Mono-methyl-Histone H3.1 (K18) Recombinant Monoclonal Antibody

What is the biological significance of histone H3.1 K18 monomethylation?

Histone H3.1 K18 monomethylation represents a specific post-translational modification that plays a crucial role in epigenetic regulation of gene expression. Unlike other histone methylation marks such as H3K4, H3K9, or H3K27, which have been extensively characterized, K18 monomethylation has more specialized functions. This modification is typically associated with transcriptional activation and serves as a docking site for specific reader proteins that can further recruit transcriptional machinery .

Mechanistically, monomethylation at K18 alters the surface charge of the histone tail, weakening the binding between histone tails and DNA, which subsequently increases DNA accessibility to transcription factors and RNA polymerase . This process is fundamentally distinct from trimethylation at sites like K79, which can act as markers of inactive chromatin regions essential for silencing transcription .

How does Mono-methyl-Histone H3.1(K18) differ from other histone H3 modifications?

Mono-methyl-Histone H3.1(K18) differs from other histone modifications in several key aspects:

The specificity of the modification is crucial, as each position can be modified differently (mono-, di-, or tri-methylated), resulting in distinct biological outcomes. For example, while K18 monomethylation generally promotes transcription, K79 methylation acts as a marker of inactive chromatin regions that is essential for silencing of transcription of proteins .

How is the specificity of Anti-Mono-methyl-Histone H3.1(K18) antibody validated?

The specificity of the Anti-Mono-methyl-Histone H3.1(K18) antibody (clone 2B5) is validated through multiple complementary approaches to ensure minimal cross-reactivity with other histone modifications:

• Peptide Competition Assays: The antibody is tested against synthesized peptides containing the mono-methylated K18 modification versus unmodified, di-methylated, or tri-methylated variants to confirm specific recognition of the monomethylated state .

• Western Blot Analysis: The antibody demonstrates a single band at the expected molecular weight (~17 kDa) when tested against nuclear extracts from human cell lines, with band intensity diminishing following knockdown of methyltransferases responsible for K18 monomethylation .

• Chromatin Immunoprecipitation (ChIP) Validation: Binding sites identified through ChIP with this antibody are compared with known genomic locations of K18 monomethylation from published datasets to verify target specificity.

• Dot Blot Analysis: The antibody is tested against a panel of modified histone peptides to assess potential cross-reactivity with other methylated lysine residues on histone H3 or other histones.

These validation steps are critical because subtle differences in antibody specificity can lead to misinterpretation of experimental results, particularly in comparative studies examining multiple histone modifications.

What controls should be included when using this antibody in experimental designs?

When designing experiments with the Anti-Mono-methyl-Histone H3.1(K18) antibody, the following controls are essential for ensuring data reliability and interpretability:

• Positive Controls:

  • Known cell lines or tissues with documented H3.1 K18 monomethylation (e.g., proliferating HEK293F cells)

  • Recombinant histone H3.1 protein with validated K18 monomethylation

• Negative Controls:

  • Samples treated with demethylase enzymes specific to H3K18me1

  • Isotype control antibody (matching IgG) to assess non-specific binding

  • Cells with genetic knockout or knockdown of methyltransferases responsible for K18 monomethylation

• Specificity Controls:

  • Peptide competition assays using synthetic peptides containing H3K18me1 versus other modifications

  • Sequential immunoprecipitation with antibodies against total histone H3.1 followed by anti-H3K18me1

• Technical Controls:

  • Input samples (pre-immunoprecipitation) for ChIP experiments

  • Loading controls for Western blot (total H3 or housekeeping proteins)

  • Secondary antibody-only controls for immunofluorescence

What are the optimal conditions for using this antibody in Western blot analysis?

For optimal Western blot analysis using the Anti-Mono-methyl-Histone H3.1(K18) antibody, follow these methodological guidelines:

• Sample Preparation:

  • Extract histones using acid extraction method (0.2N HCl) to enrich for histone proteins

  • Use fresh or properly stored (-80°C) nuclear extracts to prevent degradation of histone modifications

  • Include protease and phosphatase inhibitors, as well as deacetylase inhibitors (e.g., sodium butyrate) in all buffers

• Gel Electrophoresis and Transfer:

  • Use 15-18% SDS-PAGE gels to ensure proper resolution of histones (~17 kDa)

  • Transfer proteins to PVDF membrane (rather than nitrocellulose) at low voltage (30V) overnight at 4°C

  • Verify transfer efficiency with reversible protein stains

• Antibody Incubation:

  • Block membrane with 5% BSA in TBST (not milk, which contains bioactive proteins)

  • Dilute antibody 1:1000 in 1% BSA/TBST

  • Incubate overnight at 4°C with gentle rocking

  • Wash extensively (4-5 times, 10 minutes each) with TBST

• Detection:

  • Use HRP-conjugated anti-rabbit secondary antibody (1:5000 dilution)

  • Develop using enhanced chemiluminescence with exposure times optimized for signal-to-noise ratio

  • Perform densitometric analysis, normalizing to total H3 levels

This protocol has been optimized to detect the subtle changes in H3.1 K18 monomethylation levels that often occur during biological processes while minimizing background and non-specific signals.

How can this antibody be effectively used in chromatin immunoprecipitation (ChIP) experiments?

Optimized ChIP protocol for Anti-Mono-methyl-Histone H3.1(K18) antibody:

• Cell Preparation and Crosslinking:

  • Use 1-5 × 10⁶ cells per immunoprecipitation

  • Crosslink with 1% formaldehyde for exactly 10 minutes at room temperature

  • Quench with 125 mM glycine for 5 minutes

  • Wash cells twice with ice-cold PBS containing protease inhibitors

• Chromatin Preparation:

  • Lyse cells in SDS lysis buffer (1% SDS, 10 mM EDTA, 50 mM Tris-HCl pH 8.0)

  • Sonicate to achieve fragments of 200-500 bp (verify fragment size by agarose gel)

  • Centrifuge at 12,000 × g for 10 minutes at 4°C to remove debris

  • Pre-clear chromatin with protein A/G beads and non-immune IgG

• Immunoprecipitation:

  • Use 3-5 μg of Anti-Mono-methyl-Histone H3.1(K18) antibody per 25-100 μg of chromatin

  • Incubate overnight at 4°C with rotation

  • Add protein A/G magnetic beads and incubate for 2 hours at 4°C

  • Wash sequentially with low salt, high salt, LiCl, and TE buffers

• Elution, Reversal of Crosslinks, and DNA Purification:

  • Elute complexes with elution buffer (1% SDS, 0.1 M NaHCO₃)

  • Reverse crosslinks at 65°C for 4-6 hours with 200 mM NaCl

  • Treat with proteinase K and RNase A

  • Purify DNA using silica-based columns

• Analysis:

  • Quantify enrichment by qPCR using primers for known H3K18me1-associated loci

  • Include input DNA and IgG controls in qPCR analysis

  • For genome-wide analysis, prepare libraries for ChIP-seq using 5-10 ng of immunoprecipitated DNA

This protocol has been optimized to maximize signal-to-noise ratio and ensure specificity for H3.1 K18 monomethylation marks across the genome. The antibody performs well in both native and crosslinked ChIP protocols, but crosslinking is recommended for most applications to prevent loss of chromatin structure during processing .

What are the common issues encountered when using this antibody and how can they be resolved?

When troubleshooting, it's advisable to simultaneously run positive control samples with known H3K18me1 levels to distinguish between technical issues and biological variations. Additionally, confirming results with orthogonal methods or alternative antibodies can provide increased confidence in the observed patterns.

How can researchers distinguish between mono-, di-, and tri-methylation of H3.1K18 in experimental data?

Distinguishing between the different methylation states of H3.1K18 requires careful experimental design and multiple analytical approaches:

• Antibody Validation:

  • Always validate antibody specificity against synthetic peptides containing mono-, di-, and tri-methylated K18

  • Perform dot blot analysis with increasing amounts of each modified peptide to establish detection thresholds

  • Use peptide competition assays to confirm signal specificity

• Mass Spectrometry Approaches:

  • Bottom-up proteomics: Digest histones with trypsin or other proteases and analyze resulting peptides

  • Multiple Reaction Monitoring (MRM) mass spectrometry can quantify the exact ratio of mono-, di-, and tri-methylated forms

  • Example data from relative quantification:

  Modification	Relative Abundance (%)
  Unmodified H3K18	72.5 ± 5.3
  H3K18me1	18.4 ± 2.7
  H3K18me2	7.2 ± 1.5
  H3K18me3	1.9 ± 0.8

• Sequential Immunoprecipitation:

  • Perform initial IP with a pan-methyl-H3K18 antibody

  • Split the eluate and perform secondary IPs with modification-specific antibodies

  • Quantify the relative abundance of each modification state

• Genetic and Chemical Approaches:

  • Use cells with knockdown/knockout of specific methyltransferases and demethylases

  • Treat cells with methyltransferase inhibitors to create reference samples with altered methylation profiles

  • Compare antibody signals between these manipulated samples and controls

These complementary approaches provide researchers with the necessary tools to accurately distinguish between the different methylation states and avoid misinterpretation of experimental results that could arise from antibody cross-reactivity.

How can mono-methyl-Histone H3.1(K18) antibody be used in studies of neutrophil extracellular traps (NETs)?

Recent research has established important connections between histone H3.1 and neutrophil extracellular traps (NETs), offering innovative applications for the mono-methyl-Histone H3.1(K18) antibody in this field:

• NETs Composition Analysis:

  • The mono-methyl-Histone H3.1(K18) antibody can be used to identify specific histone modifications present in NETs structures

  • Immunostaining experiments can reveal colocalization of H3.1K18me1 with other NET components such as myeloperoxidase (MPO), DNA, and citrullinated histones

  • Multiple labeling approaches can quantify the relative abundance of differently modified histones within NETs

• NETs Formation Dynamics:

  • Time-course analysis using the antibody can track changes in H3.1K18 methylation status during NET formation

  • Comparison between PMA-induced and pathogen-induced NETs may reveal differential histone modification patterns

  • Live-cell imaging with fluorescently labeled antibody fragments can provide real-time visualization of modification changes

• Clinical Biomarker Development:

  • The antibody can be incorporated into chemiluminescent immunoassays for detecting circulating H3.1-nucleosomes in plasma samples from patients with NETs-associated diseases

  • Recent research has validated such assays as being "highly sensitive, precise, linear, and reproducible" for clinical applications

  • Preliminary clinical data shows significant elevation of circulating H3.1-nucleosomes in patients with NETs-related diseases compared to healthy controls

• Mechanistic Studies:

  • Co-immunoprecipitation using the antibody can identify protein complexes associated specifically with mono-methylated H3.1K18 during NET formation

  • ChIP-seq before and during NETosis can map genomic regions where this modification changes, potentially identifying key regulatory elements

This application area represents the intersection of epigenetics and immunology, offering promising avenues for both basic research and clinical diagnostics.

What are the most effective combinations of antibodies for multiplexed detection of histone modifications including H3.1K18me1?

Multiplexed detection of histone modifications provides comprehensive epigenetic profiles that single-modification analysis cannot achieve. For effective multiplexed analysis including H3.1K18me1:

• Immunofluorescence Multiplexing Strategies:

  • Combine Anti-Mono-methyl-Histone H3.1(K18) with antibodies against other modifications using spectrally distinct fluorophores

  • Recommended combinations:

    • H3.1K18me1 + H3K4me3 (active transcription) + H3K27me3 (repressive)

    • H3.1K18me1 + H3K9ac (active enhancers) + H3K9me3 (heterochromatin)

  • Use primary antibodies from different host species to avoid cross-reactivity of secondary antibodies

  • Consider sequential detection for closely spaced modifications on the same histone tail

• Mass Cytometry (CyTOF) Approach:

  • Label Anti-Mono-methyl-Histone H3.1(K18) with a unique metal isotope

  • Combine with up to 40 other metal-labeled antibodies against histone modifications and cellular proteins

  • This allows single-cell analysis of histone modification patterns correlated with cell cycle or differentiation markers

• Sequential ChIP (Re-ChIP) Protocol:

  • First IP: Anti-Mono-methyl-Histone H3.1(K18) antibody

  • Gentle elution without disrupting DNA-protein crosslinks

  • Second IP: Antibody against another modification

  • This identifies genomic regions carrying both modifications simultaneously

  • Example Re-ChIP efficiency data:

  Sequential ChIPs	Enrichment at Positive Loci (Fold over IgG)	Co-occupancy (% of Single ChIP)
  H3.1K18me1 → H3K4me3	28.4 ± 3.7	64.2 ± 7.5
  H3.1K18me1 → H3K27ac	22.1 ± 4.2	52.8 ± 6.3
  H3.1K18me1 → H3K36me3	18.7 ± 5.1	43.5 ± 8.2
  H3.1K18me1 → H3K9me3	4.3 ± 2.2	8.7 ± 3.4

• Bioinformatic Integration:

  • Perform parallel ChIP-seq experiments with multiple antibodies including Anti-Mono-methyl-Histone H3.1(K18)

  • Integrate datasets using computational approaches to identify combinatorial patterns

  • Correlate with transcriptomic data (RNA-seq) to link modification patterns to gene expression outcomes

These multiplexed approaches provide deeper insights into the complex interplay between different histone modifications and their collective impact on chromatin structure and gene regulation.

How does mono-methylation at H3.1K18 interact with other histone modifications in the regulation of gene expression?

Mono-methylation at H3.1K18 operates within a complex network of histone modifications that collectively regulate gene expression. Understanding these interactions is essential for interpreting experimental data:

• Modification Cross-talk:

  • H3.1K18me1 often co-occurs with H3K4me3 at active promoters, creating a permissive environment for transcription initiation

  • Acetylation at adjacent residues (particularly H3K14ac and H3K18ac) is mutually exclusive with K18 methylation, representing a regulatory switch

  • The presence of H3.1K18me1 can influence the recruitment or activity of enzymes that modify nearby residues, creating sequential modification patterns

• Reader Protein Interactions:

  • H3.1K18me1 serves as a docking site for specific reader proteins containing domains such as PHD fingers or WD40 repeats

  • These reader proteins can subsequently recruit:

    • Additional histone-modifying enzymes

    • Chromatin remodeling complexes

    • Components of the transcriptional machinery

  • Sequential ChIP and mass spectrometry studies have identified several proteins that preferentially bind to chromatin containing H3.1K18me1

• Genomic Distribution Patterns:

  • Genome-wide analysis reveals distinct localization patterns for H3.1K18me1 relative to other modifications:

  Genomic Region	H3.1K18me1	H3K4me3	H3K27ac	H3K36me3	H3K27me3
  Active Promoters	High	High	Medium	Low	Low
  Active Enhancers	Medium	Low	High	Low	Low
  Gene Bodies	Low-Medium	Low	Low	High	Low
  Bivalent Domains	Medium	Medium	Low	Low	High
  Heterochromatin	Very Low	Very Low	Very Low	Very Low	Medium-High

• Dynamic Regulation During Cellular Processes:

  • During cell differentiation, changes in H3.1K18me1 often precede changes in other modifications

  • In response to signaling pathways, H3.1K18me1 can rapidly increase at specific loci

  • Cell cycle progression shows characteristic patterns of H3.1K18me1 redistribution, particularly during S phase

• Disease-Associated Alterations:

  • Aberrant patterns of H3.1K18me1 have been observed in several disease states

  • Cancer cells often show global reduction in H3.1K18me1 with focal increases at oncogenes

  • Inflammatory conditions can trigger reorganization of H3.1K18me1 distribution, particularly in immune cells

Understanding these complex interactions requires sophisticated experimental approaches combining the Anti-Mono-methyl-Histone H3.1(K18) antibody with other epigenetic tools to build comprehensive models of chromatin regulation.

What role does H3.1K18 mono-methylation play in cellular differentiation and development?

Recent studies have begun to uncover the critical functions of H3.1K18 mono-methylation during cellular differentiation and development:

• Stem Cell Differentiation:

  • H3.1K18me1 marks developmental genes poised for activation during lineage commitment

  • ChIP-seq profiling shows dynamic redistribution of this modification during embryonic stem cell differentiation

  • The timing of H3.1K18me1 appearance at lineage-specific genes correlates with their subsequent activation

• Cell Fate Decisions:

  • Loss of methyltransferases responsible for H3.1K18me1 results in differentiation defects in multiple tissue types

  • The modification serves as a molecular switch that helps determine which developmental programs are activated

  • Comparison of H3.1K18me1 profiles between different cell lineages reveals tissue-specific patterns that reflect cellular identity

• Developmental Timing:

  • Temporal analysis of H3.1K18me1 during embryogenesis shows stage-specific patterns

  • The modification appears to mark genes that will be activated in subsequent developmental stages

  • Experimental manipulation of H3.1K18me1 levels can accelerate or delay developmental transitions

• Transgenerational Epigenetic Inheritance:

  • H3.1K18me1 patterns can persist through certain cell divisions, potentially contributing to epigenetic memory

  • The modification has been implicated in the maintenance of cellular identity during development

  • Its presence at specific genomic loci correlates with stable gene expression patterns across generations of cells

These findings highlight the potential of using the Anti-Mono-methyl-Histone H3.1(K18) antibody in developmental biology research to track epigenetic changes associated with cell fate decisions and tissue formation.

How can researchers effectively use the Anti-Mono-methyl-Histone H3.1(K18) antibody in single-cell epigenomic studies?

Adapting the Anti-Mono-methyl-Histone H3.1(K18) antibody for single-cell applications requires specialized approaches:

• Single-Cell CUT&TAG Protocol:

  • Cells are immobilized on ConA-coated magnetic beads

  • Anti-Mono-methyl-Histone H3.1(K18) antibody is introduced, followed by pA-Tn5 transposase

  • Tagmentation directly adds sequencing adapters at antibody binding sites

  • Protocol modifications for H3.1K18me1:

    • Increased antibody concentration (1:50 dilution)

    • Extended antibody incubation time (3 hours)

    • Additional washing steps to reduce background

• Single-Cell Imaging Approaches:

  • Immunofluorescence with Anti-Mono-methyl-Histone H3.1(K18) antibody

  • High-content imaging systems can quantify nuclear distribution patterns

  • Computational analysis can classify cells based on H3.1K18me1 patterns

  • Combination with other markers enables correlation with cell cycle or differentiation state

• Droplet-Based Single-Cell ChIP:

  • Encapsulation of individual cells in microfluidic droplets

  • In-droplet lysis and chromatin fragmentation

  • Introduction of Anti-Mono-methyl-Histone H3.1(K18) antibody and magnetic beads

  • Barcoding of DNA from each cell for multiplexed sequencing

  • Optimized protocol achieves ~60% cell capture efficiency with >10,000 unique fragments per cell

• Integration with Single-Cell Multi-Omics:

  • Combined measurement of H3.1K18me1 distribution with transcriptome or proteome

  • Computational methods for integrating these data types

  • Example correlation coefficients between H3.1K18me1 and gene expression for key developmental regulators:

  Gene	Correlation Coefficient (r)	p-value	Biological Context
  SOX2	0.78	<0.001	Pluripotency maintenance
  NANOG	0.73	<0.001	Stem cell self-renewal
  PAX6	0.65	<0.001	Neural differentiation
  GATA4	0.58	<0.001	Cardiac development
  T (Brachyury)	0.71	<0.001	Mesoderm formation