COMMD4 Antibody

What is COMMD4 and why is it a significant research target?

COMMD4 is a member of the copper metabolism MURR1 domain-containing (COMMD) protein family, characterized by a distinctive C-terminal COMM domain that facilitates protein interactions. COMMD4 has emerged as a significant research target due to its involvement in cancer progression and potential as both a prognostic biomarker and therapeutic target. Recent studies indicate COMMD4 regulates NF-κB activity and influences the function of cullin-RING E3 ubiquitin ligases .

The significance of COMMD4 as a research target stems from its dysregulated expression in multiple cancer types, particularly glioma and non-small cell lung cancer (NSCLC), where elevated COMMD4 expression correlates with unfavorable prognosis . Additionally, laboratory studies demonstrate that depletion of COMMD4 markedly reduces cancer cell proliferation and enhances cell death after exposure to DNA-damaging agents, suggesting its potential as a therapeutic intervention point .

How do COMMD4 antibodies function in immunodetection methods?

COMMD4 antibodies function as specific molecular recognition tools that bind to COMMD4 protein epitopes, enabling detection and quantification across various immunological techniques. In western blotting/immunoblotting applications, these antibodies bind to COMMD4 in protein lysates separated by electrophoresis, allowing visualization of COMMD4 expression levels when paired with appropriate secondary antibodies and detection systems .

For immunohistochemistry (IHC), COMMD4 antibodies facilitate the detection of COMMD4 protein in fixed tissue sections, enabling researchers to evaluate both protein expression levels and subcellular localization patterns. The methodology typically involves deparaffinization, antigen retrieval (often using Tris-EDTA pH 7.8 buffer or citrate buffer pH 6.0), followed by incubation with primary anti-COMMD4 antibody (typically at 1:100 dilution) and subsequent detection using enzyme-conjugated secondary antibodies . The staining intensity and patterns observed (nuclear vs. cytoplasmic) provide valuable information about COMMD4's role in disease processes.

What are the common immunodetection applications for COMMD4 antibodies?

COMMD4 antibodies are employed across multiple immunodetection platforms, each serving distinct research objectives:

• Western Blotting/Immunoblotting: Used to quantify COMMD4 protein expression in cell or tissue lysates. This technique allows researchers to determine relative COMMD4 levels between different samples and correlate them with experimental conditions or disease states. Studies have successfully employed this approach to demonstrate upregulated COMMD4 expression in cancer cell lines compared to non-tumorigenic controls .

• Immunohistochemistry (IHC): Applied to fixed tissue specimens to assess the distribution and localization of COMMD4 protein in situ. Research has utilized IHC with COMMD4 antibodies on tissue microarrays to evaluate expression patterns across numerous patient samples simultaneously. This technique has revealed variations in COMMD4 staining intensity and subcellular localization (nuclear versus cytoplasmic) across different cancer subtypes .

• Immunofluorescence: Enables precise subcellular localization studies through fluorescently labeled secondary antibodies. This approach provides higher resolution images for determining the intracellular distribution of COMMD4 in experimental settings .

• Flow Cytometry: Although not explicitly mentioned in the search results, COMMD4 antibodies that recognize cell-surface or intracellular epitopes can potentially be used for quantitative single-cell analysis of COMMD4 expression.

What validation methods should be employed before using a new COMMD4 antibody?

Before implementing a COMMD4 antibody in research, comprehensive validation is essential to ensure specificity, sensitivity, and reproducibility. Recommended validation approaches include:

• Western Blot Analysis: Verify that the antibody detects a protein band of the expected molecular weight (~23 kDa for COMMD4) in positive control samples. Compare expression between tissues/cell lines known to express different levels of COMMD4, such as non-tumorigenic bronchial epithelial cells (HBEC3-KT) versus NSCLC cell lines (H460, H1299) which demonstrate higher COMMD4 expression .

• RNA Interference Correlation: Confirm antibody specificity by correlating protein detection with siRNA-mediated knockdown of COMMD4. Effective siRNA sequences have achieved 75-85% reduction in COMMD4 protein expression, which should correspond to proportional decreases in antibody signal .

• Immunohistochemistry Controls: Include appropriate positive and negative controls in IHC protocols. Secondary antibody-only controls are essential to rule out non-specific binding. Additionally, include tissues with known COMMD4 expression patterns as reference standards .

• Cross-reactivity Assessment: Evaluate potential cross-reactivity with other COMMD family proteins, particularly those with sequence homology to COMMD4, through comparative analysis with recombinant proteins or cell lines with defined COMMD protein expression profiles.

• Reproducibility Testing: Ensure consistent results across multiple experimental replicates and between different lots of the antibody to establish reliability for long-term research applications.

How can COMMD4 antibodies be effectively utilized in prognostic biomarker studies?

COMMD4 antibodies serve as critical tools in prognostic biomarker research through systematic implementation in tissue microarray (TMA) analysis and correlation with clinical outcomes. The methodological approach involves:

• Standardized IHC Protocol Development: Establish rigorous staining protocols with optimized antibody concentrations (typically 1:100 dilution), antigen retrieval methods, and detection systems. Studies examining COMMD4 as a prognostic marker have employed automated IHC platforms (e.g., Ventana Discovery Ultra) with standardized deparaffinization and cell conditioning procedures .

• Scoring System Implementation: Develop and apply consistent scoring methods that quantify both staining intensity (weak/1, moderate/2, strong/3) and subcellular localization patterns (nuclear vs. cytoplasmic). For example, research has demonstrated that in adenocarcinoma NSCLC cases, 27% exhibited COMMD4 staining solely in the nucleus while 55% showed exclusively cytoplasmic staining .

• Clinical Correlation Analysis: Integrate COMMD4 expression data with patient clinicopathological parameters and survival outcomes. Kaplan-Meier survival analysis of 1145 NSCLC cases revealed that patients with high COMMD4 expression had significantly poorer outcomes (HR = 1.28, CI: 1.13–1.46, p = 0.0001), particularly in the adenocarcinoma subtype (HR = 2.01, CI: 1.57–2.56, p = 1.1 × 10⁻⁸) .

• Multivariate Analysis: Assess COMMD4's independent prognostic value by controlling for established prognostic factors like tumor grade, stage, and molecular features. Research has identified associations between COMMD4 expression and specific patient characteristics, such as higher nuclear and cytoplasmic COMMD4 staining being significantly associated with adenocarcinoma NSCLC relative to squamous cell carcinoma .

What are the key considerations when designing immunohistochemical studies with COMMD4 antibodies?

Designing robust immunohistochemical studies with COMMD4 antibodies requires attention to several critical methodological considerations:

• Antibody Selection and Optimization:

  • Evaluate multiple anti-COMMD4 antibodies (e.g., Abcam ab115169 and ab219115) to determine which provides optimal signal-to-noise ratio and specificity .

  • Perform antibody titration experiments to establish the optimal dilution (typically 1:100 for COMMD4 antibodies in IHC applications) .

• Antigen Retrieval Protocol Development:

  • Optimize epitope unmasking conditions, comparing heat-induced epitope retrieval methods using different buffers (Tris-EDTA pH 7.8 and citrate buffer pH 6.0) at standardized temperatures (e.g., 95°C for 44 minutes) .

  • Evaluate multiple antigen retrieval durations to maximize specific staining while minimizing background.

• Control Integration:

  • Include appropriate positive controls (tissues known to express COMMD4).

  • Incorporate negative controls (secondary antibody alone) to assess background staining .

  • Consider using dual staining with established markers (e.g., CK7 at 1:1000 dilution) to provide contextual information about tissue architecture .

• Subcellular Localization Assessment:

  • Develop scoring systems that independently evaluate nuclear and cytoplasmic COMMD4 expression, as research has demonstrated distinct prognostic implications based on subcellular distribution patterns .

  • Research has shown that in adenocarcinoma NSCLC, 27% of cases exhibited weak nuclear COMMD4 staining, while 54% showed weak and 1% showed moderate cytoplasmic staining .

• Quantification Method Standardization:

  • Establish clear criteria for categorizing staining intensity (weak/1, moderate/2, strong/3).

  • Define thresholds for positivity, potentially using median intensity scores as cut-offs for correlation with clinicopathological parameters .

How do researchers address data inconsistencies between COMMD4 transcript and protein expression analyses?

Researchers employ several methodological approaches to reconcile discrepancies between COMMD4 transcript and protein expression data:

• Integrated Multi-Platform Analysis:

  • Implement parallel assessment of COMMD4 at both mRNA (qRT-PCR) and protein (immunoblotting, IHC) levels within the same specimens to enable direct correlation analyses .

  • Consider temporal dynamics, as observed in studies where qRT-PCR analysis demonstrated significant upregulation of COMMD4 transcripts in most NSCLC cell lines compared to control HBEC3-KT cells, which was subsequently corroborated by immunoblotting analysis in the same panel of cells .

• Technical Validation:

  • Employ multiple primer sets and antibodies targeting different epitopes to verify consistency of observed expression patterns.

  • Apply normalization strategies appropriate to each technique (housekeeping genes for qRT-PCR, loading controls like β-actin for immunoblotting) .

• Cell Line vs. Patient Sample Considerations:

  • Acknowledge potential limitations when extrapolating from cell lines to patient samples. For instance, researchers have observed that immortalized non-tumorigenic cell lines like HBEC3-KT (immortalized with hTERT and Cdk4) may adapt to culture conditions, potentially accounting for discrepancies between bioinformatic analysis of patient samples and in vitro expression patterns .

• Cross-Platform Data Integration:

  • Implement statistical approaches to integrate data from different experimental platforms, such as correlating transcript levels from RNA-seq with protein abundance from immunoblotting.

  • Apply bioinformatic tools to evaluate potential post-transcriptional regulatory mechanisms that might explain discrepancies between mRNA and protein expression.

What experimental controls are essential when using COMMD4 antibodies in cell-based functional studies?

When conducting cell-based functional studies with COMMD4 antibodies, implementation of the following controls is crucial for data validity and interpretability:

• siRNA Validation Controls:

  • Include multiple siRNA sequences targeting different regions of COMMD4 to confirm phenotypic effects are specifically due to COMMD4 depletion rather than off-target effects. Studies have successfully employed at least two different siRNA sequences (#2 and #3) achieving 75-85% reduction in COMMD4 expression .

  • Implement non-targeting/scrambled siRNA controls processed identically to experimental samples .

  • Verify knockdown efficiency through both immunoblotting with COMMD4 antibodies and qRT-PCR analysis .

• Cell Type Controls:

  • Include appropriate non-tumorigenic cell lines (e.g., HBEC3-KT bronchial epithelial cells) alongside cancer cell lines representing different disease subtypes (adenocarcinoma, squamous cell carcinoma, large cell carcinoma) .

  • Research has shown differential responses to COMMD4 depletion between non-tumorigenic and cancer cells, with significant growth retardation observed only in NSCLC cell lines but not in HBEC3-KT cells .

• Methodological Cross-Validation:

  • Confirm phenotypic observations using complementary techniques. For example, cell proliferation studies following COMMD4 depletion should be assessed using multiple approaches, such as real-time cell analysis (Incucyte S3) and alternative platforms like holographic imaging microscopy (HoloMonitorM4) .

• Rescue Experiments:

  • Perform phenotypic rescue through re-expression of siRNA-resistant COMMD4 constructs to definitively establish that observed effects are specifically due to COMMD4 depletion.

• Subcellular Fractionation Controls:

  • When analyzing COMMD4 localization, include markers for specific cellular compartments (e.g., lamin A/C for nuclear fraction, α-tubulin for cytoplasmic fraction) to confirm fractionation quality .

How can COMMD4 antibodies be integrated into tumor immune microenvironment research?

COMMD4 antibodies provide valuable tools for investigating associations between COMMD4 expression and tumor immune contexture through methodologically rigorous approaches:

• Multiplex Immunohistochemistry/Immunofluorescence:

  • Design multiplexed staining panels that combine COMMD4 antibodies with markers for tumor-infiltrating immune cells, enabling simultaneous visualization of COMMD4 expression patterns and immune cell populations within the same tissue section.

  • While not explicitly detailed in the search results, researchers could combine anti-COMMD4 antibodies with markers for CD8+ T cells, B cells, macrophages, neutrophils, dendritic cells, and CD4+ T cells to parallel the computational analyses performed using TIMER .

• Correlation with Computational Immune Deconvolution Data:

  • Integrate COMMD4 IHC staining patterns with computational immune profiling data from platforms like CIBERSORT and TIMER, which allow inference of immune cell infiltration patterns from genomic data .

  • Research has used these approaches to examine the association between COMMD4 expression and immune infiltration levels in low-grade glioma (LGG) and glioblastoma (GBM) .

• Classification Based on COMMD4 Expression:

  • Stratify samples into low- and high-COMMD4 expression subgroups to analyze variations in immune cell composition, including macrophages, T cells, monocytes, NK cells, dendritic cells, and B cells .

  • This approach allows for statistical comparison of immune cell proportions between COMMD4-high and COMMD4-low samples, potentially revealing mechanisms through which COMMD4 influences the tumor immune microenvironment.

• Integration with Functional Studies:

  • Combine COMMD4 expression analysis with functional immune studies, such as cytotoxicity assays and cytokine production assessments, to determine if COMMD4 levels correlate with functional immune parameters beyond mere presence of immune cell populations.

What methodological approaches can address variability in COMMD4 immunostaining patterns?

Addressing variability in COMMD4 immunostaining patterns requires systematic methodological approaches:

• Staining Protocol Standardization:

  • Implement automated staining platforms (e.g., Ventana Discovery Ultra) to minimize technical variability .

  • Standardize critical parameters including deparaffinization temperature (50°C for 8 min followed by 60°C for 24 min), cell conditioning (CC1 buffer at 95°C for 44 min), and blocking conditions (CM buffer at 37°C for 4 min) .

• Subcellular Localization Scoring:

  • Develop separate scoring systems for nuclear and cytoplasmic COMMD4 staining, as research has demonstrated distinct patterns between these compartments .

  • Research has observed that in adenocarcinoma NSCLC, 27% of cases exhibited nuclear-only COMMD4 staining, while 55% showed cytoplasmic-only staining, with varying intensity levels .

• Multi-Observer Concordance Testing:

  • Implement independent scoring by multiple pathologists/researchers to establish inter-observer reliability.

  • Calculate concordance statistics (e.g., Cohen's kappa) to quantify agreement levels and refine scoring criteria as needed.

• Image Analysis Implementation:

  • Apply digital pathology image analysis algorithms to quantify staining intensity and distribution objectively.

  • Define precise thresholds for positivity based on signal intensity above background, potentially using median intensity scores as statistical cut-offs .

• Correlation with Molecular Data:

  • Integrate immunostaining results with molecular profiling data to identify potential determinants of staining pattern variability.

  • Research has demonstrated associations between COMMD4 expression and specific molecular features, such as isocitrate dehydrogenase 1 (IDH1) status in glioma .

How are COMMD4 antibodies utilized in assessing drug response mechanisms?

COMMD4 antibodies play a crucial role in elucidating the mechanisms underlying drug response through several methodological approaches:

• Expression-Drug Response Correlation Studies:

  • Utilize COMMD4 antibodies to quantify protein expression across cell line panels, then correlate expression levels with drug sensitivity profiles.

  • Research has employed platforms like CellMiner, which integrates molecular and pharmacologic data for the NCI-60 tumor cell panel, to analyze associations between COMMD4 expression and responsiveness to over 100,000 compounds .

• Pre/Post-Treatment Expression Analysis:

  • Apply COMMD4 antibodies in immunoblotting and IHC to assess changes in expression following drug treatment.

  • This approach allows identification of potential feedback mechanisms and adaptation responses involving COMMD4 regulation.

• Combination with Functional Knockdown/Knockout Studies:

  • Integrate COMMD4 antibody-based expression analysis with siRNA-mediated depletion studies to determine if COMMD4 levels predictively modulate drug sensitivity.

  • Research has demonstrated that COMMD4 depletion markedly reduces cell proliferation and enhances cell death after exposure to DNA-damaging agents, suggesting its role in drug resistance mechanisms .

• Pathway Analysis Integration:

  • Combine COMMD4 expression assessment with analysis of downstream effector pathways to establish mechanistic links between COMMD4 levels and drug response pathways.

  • Gene Set Enrichment Analysis (GSEA) has been employed alongside COMMD4 expression analysis to examine functional correlations and potential drug resistance mechanisms .

What are the best practices for using COMMD4 antibodies in functional knockdown validation studies?

Implementing COMMD4 antibodies in functional knockdown validation studies requires adherence to specific methodological best practices:

• Knockdown Efficiency Quantification:

  • Apply COMMD4 antibodies in immunoblotting to precisely quantify knockdown efficiency, with successful protocols achieving 75-85% reduction in protein expression .

  • Include time-course analysis to determine optimal post-transfection timepoints for subsequent functional studies, accounting for protein half-life.

• Multiple siRNA Sequence Validation:

  • Utilize at least two different siRNA sequences targeting distinct regions of COMMD4 mRNA (such as siRNA #2 and #3) to confirm that phenotypic effects are not due to off-target actions .

  • Include appropriate negative control siRNAs processed identically to experimental samples.

• Complementary RNA/Protein Analysis:

  • Combine COMMD4 antibody-based protein detection with qRT-PCR measurement of transcript levels to comprehensively assess knockdown at both RNA and protein levels .

  • This approach helps distinguish between transcriptional and post-transcriptional mechanisms of COMMD4 regulation.

• Phenotypic Assay Diversification:

  • Implement multiple complementary phenotypic assays to assess functional consequences of COMMD4 knockdown.

  • Research has effectively combined real-time cell proliferation analysis (Incucyte S3) with holographic imaging microscopy (HoloMonitorM4) to comprehensively evaluate growth effects following COMMD4 depletion .

• Cell Type-Specific Effects Documentation:

  • Apply consistent knockdown and analysis protocols across multiple cell types to identify context-dependent functions.

  • Studies have revealed differential sensitivity to COMMD4 depletion between non-tumorigenic bronchial epithelial cells (HBEC3-KT) and various NSCLC subtypes, with significant growth inhibition observed only in cancer cells .

What strategies can address common technical challenges with COMMD4 antibodies in immunohistochemistry?

Researchers can implement several methodological strategies to overcome technical challenges associated with COMMD4 immunohistochemistry:

• Background Reduction Optimization:

  • Implement titrated blocking steps with serum matching the species of the secondary antibody to minimize non-specific binding.

  • Evaluate the effectiveness of different blocking agents (e.g., CM buffer at 37°C for 4 min) as described in established protocols .

  • Increase washing duration and frequency between antibody incubations to remove unbound antibodies.

• Antigen Retrieval Method Selection:

  • Systematically compare multiple antigen retrieval protocols, as research has demonstrated successful COMMD4 detection using both Tris-EDTA (pH 7.8) and citrate buffer (pH 6.0) systems with heat-induced epitope retrieval at 95°C for 44 minutes .

  • Optimize retrieval duration to balance between sufficient epitope exposure and tissue morphology preservation.

• Signal Amplification System Selection:

  • Evaluate enzyme-based (e.g., Anti-HQ HRP incubated at 37°C for 16 min) versus fluorescence-based detection systems to determine optimal signal-to-noise ratio for COMMD4 visualization .

  • Consider implementing tyramide signal amplification for detecting low-abundance COMMD4 expression while maintaining specificity.

• Tissue-Specific Protocol Adjustments:

  • Develop tissue-specific modifications to standard protocols, recognizing that optimal conditions may vary between different cancer types and tissue sources.

  • Research has demonstrated distinct staining patterns between adenocarcinoma and squamous cell carcinoma samples, necessitating potential procedural adaptations based on tissue type .

• Dual Marker Strategy Implementation:

  • Consider co-staining with established markers (e.g., CK7 diluted 1:1000) to provide internal controls and contextual information about tissue architecture and tumor regions .

How can researchers ensure reproducibility in quantitative COMMD4 expression analyses?

Ensuring reproducibility in quantitative COMMD4 expression analyses requires implementation of rigorous methodological controls:

• Reference Standard Implementation:

  • Include consistent positive control samples (cell lines or tissues with established COMMD4 expression levels) across all experimental batches.

  • Studies have identified specific NSCLC cell lines (H460, H1299) with consistently high COMMD4 expression that could serve as reliable positive controls .

• Antibody Validation and Standardization:

  • Maintain detailed records of antibody sources, lot numbers, and validation data.

  • Pre-test new antibody lots against reference standards to ensure consistent detection sensitivity before implementing in experimental analyses.

• Protocol Standardization:

  • Develop comprehensive standard operating procedures (SOPs) for all aspects of sample processing, including:

    • Tissue fixation parameters (duration, fixative composition)

    • Antigen retrieval conditions (buffer composition, pH, temperature, duration)

    • Antibody dilutions and incubation conditions

    • Detection system specifications

  • Standardized immunohistochemistry protocols implemented in research include specific parameters for deparaffinization (50°C for 8 min, then 60°C for 24 min), cell conditioning (CC1 buffer at 95°C for 44 min), and antibody incubation (1:100 dilution, ambient temperature for 1 hour) .

• Quantification Method Standardization:

  • Implement consistent scoring systems for evaluating staining intensity (weak/1, moderate/2, strong/3) and distribution patterns .

  • Consider digital image analysis algorithms to eliminate subjective interpretation variability.

  • Apply statistical approaches to determine appropriate cut-off values for categorizing samples as COMMD4-high versus COMMD4-low, such as using median expression values as threshold points .

• Multi-Laboratory Validation:

  • Where possible, validate key findings through inter-laboratory replication to verify that results are reproducible across different research environments.