Di-methyl-Histone H3(K79) Monoclonal Antibody

What is the biological significance of H3K79 dimethylation?

H3K79 dimethylation occurs on lysine 79 of histone H3, which is located within the nucleosome core. Nucleosomes wrap and compact DNA into chromatin, thereby limiting DNA accessibility to cellular machineries that require DNA as a template. Histone H3 plays a central role in transcription regulation, DNA repair, DNA replication, and chromosomal stability . Specifically, methylation at lysine 79 acts as a marker of inactive chromatin regions that is critical for transcriptional silencing. Silencing proteins such as Sir3 function by blocking Dot1 methylation at this site . This modification is part of the complex "histone code" that regulates gene expression through various post-translational modifications.

How is H3K79 dimethylation enzymatically regulated?

H3K79 dimethylation is catalyzed by DOT1L methyltransferase (Disruptor of Telomeric silencing 1-like), which is unique among histone methyltransferases in that it does not contain a SET domain. Prior research has demonstrated that DOT1L exhibits exceptional selectivity for H3K79, with almost no other histone or non-histone substrates being identified . The enzyme can add one, two, or three methyl groups to lysine 79, producing mono-, di-, or trimethylated states. The methylation process involves the transfer of methyl groups from S-adenosyl methionine (SAM) to the target lysine. Unlike many other histone modifications, H3K79 methylation has not been definitively linked to specific demethylase enzymes, although histone demethylases including PADI4, LSD1, JMJD1, JMJD2, and JHDM1 have shown that methylation generally is a reversible epigenetic marker .

What are the primary research applications for Di-methyl-Histone H3(K79) Monoclonal Antibodies?

Di-methyl-Histone H3(K79) Monoclonal Antibodies are versatile research tools with applications in multiple techniques:

• Chromatin Immunoprecipitation (ChIP): For mapping genomic locations of H3K79me2 marks

• Western Blotting (WB): For detecting and quantifying H3K79me2 levels

• Peptide Arrays (PepArr): For antibody specificity testing and epitope mapping

• Immunocytochemistry/Immunofluorescence (ICC/IF): For visualizing nuclear distribution patterns

• Immunohistochemistry - Paraffin (IHC-P): For tissue section analysis

• Flow Cytometry: For quantitative analysis at the cellular level

• Chromatin IP-seq: For genome-wide profiling of H3K79me2 marks

• CUT&Tag/CUT&RUN Assays: For high-resolution mapping of protein-DNA interactions

The selected antibody format (monoclonal vs. polyclonal) and host species should be determined based on the specific experimental requirements and anticipated cross-reactivity concerns.

How should researchers design ChIP experiments using Di-methyl-Histone H3(K79) Monoclonal Antibodies?

When designing ChIP experiments with Di-methyl-Histone H3(K79) Monoclonal Antibodies, researchers should consider the following methodological approach:

• Sample Preparation: Use fresh chromatin preparations with optimal crosslinking (typically 1% formaldehyde for 10 minutes). Over-crosslinking can mask the H3K79me2 epitope.

• Sonication Parameters: Aim for chromatin fragments between 200-500bp. Test sonication efficiency with a small aliquot before proceeding.

• Antibody Selection: Choose ChIP-grade antibodies specifically validated for this application, such as rabbit recombinant monoclonal antibodies against H3K79me2 .

• Controls: Include:

  • Positive control: Antibody against total Histone H3

  • Negative control: IgG from same species as the primary antibody

  • Input chromatin: To normalize enrichment

• Immunoprecipitation: Use 2-5μg of antibody per reaction with chromatin from approximately 1 million cells. Overnight incubation at 4°C on a rotating platform is recommended.

• Washing and Elution: Perform stringent washes to minimize background, followed by elution under conditions that do not denature the antibody.

• Validation: Confirm enrichment at known H3K79me2-marked regions using qPCR before proceeding to genome-wide analysis.

For downstream applications like ChIP-seq, ensure library preparation preserves the representative nature of immunoprecipitated DNA fragments.

What are the optimal conditions for Western blotting with Di-methyl-Histone H3(K79) Monoclonal Antibodies?

For optimal Western blotting results with Di-methyl-Histone H3(K79) Monoclonal Antibodies, follow these methodological guidelines:

• Sample Preparation:

  • Extract histones using acid extraction (0.2N HCl or 0.4N H₂SO₄) followed by TCA precipitation

  • Load 5-15μg of histone extract per well

• Gel Electrophoresis:

  • Use 15-18% SDS-PAGE gels to resolve the relatively small histone proteins (~17 kDa)

  • Include recombinant methylated H3 peptides as positive controls

• Transfer Conditions:

  • Transfer to PVDF membrane (preferred over nitrocellulose for histones)

  • Use wet transfer at 30V overnight at 4°C for efficient transfer of basic proteins

• Blocking:

  • Block with 5% BSA in TBST rather than milk (milk contains casein which is phosphorylated and can increase background)

  • Block for 1 hour at room temperature

• Antibody Incubation:

  • Dilute primary antibody according to manufacturer recommendations (typically 1:1000-1:2000)

  • Incubate overnight at 4°C with gentle agitation

  • Use HRP-conjugated secondary antibodies at 1:5000-1:10000

• Detection:

  • Use enhanced chemiluminescence detection systems

  • Expected molecular weight for H3 is approximately 17 kDa

• Controls and Normalization:

  • Run parallel blots with antibodies against total H3 for normalization

  • Consider dual-color detection systems to simultaneously visualize total H3 and H3K79me2

How can researchers validate the specificity of Di-methyl-Histone H3(K79) Monoclonal Antibodies?

Validating antibody specificity is critical for reliable experimental outcomes. For Di-methyl-Histone H3(K79) Monoclonal Antibodies, implement these methodological approaches:

• Peptide Competition Assays:

  • Pre-incubate the antibody with excess H3K79me2 peptide

  • Compare results with antibody-only controls

  • Signal should be drastically reduced in the presence of competing peptide

• Peptide Array Analysis:

  • Test antibody against multiple histone peptides containing various methylation states (me0, me1, me2, me3) at K79

  • Include peptides with modifications at other lysine residues (K4, K9, K27, K36) to assess cross-reactivity

  • Quantify binding affinity to confirm specificity for H3K79me2

• DOT1L Knockout/Knockdown:

  • Analyze extracts from DOT1L-deficient cells

  • H3K79me2 signal should be significantly reduced or absent

• Recombinant Histone Testing:

  • Use recombinant histones with defined modifications as standards

  • Compare signals between unmodified H3 and H3K79me2

• Mass Spectrometry Validation:

  • Perform immunoprecipitation followed by mass spectrometry

  • Confirm enrichment of H3K79me2 peptides in the pulldown

• Sequential ChIP:

  • Perform ChIP with H3K79me2 antibody followed by a second ChIP with another H3-specific antibody

  • High overlap indicates specificity for H3

This comprehensive validation approach ensures that experimental results truly reflect H3K79me2 biology rather than antibody artifacts.

How can isotope labeling be used to track DOT1L methyltransferase activity on H3K79?

Advanced research using stable isotope labeling provides a powerful approach to track DOT1L methyltransferase activity with high specificity. The methodology involves:

• Synthesis of Heavy-Labeled Co-factors:

  • Produce 13CD3-BrSAM (bromo-deaza-S-adenosyl-L-methionine with heavy-labeled methyl groups)

  • Generate 13CD3-SAM as a control co-factor

• In Vitro Methylation Reactions:

  • Incubate nucleosomes with DOT1L enzyme and heavy-labeled co-factors

  • Allow methylation reactions to proceed under physiological conditions

• Mass Spectrometry Analysis:

  • Digest histones with trypsin or other proteases

  • Identify peptides containing H3K79

  • Detect mass shifts corresponding to incorporation of heavy-labeled methyl groups

• Specificity Validation:

  • Test multiple histone methyltransferases (SETD7, G9a, SETD2, SUB420H2)

  • Compare results with these enzymes using both 13CD3-SAM and 13CD3-BrSAM

  • Confirm selective incorporation of heavy methyl groups at H3K79 by DOT1L

Research has demonstrated excellent incorporation of heavy labels at H3K79 using both co-factors with DOT1L, while no heavy labeling was observed at other histone sites (H3K4, H3K9, H3K27, H3K36, H4K20), confirming DOT1L's selectivity. Importantly, when other methyltransferases (SETD7, G9a, SETD2, SUB420H2) were used with 13CD3-BrSAM, no incorporation was observed at their respective target sites, showing 13CD3-BrSAM's excellent selectivity for DOT1L .

This methodology provides a powerful approach to track DOT1L activity in various experimental contexts, enabling the study of factors affecting enzyme activity and substrate specificity.

What are the current approaches for studying the interplay between H3K79 dimethylation and other histone modifications?

Understanding the complex interrelationships between H3K79 dimethylation and other histone modifications requires sophisticated methodological approaches:

• Sequential ChIP (Re-ChIP):

  • Perform initial ChIP with H3K79me2 antibody

  • Use the enriched material for a second ChIP with antibodies against other modifications

  • Quantify regions carrying both modifications

  • Compare to single ChIP datasets to determine co-occurrence patterns

• Mass Spectrometry-Based Combinatorial Analysis:

  • Isolate intact histone proteins or nucleosomes

  • Employ middle-down or top-down MS approaches that preserve modification combinations

  • Quantify co-occurrence frequencies of H3K79me2 with other modifications on the same histone tail

• Multicolor ChIP-Seq:

  • Use antibody cocktails with different epitope tags

  • Develop multiplexed sequencing libraries

  • Bioinformatically resolve modification patterns

• Targeted Perturbation Experiments:

  • Manipulate specific histone-modifying enzymes (e.g., DOT1L inhibition)

  • Measure consequent changes in other modifications

  • Establish causal relationships and sequence of modification events

• Proximity Ligation Assays (PLA):

  • Detect closely positioned histone modifications in intact nuclei

  • Quantify spatial relationships between H3K79me2 and other modifications

Current research indicates that H3K79 methylation coordinates with other modifications in complex ways. Lysine methylation occurs primarily on histones H3 (Lys4, 9, 27, 36, 79) and H4 (Lys20) and has been implicated in both transcriptional activation and silencing . These modifications work together to coordinate the recruitment of chromatin modifying enzymes containing methyl-lysine binding modules such as chromodomains (HP1, PRC1), PHD fingers (BPTF, ING2), tudor domains (53BP1), and WD-40 domains (WDR5) .

How can researchers investigate the functional consequences of altered H3K79 dimethylation patterns?

Investigating the functional impacts of altered H3K79 dimethylation patterns requires a multifaceted experimental approach:

• DOT1L Inhibition or Genetic Manipulation:

  • Employ small molecule DOT1L inhibitors at various concentrations

  • Use genetic approaches (CRISPR/Cas9, RNAi) to modulate DOT1L expression

  • Create point mutations in H3K79 (K79A, K79R) to prevent methylation

• Transcriptome Analysis:

  • Perform RNA-seq following DOT1L perturbation

  • Compare expression changes with H3K79me2 ChIP-seq data

  • Identify direct transcriptional targets versus secondary effects

  • Use time-course experiments to track dynamic responses

• Chromatin Accessibility Assays:

  • Implement ATAC-seq or DNase-seq to measure chromatin accessibility changes

  • Correlate accessibility with H3K79me2 distribution

  • Map nucleosome positioning relative to H3K79me2 marks

• Functional Genomics Screens:

  • Conduct CRISPR screens in DOT1L-inhibited cells

  • Identify synthetic lethal or rescue interactions

  • Map genetic networks associated with H3K79me2 function

• Proteomic Approaches:

  • Identify readers of H3K79me2 using SILAC-based pull-downs

  • Compare protein interactomes in normal versus DOT1L-inhibited conditions

  • Map signaling pathways affected by H3K79me2 levels

• Single-Cell Analysis:

  • Measure cell-to-cell variation in H3K79me2 levels

  • Correlate with transcriptional heterogeneity

  • Track cellular differentiation trajectories

By integrating these approaches, researchers can establish causal relationships between H3K79 dimethylation patterns and specific biological processes, moving beyond correlative observations to mechanistic understanding.

What are common pitfalls in ChIP experiments with Di-methyl-Histone H3(K79) Monoclonal Antibodies and how can they be addressed?

ChIP experiments with Di-methyl-Histone H3(K79) Monoclonal Antibodies can present several technical challenges. Here are common issues and methodological solutions:

• Low Signal-to-Noise Ratio:

  • Problem: High background or weak specific signal.

  • Solutions:

    • Increase stringency of wash buffers (adjust salt concentration)

    • Optimize antibody concentration through titration experiments

    • Use ChIP-grade antibodies specifically validated for this application

    • Pre-clear chromatin with protein A/G beads before immunoprecipitation

• Epitope Masking:

  • Problem: Overfixation can mask the H3K79me2 epitope.

  • Solutions:

    • Optimize formaldehyde concentration (typically 0.75-1%)

    • Limit crosslinking time (8-10 minutes is often optimal)

    • Implement epitope retrieval steps if necessary

    • Consider native ChIP for some applications

• Cross-Reactivity:

  • Problem: Antibody recognizing other methylation states (H3K79me1 or H3K79me3).

  • Solutions:

    • Validate antibody specificity using peptide arrays

    • Perform control experiments with recombinant histones

    • Compare results with multiple antibodies from different suppliers

• Inefficient Chromatin Fragmentation:

  • Problem: Suboptimal sonication leading to inconsistent results.

  • Solutions:

    • Optimize sonication parameters (time, amplitude, pulse duration)

    • Verify fragment size distribution by gel electrophoresis

    • Consider enzymatic fragmentation alternatives

    • Ensure consistent cell numbers across experiments

• PCR Bias in ChIP-seq:

  • Problem: Amplification artifacts during library preparation.

  • Solutions:

    • Minimize PCR cycles

    • Use unique molecular identifiers (UMIs)

    • Implement multiple library replicates

    • Apply computational corrections for GC content

Addressing these challenges through methodical optimization will enhance the reliability and reproducibility of ChIP experiments targeting H3K79 dimethylation.

How can researchers troubleshoot antibody batch variation issues with Di-methyl-Histone H3(K79) Monoclonal Antibodies?

Antibody batch variation can significantly impact experimental reproducibility. Here's a methodological approach to address this challenge:

• Proactive Quality Control:

  • Validation Protocol: Establish a standardized testing procedure for each new antibody batch:

    • Peptide array analysis against modified and unmodified peptides

    • Western blot using positive control samples (e.g., cell lines with known H3K79me2 levels)

    • ChIP-qPCR for known target regions with established enrichment values

• Internal Reference Standards:

  • Create and maintain reference samples:

    • Aliquot and freeze histone extracts from standard cell lines

    • Generate recombinant or synthetic H3K79me2 standards

    • Use these standards when testing new antibody batches

• Collaborative Batch Testing:

  • Implement multi-laboratory testing for critical antibody batches

  • Share validation data through antibody validation repositories

  • Consider using commercially available ChIP-validated antibodies with consistent lot testing data

• Quantitative Benchmarking:

  • Develop quantitative metrics for antibody performance:

    • Signal-to-noise ratio in Western blots

    • Enrichment values for control regions in ChIP

    • Cross-reactivity percentages from peptide arrays

    • Compare these metrics across batches

• Mitigation Strategies:

  • Purchase larger antibody quantities from consistent lots for long-term studies

  • Consider monoclonal antibodies for greater consistency

  • Develop correction factors for batch effects in data analysis

  • Maintain detailed records of antibody batches used for each experiment

By implementing these methodological approaches, researchers can minimize the impact of batch variation and enhance the reproducibility of their epigenetic studies.

What strategies can improve the detection of low-abundance H3K79 dimethylation in challenging sample types?

Detecting low-abundance H3K79 dimethylation presents methodological challenges, particularly in limiting or difficult sample types. Consider these approaches:

• Sample Enrichment Techniques:

  • FACS Sorting: Isolate specific cell populations of interest

  • Laser Capture Microdissection: For tissue-specific analysis

  • Nuclear Isolation: Remove cytoplasmic contaminants

  • Sequential Extraction: Employ salt fractionation to enrich chromatin-bound histones

• Signal Amplification Methods:

  • Tyramide Signal Amplification (TSA): For immunofluorescence detection

  • Proximity Ligation Assay (PLA): Detect protein-protein interactions or modifications

  • 3D-SIM Super-Resolution Microscopy: For spatial resolution beyond diffraction limit

  • ChIP-qPCR with Pre-amplification: For very low input samples

• Enhanced Detection Chemistry:

  • Highly-Sensitive ECL Substrates: For Western blotting detection

  • Fluorescent Antibody Conjugates: Direct labeling to reduce background

  • Quantum Dots: For multiplexed detection with broader dynamic range

  • Mass Cytometry: For single-cell analysis of multiple modifications

• Optimized Experimental Protocols:

  • Carrier ChIP: Add carrier chromatin/DNA to very low input samples

  • Micro-ChIP: Modified protocols for small cell numbers (<10,000)

  • Modified Histone Extraction: Gentler methods to preserve modifications

  • Low-Input Library Preparation: For ChIP-seq from limited material

• Computational Enhancement:

  • Deconvolution Algorithms: To resolve mixed cell populations

  • Pattern Recognition: To identify characteristic H3K79me2 distributions

  • Integrative Analysis: Combine multiple data types to enhance confidence

  • Imputation Methods: For sparsely covered genomic regions

By combining these methodological approaches, researchers can enhance the detection of low-abundance H3K79 dimethylation in challenging biological samples, expanding the range of experimental contexts in which this important epigenetic mark can be studied.

How is Di-methyl-Histone H3(K79) being studied in the context of disease mechanisms and potential therapeutic interventions?

H3K79 dimethylation has emerged as a critical epigenetic mark with significant implications for disease mechanisms. Current research directions include:

• Cancer Biology:

  • DOT1L overexpression and aberrant H3K79 dimethylation patterns have been implicated in several cancers

  • MLL-fusion leukemias show particular dependence on DOT1L activity

  • Research is ongoing to understand how H3K79me2 distribution contributes to oncogenic gene expression programs

  • DOT1L inhibitors are being developed as potential therapeutic agents for specific leukemia subtypes

• Neurodegenerative Disorders:

  • Altered H3K79 methylation has been observed in models of neurodegenerative diseases

  • Research is exploring how these changes affect neuronal gene expression and function

  • Studies are investigating potential connections between H3K79me2 and DNA repair deficiencies in neurodegeneration

• Cardiovascular Disease:

  • Emerging evidence suggests roles for H3K79 methylation in cardiac development and disease

  • Research is examining how H3K79me2 patterns influence cardiac gene expression programs

  • Studies are investigating potential therapeutic modulation of this mark in heart failure models

• Methodological Approaches:

  • Patient-derived samples are being analyzed for H3K79me2 patterns as potential biomarkers

  • Single-cell technologies are being applied to understand heterogeneity in H3K79me2 distribution

  • CRISPR screens are identifying synthetic lethal interactions with DOT1L inhibition

  • Chemical biology approaches are developing selective DOT1L inhibitors with improved pharmacological properties

• Therapeutic Development:

  • Small molecule inhibitors targeting DOT1L are in preclinical and early clinical development

  • Precision approaches aim to selectively target cells with aberrant H3K79me2 patterns

  • Combination therapies with other epigenetic modulators are being investigated for synergistic effects

These research directions highlight the growing recognition of H3K79 dimethylation as both a disease mechanism and therapeutic target across multiple pathological contexts.

What are the latest methodological advances in genome-wide mapping of H3K79 dimethylation patterns?

Genome-wide mapping of H3K79 dimethylation has benefited from significant methodological advances, enhancing resolution, sensitivity, and biological insight:

• Single-Cell ChIP-Seq Technologies:

  • New protocols allow H3K79me2 profiling at single-cell resolution

  • Droplet-based and microfluidic approaches enable high-throughput processing

  • Computational methods address sparse data challenges inherent to single-cell approaches

  • These advances reveal cell-to-cell variation in H3K79me2 patterns within tissues

• CUT&RUN and CUT&Tag Adaptations:

  • These antibody-directed techniques offer improved signal-to-noise ratio over traditional ChIP

  • Protocols have been optimized specifically for H3K79me2 detection

  • Lower input requirements enable studies of rare cell populations

  • Higher resolution facilitates precise mapping of modification boundaries

• Long-Read Sequencing Applications:

  • Oxford Nanopore and PacBio technologies are being adapted for epigenetic analysis

  • These approaches allow simultaneous detection of multiple modifications on the same DNA molecule

  • Computational methods are improving for base modification detection on long reads

  • Integration with chromatin conformation data provides 3D context for H3K79me2 distribution

• Direct Detection Methods:

  • SMRT sequencing and nanopore approaches are being developed for direct detection of modifications

  • These methods eliminate potential biases introduced by antibody-based enrichment

  • Emerging technologies may enable real-time tracking of dynamic changes in H3K79me2

• Multi-Modal Data Integration:

  • Simultaneous profiling of H3K79me2, transcription, and chromatin accessibility

  • Spatial transcriptomics integration to map modification patterns in tissue context

  • Machine learning approaches to predict functional consequences of H3K79me2 distribution

  • Network analysis to identify regulatory hubs associated with H3K79me2 marks

These methodological advances are transforming our understanding of H3K79 dimethylation from static snapshots to dynamic, three-dimensional, and functionally integrated views across diverse biological contexts.

How do different computational approaches enhance the analysis of Di-methyl-Histone H3(K79) datasets?

Advanced computational methods are increasingly essential for extracting maximum biological insight from H3K79me2 datasets. Current approaches include:

• Peak Calling Optimizations:

  • Specialized algorithms for H3K79me2's distinctive distribution patterns

  • Machine learning approaches that incorporate chromatin state information

  • Methods that account for regional biases and sequence composition effects

  • Bayesian frameworks that integrate prior knowledge about H3K79me2 distribution

• Integrative Analysis Frameworks:

  • Multi-omics data integration platforms combining H3K79me2 with:

    • Transcriptomic data (RNA-seq, nascent RNA)

    • Chromatin accessibility (ATAC-seq, DNase-seq)

    • Other histone modifications

    • Transcription factor binding

  • Network-based approaches to identify regulatory circuits influenced by H3K79me2

  • Causal inference methods to distinguish drivers from passengers in gene regulation

• Evolutionary and Comparative Analyses:

  • Cross-species comparison of H3K79me2 conservation patterns

  • Identification of species-specific versus conserved regulatory roles

  • Evolutionary constraint analysis at H3K79me2-marked regions

  • Phylogenetic approaches to trace the evolution of DOT1L-regulated pathways

• Deep Learning Applications:

  • Convolutional neural networks for pattern recognition in H3K79me2 data

  • Attention-based models to capture long-range dependencies

  • Transfer learning approaches using pre-trained models from related epigenetic marks

  • Generative models to predict the effects of perturbations on H3K79me2 patterns

• Single-Cell Computational Methods:

  • Deconvolution algorithms for bulk H3K79me2 data

  • Imputation strategies for sparse single-cell data

  • Trajectory inference to map dynamic changes in H3K79me2 during cellular processes

  • Integration with single-cell transcriptomics and other modalities

These computational approaches transform raw H3K79me2 data into mechanistic insights about gene regulation, development, and disease, creating opportunities for hypothesis generation and experimental design that would not be possible with conventional analysis methods.