatin material to the end of a sequencing library construction and purification.
  • Existing protocols may suggest phenol-chloroform extraction or column-based nucleic acid purification, which can lead to a substantial loss of sample material and such methods may not be adaptable to a high-throughput format.
  • DNA binding proteins and chromatin modifiers can be difficult to detect reliably using ChIP protocols because of their relative low abundance on the chromatin relative to, for example, many histone tail modifications, such as H3K4me3. ChIP performed on such abundant modifications can be very efficient and robust. A high percentage of the chromatin may be in association with modified histones. Moreover, as the DNA is wrapped tightly around histones (e.g. , the nucleosome octamer), the DNA yield enriched in such experiments can be relatively high, and suffices for any downstream processes.
  • DNA binding proteins and chromatin modifiers or other proteins that do not bind the
  • DNA itself and are only a part of a complex that binds the DNA, e.g. , chromatin-associated factors
  • chromatin-associated factors are orders of magnitude less abundant across the genome and the DNA interactions of the DNA-binding proteins and associated factors are much weaker when compared to histones.
  • the low abundance and the weak interactions with DNA are among the factors that may make a ChIP for DNA-binding proteins more susceptible to small variations and a higher sensitivity is required to obtain accurate data.
  • Current methods with their inherent shortcomings in reproducibility and/or sensitivity may not allow for a large scale screen of DNA binding proteins and chromatin modifiers.
  • the shearing process which may be more sensitive to small differences when fragmenting chromatin with DNA binding proteins and may contribute to the difficulty of obtaining sufficient amounts of DNA that were in association with the DNA binding proteins; and (2) the very low amounts of DNA that can be obtained by ChIP of DNA binding proteins and chromatin modifiers may lower the overall yield.
  • Very low yields can make it difficult to purify the DNA, a step which is often necessary for subsequent analysis.
  • the low DNA yield generally obtained for ChIP assays involving DNA binding proteins and chromatin modifiers that are carried out using existing ChIP protocols can result in low reproducibility between repeats and can make it difficult to obtain reliable and unbiased data.
  • ChIP assays using antibodies directed to histone modifications usually yield sufficient DNA and the yield may be, for example, about two orders of magnitude higher than the yield from ChIP assays involving DNA binding proteins and chromatin modifiers. Due to the relatively higher DNA yield, ChIP assays involving histone modifications exhibit relatively lower susceptibility to small experimental variations, which makes such assays less prone to experimental biases. Further, existing protocols can be inefficient, time consuming and difficult if not impossible to scale it up to allow parallel processing of larger sample sizes, such as is needed in high throughput screening.
  • ChIP protocols and/or commercially available ChIP kits are not optimal for high throughput ChIP screening. They do not provide sufficient sensitivity and/or reproducibility needed to screen large numbers of DNA binding proteins and chromatin modifiers.
  • iChIP methods to obtain high quality ChlP-DNA (iChlP-DNA).
  • the methods can be carried out easily and data can be obtained reproducibly.
  • these methods are used to screen large numbers of DNA binding proteins and/or chromatin modifiers.
  • the methods provided are used to screen 10, 50, 100, 200, 500, 750, or 1000, or more DNA binding proteins and/or chromatin regulators (CRs) and modified forms thereof. Modified forms include, but are not limited to, mutants and post-translationally modified DNA binding proteins and/or chromatin modifiers.
  • the methods provided are used to screen one or more of the following DNA binding proteins and/or chromatin modifiers and modified forms thereof: ASH1L, ASH2, ATF2, ASXL1, BAP1, bcllO, Bmil, BRG1, CARM1, KAT3A/CBP, CDC73, CHD1, CHD2, CTCF, DNMT1, DOTL1, EHMT1, ESET, EZH1, EZH2, FBXL10, FRP(Plu- l), HDAC1, HDAC2, HMGA1, hnRNPAl, HP1 gamma, Hsetlb, JaridlA, JaridlC, KIAA1718_JHDM1D, KAT5, KMT4, LSD1, NFKB P100, NSD2, MBD2, MBD3, MLL2, MLL4, P300, pRB, RbAP46/48, RBP1, RbBP5, RING1B, RNApolII P S2, RNApolII P S5, ROC1, sap30
  • iChIP methods that are adapted to be suitable for semi- or full automation.
  • the ChIP methods described herein may be practiced by a person of average or limited skill in the art of chromatin immuno- precipitation or even an unskilled person.
  • the iChIP methods provided herein reduce the time required to carry out the methods and/or reduce the sample to sample variability (e.g. , improve reproducibility) in quality and/or yield, when compared to existing ChIP methods.
  • iChIP methods that are adapted to be suitable for microfluidic applications, for example, for use with DNA chips (microarrays), lab-on-chip technology, micro-propulsion, micro-thermal technologies, continuous-flow microfluidics, and/or digital (droplet-based) microfluidics.
  • a standard or positive control may give a similar signal (or is positive) about 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99%, or more of the time when conducting or repeating an experiment using the methods provided herein, given that the methods are carried out in the same manner (e.g. , incubation times) and the concentrations used are about the same.
  • Low reproducibility is indicated when a standard or positive control gives a similar signal (or is positive) in less than 75% of the time when conducting or repeating an experiment, e.g.
  • a false negative sample analysis means that a sample when analyzed (e.g. , subjected to PCR amplification, sequencing, ChlP-Seq, ChlP-on-ChIP) may not give a signal that can be unequivocally distinguished from the background or a negative control, e.g.
  • the signal may not show a difference that is statistically significant when compared to the background signal or a signal from a negative control. Such sample may then be considered a negative sample.
  • the negative sample is a false negative sample when the sample prepared under different conditions would give a signal that is distinguishable from the background signal or from the signal of a negative control.
  • Conditions that may cause a sample to be false negative sample are, e.g. , insufficient sensitivity of the detection method, insufficient quantity of starting material for the detection, and/or insufficient quality of the starting material for the detection.
  • iChIP methods that when performed provide high quality immuno-precipitated materials.
  • the purity of the immuno-precipitated materials may be greater than 70%, 75%, 80%, 85%, 90%, 95%, 98%, or more. In some embodiments, the purity it about 80% to about 90 % pure, about 90% to about 95%, about 90% to about 98%, about 90 to about 100%, about 95% to about 98%, or about 95% to about 100% pure. In particular embodiments, the purity of the immuno- precipitated materials may be 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%.
  • the iChIP methods provided herein improve yield of immuno- precipitated material.
  • Improved yield means a yield of immuno-precipitated material (e.g. , chromatin/DNA) that is greater compared to existing protocols and allows the subsequent detection of low abundance signal that would not be detectable or would give a signal that is not significantly different or not statistically different from a background signal using immuno-precipitated material from existing protocols.
  • the yield of immuno-precipitated material produced by the methods described herein may be improved over existing methods by about 10% to about 500%, or about 10% to about 100%. In some embodiments, the improvement is by about 10% to about 25% or about 25% to about 50%.
  • the yield is greater improved by than 10%, greater than 20%, greater than 30%, greater than 40%, greater than 50%, greater than 60%, greater than 70%, greater than 80%, greater than 90%, greater than 100%, greater than 200%, or more.
  • chromatin-bound factors may be immuno-precipitated that are of low abundance in the chromatin fraction, e.g. , certain transcription factors. Chromatin (DNA) binding sites of such factors may not be identified using existing protocols.
  • immuno-precipitation using antibodies specific for certain abundant histone modifications e.g. , certain histone H3 tail modifications
  • the signals that are detected may vary depending on the assays and applications used.
  • Low abundance chromatin-associated factors are factors that can be found at one or more sites on the chromatin and/or that may associate with chromatin in a transient manner.
  • Examples of low abundance chromatin-associated factors include, but are not limited to, transcription factors (e.g. , tumor suppressors, oncogenes, cell cycle regulators, development and/or differentiation factors, general transcription factors (TFs)), activator (e.g. , histone acetyl transferase (HAT)) complexes, repressor (e.g. , histone deacetylase (HDAC)) complexes, co-activators, co-repressors, other chromatin-remodelers, e.g.
  • transcription factors e.g. , tumor suppressors, oncogenes, cell cycle regulators, development and/or differentiation factors, general transcription factors (TFs)
  • activator e.g. , histone acetyl transferase (HAT)
  • histone (de-) methylases DNA methylases
  • replication factors and the like.
  • factors may interact with the chromatin (DNA, histones) at particular phases of the cell cycle (e.g., Gl, S, G2, M- phase), upon certain environmental cues (e.g., growth and other stimulating signals, DNA damage signals, cell death signals) upon transfection and transient or stable expression (e.g., recombinant factors) or upon infection (e.g., viral factors).
  • Abundant factors are constituents of the chromatin, e.g., histones.
  • Histones may be modified at histone tails through posttranslational modifications which alter their interaction with DNA and nuclear proteins and influence for example gene regulation, DNA repair and chromosome condensation.
  • the H3 and H4 histones have long tails protruding from the nucleosome which can be covalently modified, for example by methylation, acetylation, phosphorylation, ubiquitination, sumoylation, citrullination and ADP-ribosylation.
  • the core of the histones H2A and H2B can also be modified. Combinations of modifications are thought to constitute the so-called "histone code” (Strahl and Allis (2000) Nature 403 (6765): 41-5; Jenuwein and Allis (2001) Science 293 (5532): 1074-80). Such modifications can also be analyzed by ChlP.
  • ChlP methods are provided that allow sample processing in a high-throughput manner. For example, 10, 50, 100, 200, 500, 750, 1000, or more chromatin- associated factors and/or chromatin modifications may be immuno-precipitated and/or analyzed in parallel. In one embodiment, up to 96 samples may be processed at once, using e.g., a 96-well plate. In other embodiments, fewer or more samples may be processed, using e.g., 6-well, 12-well, 32-well, 384-well or 1536-well plates. In some embodiments, ChlP methods are provided that can be carried out in tubes, such as, for example, common 1.5 ml, 2.0 ml, 15 ml, 50 ml size tubes. These tubes may be arrayed in tube racks, floats or other holding devices.
  • the immune-precipitated chromatin may be prepared from harvested cells (e.g., subsequently subjected to sonication). In certain embodiments, the immune-precipitated chromatin may be prepared from a single sample of about 1 million to about 20 million cells, or more. In certain embodiments, immune- precipitated chromatin may be prepared from a single sample of about the 5 cells to about 1 million cells.
  • a sample may comprise about 50 cells, about 100 cells, about 150 cells, about 200 cells, about 300 cells, about 400 cells, about 500 cells, about 1000 cells, about 2000 cells, about 3000 cells, about 4000 cells, about 5000 cells, about 10,000 cells, about 20,000 cells, about 30,000 cells, about 40,000 cells, about 50,000 cells, about 100,000 cells, about 200,000 cells, about 300,000 cells, about 400,000 cells, about 500,000 cells, or about 1,000,000 cells.
  • a sample may comprise about 100 cells to about 10,000 cells, or about 10,000 cells to about 100,000 cells, or more.
  • methods for ChlP are provided.
  • the methods comprise optimized sonication conditions for shearing the immuno-precipitated chromatin.
  • sample tubes are held on ice.
  • This practice can introduce experimental variation because the ice itself changes during the sonication, and between experiment to experiment, which may limit the reproducibility of the process.
  • the sample temperature can change during the process, in an uncontrolled manner, which can also increase the variations between repeats.
  • the temperature of the sample is not kept constant, the state of different molecular areas within the tube can change in an uncontrolled way.
  • Another difficulty is that the samples commonly are not located in a specific position, e.g., due to the variations in the amounts of ice and/or due to inaccuracy with the placement of the tube. This may lead to sample-to-sample variation.
  • the methods comprise optimized sonication conditions for shearing the immuno-precipitated chromatin, wherein the optimization includes, but is not limited to, optimizing: i) sample cooling conditions, ii) sample volume, iii) probe size, iv) sample/probe contact, v) total duration of sonication, vi) duration of sonication pulse, vii) duration of cooling down phase between pulses, viii) pulse intensity, ix) pulse frequency, and/or x) pulse cycles. Specific optimization steps are described herein.
  • the exact probe location is maintained in all samples, that is, the three- dimensional spatial orientation of the probe in a well or tube: a) the distance from the bottom of the well or tube to the tip of the probe; b) the distance from any of the walls of the well or tube to the tip of the probe; c) the distance of the tip of the probe to the surface of the sample liquid (i.e., the immersion depth of the probe).
  • the exact spatial orientation can be set and maintained using mechanical or electromechanical devices, for example, by using levers that control the insertion of the sonication probe into the tube or well.
  • the temperature of the sample and/or cooling device is kept constant, e.g., by controlling the flow of coolant, the thermal capacity of the coolant, the size of the contact surface of the cooling device and the well or tube and/or adapting the thickness of the well or tube to optimize thermal exchange between the cooling device and the well of tube.
  • sonication conditions include:
  • the time per repeat is adjusted according to the cell source used. For example, the time per repeat for dendritic cells is 5 minutes (a total of 15 minutes of sonication), for embryonic stem cells is 5.5 minutes (16.5 minutes total), and for K-562 cells (immortalized myelogenous leukemia line) is 4.5 minutes (13.5 minutes total).
  • the number of repeats are one, two, four or five.
  • the amplitude is 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, or 65%.
  • the pulse is 0.3, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0 or 1.1 seconds.
  • the pause is 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or 2.0 seconds.
  • the pause is 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, or 10.0 seconds.
  • the time per repeat is 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5 or 10 minutes. It would be appreciated by one of ordinary skill that the sonicator described herein can be substituted with a different make or model and that the amplitude, pulse and pause time can routinely be adjusted according to the specification of the substitute sonicator device.
  • cell lines that may be subjected to the sonication conditions described herein include, but are not limited to, 293-T (human kidney), 3T3 (mouse embryonic fibroblast), 721 (human melanoma), 9L (rat glioblastoma), A2780 (human ovary), A172 (human glioblastoma), A20 (murine B lymphoma), A253 (human head and neck carcinoma), A431 (human skin epithelium squamous carcinoma), A-549 (human lung carcinoma epithelium), ALC (murine bone marrow stroma), B16 (murine melanoma), B35 (rat neuroblastoma),
  • BHK-21 hamster kidney fibroblast
  • BR 293 human breast cancer
  • Cal-27 human tongue squamous cell carcinoma
  • CHO Choinese hamster ovary
  • COR-L23 human lung
  • COS-7 Cercopithecus aethiops, kidney fibroblast
  • COV-434 human ovary metastatic granulosa cell carcinoma
  • CML Tl chronic myeloid leukaemia T- lymphocyte 1
  • CMT canine mammary gland tumor epithelium
  • CT26 murine colorectal carcinoma
  • D17 canine osteosarcoma
  • EL4 mimouse T cell leukemia
  • FM3 human lymph node melanoma
  • H1299 human lung cancer
  • H69 human lung
  • HEK-293 human embryonic kidney
  • HeLa human cervical cancer epithelium
  • HL-60 human leukemia myeloblast
  • HMEC human mammary epithelial
  • HT-29 human colon epithel
  • T84 human colorectal carcinoma
  • THP1 human AML
  • U373 human glioblastoma-astrocytoma
  • U937 human lymphoma
  • WT-49 human lymphoblastoid
  • the methods for iChIP comprise introducing a nucleic acid purification step of immuno-precipitated chromatin.
  • the nucleic acid purification step comprises attaching the nucleic acid to a suitable substrate or object as a means of separating and isolating the nucleic acid from a reaction mix (e.g., comprising buffers and/or proteins).
  • a reaction mix e.g., comprising buffers and/or proteins.
  • the substrate is a silica-coated surface or object.
  • the substrate or object is coated with a molecule that is suitable for affinity interactions.
  • molecules suitable for affinity interactions may comprise nucleic acids, such as small oligonucleotides.
  • Such small oligonucleotides may comprise poly-T sequences that can be utilized in affinity interactions with nucleic acids that comprise poly-A tails.
  • Poly-A tails may be added to the nucleic acids enzymatically.
  • Other molecules suitable for affinity interactions can be avidin or streptavidin that can be utilized in affinity interactions with nucleic acids that comprise biotin (e.g., biotinylated DNA).
  • Silica or other molecules facilitating affinity interactions to nucleic acids may coat any surface or object, such as a bead, a resin, a gel, a well, a tube, a membrane, a filter or a surface thereof.
  • high-throughput iChIP methods are provided, the methods include one or more of the following steps:
  • the affinity molecule in (c) is an antibody that is coupled to a substrate by affinity interaction.
  • an antibody that is specific for a chromatin- associated factor bound (cross-linked) to the nucleic acid fragment is coupled to a substrate or object (e.g., a bead, a membrane, or a well/tube surface) via a second affinity molecule that facilitates interaction with the antibody (e.g., protein A or protein G) that is itself coupled to the substrate.
  • Nucleic acid bound (cross-linked) to a chromatin-associated factor recognized and bound by the specific antibody may then be isolated by capturing this antibody via the second affinity molecule.
  • the affinity molecule in (c) is an affinity tag that is coupled to a substrate by affinity interaction.
  • a chromatin-associated factor bound (cross- linked) to the nucleic acid fragment may contain an affinity tag (e.g., FLAG, myc, biotin) and is coupled to a substrate or object (e.g., a bead, a membrane, or a well/tube surface) via a second affinity molecule (e.g., an antibody: anti-FLAG, anti-myc antibody, or streptavidin, avidin) that is itself coupled to the substrate or object.
  • Nucleic acid bound (cross-linked) to a chromatin-associated factor containing the affinity tag may then be isolated by capturing the factor via the second affinity molecule.
  • affinity molecule is, for example, coupled to a bead
  • separation of the specifically bound nucleic acid fragment may comprise gravity (e.g., settling or centrifuging) or in case the bead is a magnetic bead, a magnetic field and washing steps to reduce or eliminate the fraction of non-specific nucleic acid fragments.
  • secondary affinity molecule is, for example, coupled to a membrane or well/tube surface the membrane or well/tube surface may be subjected to washing steps without the need for settling, centrifuging or the use of a magnetic field.
  • isolating the nucleic acid fragments in (d) is carried out using an interaction between the nucleic acid and an affinity molecule (e.g., poly-T oligonucleotides, avidin/streptavidin) if the nucleic acid is suitably modified for this interaction (e.g., is modified to contain poly-A tails or is biotinylated) or affinity surfaces such as silica, which interact with nucleic acids e.g., under specific buffer (pH and salt) conditions.
  • an affinity molecule e.g., poly-T oligonucleotides, avidin/streptavidin
  • isolation of the nucleic acid fragment may comprise gravity (e.g., settling or centrifuging) or in case the bead is a magnetic bead, a magnetic field.
  • affinity molecule or surface is, for example, coupled to a membrane or well/tube surface the membrane or well/tube surface may be used without the need for settling, centrifuging or the use of a magnetic field to immobilize and isolate the nucleic acid fragment.
  • Nucleic acid fragments in (d) immobilized in such way can further be subjected to washing steps to reduce or eliminate any residual undesired components of the reaction mix, e.g., polypeptides, peptide fragments, phospholipids.
  • iChIP methods may be adapted for a high-throughput format (e.g., the parallel processing of several samples, such as 6, 12, 24, 96 or more samples) by combining the improvements described herein.
  • a high-throughput iChIP method may include the steps of:
  • a first affinity molecule specific for the chromatin-associated factor e.g., antibody
  • immobilizing the first affinity molecule on a surface e.g., a bead/magnetic bead, the surface of the reaction container (e.g., a well, tube, or nanodroplet), or a membrane coated with a second affinity molecule specific for the first affinity molecule
  • nucleic acid fragments from chromatin-associated factors, g) immobilizing nucleic acid fragments in (f) on a surface, e.g., a bead/magnetic bead, the surface of the reaction container (e.g., a well or tube), or a membrane coated with an affinity molecule specific for the nucleic acid fragments (e.g., silica, DNA fragment- specific oligonucleotides (e.g., poly-T), or tag-specific affinity molecule (e.g.,
  • biotin/avidin/streptavidin this may be done in a convenient format, e.g., a 96-well plate or microfluidic chamber/channel, and
  • nucleic acid in (f) is immobilized in (g) then all steps may be carried out in a convenient high-throughput format (e.g., 96-well plates, or microfluidic chambers/channels) and may be carried out in a fully automated or semi- automated process.
  • a convenient high-throughput format e.g., 96-well plates, or microfluidic chambers/channels
  • nucleic acid purification may be advantageous over existing protocols that suggest nucleic purification using, for example, phenol extraction or column-based purification methods (e.g., QIAGEN MINELUTETM kit). Such nucleic acid purification steps would make necessary sample transfer and handling difficult to automate. As described herein, in some embodiments, it is disadvantageous to omit nucleic acid purification. Subsequent nucleic acid analysis (such as sequencing (e.g., ChlP-seq), real-time PCR (qPCR), DNA microarrays hybridization/ChlP-on-chip) may be impacted by sequencing (e.g., ChlP-seq), real-time PCR (qPCR), DNA microarrays hybridization/ChlP-on-chip) may be impacted by
  • chromatin shearing DNA fragmentation
  • enzymatic digestion e.g., micrococcal nuclease
  • hydrodynamic shearing e.g., hydrodynamic shearing
  • sonication e.g., acetylation
  • thermal and sequence-specific biased shearing e.g., sequence- specificity of enzymatic fragmentation
  • thermal degradation e.g., heat denaturation and complex stripping of proteins
  • SONICMANTM Small Computer Network
  • the SONICMANTM platform uses disposable gasketed pin lids (in variable lengths to accommodate different well dimensions of microplates) to transfer sonic energy to each individual well and to prevent well-to-well cro s s-contamination .
  • AFA Adaptive Focused AcousticsTM
  • immobilization of the factor-bound sheared chromatin fragments and subsequent eluted complex-free nucleic acid fragments using affinity-based immobilization methods described herein allows robotic dispensing and aspiration of wash solutions and elution buffers, as well as sample transfer into new reaction containers (e.g., multi- well/micro plates).
  • silica is used to immobilize nucleic acid fragments. Without wanting to be bound by any particular theory, it is thought that the highest DNA adsorption efficiencies occur in the presence of buffer solution with a pH at or below the pKa of the surface silanol groups. It is thought that a decrease of the negative charge on the silica surface due to the high ionic strength of the buffer leads to a decrease in the electrostatic repulsion between the negatively charged DNA and the negatively charged silica.
  • the buffer may also reduce the activity of water molecules possibly causing the silica surface and DNA to become less coordinated by water molecules which may aide the DNA to adsorb to the silica surface.
  • guanidinium HC1 in a GuHCl-based DNA loading buffer, a chaotrope, denatures biomolecules by disrupting the coordinating water molecules around them. This may allow positively charged ions to form a salt bridge between the negatively charged silica and the negatively charged DNA backbone in high salt concentration.
  • the DNA can then be washed with high salt and ethanol, and ultimately eluted with low salt (e.g., Tris-ethylenediaminetetraacetic acid (EDTA) (TE) at pH 8.4).
  • EDTA Tris-ethylenediaminetetraacetic acid
  • the purified nucleic acid fragments may be transferred to a new reaction container (e.g., a 96-well plate) and used for detection of chromatin regions that are enriched for the chromatin-associated factor that is bound by the specific antibody used for iChIP, e.g., by quantitative real-time (qPCR), ChlP-string arrays, or DNA sequencing.
  • a new reaction container e.g., a 96-well plate
  • the samples may be amplified, e.g., using a whole genome amplification method, which optionally may be carried out in a high throughput manner. These amplified DNA samples may then be used for the detection of enriched regions. Chromatin profiling methods are known in the art. For example, chromatin immunoprecipitation-massively parallel DNA sequencing (ChlP-Seq) is used to analyze a set of DNA-associated proteins. It can be used to precisely map global DNA binding sites for any protein of interest, e.g., transcription factor, restriction enzyme, or other chromatin associated proteins, on a genome scale. Chromatin immunoprecipitation may also be combined with microarray "ChlP-on-chip," which requires a hybridization array.
  • Chromatin immunoprecipitation may also be combined with microarray "ChlP-on-chip," which requires a hybridization array.
  • iChIP Integrated DNA-binding protein
  • iChIP Integrated DNA-binding protein
  • massively parallel sequence analyses may be used in conjunction with whole-genome sequence databases to analyze the interaction pattern of a protein of interest (e.g., transcription factors, polymerases or transcriptional machinery) with DNA or to analyze the pattern of an epigenetic chromatin modification of interest (e.g., histone modifications or DNA modifications).
  • a protein of interest e.g., transcription factors, polymerases or transcriptional machinery
  • an epigenetic chromatin modification of interest e.g., histone modifications or DNA modifications.
  • ChIP may be used, in some embodiments, to selectively enrich for DNA sequences bound by a particular protein (e.g., transcription factor or histone, see Examples) in living cells by cross-linking DNA-protein complexes and using an antibody that is specific against a protein of interest. After precipitation of chromatin, oligonucleotide adapters may be added to the small stretches of DNA that are bound to the protein of interest to enable massively parallel sequencing. After size selection, the resulting iChlP-DNA fragments can be sequenced simultaneously using, for example, a genome sequencer. A single sequencing run can scan for genome-wide associations with high resolution. For ChlP-on-chip sets of tiling arrays (of overlapping probes designed to densely represent a genomic region of interest) may be utilized, in certain embodiments.
  • a particular protein e.g., transcription factor or histone, see Examples
  • oligonucleotide adapters may be added to the small stretches of DNA that are bound to the protein of interest to enable massive
  • Massively parallel sequencing is known in the art and many sequencing methods may be used. Some technologies may use cluster amplification of adapter-ligated ChIP DNA (or iChIP DNA) fragments on a solid flow cell substrate. The resulting high density array of template clusters on the flow cell surface may then be submitted to sequencing-by- synthesis in parallel using for example fluorescently labeled reversible terminator nucleotides.
  • Templates can be sequenced base-by-base during each read.
  • the resulting data may be analyzed using data collection and analysis software that aligns sample sequences to a known genomic sequence.
  • Sensitivity of this technology may depend on factors such as the depth of the sequencing run (e.g., the number of mapped sequence tags), the size of the genome, and the distribution of the target factor. By integrating a large number of short reads, highly precise binding site localization may be obtained.
  • ChlP-Seq data can be used to locate the binding site within few tens of base pairs of the actual protein binding site, and tag densities at the binding sites may allow quantification and comparison of binding affinities of a protein to different DNA sites.
  • a difficulty concerning ChIP protocols is the validation of the success of ChIP protocols for DNA-binding proteins.
  • Current validation methods are difficult and time consuming.
  • One common way of validation that may be used in some embodiments described herein is to focus on one specific DNA-binding protein of interest and to assess the genomic regions that are in association with the DNA-binding protein of interest by trial and error and other empiric approaches, utilizing for example qPCR.
  • the iChlP assay validation by qPCR precedes ChlP-DNA sequencing.
  • Another approach involved the over-expression of a tagged version of the DNA-binding protein of interest (e.g., described in Nishiyama et al. Cell Stem Cell 5(4):420-433, 2009). This can only be performed in vitro (e.g., by transfecting cell lines) and, in certain instances, it may be difficult to discern the relevance of the non-physiological data obtained from an over-expressed protein.
  • Figure 25 (Nanostring Technologies, Seattle, WA).
  • methods are provided that probe ChlP-DNA derived from iChlP assays performed as described herein in a high-throughput manner using the NANOSTRING ® platform as depicted in Figure 24 and exemplified in Figure 26.
  • probes are specific for genomic regions that are bound by DNA-binding proteins and/or chromatin-associated factors.
  • 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 15000, 20000, 50000, or 100000 probes specific for genomic regions are represented using the
  • NANOSTRING ® platform uses color-coded
  • the NCOUNTER ® Analysis System utilizes a digital technology that is based on direct multiplexed measurement of gene expression with a sensitivity of less than one copy per cell.
  • the NANOSTRING ® technology is described, for example, in Geiss et al. Nat. Biotech.
  • any one of the preceding methods may further comprise pre-amplification of chromatin and/or nucleic acids.
  • the methods further comprise adding to the chromatin or nucleic acid fragments a sequence (e.g., a Yl recognition site) that will facilitate pre-amplification of the chromatic or nucleic acid.
  • pre-amplification renders the methods described herein more sensitive.
  • TFs sequence-specific transcription factors
  • Genomic approaches such as correlation analysis of gene expression profiles (Segal et al, 2003), and more recently RNAi perturbation followed by gene expression readouts (Amit et al, 2009), have provided an initial glimpse into the complexity of mammalian gene regulation.
  • Such approaches cannot distinguish direct from indirect effects and cannot address network redundancy and temporal regulation, providing limited insight into the underlying regulatory mechanisms.
  • a complementary approach is to measure the temporal in vivo binding of TFs to cis regulatory regions under relevant stimuli.
  • Recent advances in genomic technologies allow for unbiased and accurate genome- wide characterization of TF binding using ChIP followed by DNA sequencing (ChlP-Seq) (Barski et al., 2007; Johnson et al., 2007; Mikkelsen et al., 2007).
  • ChIP has remained a relatively low throughput, labor intensive and costly protocol (Barski et al., 2007; Johnson et al., 2007; Mikkelsen et al., 2007;Solomon and Varshavsky, 1985). Because of this, ChIP studies in mammalian systems are typically limited to measuring the binding of a handful of selected regulatory proteins at a single time point. As a result, little is known about the genome wide dynamics of protein- DNA interaction networks.
  • iChIP was developed - a reproducible, high throughput and cost-effective method for ChIP coupled to multiplexed massively parallel sequencing.
  • iChIP was used to investigate the principles of gene regulation in the model system of primary innate immune dendritic cells (DCs) (Katsnelson, 2006) stimulated with a pathogen component lipopolysaccharide (LPS).
  • DCs primary innate immune dendritic cells
  • LPS pathogen component lipopolysaccharide
  • DCs activate a robust, specific, and reproducible response that unfolds over several hours, involves changes of thousands of genes (Amit et al., 2009), and plays a critical role in directing the host immune response.
  • DCs are an attractive model system for dissecting a dynamic transcriptional response.
  • iChIP was used to build genome- wide dynamic maps of TF localization to DNA in the response of DCs to LPS. Antibodies were screened for the 184 most expressed transcription factors and identified ChlP-Seq grade antibodies for 29 TFs, 4 RNA polymerase components, chromatin modifiers and 3 epigenetic modifications. Using these validated antibodies, iChIP was performed across four time points upon LPS stimulation. The TFs vary substantially in their binding dynamics, number of binding events, preferred genomic locations and interactions with other TFs.
  • iChIP A high-throughput method for mapping Protein-DNA interactions
  • iChIP is an automated method for systematic mapping of in vivo protein-DNA binding that greatly increases the throughput, while significantly reducing the labor and cost required for ChlP-Seq.
  • iChIP uses magnetic beads for chromatin immunoprecipitation and DNA purifications, thus eliminating the need for laborious manual washes, DNA purification and gel extraction steps ( Figure 1).
  • the entire iChIP process is performed in the same well, reducing sample loss due to transfer of material, a significant source of variability given the small amounts of DNA involved ( Figure 9).
  • iChIP further leverages the yield of current next-generation sequencing by multiplexing an arbitrary number of different indexed sequencing adapters, 96 in our case, to combine samples in a single flow cell ( Figure 1A).
  • iChIP was used to reconstruct the TF binding network following a time course of LPS stimulation of primary mouse dendritic cells (DCs). RNA-Seq was first used across these time points to identify the TFs expressed in DC. 271 commercially available antibodies targeting the 184 expressed TFs were collected ( Figure IB). Each antibody was tested using a signature readout (Geiss et al., 2008) ('ChlP-String') that measures selected genomic DNA regions with high regulatory activity (De Santa et al., 2010; Kim et al., 2010) ( Figure 1A bottom; Figure 1C).
  • candidates containing H3K4me3+ were identified and those that overlapped a known (Pruitt et al., 2007) or reconstructed (Guttman et al., 2010) transcription start site were retained ( Figure 2A, Figure 12). Notably, -75% of the identified promoters were bound by at least one of the TFs.
  • candidates containing H3K4me3+ were identified and those that overlapped a known (Pruitt et al., 2007) or reconstructed (Guttman et al., 2010) transcription start site were retained (Figure 2A, Figure 12). Notably, -75% of the identified promoters were bound by at least one of the TFs.
  • H3K4mel+ were identified and those that were also bound by at least one TF were retained(See for example the Illab loci in Figure 2A). Altogether, 48,163 enhancers and 11,252 promoters were identified.
  • the known motifs for 19 (-65%) of the TFs were identified, as well as novel motifs for an additional four (-13%) TFs ( Figure 14).
  • the highest scoring motif (E ⁇ e-100) found for the TF E2f4 is the cell cycle genes homology region (CHR), a previously identified regulatory element found adjacent to a handful of cell cycle genes, which appears in tandem to an E2f canonical motif (Lange-zu Dohna et al., 2000).
  • the TFs vary substantially in both number and location of binding events.
  • Runxl binds on the 3' UTR of inflammatory genes
  • Runxl also known as Amll
  • the runt domain 1 factor Runxl is an important transcription factor for normal hematopoiesis whose translocations are involved in several types of leukemia (Pabst and Mueller, 2007).
  • our ChIP data shows that Runxl binds many genes at their 3' end (Figure 3A).
  • TFs range from primarily static to primarily dynamic binders
  • TFs vary substantially in the extent of dynamic changes in their binding during the response.
  • the Ifit locus ( Figure 5A) provides an illustrative example. While PU.l is bound at the same level in both unstimulated and stimulated cells (Figure 5A, top inset), Statl binding appears only during the late stages of LPS response ( Figure 5A, bottom inset).
  • a Turnover score defined as the fraction of regions bound by the factor that changed from the unstimulated state by at least 3-fold was calcualted (Figure 5B and Figure 17). This score varies dramatically between TFs, with PU. l and Cebpb, being mostly static ( ⁇ 20% Turnover), 16 factors having moderate dynamic binding (e.g. Irf4 and Junb, 20-65% Turnover), and 10 factors being mostly dynamic (e.g. , Statl and Rela, >65% Turnover). The extent of gain versus loss of binding also varies between TFs with 7 factors showing almost exclusive (>90%) gain of binding (e.g.
  • Control of the transcriptional response consists of several layers of regulation (Yosef and Regev, 2011): (1) establishing the basal level pre- stimulation, (2) establishing the induction or repression level after stimulation, and (3) controlling the timing of the transcriptional response. Focusing on TF binding at each gene pre- stimulation, the overall number of TF binding events correlated with basal gene expression levels (Figure 6D, p ⁇ 10-20), with genes induced post-stimulation ( Figure 6D, p ⁇ 10-20), and with early onset of induction (p ⁇ 10-10).
  • Irf2 was the only TF with significantly enriched pre- stimulation binding in genes that are expressed at low levels pre- stimulation and then strongly induced post- stimulation (Figure 20C, p ⁇ 10-3). Consistent with a previous report (Harada et al., 1989), Irf2 may act as a repressor antagonizing l function. It was hypothesized that the M2 circuit functions as a repressor in unstimulated DCs to 'poise' anti- viral genes for the response. Consistent with this hypothesis, the effect of M2 knocked down in unstimulated DCs was almost entirely up-regulation (20% of genes bound by M2 are affected by the knockdown, 91% of them are up-regulated; Figure 21).
  • the binding data was integrated with RNAi perturbation data for the same TFs in the same cells under the same stimuli on a selected set of signature genes (Amit et al., 2009) ( Figures 7B, 7C). It was found that overall 35% of the binding events involving the signature set were associated with up or down regulation of the target gene. Further, it was found that 59% of the knockdown effects were indirect ⁇ e.g., the TF showed no binding but knock-down affected the gene expression). Considering specific TFs, various levels of redundancy were observed.
  • TFs While some TFs affected a relatively small portion of the genes they bind ⁇ e.g., Fos, l, Nfkb2 and Maff), others were non-redundant (or more sensitive up to 4 fold decrease in their expression following knock down) and affected a large fraction of the genes to which they bind ⁇ e.g. , Statl and Stat2).
  • Figures 6C, 20 The remaining factors (Figure 8A) tended to bind a smaller number of regions from certain functional categories (e.g., Statl with anti-viral genes; Figure 6C) and dynamically coincide with the induction of genes post-stimulation (Figure 6E). This was consistent with a spatial-temporal layered organization where Pioneer factors potentiate binding by opening previously inaccessible sites (Bossard and Zaret, 1998; Cirillo et al., 2002; Cirillo and Zaret, 1999; Heinz et al., 2010; Lupien et al., 2008) (Figure 8B, left).
  • beads were washed once (200ul) in a binding/blocking buffer (PBS, 0.5% Tween 20, 0.5% BSA), incubated with 10 ⁇ g of antibody in binding/blocking buffer for 1 hour at room temperature, and then washed to remove excess antibody, 96 well magnet was used (Invitrogen) in all further steps.
  • PBS 0.5% Tween 20, 0.5% BSA
  • Solid-phase reversible immobilization (SPRI) cleanup steps were performed using the Bravo liquid handling platform (Agilent) using a modified version of (Fisher et al., 2011).
  • 120 ⁇ SPRI AMPure XP beads (Agencourt) were added to the reverse-crosslinked samples, pipette-mixed 15 times and incubated for 2 minutes. Supernatant were separated from the beads using a 96-well magnet for 4 minutes. Beads were washed on the magnet with 70% ethanol and then air dried for 4 minutes.
  • the DNA was eluted in 40 ⁇ EB buffer (10 mM Tris-HCl pH 8.0) by pipette mixing 25 times.
  • a general SPRI cleanup involves addition of buffer containing 20% PEG and 2.5 M NaCl to the DNA reaction products (without moving them from their original well position). After thorough mixing and a 2-minute incubation at room temperature, plates are transferred to a magnet plate, incubated for 4 minutes and supernatant removed. Beads are then washed on the magnet with 150 ⁇ 70% ethanol and then air dried for 4 minutes. The DNA is eluted with 40 ⁇ of EB buffer by pipette mixing 25 times. Reagent kits are prepared in advance for all enzymatic steps (New England Biolabs).
  • the DNA end-repair was performed by adding 27 ⁇ of a master mix (17 ⁇ master mix (5 ⁇ T4 buffer, 5 ⁇ BSA-lmg/ml, 5 ⁇ ATP-10mM- 2 ⁇ dNTPs 10 mM), 5 ⁇ T4 PNK enzyme, 5 ⁇ T4 polymerase (3 units) to each well.
  • a master mix (5 ⁇ T4 buffer, 5 ⁇ BSA-lmg/ml, 5 ⁇ ATP-10mM- 2 ⁇ dNTPs 10 mM
  • 5 ⁇ T4 PNK enzyme 5 ⁇ T4 polymerase (3 units)
  • Samples were incubated in a thermal cycler at 12C for 15 min, 25C for 15 min, and finally cooled to 4 °C.
  • the SPRI bead clean up method was used to purify the product (147 ⁇ of 20% PEG, 2.5 M NaCl was added to each sample and eluted in 40 ⁇ EB).
  • the A-base addition was performed by adding 20 ⁇ master mix (17 ⁇ A-base add mix, 3 ⁇ Klenow (3'- >5' exonuclease) to each well and incubated at 37 °C for 30 minutes in a thermal cycler.
  • SPRI bead clean up method was used to purify the product (132 ⁇ of 20% PEG, 2.5 M NaCl was added to each sample and eluted in 19 ⁇ EB).
  • Adaptor ligation was performed by adding 34 ⁇ of a master mix (29 ⁇ 2x DNA ligase buffer, 5 ⁇ DNA ligase) to each well. 5 ⁇ PE Indexed oligo adaptors (0.75 uM ) was added to each well and samples were incubated 25C for 15 min in a thermal cycler. SPRI bead clean up with size selection was used to purify the ligated products (15.5 ⁇ of 20% PEG, 2.5 M NaCl was added to each sample and eluted in 40 ⁇ EB).
  • enrichment PCR was performed by adding 10 ⁇ of a master mix (2 ⁇ Forward/Reverse Index Primer, 0.5 ⁇ dNTP mix, 5 ⁇ lOx Pfu Ultra Buffer, 1 ⁇ Pfu Ultra- II Fusion, 1.5 ⁇ Nuclease free water) to each well. Plate was transferred to a thermal cycler and ran a Pfu amplification program at 95 °C for 2 min, 16 cycles of: 95 °C for 30 sec, 55 °C for 30 sec, 72 °C for 60 sec, and finally 72 °C for 10 min. The final SPRI clean up coupled to size selection was performed (35 ⁇ SPRI beads was added to each sample and eluted in 40 ⁇ ). Sample concentrations were measured and 5 ⁇ was used for ChlP-String enrichment validation. For a detailed Automated iChIP setup procedure on the Bravo liquid handling platform. Enrichment validation: ChlP-String, DNA measurement on Nanostring
  • nCounter Details on the nCounter system are presented in full in (Geiss et al., 2008).
  • a custom CodeSet constructed to detect a total of 786 probes covering -200 genes (for detailed design of the Nanostring code-set see below) was used.
  • 5 ⁇ of iChIP libraries DNA where denatured at 95 °C for 5 minutes and immediately cooled on ice.
  • the denatured DNA product was applied directly into the hybridization reaction (5X SSPE, 0.1% Tween-20), and incubated at 65 °C for 16 hours in a PCR machine with a heated lid.
  • the samples were loaded onto the nCounter prep station followed by quantification using the nCounter ® Digital Analyzer 2.
  • nCounter ® probe-set as designed that target regulatory regions that are active during immune stimulation.
  • a list of 185 induced post stimulation in DCs was first selected together with a set of 16 control genes that are either not expressed (Cryaa, Pckl, Hbb-bl, Gabrbl, Drd2, Pou5fl, Sox2) or that are expressed but their expression remains unchanged (Gapdh, Meal, Ndufa7, Ndufs5, Rbm6, Shfml, Tbca, Tomm7, Ywhaz) upon stimulation with LPS.
  • BMDCs bone marrow-derived dendritic cells
  • the RNAA+- Seq libraries were prepared using the 'dUTP second strand (strand specific) protocol as described in (Levine et al 2010). Briefly, extracts were treated with DNase (Ambion 2238). Polyadenylated RNAs were selected using Ambion's MicroPoly(A)Purist kit (AM1919M) and RNA integrity confirmed using Bioanalyzer (Agilent). RNA was fragmented by incubation in RNA fragmentation buffer (Affymetrix) at 80 °C for 4 minutes.
  • RNA fragmentation buffer Affymetrix
  • RNA was mixed with 3 ⁇ g random hexamers (Invitrogen), incubated at 70 °C for 10 min, and placed on ice briefly before starting cDNA synthesis.
  • First-strand cDNA was synthesized with this RNA primer mix by adding 4 ⁇ 5x first-strand buffer, 2 ⁇ 100 mM DTT, 1 ⁇ 10 mM dNTPs, 4 ⁇ g of actinomycin D, 200 U Superscript III and 20 U
  • Second- strand cDNA was synthesized by adding 4 ⁇ of 5x first-strand buffer, 2 ⁇ of 100 mM DTT, 4 ⁇ of 10 mM dNTPs with dTTP replaced by dUTP (Sigma), 30 ⁇ of 5x second-strand buffer, 40 U of Escherichia coli DNA polymerase, 10 U of E. coli DNA ligase and 2 U of E. coli RNase H, and incubating at 16 °C for 2 h.
  • cDNA was eluted using the Qiagen MiniElute kit with 30 ⁇ of the manufacturer's EB buffer.
  • DNA ends were repaired using dNTPs and T4 polymerase (NEB), followed by purification using the MiniElute kit.
  • Adenine was added to the 3' end of the DNA fragments using dATP and Klenow exonuclease (NEB; M0212S) to allow adaptor ligation, and fragments were purified using MiniElute.
  • Adaptors were ligated and incubated for 15 min at room temperature (25 °C). Phenol/chloroform/isoamyl alcohol (Invitrogen 15593-031); extraction followed to remove the DNA ligase. The pellet was then resuspended in 10 ⁇ EB buffer.
  • PCR was performed with Phusion High-Fidelity DNA Polymerase with the manufacturer's GC buffer (New England Biolabs) and 2 M betaine (Sigma). PCR conditions were 30 s at 98 °C; 16 cycles of 10 s at 98 °C, 30 s at 65 °C, 30 s at 72 °C; 5 min at 72 °C; forever at 4 °C. Products were run on a polyacrylamide gel for 60 min at 120 V.
  • PCR 22 products were cleaned up with Agencourt AMPure XP magnetic beads (A63880) to completely remove primers and the product was submitted for Illumina sequencing. All libraries were sequenced using the Illumina Genome Analyzer (GAII). Two lanes for each sample were sequenced,
  • Bone marrow cells were infected with lentiviruses as previously described (Amit et al., 2009). Five shRNAs were tested for knock down efficiency using qPCR of the target gene. shRNAs with >75 knockdown efficacy were selected. Measurements of gene expression in unstimulated cells were carried out using a signature gene set in the nCounter Digital Analyzer as previously described (Amit et al., 2009). Lentivirus-infected cells were composed of -90% CD11C+ cells, which was comparable to sorted BMDCs (Amit et al., 2009). nCounter data analysis
  • the following pipeline was used to analyze the Irf2 knockdown data and re-analyze the knockdown data from (Amit et al., 2009).
  • the nanostring count values were divided by the sum of counts that are assigned with a set of control genes that are the least affected by shRNAs and LPS stimulation (10 gene altogether, including Ndufa7, Tbca, and Tomm7; see (Amit et al., 2009)).
  • a fold change ratio was computed by comparing to five control samples infected with non-targeting shRNA.
  • Nuclear extracts from mouse bone marrow dendritic cells were prepared by using NE-PER nuclear and cytoplasmic extraction reagents (Thermo scientific, USA), and following the instructions of the manufacturer with minor modifications. Briefly, 10 million cells were harvested by centrifugation and washed with PBS. Cells were transferred to an eppendorf tube and 1ml of CER I buffer was added. Cells were resuspended by vigorous vortexing for 15 sec followed by incubation on ice for 10 min. 55 ⁇ of ice-cold CER II buffer was added and vortexed for 5 sec on the highest setting.
  • Prestained protein molecular mass marker (BioRad) was run to monitor electrophoretic transfer and to determine relative size. Membranes were probed with antibodies and visualized by the enhanced chemiluminescence (ECL, Amarsham) method according to the instructions of the manufacturer.
  • ChIP Sequencing was done on Illumina HiSeq-2000 at the Broad Institute sequencing center. Pooled libraries were sequenced in -12 samples per lane at a sequencing depth of ⁇ 8 million aligned reads per sample. Initially, several libraries were sequenced using different sets of read lengths with and without paired-end reads test impact on analysis. The optimal read length for both cost and sensitivity was 44 bases (8 bases in this scheme are used for the indexes) with which all late libraries were sequenced.
  • Reads for each index and each lane was aligned to the mouse reference mouse genome NCBI37, using BWA (Li and Durbin, 2009) version 0.5.7 with parameters 24 -q 5 -1 32 -k 2 -t 4 -o 1 -f for the aln command and -P -a 600 -f for the sampe command. After combining reads from different lanes corresponding to the same timepoint, an average of 11,249,898 (7,370,024 sd) reads per timepoint were aligned for transcription factors and an average of 22,678,617 (10,176,016 sd) for chromatin and PolII libraries.
  • RNA sequencing was done for samples obtained from DCs pre-stimulation, 1, 2, 4 and 6 hours post stimulation and performed on an Illumina GA-II using 2 lanes per sample and a read length of 76 bases. All reads were aligned to the mouse reference genome (NCBI 37, MM9) using the TopHat aligner (version 1.1.4(Kim and Salzberg, 2011).
  • TopHat uses a two-step mapping process, first using Bowtie (Langmead et al., 2009) to align all reads that map directly to the genome (with no gaps), and then mapping all reads that were not aligned in the first step using gapped alignment.
  • TopHat uses canonical and non-canonical splice sites to determine possible locations for gaps in the alignment.
  • the EST database was used, which was downloaded from the UCSC genome browser (Fujita et al., 2011) to improve TopHat sensitivity for splice alignments.
  • TopHat parameters were used: - g 15 -r 250—library- type fr-firststrand -G spliced.est.gtf -p 4, where the spliced.est.gtf file was downloaded from UCSC. An average of 73 million uniquely aligned reads were obtained, of which an average 55 million aligned in proper pairs and 15 million aligned spanning a putative spliced junction. In addition, 4SU labeled libraries collected from unstimulated DCs were used every hour for 6 hours after stimulation.
  • a significant region R is score by the enrichment score:
  • N is the total number or reads
  • L is the length of the alignable genome
  • NR the total reads overlapping the region
  • IR is the length of the region R.
  • Regions of open chromatin tend to generate more reads than regions of less accessible DNA regardless of enrichment for a specific antibody target.
  • whole cell extract (WCE) libraries were used as our null set. For every library C, scripture significant regions were further filtered by running a fixed window of 150 bases (b) across the region computing
  • RNA-Seq Transcriptome annotation and quantification
  • Top-Hat alignments were processed by Scripture (Guttman et al., 2010) to obtain significantly expressed transcripts for each time course. Only multi-exonic transcripts were retained.
  • Scripture was run using the following parameters to find transcripts one chromosome at a time:
  • the RNASeq time course data of DC activated with LPS as used.
  • a list of 1885 transcription factors was filtered for maximal expression (in our RNA-Seq data) at any of the time points.
  • the list was then manually curated to remove any gene that is not a sequence specific TF (e.g., general transcription machinery, and chromatin modifiers).
  • Any TF that was expressed at any of the time points above 15 RPKM (for RPKM calls see below) was designated as "expressed” and further selected as a TF target, and was screened for potential antibodies in commercial antibody vendors databases.
  • the peaks were filtered by requiring a minimum score of 20. Peaks overlapping a promoter region that where closer to a transcriptional start site than to a 3'UTR were considered promoter peaks. Peaks were classified as 3'UTR whenever they were within lkb of an annotated 3' end and no transcriptional start site was closer. Peaks overlapping enhancer regions were classified as enhancer bound. The remaining peaks were classified as intronic, exonic or "other" whenever they overlapped an annotated intron, exon or neither.
  • a binomial p-value was used to assess their overlap in the genome as described in (McLean et al., 2010).
  • the number of hits is set to the number of x peaks that fall within 500 bp away from some peak of y.
  • the background probability set to the length of regions associated with y (i.e., taking 500 bp margin around each of its peaks) divided by the overall length of genomic regions that are associated with at least one ChIP assay.
  • a similar computation was performed for assessing the overlap of ChIP assay peaks with annotated genomic regions. To compute the overlap of assay x with region y, the number of hits was set to the number of x peaks that overlap with y.
  • the background probability was set to the length of regions associated with y divided by the overall length of the 27 genome.
  • the regions used include: (i) regulatory features annotations from ensembl (Flicek et al., 2010), (ii) regulatory features found by the oregano algorithm(Griffith et al., 2008), (iii) conserved regions annotated by the multiz30way algorithm, here regions with multiz30way score>0.7 were considered, and (iv) repeat regions annotated by RepeatMasker (website:repeatmasker.org). Region coordinates (ii - iv) were downloaded from the UCSC genome browser. Computation of the percent of bound motifs
  • every peak was scored with an enrichment of 20 or more by evaluating the match to the motif within the peak using the standard log-odds ratio score of the probability of a given k-mer being generated by the inferred position weight matrix and the probability of the k-mer being generated by a neutral model of 40% GC, the mouse genome wide GC content percent.
  • the 10 th percentile was the used in a genome wide scan for available motifs.
  • the percent of bound motifs was the ratio of motifs scoring higher than the cutoff to the number of genome wide matches above the cutoff.
  • a TF-region association matrix was first defined having columns corresponding to TF binding in the four studied time points (altogether 4 columns per TF), and rows correspond to regions (promoters and enhancers).
  • the association value is the sum of enrichment scores over all the peaks of the TF that fall within the given region at the given time point. Only peaks that had a sufficiently high enrichment score during at least one time point were considered. In this analysis, a cutoff of 26.9 was used, which corresponded to the mean + 0.25*std of all peak enrichment scores. This cutoff also corresponded to the top -33% scoring peaks.
  • Abinary version of the association matrix was defined. In the binary TF- region association matrix, each factor was associated with four columns (one for each time point).
  • the values in the binary matrix were "1" if the TF has at least one peak within the region that has an enrichment score over the cutoff value (and "0" otherwise).
  • a categorical TF-gene association matrix was also defined, where each factor is 28 associated with one column as above.
  • the values in the matrix are determined in accordance to the regions associated with the gene using the categorical TF-region association matrix. If there are no bound enhancers or promoters, the value will be "none". Otherwise, if at least 50% of the bound enhancers or at least 50% of the bound promoters are associated with gain, then the value will be "gain”. If at least 50% of the bound enhancers or at least 50% of the bound promoters are associated with loss, then the value will be "loss" (in the rare event (2.5% of the cases) where both conditions hold, the entry is marked as "static”). If both conditions do not hold, the entry was marked as "static".
  • the binary and categorized matrices were used throughout the analysis as a references for defining TF binding events and the dynamics of these events.
  • N is the overall number of regions bound at that time point
  • B is the number of regions bound by the first TF at the given time point
  • n is the number of regions bound by the second TF at the given time point
  • b is the number of regions bound by both at the given time point.
  • the analysis was limited to regions with at least two binding events. Further, to get more specific results, highly occupied target (HOT) regions with 10 or more bound TF were filtered out. TF pairs that had a p-value lower than 10-3 during at least one time point are shown (Figure 4B).
  • the Hamming distance percentage of elements that are either bound by both TFs or not bound by both TFs was used.
  • N is the background set of genes.
  • B is the size of the cluster
  • n is the number of genes that have the investigated property ⁇ i.e., a functional group from MsigDB, or genes annotated as inflammatory or anti-viral 4
  • b is the number of genes that belong to the cluster and to the annotated set
  • N is the background set of genes.
  • the pcomp Matlab function was applied to the 28x6 dimensional matrix consistent of all transcription factors (excluding Atf4 for which there was only one time point) and six binding characteristics scored: log of the number of bound regions, percent of dynamic binding events, promoter to enhancer binding ratio, percent of regions bound in isolation, running score, and percent of genome wide motifs bound by the TF. All covariates were standardized (mean zero, STD 1) prior to the analysis.
  • the biplot Matlab function was used to present the TFs projections and the loading of the different covariates for the first three principle components (Figure 8A). Notably, the first three principal components account for 88% of the variance in the data.
  • Mammalian genomes can give rise to hundreds of cell types; each with distinct functions and responses, but the mechanisms by which this plasticity is encoded in the TF- DNA networks is only partly understood. It was found that the response of primary innate DCs to pathogen stimulus is orchestrated by a multilayered TF network with at least three major layers.
  • a first layer consists of Pioneer TFs, which have been previously shown to play a role in establishing cell identity by shaping the cells epigenetic state during
  • Primers that are already loaded into cis-regulatory elements, most prominently at early-induced genes, may in some embodiments, 'poise' genes for induction under irrelevant conditions.
  • they may serve as beacons, to direct other TFs or post-translation modifying enzymes to the appropriate site to tune gene-expression, a recruitment role previously suggested for the pioneer factors Cebpb, PU. l, E2a and Ebf (Cirillo et al., 2002; Cirillo and Zaret, 1999; Heinz et al., 2010).
  • a third layer of TFs is more closely associated with a specific signaling cascade or regulatory pathway (Figure 6C, Table S7). Factors in this Transducer layer work together with Primer factors to regulate genes in specific gene programs ( Figure 8B). In certain embodiments, this model may generalize to other transcriptional responses in different cell types.
  • a more complete understanding of mammalian regulatory circuits may require comprehensive mapping of TFs across a range of cell states, conditions, and responses.
  • a map of the differences across individuals in a population and across evolutionary history will provide critical insights into the mammalian regulatory code and their role in human disease. This may extend the layered organization to other cellular states, and may enable efficient engineering of cellular identities by controlling the expression and timing of different regulatory layers.
  • quadrants 1 and 2 of each position in which a sample is located are quadrants 1 and 2 of each position in which a sample is located).
  • Tips are knocked off for disposal at quadrant 1 in column one of an empty 70 ⁇ ST VI 1.
  • 61 Repeat steps 1 through 3 for all subsequent columns on the sample which contain samples. Clean tips should be used each time mastermix is aliquotted into a new column on the sample plate. For column 1, put the tips on in column 1 of the tip box and off in column 1 of the tip trash. For column 2 of the sample plate, put the tips on in column 3 of the tip box and off in column 3 of the tip trash. For column 3 of the sample plate, put the tips on in column 5 of the tip box and off in column 5 of the tip trash.
  • thermocycler 101 Once the protocol is complete, seal well containing sample with ABI optical caps and place the sample plate on thermocycler. (Thermoprofile consists solely of 37°C for 30 minutes then held at 4°C indefinitely).
  • thermocycler Once the protocol is complete, seal wells containing sample with ABI optical caps and place the sample plate on thermocycler. (Thermoprofile consists solely of 25 °C for 15 minutes then held at 4°C indefinitely).
  • Clean tips should be used each time mastermix is aliquotted into a new column on the sample plate.
  • For column 2 of the sample plate put the tips on in column 3 of the tip box and off in column 3 of the tip trash.
  • thermocycler 101 Once the protocol is complete, seal wells containing sample with ABI optical caps and place the sample plate on thermocycler. (Thermoprofile is diagramed in Table 5).
  • IGV Integrative Genomics Viewer
  • General documentation is available at website:broadinstitute.org/igv.
  • the iChiP enabled IGV can be launched directly from website:broadinstitute.org/igv/ichip. This link will download and install IGV 2.0 with iChIP extensions, and open the iChIP dataset at the ilia locus.
  • the iChIP extensions include a new command bar and popup menu.
  • BMDC bone marrow-derived dendritic cells
  • the approximate cell numbers may be 3 million cells per dish. Tissue culture dishes and table top centrifuge should be cooled to 4°C.
  • PBS + protease inhibitor PBS+PI
  • crosslinked cells can be flash frozen at -80 °C.
  • estimate DNA e.g., QBITTM assay.
  • nCounter ® is designed to compare reads from specific probes across different RNA samples; however, evaluating ChIP samples by the nCounter ® requires comparison of reads originating from different probes.
  • both the different ChlP-string assays and the probes were normalized by the median. This accounted for the differences in the loading amounts of DNA in each ChlP-string assay. Furthermore, this normalization also adjusts the fluctuations of the diverse probes.
  • Z- score transformation for the ChIP sample was applied, followed by zeroing negative values, in order to reduce background noise. High outliers were subjected to threshold. 122 antibodies were evaluated.
  • 10 x TE 100 mM Tris-HCl pH8.0, 10 mM EDTA pH 8.0,
  • RIPA buffer STE + 1% Triton X-100, 0.1% SDS, 0.1% DOC
  • LiCl wash buffer TE, 250mM LiCl, 0.5% NP-40, 0.5% DOC
  • Hepes Buffer, IM solution Mediatech Inc. (Manassas, VA) Cat# 25-060-CI 100 ml
  • IM KC1 (ACS grade) : Boston Bioproducts, MT-250, 100 ml
  • Triton X-100 Sigma (St. Louis, MO) T8787-100 ml
  • Proteinase K 1. Invitrogen (Carlsbad, CA) Proteinase K solution #25530-049
  • MINELUTE QIAGEN kit MINELUTE purification kit (50) #28004
  • Phenol CHC1 3 : isoamyl alcohol (25:24: 10) : Invitrogen 15593-031 100 ml
  • ChIP- string was then used to evaluate the efficiency and accuracy of the data.
  • the SCN ChIP- string data was then compared to to other ChIP- string data (derived using 20 million cells, and successfully sequenced). All the samples from SCN correlated with the ChIP- string data of 20 million cells.
  • Figure 27 represents ChlP-string experiments for 10,000 20,000 and 100,000 cells.
  • the matrix represents the correlation values of each ChlP-string experiment to the rest.
  • Each antibody correlates within the SCN and with the relevant 20 million cell ChlP-string data.
  • ChlP-string probe signals were normalized by experiment (e.g. , column)
  • ChlP-string experiments were calculated. This provided a background distribution, independent of the CR experiments.
  • K562 erythrocytic leukaemia cells were grown according to standard protocols in RPMI 1640 media (Invitrogen, 22400105) supplemented with 10% fetal bovine serum (FBS, Atlas Biologicals, F-0500-A) and 10% Penicillin/Streptomycin
  • HI ES cells were grown in TeSR media on Matrigel (Cellular
  • H3K27me3, H3K9me3 and CTCF was used to identify 10 major chromatin states and annotate the genome accordingly. These states corresponded to distinct annotations, including active promoters, poised promoters, weakly and strongly transcribed regions, weak and strong enhancers, Polycomb-repressed regions, heterochromatic regions and CTCF sites.
  • the nCounter ® was originally designed for the non-enzymatic capture and counting of -800 individual RNA molecules in a single multiplexed reaction.
  • the method employed color-coded, molecular bar-codes (reporters), solid phase capture, and high-resolution fluorescent imaging to digitally count individual nucleic acids.
  • Each custom codeset contained two sequence- specific probes (each 35-50 bases) for every 100 base region of interest: (i) a biotinylated capture probe that contains complementary sequence to the 5' of the particular target region; (ii) a uniquely color-coded reporter probe, complementary to the 3' of the target region.
  • the capture and reporter probes were hybridized to the target of interest, forming a tripartite structure (capture probe:target:reporter probe) that is then purified via universal affinity tags present in both capture and reporter probe molecules.
  • the purified complexes were bound to the imaging surface via the biotin moiety on the capture probe, aligned via electrophoresis, immobilized and imaged.
  • An image analysis algorithm was used to count the barcodes associated with a single molecule of each target sequence.
  • Target genomic sequences were screened for optimal sets of nCounter ® probe pairs as described previously for RNA quantification (Geiss, et al.
  • RNA detection was adapted for DNA detection by incorporating a denaturation step prior to hybridization (95 °C for 5 minutes, followed by flash cooling on ice).
  • the single- stranded denatured product was then applied directly into the hybridization reaction (5X SSPE, 0.1% Tween-20), and incubated at 65 °C overnight in a heat block with a heated lid.
  • Automated purification of hybridized complexes and binding to the sample cartridge were performed using the nCounter ® prep station and reagents, according to standard procedures (NanoString Technologies, Master Kits).
  • immunoprecipitated DNA samples were first amplified using a whole genome amplification kit (Sigma- Aldrich, WGA2), according to the manufacturer's protocol.
  • the antibody screen was based on ChIP- string data consisting of digital counts for each probe (rows) over all CR experiments (columns).
  • the original analysis procedure includes the following steps. (1) Each measurement was divided by the median of all counts in the experiment in which it was taken (the column median); (2) Each value was further divided by the median of the values for that probe across all experiments (the row median); (3) The values were standardized based on the distribution of values within each experiment, subtracting the mean of each column and dividing by its standard deviation; after this step, the scores were compared to a Normal distribution with mean of 0 and variance of 1.
  • Probes with large positive values corresponded to enriched loci; (4) To reduce the effect of background variations, or outliers, on the correlations (calculated in the next step), any value less than 0 was set to zero, and values higher than 5 were set to 5; (5) Pearson correlations were calculated between each pair of samples (experiments), and formed the basis for clustering the experiments using average-linkage, hierarchical agglomerative clustering; (6) The resulting clusters were visually inspected to pass/fail individual antibodies in the following way: (i) antibodies that clustered with IgG controls were designated as 'failed' in the assay, with a few exceptions made for antibodies that also showed high correlation with one (or more) histone modification experiments; (ii) experiments in which the pre- standardization signal was flat and contained no strongly enriched probes were also considered 'failed'.
  • Step 2 of Screening Method 1 was designed to adjust for the different baseline signals of individual probes by considering the median signal for one probe across all experiments.
  • a large screen such as this study
  • most of the data for a given probe comes from the background distribution: many antibodies work poorly (and are thus background) and, moreover, the corresponding locus is only expected to be enriched for some of the epitopes. In a small screen with few antibodies, in some instances, this procedure can result in bias.
  • a second approach was devised, which estimates the background distribution from the set of control experiments where no enrichment is expected (IgG and WCE). The background distribution was then applied in an automated fashion to determine whether a given CR experiment was significantly different from the control experiments, in which case the antibody was 'passed' .
  • the Partitioning Around Medoids (PAM) algorithm from a R cluster package was used to cluster experiments (3 clusters) and probes (4 clusters) from the reference set (see below).
  • the section of the probe with the highest signals was identified by experiment cluster grid (12 sections), and the corresponding probes were removed.
  • the remaining probes were divided into 11 sets based on combinatorial enrichments in ChIP- string experiments for histone modifications (H3K4mel, H3K4me3, H3K9me3, and H3K27me3). These sets were also derived using the pam algorithm, and roughly correspond to the chromatin states used for probe design.
  • a background distribution of median ranks was calculated for each set based on the 18 control experiments. For each CR experiment, the ranks for each set were compared against the background distribution. An antibody passed the screen if any of the 11 probe sets had a median rank above the 99th percentile of the background distribution.
  • the PAM algorithm is a k-medoids algorithm, which is a clustering algorithm related to the k-means algorithm and the medoidshift algorithm. Both the fc-means and fc-medoids algorithms are partitional (breaking the dataset up into groups) and both attempt to minimize squared error, the distance between points labeled to be in a cluster and a point designated as the center of that cluster. In contrast to the k-means algorithm, fc-medoids chooses datapoints as centers (medoids or exemplars).
  • fc-medoid is a classical partitioning technique of clustering that clusters the data set of n objects into k clusters known a priori.
  • a useful tool for determining k is the silhouette.
  • a medoid can be defined as the object of a cluster, whose average dissimilarity to all the objects in the cluster is minimal, i.e., it is a most centrally located point in the cluster.
  • Medoids (PAM) algorithm and is as follows (Theodoridis et ah, Pattern Recognition 3rd ed.. p.635 (2006)).: (1) initialize by randomly selecting k of the n data points as the medoids; (2) associate each data point to the closest medoid ("closest” refers to the use of any valid distance metric, most commonly Euclidean distance, Manhattan distance or Minkowski distance); (3) for each medoid m, for each non-medoid data point o, swap m and o and compute the total cost of the configuration; (4) select the configuration with the lowest cost; and (5) repeat steps 2 to 5 until there is no change in the medoid.
  • closest refers to the use of any valid distance metric, most commonly Euclidean distance, Manhattan distance or Minkowski distance
  • composition it is to be understood that methods of using the composition for any of the purposes disclosed herein are included, and methods of making the composition according to any of the methods of making disclosed herein or other methods known in the art are included, unless otherwise indicated or unless it would be evident to one of ordinary skill in the art that a contradiction or inconsistency would arise.
  • compositions of the invention can be excluded from any one or more claims, for any reason, whether or not related to the existence of prior art.
  • IRF-1 and IRF-2 Structurally similar but functionally distinct factors, bind to the same regulatory elements of IFN and IFN-inducible genes. Cell 58, 729-739.
  • CDE/CHR tandem element regulates cell cycle-dependent repression of cyclin B2 transcription.
  • NF-kappa B a pleiotropic mediator of inducible and tissue-specific gene control.
  • Negre N., Brown, CD., Ma, L., Bristow, C.A., Miller, S.W., Wagner, U., Kheradpour, P., Eaton, M.L., Loriaux, P., Sealfon, R., et al. (2011).
  • NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35, D61-65.
  • RNA Rabani, M., Levin, J.Z., Fan, L., Adiconis, X., Raychowdhury, R., Garber, M., Gnirke, A., Nusbaum, C, Hacohen, N., Friedman, N., et al. (2011). Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat Biotechnol 29, 436-442.
  • Module networks identifying regulatory modules and their condition— -specific regulators from gene expression data. Nat Genet 34, 166— -176.
  • HMG I(Y) The high mobility group protein HMG I(Y) is required for NF— -kappa B— -dependent virus induction of the human IFN— -beta gene.
  • HDAC1 Santa cruz,6298X motif,39531 81598 motif,40967

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Abstract

La présente invention concerne des procédés et des kits d'immunoprécipitation de la chromatine.
PCT/US2011/054072 2010-09-29 2011-09-29 Procédés d'immunoprécipitation de la chromatine WO2012047726A1 (fr)

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WO2015159295A1 (fr) * 2014-04-17 2015-10-22 Yeda Research And Development Co. Ltd. Procédés et kits pour analyser les fragments se liant à l'adn liés à l'adn
US9411930B2 (en) 2013-02-01 2016-08-09 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US9715573B2 (en) 2015-02-17 2017-07-25 Dovetail Genomics, Llc Nucleic acid sequence assembly
WO2018020489A1 (fr) 2016-07-24 2018-02-01 Yeda Research And Development Co. Ltd. Méthodes et kits pour analyser des fragments de liaison à l'adn liés à l'adn
US9957548B2 (en) 2015-03-30 2018-05-01 General Electric Company Methods of capturing sperm nucleic acids
CN108290285A (zh) * 2015-11-17 2018-07-17 Abb瑞士股份有限公司 用于在机器人系统中优化工作周期的方法
US10030241B2 (en) 2015-03-30 2018-07-24 General Electric Company Methods and kits for capturing sperm nucleic acids
CN108330181A (zh) * 2018-02-27 2018-07-27 苏州睿迈英基因检测科技有限公司 一种适用于少细胞的染色质免疫共沉淀测序方法及其试剂盒和应用
US10089437B2 (en) 2013-02-01 2018-10-02 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US10457934B2 (en) 2015-10-19 2019-10-29 Dovetail Genomics, Llc Methods for genome assembly, haplotype phasing, and target independent nucleic acid detection
US10526641B2 (en) 2014-08-01 2020-01-07 Dovetail Genomics, Llc Tagging nucleic acids for sequence assembly
US10732185B2 (en) * 2014-02-03 2020-08-04 The University Of Chicago Compositions and methods for quantitative assessment of DNA-protein complex density
CN112359092A (zh) * 2020-10-22 2021-02-12 中国农业科学院农业基因组研究所 一种基因组短片段文库的构建方法
US10947579B2 (en) 2016-05-13 2021-03-16 Dovetail Genomics, Llc Recovering long-range linkage information from preserved samples
US10975417B2 (en) 2016-02-23 2021-04-13 Dovetail Genomics, Llc Generation of phased read-sets for genome assembly and haplotype phasing
WO2021243289A1 (fr) * 2020-05-29 2021-12-02 The General Hospital Corporation Systèmes et procédés de modification stable et héréditaire par édition de précision (shape)
US11667677B2 (en) 2017-04-21 2023-06-06 The General Hospital Corporation Inducible, tunable, and multiplex human gene regulation using CRISPR-Cpf1
US11807896B2 (en) 2015-03-26 2023-11-07 Dovetail Genomics, Llc Physical linkage preservation in DNA storage

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US10529443B2 (en) 2013-02-01 2020-01-07 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US10825553B2 (en) 2013-02-01 2020-11-03 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US11935626B2 (en) 2013-02-01 2024-03-19 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US9411930B2 (en) 2013-02-01 2016-08-09 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US9910955B2 (en) 2013-02-01 2018-03-06 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US10089437B2 (en) 2013-02-01 2018-10-02 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US11081209B2 (en) 2013-02-01 2021-08-03 The Regents Of The University Of California Methods for genome assembly and haplotype phasing
US10732185B2 (en) * 2014-02-03 2020-08-04 The University Of Chicago Compositions and methods for quantitative assessment of DNA-protein complex density
US11965890B2 (en) 2014-02-03 2024-04-23 The University Of Chicago Compositions and methods for quantitative assessment of DNA-protein complex density
US10590467B2 (en) 2014-04-17 2020-03-17 Yeda Research And Development Co. Ltd. Methods and kits for analyzing DNA binding moieties attached to DNA
WO2015159295A1 (fr) * 2014-04-17 2015-10-22 Yeda Research And Development Co. Ltd. Procédés et kits pour analyser les fragments se liant à l'adn liés à l'adn
US10526641B2 (en) 2014-08-01 2020-01-07 Dovetail Genomics, Llc Tagging nucleic acids for sequence assembly
US10318706B2 (en) 2015-02-17 2019-06-11 Dovetail Genomics, Llc Nucleic acid sequence assembly
US11600361B2 (en) 2015-02-17 2023-03-07 Dovetail Genomics, Llc Nucleic acid sequence assembly
US9715573B2 (en) 2015-02-17 2017-07-25 Dovetail Genomics, Llc Nucleic acid sequence assembly
US11807896B2 (en) 2015-03-26 2023-11-07 Dovetail Genomics, Llc Physical linkage preservation in DNA storage
US10030241B2 (en) 2015-03-30 2018-07-24 General Electric Company Methods and kits for capturing sperm nucleic acids
US9957548B2 (en) 2015-03-30 2018-05-01 General Electric Company Methods of capturing sperm nucleic acids
US10457934B2 (en) 2015-10-19 2019-10-29 Dovetail Genomics, Llc Methods for genome assembly, haplotype phasing, and target independent nucleic acid detection
CN108290285A (zh) * 2015-11-17 2018-07-17 Abb瑞士股份有限公司 用于在机器人系统中优化工作周期的方法
US10975417B2 (en) 2016-02-23 2021-04-13 Dovetail Genomics, Llc Generation of phased read-sets for genome assembly and haplotype phasing
US10947579B2 (en) 2016-05-13 2021-03-16 Dovetail Genomics, Llc Recovering long-range linkage information from preserved samples
WO2018020489A1 (fr) 2016-07-24 2018-02-01 Yeda Research And Development Co. Ltd. Méthodes et kits pour analyser des fragments de liaison à l'adn liés à l'adn
US11667677B2 (en) 2017-04-21 2023-06-06 The General Hospital Corporation Inducible, tunable, and multiplex human gene regulation using CRISPR-Cpf1
CN108330181A (zh) * 2018-02-27 2018-07-27 苏州睿迈英基因检测科技有限公司 一种适用于少细胞的染色质免疫共沉淀测序方法及其试剂盒和应用
WO2021243289A1 (fr) * 2020-05-29 2021-12-02 The General Hospital Corporation Systèmes et procédés de modification stable et héréditaire par édition de précision (shape)
CN112359092A (zh) * 2020-10-22 2021-02-12 中国农业科学院农业基因组研究所 一种基因组短片段文库的构建方法
CN112359092B (zh) * 2020-10-22 2023-09-19 中国农业科学院农业基因组研究所 一种基因组短片段文库的构建方法

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