MET (Ab-1234) Antibody is a rabbit polyclonal antibody that specifically recognizes the region around the tyrosine 1234 phosphorylation site of the MET receptor (also known as hepatocyte growth factor receptor or c-Met). The antibody is raised against a synthesized non-phosphopeptide derived from human Met with the sequence K-E-Y(p)-Y-S, corresponding to the area surrounding the tyrosine 1234 phosphorylation site . This antibody is designed to detect endogenous levels of total MET protein, making it valuable for studying MET signaling pathways.
The MET (Ab-1234) Antibody has been primarily validated for Western Blotting (WB) applications . While ELISA has also been reported as a potential application , the antibody's primary strength lies in protein detection via immunoblotting techniques. The recommended dilution for Western Blot applications ranges from 1:500 to 1:3000, though optimal concentrations should be determined empirically for each experimental system .
For optimal preservation of antibody activity, MET (Ab-1234) Antibody should be stored at -20°C or -80°C . The antibody is typically supplied in phosphate buffered saline (without Mg²⁺ and Ca²⁺), pH 7.4, containing 150mM NaCl, 0.02% sodium azide, and 50% glycerol . Repeated freeze-thaw cycles should be avoided to prevent degradation of the antibody. For long-term storage, aliquoting the antibody upon receipt is recommended to minimize freeze-thaw cycles.
While MET (Ab-1234) Antibody detects total MET protein regardless of phosphorylation status, researchers interested in specifically studying the activation state of MET should consider using phospho-specific antibodies targeting Y1234/Y1235 . Experimental approaches to differentiate between phosphorylated and non-phosphorylated forms include:
Parallel Western blots with both total MET antibody and phospho-specific antibodies
Immunoprecipitation with one antibody followed by immunoblotting with the other
Phosphatase treatment of cell lysates as a control to confirm phospho-specificity
Stimulation experiments with HGF to induce phosphorylation compared to non-stimulated controls
Scientific data demonstrates that phospho-MET (Y1234/Y1235) can be effectively detected in pervanadate-treated cells (e.g., MDA-MB-468 human breast cancer cell line) compared to untreated controls . Immunofluorescence studies also show that phospho-MET antibodies selectively stain HGF-stimulated cells but not non-stimulated cells .
For optimal Western blot detection of MET using Ab-1234 antibody, the following methodological considerations are recommended:
Sample Preparation:
Lyse cells in buffers containing phosphatase inhibitors (especially when studying phosphorylation)
Use reduced protein loading (30-50 μg/lane) to minimize background
Denature samples at 95°C for 5 minutes in reducing sample buffer
Electrophoresis and Transfer Conditions:
Use 7.5% or 4-15% gradient SDS-PAGE gels due to the relatively large size of MET (~145 kDa)
Perform wet transfer at 30V overnight at 4°C for efficient transfer of high molecular weight proteins
Antibody Incubation:
Detection:
These conditions should be optimized based on the specific experimental system and cell types being studied.
Cross-reactivity and non-specific binding can compromise experimental results. To address these issues:
Validation Controls:
Optimization Strategies:
Titrate antibody concentration to determine optimal signal-to-noise ratio
Adjust blocking conditions (try different blocking agents such as BSA vs. milk)
Increase washing stringency with higher salt concentration or mild detergents
Pre-adsorb the antibody with non-relevant proteins
Technical Considerations:
For immunofluorescence applications, include secondary-only controls
For immunoprecipitation studies, include isotype control antibodies
When possible, confirm results with an alternative MET antibody targeting a different epitope
MET signaling can be studied using complementary experimental approaches:
Stimulation Experiments:
Downstream Signaling Analysis:
After detecting MET activation, probe for downstream targets:
PI3K/AKT pathway (phospho-AKT)
RAS/ERK pathway (phospho-ERK)
STAT3 pathway (phospho-STAT3)
PLCG1 activation
MET Inhibitor Studies:
Combine with selective MET inhibitors to confirm specificity of observed effects
Monitor dose-dependent inhibition of MET phosphorylation using both total and phospho-specific antibodies
Co-Immunoprecipitation:
Use MET (Ab-1234) Antibody for immunoprecipitation followed by detection of associated proteins
Identify interaction partners of MET following HGF stimulation
When experiencing weak or absent signals in Western blot applications:
Sample Preparation Issues:
Ensure sufficient protein is loaded (start with 50-75 μg per lane)
Verify sample integrity by checking housekeeping proteins
Add fresh protease and phosphatase inhibitors to lysis buffers
Avoid excessive heating which may cause protein aggregation
Detection Sensitivity:
Technical Parameters:
Experimental Design:
MET can exist in multiple forms, including the full-length precursor (~170 kDa), mature form (~145 kDa), and various proteolytic fragments. To distinguish between these forms:
Gel Electrophoresis Conditions:
Use lower percentage gels (6-8%) for better separation of high molecular weight forms
Consider longer run times to achieve better resolution between closely migrating forms
Use gradient gels (4-15%) to simultaneously detect fragments of different sizes
Data Interpretation:
Validation Approaches:
Compare with antibodies targeting different MET domains
Use recombinant expression of specific MET variants as controls
Perform peptide competition assays to confirm band identity
When conducting comparative studies of MET expression:
Sample Normalization:
Carefully quantify and equalize total protein loading across samples
Include multiple housekeeping proteins as loading controls
Consider normalizing to total protein (using stain-free technology or Ponceau S)
Calculate relative expression using densitometry with appropriate normalization
Experimental Controls:
Include positive control samples with known MET expression
Process all samples simultaneously under identical conditions
Use the same antibody lot for all experiments in a comparative study
Consider running a standard curve with recombinant MET protein
Technical Considerations:
Be aware that different cell lysis methods may extract MET with varying efficiency
Consider that membrane proteins like MET may require specialized extraction buffers
Account for post-translational modifications that may affect antibody recognition
Remember that different tissues may contain varying levels of proteases that could affect MET integrity
Integrating MET detection into multiplexed experimental approaches requires careful planning:
Multi-Color Western Blotting:
Strip and reprobe membranes for different signaling components
Use fluorescently-labeled secondary antibodies with spectrally distinct fluorophores
Separate proteins with similar molecular weights on different membranes
Consider using antibodies from different host species to allow simultaneous probing
Phosphorylation Profiling:
Combine total MET detection with phospho-specific antibodies targeting different residues
Monitor multiple downstream pathways in parallel (PI3K/AKT, MAPK, STAT3, etc.)
Use phospho-protein arrays to screen for activation of multiple pathways simultaneously
Multi-Parameter Flow Cytometry:
Combine surface MET detection with intracellular phospho-protein staining
Perform cell cycle analysis in conjunction with MET signaling assessment
Correlate MET expression with functional cellular readouts
Imaging-Based Approaches:
Perform co-localization studies with MET and interacting proteins
Use proximity ligation assays (PLA) to detect protein-protein interactions involving MET
Combine with fluorescent HGF to monitor ligand-receptor interactions
When investigating MET phosphorylation dynamics and inhibitor responses:
Time-Course Experiments:
Monitor both rapid (minutes) and sustained (hours) phosphorylation changes
Include appropriate time points to capture transient signaling events
Collect samples at consistent time points across experimental conditions
Dose-Response Studies:
Use a wide range of HGF concentrations (typically 1-100 ng/mL)
For inhibitors, test concentrations spanning at least 3 orders of magnitude
Plot dose-response curves to determine EC50/IC50 values
Combination Studies:
Test MET inhibitors in combination with inhibitors of parallel or downstream pathways
Analyze synergistic or antagonistic effects using appropriate statistical methods
Consider the temporal sequence of drug administration
Resistance Mechanisms:
Develop resistant cell models through prolonged exposure to MET inhibitors
Compare MET phosphorylation patterns between sensitive and resistant cells
Investigate alternative signaling pathways that may compensate for MET inhibition
Quantitative analysis of Western blot data requires rigorous methodology:
Image Acquisition:
Capture images within the linear dynamic range of the detection system
Avoid saturated pixels which compromise quantitation
Include a standard curve when absolute quantification is required
Densitometric Analysis:
Use appropriate software (ImageJ, Image Studio, etc.) for band quantification
Define consistent region-of-interest (ROI) dimensions across all lanes
Subtract local background for each measurement
Normalize to appropriate loading controls or total protein
Statistical Analysis:
Perform experiments with sufficient biological replicates (minimum n=3)
Apply appropriate statistical tests based on data distribution
Consider using ANOVA with post-hoc tests for multiple comparisons
Report both statistical significance and effect size
Data Visualization:
Present both representative blot images and quantitative graphs
Include error bars representing standard deviation or standard error
Clearly indicate statistical significance levels
Consider using heatmaps for time-course or multi-parameter data
MET (Ab-1234) Antibody could be adapted for use in evolving single-cell methodologies:
Single-Cell Western Blotting:
Optimize antibody concentrations for microfluidic-based single-cell Western platforms
Develop multiplexed protocols combining MET with other signaling molecules
Correlate MET expression/activation with cellular phenotypes at single-cell resolution
Mass Cytometry (CyTOF):
Conjugate MET antibodies with metal isotopes for mass cytometry
Develop panels combining MET detection with 30+ additional markers
Apply to heterogeneous tumor samples to identify distinct cellular subpopulations
Spatial Transcriptomics Integration:
Combine antibody-based MET protein detection with spatial transcriptomics
Correlate protein expression with mRNA levels at single-cell resolution
Map MET activation patterns within the tissue microenvironment
Live-Cell Imaging Applications:
Develop non-disruptive labeling strategies based on MET antibody fragments
Monitor real-time changes in MET localization and clustering
Correlate with functional cellular responses at single-cell level
When utilizing MET (Ab-1234) Antibody in therapeutic development:
Mechanism-of-Action Studies:
Determine whether candidates affect total MET levels or phosphorylation status
Investigate effects on MET dimerization, internalization, and degradation
Assess impact on downstream signaling pathway activation
Resistance Mechanism Investigation:
Monitor changes in MET expression/phosphorylation during treatment resistance development
Identify compensatory signaling pathways activated upon MET inhibition
Develop rational combination strategies based on observed resistance mechanisms
Biomarker Development:
Correlate baseline MET expression/activation with treatment response
Develop quantitative assays for patient stratification
Identify threshold levels of MET activation predictive of therapeutic response
Translational Research:
Compare antibody performance across preclinical models and patient samples
Develop standardized protocols for clinical biomarker assessment
Validate antibody specificity in complex tissue microenvironments
Integrating computational methods with experimental data can provide deeper insights:
Pathway Modeling:
Incorporate quantitative MET activation data into mathematical models
Simulate downstream signaling dynamics under various conditions
Predict response to combination therapies targeting multiple nodes
Machine Learning Applications:
Develop algorithms to classify cellular responses based on MET signaling patterns
Identify subtle signaling signatures associated with specific outcomes
Integrate MET data with multi-omics datasets for comprehensive analysis
Image Analysis Automation:
Implement deep learning for automated quantification of immunofluorescence data
Develop algorithms for cell-type identification based on MET expression patterns
Enable high-throughput analysis of spatial heterogeneity in tissue samples
Systems Biology Integration:
Map MET signaling within the broader cellular interactome
Identify context-dependent signaling networks
Model feedback and cross-talk mechanisms affecting MET function