HNF1B Antibody

What is HNF1B and why is it important to study?

HNF1B (also known as TCF2, FJHN, HNF-1-beta) is a transcription factor belonging to the homeodomain-containing superfamily. It plays a crucial role in nephronogenesis (kidney development), and its dysfunction has been linked to kidney developmental disorders . HNF1B can function either as a tumor suppressor or oncogene depending on the cancer type, making it a valuable target for oncological research . Its protein is approximately 61.3 kilodaltons in mass and contains 557 amino acids, encoded by the HNF1B gene (ID: 6928) .

What applications are HNF1B antibodies typically used for?

HNF1B antibodies are used in multiple research applications, including:

• Western Blot (WB): For detecting protein expression levels and molecular weight verification

• Immunohistochemistry (IHC): For examining tissue localization and expression patterns

• Immunofluorescence (IF)/Immunocytochemistry (ICC): For cellular localization studies

• Co-Immunoprecipitation (CoIP): For protein-protein interaction studies

• Flow Cytometry (FC): For quantifying expression in cell populations

• ELISA: For quantitative protein detection

The application versatility varies by antibody clone and manufacturer, with most antibodies showing reactivity against human, mouse, and rat HNF1B proteins .

How should I select the appropriate HNF1B antibody for my research?

When selecting an HNF1B antibody, consider:

• Application compatibility: Verify that the antibody has been validated for your specific application (WB, IHC, IF, etc.)

• Species reactivity: Ensure the antibody recognizes HNF1B in your model species (human, mouse, rat, etc.)

• Antibody type: Determine whether polyclonal, monoclonal, or recombinant antibodies best suit your needs

• Immunogen information: Review the immunogen used to generate the antibody to understand its epitope specificity

• Supporting validation data: Examine published citations and manufacturer-provided validation data

• Clone information: For monoclonal antibodies, specific clones may perform better in certain applications (e.g., clone EPR6334(2) for WB and IHC-p)

Ideally, select antibodies with published validation in applications similar to your intended use.

What are the recommended dilutions for different applications of HNF1B antibodies?

Based on published protocols, the following dilutions are typically recommended:

It's important to note that optimal dilutions may vary by antibody lot, sample type, and detection method. Titration experiments are recommended to determine optimal concentrations for your specific experimental conditions .

How can I distinguish between specific and non-specific binding of HNF1B antibodies?

To ensure specific binding and minimize artifacts:

• Always include proper controls:

  • Positive controls: Use tissues or cell lines known to express HNF1B (e.g., L02 cells, Jurkat cells, or MDCK cells for WB)

  • Negative controls: Include tissues or cells with no/low HNF1B expression

  • For IHC, normal tubular epithelia serve as excellent internal positive controls, while connective tissue, smooth muscle, and adipose tissue function as negative controls

• Validate with multiple detection methods:

  • Complement antibody-based detection with orthogonal techniques (RT-PCR, RNA-seq)

  • Use multiple antibodies targeting different epitopes

• Perform knockdown/knockout validation:

  • Demonstrate reduced signal in HNF1B knockdown/knockout samples

  • Several publications have utilized this approach to validate HNF1B antibody specificity

• For IHC/IF, confirm that staining is primarily nuclear, consistent with HNF1B's function as a transcription factor

How does HNF1B expression vary across different renal tumor types, and what implications does this have for antibody-based detection?

HNF1B expression shows significant variation across renal tumor subtypes, which has important implications for antibody-based detection and interpretation:

• Clear Cell Renal Cell Carcinoma (ccRCC):

  • Shows consistent HNF1B expression

  • Expression decreases with higher tumor grade and higher T stage

  • Lower expression correlates with poorer prognosis

• Papillary Renal Cell Carcinoma (papRCC):

  • Demonstrates homogeneous positive HNF1B expression

  • May function as an oncogene in this context

• Chromophobe Renal Cell Carcinoma (chRCC):

  • Predominantly shows complete negativity for HNF1B

  • Only rare cases exhibit weak to moderate positive staining

  • May act in a tumor suppressive fashion in this context

• Renal Oncocytoma (RO):

  • Shows consistent positive expression

These patterns suggest that HNF1B antibodies can potentially serve as diagnostic markers to differentiate between renal tumor subtypes, particularly in distinguishing chRCC from other renal neoplasms. When conducting immunohistochemical studies, it's essential to optimize staining protocols for each tumor type and account for these expression differences when interpreting results .

What antibody validation strategies should be implemented when studying HNF1B mutations or epigenetic modifications?

When studying HNF1B mutations or epigenetic modifications, standard antibody validation is insufficient. Consider these specialized approaches:

• For mutation studies:

  • Verify antibody epitope location relative to common mutations

  • Use antibodies targeting different epitopes to detect truncated or mutant proteins

  • Complement with genetic analysis methods (e.g., the amplicon NGS approach described in the literature)

  • Include wild-type and mutant controls to assess antibody specificity

• For epigenetic modification studies:

  • Correlate antibody signal with methylation status determined by bisulfite conversion and High Resolution Melting (HRM) Analysis

  • Include methylated and unmethylated controls at various percentages (100%, 20%, 10%, 5%, 0%)

  • Focus on key CpG islands in the HNF1B promoter region that correlate with expression changes (-457 to -202 bp before TSS)

  • Validate findings with expression analysis (RT-PCR or RNA-seq)

• For both approaches:

  • Use tissue microarrays (TMAs) to simultaneously analyze multiple samples

  • Implement quantitative scoring methods (H-score) to assess expression levels objectively

  • Account for heterogeneity by analyzing multiple regions of the same sample

What is the optimal protocol for immunohistochemical detection of HNF1B in renal tissues?

For optimal immunohistochemical detection of HNF1B in renal tissues:

• Sample preparation:

  • Use 4 μm thick sections of formalin-fixed, paraffin-embedded (FFPE) tissue

  • Tissue microarrays (TMAs) can be employed for high-throughput analysis

• Antigen retrieval:

  • Perform heat-induced epitope retrieval with citrate buffer (pH 6.0)

  • Alternative: Tris-EDTA buffer at pH 9.0 may yield better results for some samples

• Antibody incubation:

  • Use validated anti-HNF1B antibodies (e.g., polyclonal antibody, dilution 1:500, Sigma-Aldrich HPA002083)

  • Automated staining systems like Ventana BenchMark ULTRA enhance reproducibility

• Detection system:

  • Visualize using a sensitive detection system (e.g., OptiView DAB IHC Detection Kit)

• Evaluation:

  • Assess only nuclear staining as positive (consistent with HNF1B's function)

  • Quantify using the H-score method: [1 × (% of cells 1+) + 2 × (% of cells 2+) + 3 × (% of cells 3+)]

  • This yields scores ranging from 0-300, allowing for objective comparison

• Controls:

  • Internal positive control: normal tubular epithelia

  • Internal negative control: connective tissue, smooth muscle, adipose tissue

How can I optimize Western blot protocols for detecting HNF1B?

For optimal Western blot detection of HNF1B:

• Sample preparation:

  • Extract proteins using RIPA or NP-40 buffer with protease inhibitors

  • For nuclear proteins like HNF1B, nuclear extraction protocols may yield cleaner results

  • Load 20-50 μg of total protein per lane

• Gel electrophoresis:

  • Use 8-10% SDS-PAGE gels to adequately resolve the 58-65 kDa HNF1B protein

  • Include positive control lysates (e.g., L02, Jurkat, or MDCK cells)

• Transfer and blocking:

  • Transfer to PVDF membranes (preferred over nitrocellulose for nuclear proteins)

  • Block with 5% non-fat milk or BSA in TBST for 1-2 hours

• Antibody incubation:

  • Primary antibody: Dilute HNF1B antibody 1:500-1:1000 in blocking buffer

  • Incubate overnight at 4°C with gentle agitation

  • Secondary antibody: Use appropriate HRP-conjugated secondary at 1:5000-1:10000

• Detection and analysis:

  • Develop using enhanced chemiluminescence (ECL)

  • Expected molecular weight: 58-65 kDa (observed range)

  • Verify specificity with knockdown/knockout controls

• Troubleshooting:

  • If detecting multiple bands, optimize antibody concentration and washing steps

  • For weak signals, increase antibody concentration or protein loading

  • For high background, extend blocking time or increase washing stringency

What considerations are important when designing experiments to study HNF1B expression in relation to cancer progression?

When designing experiments to study HNF1B expression in cancer progression:

• Sample selection:

  • Include diverse tumor grades and stages to capture progression-related changes

  • Analyze matched normal and tumor tissues from the same patients

  • Consider inclusion of precancerous lesions to study early changes

• Multi-modal assessment:

  • Combine protein detection (IHC/WB) with genetic and epigenetic analyses

  • Screen for HNF1B mutations using amplicon NGS covering coding exons and intronic boundaries

  • Assess promoter methylation status, focusing on CpG islands associated with expression changes

• Quantitative analysis:

  • Use objective scoring methods (H-score) for IHC to enable statistical analyses

  • Correlate expression with clinicopathological parameters (grade, stage, survival)

  • Remember that HNF1B's role may be context-dependent (e.g., in ccRCC, decreased expression correlates with higher tumor grade and T stage)

• Functional validation:

  • Complement expression studies with in vitro functional assays

  • Consider the different roles of HNF1B across tumor types (tumor suppressor in ccRCC and chRCC vs. oncogene in papRCC)

  • Design knockdown/overexpression experiments based on the presumed role in your specific cancer model

• Technical considerations:

  • Account for tumor heterogeneity by analyzing multiple regions

  • Include appropriate controls for each technique

  • Consider batch effects when processing large sample sets

How should I interpret variations in HNF1B staining patterns and intensity in immunohistochemistry?

When interpreting variations in HNF1B immunohistochemical staining:

• Subcellular localization:

  • Normal pattern: Predominantly nuclear staining (consistent with transcription factor function)

  • Aberrant cytoplasmic staining may indicate protein mislocalization or specificity issues

  • Only evaluate nuclear staining as positive for quantification purposes

• Staining intensity variations:

  • Quantify using a structured approach like H-score methodology

  • Stratify positive cells by intensity: 1+ (weak), 2+ (moderate), 3+ (strong)

  • Calculate final H-score: [1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+)]

  • This yields values between 0-300 for objective comparison

• Tumor type-specific considerations:

  • ccRCC: Expect positive expression that decreases with higher tumor grade

  • papRCC: Expect homogeneous positive expression

  • chRCC: Expect predominantly negative staining

  • RO: Expect consistent positive expression

• Heterogeneity assessment:

  • Evaluate multiple tumor regions when possible

  • Note any intratumoral variation in expression patterns

  • Consider correlation with morphological features

• Prognostic implications:

  • In ccRCC, decreased expression correlates with higher grade and stage

  • Interpret findings in the context of other molecular and clinical parameters

What are the most common technical artifacts in HNF1B detection and how can they be distinguished from genuine findings?

Common technical artifacts in HNF1B detection and their resolution:

• False positives:

  • Non-specific binding: May appear as diffuse background staining

  • Edge artifacts: Stronger staining at tissue edges

  • Trap antibodies: Can cause staining in necrotic areas or extracellular space

  • Resolution: Compare with negative controls; focus on nuclear staining pattern; optimize antibody concentration and washing steps

• False negatives:

  • Inadequate antigen retrieval: May result in weak/absent staining despite HNF1B presence

  • Epitope masking: Fixation can hide antibody binding sites

  • Resolution: Optimize antigen retrieval method (try both citrate buffer pH 6.0 and TE buffer pH 9.0); extend retrieval time; verify with alternative antibodies targeting different epitopes

• Interpretation challenges:

  • Cross-reactivity: Some antibodies may detect related proteins (e.g., HNF1A)

  • Variable fixation: Different fixation times across samples can affect staining

  • Resolution: Use multiple antibodies; standardize fixation protocols; include positive controls

• Western blot-specific issues:

  • Multiple bands: May indicate isoforms, degradation products, or non-specific binding

  • Migration variations: HNF1B may run at 58-65 kDa rather than the calculated 61 kDa

  • Resolution: Use positive controls (L02, Jurkat, MDCK cells); run knockdown controls; optimize lysis conditions

• Distinguishing artifacts from genuine findings:

  • Consistency: True findings should be reproducible across technical replicates

  • Biological coherence: Findings should align with known biology (nuclear localization, expected molecular weight)

  • Control validation: Compare with appropriate positive and negative controls

  • Orthogonal validation: Confirm with alternative methods (e.g., RNA expression, promoter methylation status)

How is HNF1B being investigated as a biomarker in renal cancer and other malignancies?

HNF1B is emerging as a significant biomarker across multiple cancer types, with particularly notable applications in renal cancers:

• Diagnostic applications:

  • Differential diagnosis of renal tumor subtypes:

    • Strong expression in ccRCC and papRCC

    • Near absence in chRCC

    • Consistent expression in oncocytomas

  • This expression pattern could potentially aid in distinguishing challenging cases

• Prognostic implications:

  • In ccRCC: Decreased HNF1B expression correlates with higher tumor grade and higher T stage

  • This suggests potential utility as a prognostic marker in predicting disease aggressiveness and patient outcomes

• Tumor biology insights:

  • Dual role as tumor suppressor or oncogene depending on cancer type:

    • Potential tumor suppressor in ccRCC and chRCC

    • Potential oncogene in papRCC

  • Understanding these context-dependent functions may reveal new therapeutic approaches

• Integration with molecular profiling:

  • Combined analysis of HNF1B expression, mutation status, and promoter methylation

  • This multi-modal approach provides deeper insights than any single parameter alone

• Methodological considerations:

  • Standardized IHC protocols using validated antibodies

  • Quantitative scoring systems (H-score) for objective assessment

  • Integration with genetic and epigenetic analysis for comprehensive profiling

What are the challenges in correlating HNF1B genetic alterations with protein expression detected by antibodies?

Several challenges exist when correlating HNF1B genetic alterations with protein expression detected by antibodies:

• Epitope-specific effects:

  • Mutations may affect the antibody recognition site while the protein remains expressed

  • Different antibodies targeting different epitopes may yield discordant results

  • Solution: Use multiple antibodies targeting different HNF1B regions

• Post-transcriptional and post-translational regulation:

  • Genetic analysis (DNA level) may not reflect mRNA expression

  • mRNA expression may not correlate with protein levels due to translational regulation

  • Post-translational modifications may affect antibody binding or protein stability

  • Solution: Integrate multi-omics approaches (genomics, transcriptomics, proteomics)

• Epigenetic complexities:

  • Promoter methylation status significantly affects HNF1B expression

  • The relationship between methylation and expression may be non-linear

  • Key CpG islands (-457 to -202 bp before TSS) show strongest correlation with expression

  • Solution: Conduct targeted methylation analysis of specific regulatory regions

• Technical considerations:

  • Sensitivity differences between genetic and protein detection methods

  • Tumor heterogeneity may result in sampling discordance between methods

  • Limited detection of low-frequency variants in bulk tissue analysis

  • Solution: Analyze matched samples from the same tissue region; consider single-cell approaches

• Functional impact assessment:

  • Not all mutations affect protein function equally

  • Silent mutations or non-disruptive variants may have minimal impact on protein levels

  • Solution: Focus on functionally relevant mutations (e.g., nonsense, frameshift, key missense)