HPGD Antibody, HRP conjugated

What is HPGD Antibody, HRP conjugated and what is its primary biochemical function?

HPGD Antibody, HRP conjugated is a rabbit polyclonal antibody that specifically targets 15-hydroxyprostaglandin dehydrogenase [NAD+] protein and is chemically linked to horseradish peroxidase (HRP) enzyme. This conjugation enables direct detection in immunoassays without requiring secondary antibodies. The target protein, HPGD, plays a crucial role in prostaglandin inactivation, contributing to the regulation of multiple physiological processes controlled by prostaglandin levels. HPGD catalyzes the NAD-dependent dehydrogenation of lipoxin A4 to form 15-oxo-lipoxin A4 and has demonstrated capability to inhibit in vivo proliferation of colon cancer cells .

The antibody specifically recognizes the recombinant Human 15-hydroxyprostaglandin dehydrogenase [NAD(+)] protein (amino acids 14-130), making it suitable for detecting this 29-34 kDa protein in human samples .

What are the primary applications for HPGD Antibody, HRP conjugated in research settings?

HPGD Antibody, HRP conjugated is primarily optimized for ELISA (Enzyme-Linked Immunosorbent Assay) applications according to manufacturer specifications . While this specific conjugated antibody is recommended for ELISA, HRP-conjugated antibodies generally serve multiple immunodetection purposes. The HRP enzyme label enables visualization through chromogenic reactions using substrates such as diaminobenzidine (DAB), ABTS, TMB, or TMBUS in the presence of hydrogen peroxide .

Detection of human HPGD has been successfully demonstrated in western blots of human liver tissue using other HPGD antibodies subsequently detected with HRP-conjugated secondary antibodies, suggesting potential expanded applications for directly conjugated versions . The direct conjugation offers advantages in streamlining protocols by eliminating secondary antibody incubation and washing steps.

What are the optimal storage conditions for maintaining HPGD Antibody, HRP conjugated activity?

For maximum stability and activity retention of HPGD Antibody, HRP conjugated, manufacturers recommend storage at -20°C or -80°C upon receipt . Critically important is avoiding repeated freeze-thaw cycles, which can significantly degrade antibody performance. The antibody is supplied in liquid form containing 50% glycerol, 0.01M PBS (pH 7.4) with 0.03% Proclin 300 as a preservative .

For HRP-conjugated antibodies in general, it is well-established that performance diminishes over time even with optimal storage conditions, with degradation accelerating at higher temperatures and increased dilution . Several factors contribute to this degradation, including HRP enzyme instability and antibody structural changes. To extend shelf-life and maintain activity, specialized stabilizers like LifeXtend™ can protect against multiple degradation factors .

How does temperature and pH affect the enzymatic activity of HPGD Antibody, HRP conjugated during experimental procedures?

The enzymatic activity of HPGD Antibody, HRP conjugated is influenced by both temperature and pH conditions during experimental procedures. The optimal pH range for maintaining antibody-HRP conjugate integrity is 6.5-8.5, with significant deviations potentially reducing binding efficiency and enzymatic activity . The HRP enzyme component functions optimally around pH 7.0 with activity declining sharply below pH 5.0 or above pH 9.0.

Temperature management is particularly critical during incubation steps. While room temperature incubations are common practice, extended exposure to temperatures above 25°C can accelerate HRP denaturation. For overnight incubations, 4°C is recommended to preserve enzymatic activity. Additionally, the diluent buffer composition (containing 50% glycerol and 0.01M PBS at pH 7.4) provides some protection against thermal denaturation . When designing experimental protocols, researchers should incorporate temperature and pH controls to ensure consistent signal generation across experimental replicates.

What methodological considerations are important when studying HPGD expression in relation to tumor immune infiltration?

Research demonstrates that HPGD expression correlates significantly with specific immune cell populations in the tumor microenvironment. When designing experiments to study these relationships, researchers should implement the following methodological approaches:

• Utilize computational algorithms such as CIBERSORT with appropriate parameters (permutation number set to 100, quantile normalization enabled) to assess tumor-infiltrating immune cell populations in relation to HPGD expression levels .

• Examine both positive correlations (with resting mast cells, dendritic cells, naïve B cells, and regulatory T cells) and negative correlations (with memory B cells, M0 macrophages, and activated mast cells) as described in published findings .

• Incorporate Gene Set Variation Analysis (GSVA) to investigate specific signaling pathways associated with differential HPGD expression, including KRAS signaling regulation, bile acid metabolism, estrogen response, and apoptosis pathways that are enriched in high HPGD expression samples .

• Consider evaluating co-expressed genes, as HPGD expression has been significantly correlated with specific genes including EDAR (correlation coefficient 0.45), KRT13 (0.41), and PADI1 (0.39) .

• Implement robust statistical methods with appropriate significance thresholds (p<0.05) and multiple testing corrections when identifying correlations between HPGD and other molecular or cellular parameters .

What experimental design considerations are important for validating HPGD Antibody, HRP conjugated specificity in complex tissue samples?

Validating antibody specificity is critical for accurate interpretation of experimental results. For HPGD Antibody, HRP conjugated, researchers should implement a comprehensive validation strategy:

• Include positive control tissues known to express HPGD (such as human liver tissue, which has been demonstrated to express detectable levels of HPGD protein) .

• Incorporate negative controls where HPGD expression is absent or has been experimentally reduced through siRNA knockdown or CRISPR deletion.

• Perform antibody dilution series experiments to demonstrate dose-dependent detection, which supports specificity.

• Compare results obtained with the HRP-conjugated antibody to those using a non-conjugated HPGD antibody with separate HRP-conjugated secondary antibody detection to evaluate potential impacts of HRP conjugation on binding properties.

• Conduct peptide competition assays using the specific immunogen (recombinant Human 15-hydroxyprostaglandin dehydrogenase [NAD(+)] protein, amino acids 14-130) .

• Validate protein detection with orthogonal techniques such as qPCR for HPGD mRNA expression, while acknowledging that protein and mRNA levels may not always correlate due to post-transcriptional regulation.

What are common causes of high background signal when using HPGD Antibody, HRP conjugated in ELISA, and how can these be mitigated?

High background signal when using HPGD Antibody, HRP conjugated in ELISA applications can stem from multiple sources. The following systematic approach addresses common issues and their solutions:

• Insufficient blocking: Ensure thorough blocking with appropriate buffers containing proteins that don't cross-react with the antibody. Optimize both blocking time and buffer composition.

• Improper washing: Implement more stringent washing protocols with appropriate buffers between reaction steps. Increasing wash cycles or volume can significantly reduce background.

• Buffer incompatibility: The antibody preparation contains 50% Glycerol and 0.01M PBS (pH 7.4) , which may interact with certain assay components. Testing alternative dilution buffers may reduce non-specific binding.

• Antibody concentration: Titrate the antibody to determine the optimal concentration that provides specific signal with minimal background. Over-concentration frequently leads to increased non-specific binding.

• Storage degradation: Degraded antibody can contribute to non-specific binding. Strictly follow storage recommendations (-20°C or -80°C) and avoid freeze-thaw cycles .

• Substrate reaction time: Excessive substrate incubation can lead to increased background. Optimizing the enzyme-substrate reaction time and using substrate solution prepared immediately before use can improve signal-to-noise ratio.

How should researchers modify protocols when transferring from ELISA to other applications like Western blotting or IHC?

When adapting HPGD Antibody, HRP conjugated from ELISA to other applications, several key modifications are necessary:

• Antibody concentration adjustment: While a specific concentration may work for ELISA, Western blotting typically requires different concentrations, usually starting with 1:1000 dilution as a baseline (similar to other HRP-conjugated antibodies) .

• Buffer optimization: For Western blotting, consider using specialized blocking buffers that minimize background on membrane surfaces. The presence of 50% glycerol in the antibody preparation may affect membrane binding dynamics differently than in plate-based ELISAs.

• Detection system adaptation: For Western blotting, enhanced chemiluminescence (ECL) systems often provide better sensitivity than chromogenic detection. Substrate choice should be optimized based on expected expression levels of HPGD.

• Incubation conditions: While ELISA might use shorter incubation times at room temperature, Western blotting might benefit from longer incubations (1-2 hours at room temperature or overnight at 4°C) to enhance specific binding.

• Sample preparation: Ensure complete denaturation of samples for SDS-PAGE using appropriate reducing conditions, as demonstrated in published Western blot detection of 15-PGDH/HPGD .

• Positive controls: Include human liver tissue lysate as a positive control, which has been validated to express 15-PGDH/HPGD at approximately 29-34 kDa under reducing conditions .

How should researchers interpret discrepancies between HPGD protein detection and HPGD gene expression data?

When encountering discrepancies between HPGD protein detection using HRP-conjugated antibodies and gene expression data, researchers should consider multiple biological and technical factors:

• Post-transcriptional regulation: HPGD protein levels may not directly correlate with mRNA levels due to microRNA regulation, RNA stability differences, or translation efficiency variations.

• Post-translational modifications: Modifications might affect antibody binding without altering gene expression. The epitope recognized by the HPGD Antibody (amino acids 14-130 of the HPGD protein) may be subject to modifications in certain cellular contexts.

• Methodological sensitivity differences: RNA-seq and qPCR typically have different detection sensitivities compared to protein detection methods, potentially leading to apparent discrepancies.

• Temporal dynamics: Gene expression changes may precede detectable protein changes or vice versa, depending on mRNA and protein half-lives.

• Sample heterogeneity: In complex samples like tumor tissues, the proportion of HPGD-expressing cells may differ from the proportion contributing to bulk RNA measurements.

To resolve these discrepancies, researchers should employ multiple detection methods, including functional assays measuring HPGD enzyme activity, and consider cellular localization studies to detect potential compartmentalization of the protein.

What statistical approaches are most appropriate for analyzing correlations between HPGD expression and clinical parameters?

For rigorous analysis of correlations between HPGD expression and clinical parameters, researchers should implement the following statistical approaches:

• Correlation analysis: When examining relationships between HPGD and other biomarkers or clinical variables, select appropriate methods based on data distribution:

  • Pearson correlation for normally distributed data

  • Spearman or Kendall correlation for non-parametric data

  • Set significance thresholds at p<0.05 as commonly used in published studies

• Survival analysis: Apply Kaplan-Meier curves with log-rank tests to compare patient groups with high versus low HPGD expression.

• Multivariate analysis: Implement Cox proportional hazards regression to assess whether HPGD expression is an independent prognostic factor when accounting for other clinical variables.

• Immune correlation analysis: For immune infiltration studies, utilize the CIBERSORT algorithm with permutation number set to 100 and quantile normalization enabled, as demonstrated in relevant research .

• Pathway analysis: Apply Gene Set Variation Analysis (GSVA) to understand pathway-level changes associated with differential HPGD expression .

• Co-expression network analysis: Identify genes significantly correlated with HPGD expression using appropriate correlation coefficients and significance thresholds (p<0.05) .

How can researchers integrate HPGD protein expression data with immune cell infiltration analysis in cancer research?

To effectively integrate HPGD protein expression data with immune cell infiltration analyses in cancer research, researchers should implement the following methodological approach:

• Computational deconvolution: Use established algorithms like CIBERSORT to quantify immune cell populations in relation to HPGD expression levels . Research has demonstrated that HPGD expression positively correlates with resting mast cells, dendritic cells, naïve B cells, and regulatory T cells, while negatively correlating with memory B cells, M0 macrophages, and activated mast cells .

• Multiplex immunohistochemistry: Employ multiplex staining to directly visualize and quantify HPGD-expressing cells in spatial relation to immune cell populations within the same tissue section.

• Pathway enrichment analysis: Analyze biological pathways differentially activated in high versus low HPGD-expressing samples. Published research indicates that high HPGD expression is associated with enrichment in KRAS signaling regulation, bile acid metabolism, estrogen response, and apoptosis pathways .

• Correlation network analysis: Construct correlation networks between HPGD, its co-expressed genes, and immune cell markers to identify potential regulatory relationships and biological mechanisms.

• Functional validation: Design experiments to test hypothesized relationships between HPGD and immune cell function, potentially using in vitro co-culture systems or animal models with HPGD manipulation.

• Clinical correlation: Correlate integrated HPGD and immune profiles with patient outcomes, treatment responses, and other clinical parameters to establish potential prognostic or predictive value.

This integrated approach allows researchers to develop comprehensive models of how HPGD influences and is influenced by the tumor immune microenvironment, potentially revealing new therapeutic targets or biomarkers.

What buffer composition factors are critical when using HPGD Antibody, HRP conjugated in research applications?

Buffer composition significantly impacts the performance of HPGD Antibody, HRP conjugated across research applications. Critical factors to consider include:

• pH range: Maintain pH between 6.5-8.5 for optimal antibody stability and function . Significant deviation from this range can reduce binding efficiency and compromise HRP enzymatic activity.

• Glycerol concentration: The antibody is supplied in 50% glycerol , which provides stability during storage. When diluting for use, keep final glycerol concentration below 10% to avoid interference with binding kinetics.

• Incompatible components: Buffer should not contain thiomersal/thimerosal, merthiolate, sodium azide, glycine, proclin (except the 0.03% Proclin 300 already in the formulation), or nucleophilic components like primary amines or thiols . These compounds can inhibit HRP activity or disrupt antibody structure.

• Protein additives: Keep BSA or gelatin concentrations below 0.1% if needed as carriers . Higher concentrations may interfere with specific binding.

• Tris concentration: If using Tris buffer, maintain concentration below 50mM to avoid interference with the conjugation chemistry and HRP activity .

The table below summarizes optimal buffer component levels for working with HRP-conjugated antibodies:

What considerations are important when multiplexing HPGD detection with other biomarkers?

Multiplexing HPGD detection with other biomarkers requires careful experimental design to ensure specific detection without cross-reactivity. Key considerations include:

• Antibody compatibility: When combining HPGD Antibody, HRP conjugated with other antibodies, select those raised in different host species to avoid cross-reactivity of secondary detection systems.

• Sequential detection strategies: For multiple HRP-conjugated antibodies, implement sequential detection protocols with complete HRP inactivation between rounds using hydrogen peroxide or other quenching methods.

• Substrate selection: Choose substrates that generate distinct and distinguishable signals when using multiple HRP-conjugated antibodies. Consider chromogenic substrates that produce different colors or fluorescent substrates with non-overlapping emission spectra.

• Signal amplification needs: Assess whether tyramide signal amplification or other enhancement methods are needed for detecting low-abundance targets alongside HPGD.

• Co-expression analysis: Based on published data, consider multiplexing HPGD with its highly correlated genes like EDAR, KRT13, and PADI1 , which may provide context for understanding HPGD's biological role in specific samples.

• Controls: Include appropriate controls for each biomarker individually and in combination to identify any unexpected interactions or non-specific binding within the multiplex system.

This methodical approach ensures reliable simultaneous detection of HPGD and other biomarkers, enabling comprehensive analysis of complex biological systems and relationships.