EER5 Antibody

What are the common issues with commercially available ERβ antibodies?

Most commercially available ERβ antibodies demonstrate significant problems with specificity. In a comprehensive validation study of 13 antibodies, only one (monoclonal PPZ0506) showed adequate specificity for IHC applications . The most commonly used antibodies in the field, monoclonals PPG5/10 and 14C8, yielded false positive results in control tissues known to lack ERβ expression . This lack of specificity has likely contributed to contradictory findings regarding ERβ expression patterns and functions across different tissues and disease states .

How can researchers rigorously validate an ERβ antibody for their specific application?

Rigorous validation requires a multi-technique approach using appropriate controls:

• Positive and negative controls: Use cell lines with confirmed ERβ expression (by RNA-seq) and those lacking expression. For example, researchers have used ERβ-transduced cell lines as positive controls and parental lines as negative controls .

• Multiple applications assessment: Validate the antibody using:

  • Immunohistochemistry on formalin-fixed paraffin-embedded (FFPE) samples

  • Western blotting with recombinant ERβ protein as reference

  • Immunoprecipitation followed by mass spectrometry to confirm the antibody is capturing ERβ protein

• Cross-validation with transcript data: Compare antibody staining patterns with RNA-seq data from repositories like GTEx to ensure concordance between protein and transcript expression patterns .

• Antibody stability testing: Some antibodies (like 14C8) may lose specificity during storage, so researchers should perform regular validation checks .

What explains the discrepancies in ERβ tissue expression reported in the literature?

The contradicting reports of ERβ expression across tissues can be attributed to several factors:

• Antibody specificity issues: Most studies have relied on antibodies now shown to lack specificity (particularly PPG5/10 and 14C8) .

• Inadequate controls: Many studies failed to include appropriate positive and negative controls or did not verify expression at the transcript level .

• Application-dependent performance: Some antibodies may perform adequately in one application (e.g., Western blot) but fail in others (e.g., IHC) .

• Storage and handling variations: Antibody performance can degrade with time and improper storage, as demonstrated with clone 14C8, which lost ability to recognize recombinant ERβ after extended storage .

• Cross-reactivity with other proteins: Many ERβ antibodies cross-react with unrelated proteins, generating false positive signals in tissues that don't express the receptor .

How does the specificity of ERβ antibodies compare when used across different experimental techniques (IHC, WB, IP-MS)?

Antibody performance varies substantially across different techniques:

• PPZ0506: Shows consistent specificity across IHC, Western blot, and immunoprecipitation-mass spectrometry. It generates clean bands of expected size (60 kDa) in Western blot, specific nuclear staining in IHC of positive control cells, and robustly binds ERβ protein in IP-MS experiments .

• 14C8: Demonstrates inconsistent performance. While it showed the expected staining pattern in IHC of control cells, it produced multiple bands in Western blot. In IP-MS, ERβ could be detected only at low confidence in one of two replicates .

• PPG5/10: Lacks specificity across all methods. It shows false positivity in IHC, generates unspecific bands (75-100 kDa) in Western blot, and failed to capture ERβ protein in IP-MS experiments .

This inconsistency highlights the importance of validating antibodies for each specific application rather than assuming performance will translate across methods.

What is the optimal protocol for using the validated PPZ0506 antibody in IHC applications?

Based on the limited available research on the PPZ0506 antibody, researchers should consider the following protocol for IHC applications:

• Sample preparation: Use standard FFPE tissue processing with sections of 4-5 μm thickness.

• Antigen retrieval: Perform heat-induced epitope retrieval in citrate buffer (pH 6.0).

• Blocking: Block with appropriate serum (typically 5% normal goat serum) to reduce background staining.

• Primary antibody: Apply PPZ0506 at the manufacturer's recommended dilution (typically 1:100 to 1:200) and incubate overnight at 4°C.

• Detection system: Use a polymer-based detection system compatible with mouse monoclonal antibodies.

• Controls: Always include positive controls (tissues known to express ERβ: testis, ovary, lymphoid cells) and negative controls (tissues lacking ERβ expression) .

• Interpretation: Look for nuclear staining pattern, as ERβ is a nuclear receptor. Be aware that the staining pattern will be more restricted than previously reported with other antibodies .

How can researchers troubleshoot contradictory results when comparing ERβ antibody data with transcriptomic data?

When facing discrepancies between antibody staining and transcriptomic data, researchers should:

• Re-validate the antibody: Confirm antibody specificity using positive and negative controls and multiple techniques .

• Assess transcript detection sensitivity: Low-abundance transcripts might be missed by some RNA-seq protocols. Consider using more sensitive methods like qPCR with well-validated primers .

• Examine post-transcriptional regulation: Investigate whether post-transcriptional mechanisms might affect protein expression independently of mRNA levels.

• Consider protein stability and turnover: Differences in protein half-life could explain some discrepancies between transcript and protein levels.

• Evaluate antibody cross-reactivity: Test whether the antibody cross-reacts with related proteins (e.g., ERα) or unrelated proteins of similar molecular weight .

• Use orthogonal approaches: Consider techniques that don't rely on antibodies, such as CRISPR-Cas9 tagging of endogenous ERβ, to confirm expression patterns.

What are the best approaches for multiplex detection of ERβ with other markers?

For researchers needing to co-localize ERβ with other proteins:

• Sequential immunofluorescence: Use fluorescently-labeled secondary antibodies against different species of primary antibodies (e.g., mouse anti-ERβ with rabbit anti-second protein).

• Tyramide signal amplification: This technique can enhance detection sensitivity for low-abundance proteins like ERβ in multiplex settings.

• Chromogenic multiplex IHC: Use sequential staining with different chromogens, but be aware of potential cross-reactivity issues.

• Antibody stripping and re-probing: For sequential detection on the same slide, validate that the stripping protocol completely removes the first antibody.

• Controls for specificity: Use blocking peptides or absorption controls to verify that each antibody maintains specificity in the multiplex setting .

• Validation with single-staining: Always validate multiplex findings with parallel single-staining experiments to confirm that antibody performance isn't affected by the multiplex protocol.

How should researchers address the potential impact of fixation and tissue processing on ERβ antibody performance?

Tissue preservation can significantly impact antibody binding:

• Fixation optimization: Compare different fixation protocols (duration and fixative type) to determine optimal conditions for ERβ detection.

• Antigen retrieval comparison: Test multiple antigen retrieval methods (heat-induced vs. enzymatic, different pH buffers) to optimize epitope exposure.

• Fresh vs. archival tissue: Be aware that long-term storage of FFPE blocks can affect antigen detection; validate antibodies on both fresh and archived samples.

• Frozen vs. FFPE comparison: If possible, compare antibody performance on matched frozen and FFPE samples to assess fixation effects.

• Internal controls: Include tissues with known ERβ expression in each staining batch to control for processing variables .

• Pre-analytical variable documentation: Carefully document fixation time, processing protocols, and storage duration to account for these variables in result interpretation.

How can researchers accurately score and quantify ERβ expression in tissue samples?

For standardized ERβ assessment:

• Nuclear-specific scoring: Since ERβ is a nuclear receptor, scoring should focus on nuclear staining intensity and percentage of positive cells .

• Blinded assessment: Use multiple blinded observers to score samples to reduce bias.

• Digital image analysis: Consider automated quantification using digital pathology software for reproducible assessment of staining intensity and distribution.

• Scoring scale standardization: Develop a clear scoring system (e.g., 0-3+ intensity, with percentage of positive cells) and validate inter-observer reproducibility.

• Positive control calibration: Use known positive tissues (testis, ovary) to calibrate scoring thresholds in each experiment .

• Cut-off determination: Establish biologically relevant cut-offs for positivity based on correlation with functional outcomes or transcript levels.

What considerations should guide interpretation of conflicting literature on ERβ expression in cancer tissues?

When evaluating conflicting reports:

• Antibody identification: Note which antibody was used in each study, with particular skepticism toward findings based solely on PPG5/10 or 14C8 .

• Validation methodology: Assess whether appropriate controls and validation methods were employed.

• Transcript correlation: Consider whether protein expression findings were correlated with transcript data.

• Methodology details: Evaluate the completeness of methodological reporting, including antigen retrieval, antibody dilution, and scoring criteria.

• Sample size and heterogeneity: Consider whether sample sizes were adequate and tissue heterogeneity was accounted for.

• Functional validation: Give greater weight to studies that correlated expression with functional outcomes or mechanistic studies.