MTF2 Antibody

What is MTF2 and why is it significant in research?

MTF2 (metal response element binding transcription factor 2) is a 536 amino acid protein that contains two PHD-type zinc fingers and belongs to the Polycomblike family. Its significance stems from its critical role in epigenetic regulation, particularly through binding to H3K36me3, a mark for transcriptional activation, and its subsequent recruitment of the PRC2 complex. This interaction enhances PRC2 H3K27me3 methylation activity and regulates transcriptional networks during embryonic stem cell self-renewal and differentiation . Recent research has also implicated MTF2 in cancer progression, particularly hepatocellular carcinoma, making it an important target for both developmental biology and cancer research .

What are the common applications for MTF2 antibodies?

MTF2 antibodies have been validated for multiple research applications with specific recommended dilutions:

These applications have been positively validated in various cell lines including HepG2, Jurkat, mouse ES cells, 293 cells, HeLa, K-562, HSC-T6, and NIH/3T3 cells, as well as in tissue samples .

What is the recommended protocol for MTF2 antibody in Western blot analysis?

For optimal Western blot results with MTF2 antibody:

• Prepare protein lysates from appropriate cellular or tissue samples (validated samples include HepG2, Jurkat, HEK-293 cells)

• Separate proteins by SDS-PAGE (10-12% gel recommended)

• Transfer proteins to PVDF or nitrocellulose membrane

• Block membrane with 5% non-fat milk in TBST for 1 hour at room temperature

• Incubate with primary antibody diluted in blocking buffer (1:200-1:1000 for polyclonal 16208-1-AP or 1:5000-1:50000 for monoclonal 68713-1-Ig)

• Incubate overnight at 4°C with gentle agitation

• Wash membrane 3-5 times with TBST

• Incubate with appropriate HRP-conjugated secondary antibody

• Develop using ECL detection system

When interpreting results, note that MTF2 typically appears at molecular weights of 60-67 kDa or 75 kDa depending on the isoform and post-translational modifications .

How should I optimize antigen retrieval for MTF2 IHC in different tissue types?

Antigen retrieval optimization is critical for MTF2 immunohistochemistry. Based on published protocols:

• Primary recommendation: Use TE buffer at pH 9.0 for heat-induced epitope retrieval

• Alternative approach: Citrate buffer at pH 6.0 may be effective for certain tissue types

For neural tissues (e.g., brain samples), the TE buffer approach has been validated more extensively. When working with liver tissues (particularly in HCC studies), a comparative assessment of both methods is advisable. General protocol:

• Deparaffinize and rehydrate tissue sections

• Perform heat-induced epitope retrieval using either buffer system

• Allow slides to cool to room temperature

• Proceed with peroxidase blocking (3% H₂O₂, 10 minutes)

• Blocking with serum (5-10% normal serum, 1 hour)

• Apply MTF2 antibody at 1:20-1:200 dilution

• Incubate overnight at 4°C in a humidified chamber

For tissues with high background, additional optimization may include extending blocking time or implementing a biotin-avidin blocking step when using biotinylated detection systems .

What controls should be included when using MTF2 antibody in experimental design?

A robust experimental design for MTF2 antibody applications should include:

• Positive control samples: Use tissues or cell lines with confirmed MTF2 expression:

  • For WB: HepG2, Jurkat, mouse ES cells, or 293 cells

  • For IHC: Human brain tissue

  • For IF/ICC: Mouse ES cells

• Negative controls:

  • Primary antibody omission (buffer only)

  • Isotype control (rabbit IgG for polyclonal or mouse IgG2b for monoclonal)

  • MTF2 knockdown/knockout samples when available

• Loading controls for Western blot:

  • Housekeeping proteins (β-actin, GAPDH, α-tubulin)

  • Total protein staining (Ponceau S, REVERT)

• Technical validation:

  • Concentration gradient to determine optimal antibody dilution

  • Multiple exposure times for Western blot

  • Different fixation conditions for IHC/ICC

Including these controls helps distinguish specific from non-specific signals and validates the antibody's performance in each experimental system .

How do I address discrepancies in observed molecular weights for MTF2?

The calculated molecular weight for MTF2 is 61 kDa (536 amino acids), but observed molecular weights vary between 55-75 kDa in Western blot analyses . To address these discrepancies:

• Isoform identification: MTF2 exists in several isoforms with molecular weights ranging from 55-60 kDa, plus a smaller isoform around 29 kDa. Use RNA sequencing or RT-PCR to identify which isoforms are expressed in your experimental system.

• Post-translational modifications: Higher observed weights (67-75 kDa) likely reflect post-translational modifications such as phosphorylation, ubiquitination, or SUMOylation. Consider:

  • Phosphatase treatment of lysates before Western blot

  • Immunoprecipitation followed by mass spectrometry

  • Specific inhibitors of post-translational modifications

• Resolution techniques:

  • Use gradient gels (4-15%) for better separation

  • Extend running time for improved resolution

  • Consider Phos-tag gels if phosphorylation is suspected

• Validation approaches:

  • Compare results from multiple antibodies targeting different epitopes

  • Include recombinant MTF2 protein as a standard

  • Validate with siRNA/shRNA knockdown samples

These methodological approaches can help determine which form of MTF2 is being detected and explain variations across experimental systems .

How can I effectively design ChIP experiments using MTF2 antibody?

Chromatin immunoprecipitation (ChIP) with MTF2 antibody requires specific optimization given MTF2's role in recruiting PRC2 and its interaction with H3K36me3. A comprehensive ChIP protocol includes:

• Cross-linking optimization:

  • Standard: 1% formaldehyde for 10 minutes at room temperature

  • For MTF2: Consider dual cross-linking with 1 mM disuccinimidyl glutarate (DSG) for 30 minutes followed by formaldehyde

• Chromatin fragmentation:

  • Sonicate to achieve fragments of 200-500 bp

  • Verify fragmentation efficiency by agarose gel electrophoresis

  • Adjust sonication parameters based on cell/tissue type

• Immunoprecipitation:

  • Pre-clear chromatin with protein A/G beads

  • Use 4-10 μg MTF2 antibody per ChIP reaction

  • Include IgG control and positive control (H3K27me3 antibody)

  • Incubate overnight at 4°C with rotation

• Washing and elution:

  • Use progressively stringent wash buffers

  • Elute DNA-protein complexes and reverse cross-links

• Analysis strategies:

  • qPCR targeting known MTF2 binding regions

  • ChIP-seq for genome-wide binding profile

  • Integration with RNA-seq or H3K27me3 ChIP data

Based on published applications, MTF2 ChIP experiments have successfully identified its role in transcriptional regulation and binding preferences for specific genomic regions .

What are the key considerations when studying MTF2's role in epithelial-mesenchymal transition (EMT) and cancer progression?

Research has demonstrated MTF2's involvement in promoting epithelial-mesenchymal transition, particularly in hepatocellular carcinoma. When investigating this role:

• Experimental models:

  • Cell lines: HepG2 provides a validated model for MTF2 overexpression studies

  • Patient-derived xenografts for in vivo studies

  • Tissue microarrays for clinical correlation (240+ HCC specimens recommended)

• Key readouts and markers:

  • MTF2 expression level (RNA and protein)

  • Snail transcription (direct target of MTF2 regulation)

  • EMT markers (E-cadherin, N-cadherin, vimentin)

  • Cell migration and invasion assays

  • In vivo metastasis models

• Mechanistic studies:

  • ChIP analysis of MTF2 binding to Snail promoter

  • Co-immunoprecipitation to identify MTF2 protein complexes

  • Reporter assays for Snail transcriptional activity

  • PRC2 recruitment and H3K27me3 enrichment analysis

• Clinical correlations:

  • MTF2 expression in relation to alpha-fetoprotein (AFP) levels

  • Survival analysis with defined cutoffs (H-score ≥ 102)

  • Multi-parameter analysis with other prognostic factors

How does MTF2 interact with the PRC2 complex, and what methods can detect these interactions?

MTF2 functions by binding to H3K36me3 and recruiting the PRC2 complex to enhance H3K27me3 methylation activity. To study these complex interactions:

• Biochemical approaches:

  • Co-immunoprecipitation using MTF2 antibody followed by Western blot for PRC2 components (EZH2, SUZ12, EED)

  • Reciprocal IP with PRC2 component antibodies

  • Size exclusion chromatography to isolate intact complexes

  • Mass spectrometry for unbiased identification of interaction partners

• Chromatin interaction studies:

  • Sequential ChIP (ChIP-reChIP) for MTF2 followed by PRC2 components

  • Proximity ligation assay (PLA) to visualize interactions in situ

  • ChIP-seq correlation analysis between MTF2 and PRC2 binding sites

  • CUT&RUN or CUT&Tag for higher resolution binding profiles

• Functional validation:

  • Domain mutation analysis to identify interaction interfaces

  • In vitro reconstitution with recombinant proteins

  • H3K27me3 methyltransferase assays in the presence/absence of MTF2

  • CRISPR-Cas9 editing of key interaction domains

• Genomic approaches:

  • Integrated analysis of H3K36me3, MTF2 binding, PRC2 occupancy, and H3K27me3 patterns

  • Chromatin conformation capture techniques to identify long-range interactions

  • Single-cell approaches to investigate heterogeneity in these interactions

These methodological approaches can help elucidate the molecular mechanisms of MTF2's role in epigenetic regulation and transcriptional control .

How can single-cell approaches be applied to study MTF2 function in heterogeneous tissues?

Single-cell technologies offer powerful approaches to study MTF2 function across heterogeneous cell populations:

• Single-cell RNA sequencing (scRNA-seq):

  • Correlate MTF2 expression with cell-type specific transcriptional programs

  • Identify differential MTF2 expression across developmental trajectories

  • Requirements:

    • Fresh tissue dissociation protocols optimized for nuclear integrity

    • Computational frameworks for trajectory analysis

    • Integration with bulk RNA-seq data

• Single-cell ChIP-seq and CUT&Tag:

  • Map MTF2 binding sites in rare cell populations

  • Correlate with chromatin states at single-cell resolution

  • Technical considerations:

    • Low input protocols (1,000-10,000 cells minimum)

    • Spike-in controls for quantitative comparison

    • Specialized bioinformatic pipelines for sparse data

• Spatial transcriptomics:

  • Visualize MTF2 expression in tissue context

  • Correlate with EMT markers in tumor microenvironments

  • Methods:

    • RNAscope combined with IF for MTF2 protein

    • Digital spatial profiling

    • Spatial ATAC-seq for chromatin accessibility

• Live cell imaging approaches:

  • CRISPR-Cas9 knock-in of fluorescent tags

  • Monitoring MTF2 dynamics during differentiation or EMT

  • Correlation with PRC2 component localization

These emerging approaches can reveal context-specific functions of MTF2 in development and disease, particularly in understanding its role in transcriptional regulation during cell fate decisions .

What considerations are important when targeting MTF2 for therapeutic development in cancer?

Based on research showing MTF2's role in cancer progression, particularly in HCC, several considerations for therapeutic targeting include:

• Target validation strategies:

  • Genetic depletion models (siRNA, shRNA, CRISPR-Cas9)

  • Patient-derived xenografts with varying MTF2 expression levels

  • Correlation of MTF2 levels with therapy response

  • Assessment across multiple cancer types beyond HCC

• Potential targeting approaches:

  • Small molecule inhibitors of MTF2-H3K36me3 interaction

  • Degraders (PROTACs) targeting MTF2 protein

  • Disruption of MTF2-PRC2 protein-protein interactions

  • Epigenetic editing to alter MTF2 expression

• Predictive biomarkers:

  • MTF2 expression levels by IHC (H-score ≥ 102 as demonstrated cutoff)

  • Combined assessment with AFP levels

  • Snail expression as downstream effector

  • EMT marker signature

• Resistance mechanisms and combination strategies:

  • Compensatory epigenetic modifications

  • Alternative PRC2 recruitment mechanisms

  • Combination with existing epigenetic therapies

  • Integration with EMT-targeting approaches

Research has demonstrated that MTF2 knockdown suppresses tumorigenesis and intrahepatic metastasis in vivo, suggesting therapeutic potential. Further development would require detailed characterization of structure-function relationships and identification of druggable pockets or interaction surfaces .

How do I implement multiplexed detection systems to study MTF2 in complex regulatory networks?

To understand MTF2's role in complex regulatory networks, multiplexed detection systems provide comprehensive insights:

• Multiplexed immunofluorescence/immunohistochemistry:

  • Tyramide signal amplification (TSA) for sequential staining

  • Panel design: MTF2 + PRC2 components + H3K27me3 + H3K36me3 + cell type markers

  • Cyclic immunofluorescence for 10+ markers on the same section

  • Analytical approach:

    • Spectral unmixing for overlapping fluorophores

    • Single-cell quantification of co-expression

    • Spatial relationship analysis

• Multi-omics integration:

  • Paired ChIP-seq and RNA-seq from the same samples

  • ATAC-seq for chromatin accessibility

  • DNA methylation profiling

  • Integration strategies:

    • Correlation networks

    • Causal inference modeling

    • Machine learning approaches

• Proximity-based interaction mapping:

  • BioID or APEX2 proximity labeling with MTF2 as bait

  • Split-BioID for conditional interactions

  • IP-MS with quantitative labeling (TMT, iTRAQ)

  • Validation by PLA in tissue samples

• Functional genomics screening:

  • CRISPR interference/activation screens targeting MTF2 network

  • Synthetic lethality approaches

  • Epistasis analysis with PRC2 components

  • Readouts:

    • Transcriptional reporters

    • Cell phenotyping

    • In vivo metastasis models

These multiplexed approaches enable systems-level understanding of MTF2 function within epigenetic regulatory networks and its impact on transcriptional programs in development and disease .

How should I validate MTF2 antibody specificity for my particular experimental system?

Comprehensive antibody validation is essential to ensure reliable results with MTF2 antibodies. Implement these approaches:

• Genetic validation:

  • CRISPR/Cas9 knockout of MTF2

  • siRNA/shRNA knockdown (multiple sequences)

  • Rescue experiments with exogenous MTF2 expression

  • Expected results: Signal reduction or elimination in knockout/knockdown samples

• Independent antibody validation:

  • Compare multiple antibodies targeting different epitopes (e.g., 16208-1-AP and 68713-1-Ig)

  • Correlation of staining patterns across applications

  • Epitope mapping to confirm binding specificity

• Recombinant protein controls:

  • Peptide competition assays

  • Western blot with recombinant MTF2 protein

  • Pre-adsorption studies for immunostaining applications

• Application-specific validation:

  • For WB: Confirm expected molecular weight (60-67 kDa, 75 kDa)

  • For IHC/IF: Pattern consistency with known biology

  • For ChIP: qPCR validation of known binding sites

  • For IP: Mass spectrometry validation of pulled-down proteins

• Orthogonal method comparison:

  • Correlation between protein (antibody) and mRNA (qPCR, RNA-seq) expression

  • Independent methods measuring the same parameter

These validation steps should be performed in your specific experimental system rather than relying solely on previous validations in other contexts .

What are the optimal sample preparation methods for detecting MTF2 in different applications?

Sample preparation significantly impacts MTF2 detection across different applications:

• Western blot sample preparation:

  • Lysis buffer: RIPA buffer with protease inhibitors (complete protease inhibitor cocktail)

  • Include phosphatase inhibitors (sodium fluoride, sodium orthovanadate)

  • Sonication recommended (3-5 pulses, 10s each)

  • Protein concentration: 20-50 μg per lane

  • Optimal sample handling: Snap freeze lysates, avoid multiple freeze-thaw cycles

• Immunohistochemistry/Immunofluorescence:

  • Fixation: 10% neutral buffered formalin, 24-48 hours

  • Paraffin embedding: Standard protocols

  • Section thickness: 4-5 μm optimal

  • Antigen retrieval: TE buffer pH 9.0 (primary) or citrate buffer pH 6.0 (alternative)

  • Cell fixation (ICC): 4% paraformaldehyde, 15 minutes at room temperature

• Immunoprecipitation:

  • Cell/tissue lysis: Non-denaturing lysis buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100)

  • Input amount: 1.0-3.0 mg total protein

  • Antibody amount: 0.5-4.0 μg MTF2 antibody

  • Pre-clearing: 1 hour with protein A/G beads

  • Incubation: Overnight at 4°C with rotation

• ChIP sample preparation:

  • Cross-linking: 1% formaldehyde, 10 minutes at room temperature

  • Quenching: 125 mM glycine, 5 minutes

  • Cell number: 1-5 x 10^6 cells per IP

  • Sonication: Optimize to achieve 200-500 bp fragments

  • Quality control: Verify fragmentation by agarose gel electrophoresis

Each application requires specific optimization, particularly regarding buffer compositions and extraction conditions to preserve MTF2 protein integrity and interactions .

How can I quantitatively analyze MTF2 expression in tissue microarrays for clinical correlations?

For rigorous quantitative analysis of MTF2 in tissue microarrays (TMAs) and clinical samples:

• Staining protocol standardization:

  • Batch processing to minimize technical variation

  • Automated staining platforms when available

  • Inclusion of control tissues on each TMA

  • Recommended antibody dilution: 1:20-1:200 for IHC

• Scoring systems:

  • H-score method: Intensity (0-3) × percentage of positive cells (0-100)

  • Cutoff determination: ROC curve analysis or median split (102 is validated cutoff)

  • Digital image analysis:

    • Whole slide scanning at 20-40x magnification

    • Cell segmentation algorithms

    • Intensity calibration with control slides

• Statistical analysis approaches:

  • Correlation with clinicopathologic parameters:

    • Categorical variables: Chi-square or Fisher's exact test

    • Continuous variables: Student's t-test or Mann-Whitney U test

  • Survival analysis:

    • Kaplan-Meier curves with log-rank test

    • Cox proportional hazards models (univariate and multivariate)

  • Multiple testing correction:

    • Bonferroni or false discovery rate methods

    • Bootstrap validation

• Validation cohorts:

  • Independent patient cohorts

  • Meta-analysis with published datasets

  • Integration with molecular subtypes

How does MTF2 function integrate with the broader epigenetic landscape in development and disease?

MTF2's role within the broader epigenetic landscape involves complex interactions with chromatin modifications and regulatory networks:

• Bivalent chromatin regulation:

  • MTF2 mediates between activating (H3K36me3) and repressive (H3K27me3) marks

  • Methodology for investigation:

    • Sequential ChIP for co-occupancy analysis

    • Genome-wide correlation of histone modifications

    • CUT&RUN for high-resolution mapping

• Developmental context specificity:

  • Embryonic stem cell self-renewal and differentiation

  • Lineage commitment mechanisms

  • Cell type-specific binding patterns

  • Research approaches:

    • Conditional knockout models

    • Time-course analysis during differentiation

    • Single-cell trajectory mapping

• Disease-specific rewiring:

  • Cancer-specific targets (e.g., Snail in HCC)

  • Comparison across cancer types

  • Crosstalk with oncogenic signaling

  • Experimental designs:

    • Patient-derived models

    • CRISPR screens for synthetic interactions

    • Network perturbation analysis

• Integration with other epigenetic mechanisms:

  • DNA methylation interplay

  • Chromatin accessibility regulation

  • Non-coding RNA interactions

  • Long-range chromatin interactions

  • Multi-omics methodologies:

    • Integrated ChIP-seq, ATAC-seq, RNA-seq

    • HiC or HiChIP for 3D genome organization

    • Machine learning for pattern identification

Understanding these integrated functions provides deeper insights into MTF2's role in normal development and disease pathogenesis, particularly its context-dependent functions in transcriptional regulation .

What are emerging techniques for studying MTF2 protein dynamics and interactions in living cells?

Cutting-edge approaches for investigating MTF2 dynamics in living systems include:

• Live cell imaging technologies:

  • CRISPR knock-in of fluorescent tags (mNeonGreen, Halo-tag)

  • Optimized tag position: C-terminal tagging preserves PHD finger function

  • Photoactivatable or photoconvertible fluorophores for pulse-chase

  • Advanced microscopy approaches:

    • Lattice light-sheet for 3D imaging with reduced phototoxicity

    • Single-molecule tracking for diffusion dynamics

    • FRAP (Fluorescence Recovery After Photobleaching) for binding kinetics

• Proximity labeling in living cells:

  • TurboID or miniTurbo fusion with MTF2

  • Spatial-specific labeling (nuclear compartment-restricted)

  • Temporal control with inducible systems

  • Analysis workflow:

    • Streptavidin pulldown of biotinylated proteins

    • Mass spectrometry identification

    • Validation by co-IP or immunofluorescence

• Optogenetic approaches:

  • Light-inducible MTF2 recruitment systems

  • Spatiotemporal control of PRC2 complex assembly

  • Measurement of downstream effects on histone modifications

  • Experimental design:

    • CRY2-CIB1 or iLID system adaptation

    • Live cell monitoring of H3K27me3 dynamics

    • Single-cell transcriptional readouts

• Phase separation investigation:

  • Examination of MTF2's potential role in nuclear condensates

  • FRAP analysis of droplet dynamics

  • 1,6-hexanediol sensitivity assays

  • Correlative light-electron microscopy for ultrastructural analysis

These advanced techniques enable real-time investigation of MTF2 function and regulation, providing insights into the dynamic nature of epigenetic regulation beyond static snapshots .

How can computational approaches enhance our understanding of MTF2 binding specificity and functional outcomes?

Computational methods offer powerful approaches to decipher MTF2 function across genomic contexts:

• Motif analysis and binding prediction:

  • De novo motif discovery from ChIP-seq data

  • Machine learning approaches for binding site prediction

  • Integrative analysis with chromatin features

  • Methodology:

    • MEME suite for motif identification

    • Support vector machines or deep learning for sequence context

    • Feature importance analysis for contributing factors

• Network inference from multi-omics data:

  • Gene regulatory network reconstruction

  • ChIP-seq, RNA-seq, ATAC-seq integration

  • Bayesian approaches for causal relationships

  • Tools and approaches:

    • SCENIC for transcription factor networks

    • Cicero for cis-regulatory networks

    • CellOracle for perturbation prediction

• Structural biology and molecular dynamics:

  • Homology modeling of MTF2-chromatin interactions

  • Molecular dynamics simulations of PHD finger binding

  • Protein-protein docking with PRC2 components

  • Computational requirements:

    • AlphaFold2 for structure prediction

    • GROMACS or NAMD for dynamics simulations

    • High-performance computing resources

• Clinical data mining and integration:

  • Multi-cancer analysis of MTF2 expression patterns

  • Correlation with mutation landscapes

  • Survival prediction models incorporating MTF2

  • Data sources and approaches:

    • TCGA and ICGC databases

    • Single-cell atlases

    • Cox proportional hazards with regularization

    • Random forest for feature importance

• Perturbation response prediction:

  • Network-based prediction of MTF2 inhibition effects

  • In silico modeling of combination therapies

  • Identification of synthetic lethal interactions

  • Validation approaches:

    • CRISPR screens

    • Drug combination testing

    • Patient-derived organoid models

These computational approaches complement experimental methods and provide systems-level insights into MTF2 function and its potential as a therapeutic target .