PDE11A Antibody, HRP conjugated

What is PDE11A and why is it important in scientific research?

PDE11A (Phosphodiesterase 11A) is a member of the phosphodiesterase family of enzymes that hydrolyzes both cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) with similar Vmax values and Km values of 1.04 μM and 0.52 μM, respectively . This dual-substrate specificity makes PDE11A unique among PDEs and potentially important in regulating both signaling pathways simultaneously. PDE11A has gained scientific importance due to its expression in multiple tissues and its emerging role as a potential biomarker in several pathological conditions, most notably glioblastoma where it is significantly overexpressed compared to normal brain tissue . Understanding PDE11A's function and expression patterns requires specific antibodies for detection and quantification in various experimental settings.

Which tissue types express PDE11A and at what levels?

PDE11A shows variable expression across different tissue types. According to tissue distribution studies, PDE11A mRNA occurs at highest levels in skeletal muscle, prostate, kidney, liver, pituitary, salivary glands, and testis . Recent studies have also shown significant PDE11A overexpression in glioblastoma cell lines (U87-MG, U251-MG, and U343-MG) compared to control HaCaT cells, both at protein and mRNA levels . Developmental studies using immunohistochemistry have tracked PDE11A expression from early embryonic stages (e9.5) through adulthood, revealing dynamic expression patterns during development . For comprehensive tissue expression profiling, researchers should consider using validated antibodies in conjunction with techniques like qRT-PCR to correlate protein and mRNA expression levels.

What are the known isoforms of PDE11A and how can antibodies differentiate between them?

PDE11A exists in multiple isoforms. Research has detected at least three major transcripts of approximately 10.5, 8.5, and 6.0 kb, suggesting the existence of multiple subtypes . This is further supported by Western blotting studies that have identified three distinct protein isoforms with approximate molecular weights of 78, 65, and 56 kDa in human tissues . The isoform PDE11A1 has been well-characterized with a complete open reading frame encoding a 490-amino acid enzyme with a predicted molecular mass of 55,786 Da . When selecting a PDE11A antibody, researchers should verify which epitope is targeted and whether it can distinguish between specific isoforms. For example, the polyclonal antibody described in result targets a sequence corresponding to amino acids 712-933 of human PDE11A, which may recognize specific isoforms depending on their sequence conservation in this region.

What experimental applications are PDE11A antibodies suitable for?

PDE11A antibodies have been validated for several experimental applications. Based on the search results, these applications include:

• Western blotting: For detecting PDE11A protein expression in cell lines and tissue samples

• Immunohistochemistry (IHC): For visualizing PDE11A expression in tissue sections, including tissue arrays and developmental studies

• Immunofluorescence (IF): Some antibodies are validated for subcellular localization studies

When selecting an antibody for a specific application, researchers should verify the validation data for that particular application. For instance, the polyclonal antibody CAB16121 is specifically validated for Western blot applications and shows high reactivity with human, mouse, and rat samples .

How can PDE11A antibodies be utilized to study glioblastoma progression and prognosis?

Recent research has identified PDE11A as a potential biomarker for glioblastoma (GBM). Studies have shown that PDE11A is significantly overexpressed in glioblastoma cell lines and patient tissue samples compared to normal controls . Importantly, Kaplan-Meier survival analysis using the REMBRANDT cohort showed that high PDE11A mRNA expression correlated with poor survival in glioma patients, indicating that PDE11A expression levels may have prognostic value .

For studying GBM progression and prognosis with PDE11A antibodies, researchers can:

• Perform immunohistochemistry on tissue microarrays containing samples from different stages of glioma progression to correlate PDE11A expression with disease advancement

• Combine Western blotting quantification of PDE11A with patient outcome data to establish threshold values for prognostic classification

• Use dual immunostaining with other established GBM markers to create a more comprehensive prognostic panel

• Validate findings by comparing protein expression (detected by antibodies) with mRNA expression data

These approaches should be accompanied by proper statistical analysis to establish PDE11A's value as a prognostic biomarker.

What methodological considerations are critical when validating PDE11A antibody specificity?

Validation of antibody specificity is crucial for reliable research outcomes. For PDE11A antibodies, consider these methodological approaches:

• siRNA knockdown controls: Use siRNA against PDE11A (like the validated sequence: sense 5′-ACUAUCGGAUGGUUCUAUATT−3′ and anti-sense 5′-UAUAGAACCAUCCGAUAGUTT−3′) to create knockdown cells showing reduced antibody signal compared to control siRNA-treated cells

• Peptide competition assays: Pre-incubate the antibody with excess immunogenic peptide (for example, the sequence corresponding to amino acids 712-933 of human PDE11A for CAB16121) before application to samples, which should eliminate specific binding

• Multiple antibody validation: Use antibodies targeting different epitopes of PDE11A to confirm consistent detection patterns

• Knockout/knockdown validation: Compare antibody reactivity in wild-type versus Pde11a knockout or knockdown samples, though care must be taken as some knockout models may still express truncated forms of PDE11A

• Cross-species reactivity assessment: Test antibody performance across species with known PDE11A sequence homology to ensure consistent detection based on epitope conservation

A comprehensive validation should incorporate multiple approaches, properly documented with quantitative analysis of specificity metrics.

How can researchers optimize PDE11A detection in fixed tissue versus fresh samples?

Optimization strategies differ between fixed tissue and fresh samples:

For fixed tissue samples:

• Optimize fixation time: Extended formalin fixation can mask epitopes; the protocol in search result used 15 minutes in 4% PFA followed by PBS washes

• Implement antigen retrieval: Heat-induced epitope retrieval in citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) may improve antibody access to PDE11A epitopes

• Adjust antibody concentration: Titrate antibody dilutions (e.g., starting with 1:75 as used in )

• Extend primary antibody incubation: Overnight incubation at 4°C improved results in developmental studies

• Use detection amplification systems: For tissues with lower PDE11A expression, consider tyramide signal amplification

For fresh/frozen samples:

• Optimize fixation protocol: Brief fixation (60 minutes in 4% formaldehyde) followed by PBS washes maintained epitope accessibility in embryonic tissues

• Section thickness: 8-10μm sections provided sufficient signal while maintaining tissue integrity

• Block endogenous peroxidase activity: Particularly important for HRP-conjugated antibodies to reduce background

• Include detergent optimization: Adjust Triton X-100 concentration to balance membrane permeabilization with epitope preservation

In both cases, include appropriate positive controls (tissues known to express PDE11A such as skeletal muscle, prostate, or GBM samples) and negative controls (secondary antibody only).

What are the optimal parameters for quantitative analysis of PDE11A expression using antibody-based methods?

For accurate quantitative analysis of PDE11A expression:

Western Blotting Quantification:

• Standardize protein loading using validated housekeeping proteins (β-actin was used in study )

• Use recombinant PDE11A standards for absolute quantification

• Implement digital image analysis with appropriate software for densitometry

• Calculate relative expression using the ratio of PDE11A to housekeeping protein band intensity

• Run samples in triplicate for statistical validity

Immunohistochemistry Quantification:

• Use standardized staining protocols across all samples

• Include calibration slides in each batch

• Apply digital pathology approaches:

  • H-score calculation (intensity × percentage of positive cells)

  • Automated image analysis for consistent scoring

  • Consider subcellular localization patterns in the analysis

• Use multiple fields per sample (minimum 5-10) for representative quantification

Correlation with mRNA Data:
To validate protein expression findings, correlate with mRNA quantification:

• Design qRT-PCR primers spanning specific exons (as in study )

• Use validated reference genes (GAPDH was used in study )

• Apply the comparative Ct method (2^-ΔΔCt) for relative quantification

• Calculate Pearson's correlation coefficient between protein and mRNA data

This multi-method approach provides more reliable quantitative data on PDE11A expression.

What are the common sources of false positives in PDE11A antibody experiments and how can they be mitigated?

False positives in PDE11A antibody experiments can arise from several sources:

• Cross-reactivity with related phosphodiesterases: PDE11A shares sequence homology with other PDE family members, particularly PDE5 as noted in search result . To mitigate:

  • Select antibodies against unique regions of PDE11A

  • Validate specificity using overexpression systems with individual PDE family members

  • Perform peptide competition assays to confirm specificity

• Non-specific binding: To reduce:

  • Optimize blocking conditions (5% BSA or 5% non-fat milk in TBST)

  • Include 0.1-0.3% Tween-20 in wash buffers

  • Titrate antibody concentration to determine optimal dilution

  • Consider using monoclonal antibodies for higher specificity

• Endogenous peroxidase activity (particularly relevant for HRP-conjugated antibodies): To address:

  • Include a peroxidase quenching step (3% H₂O₂ for 10-15 minutes)

  • For tissue sections, treat with H₂O₂ in methanol to penetrate membranes effectively

• Fc receptor binding: To minimize:

  • Include human or animal serum (matching secondary antibody source) in blocking buffer

  • Consider using F(ab')₂ fragments instead of whole IgG

• Batch variation: To control:

  • Use antibodies from the same lot for comparative studies

  • Include standard positive controls in each experiment

Proper experimental design with appropriate controls is essential for distinguishing true PDE11A signal from false positives.

What controls should be included in experiments using PDE11A antibodies?

A robust experimental design should include these controls:

Positive Controls:

• Tissues/cells known to express high levels of PDE11A (skeletal muscle, prostate, glioblastoma cell lines like U87-MG)

• Recombinant PDE11A protein (if available)

• Overexpression systems (transfected cells expressing PDE11A)

Negative Controls:

• Primary antibody omission (secondary antibody only)

• Isotype control (non-specific IgG from same species as primary antibody)

• PDE11A-knockdown samples using validated siRNA (sense 5′-ACUAUCGGAUGGUUCUAUATT−3′ and anti-sense 5′-UAUAGAACCAUCCGAUAGUTT−3′)

• Pre-adsorption control (antibody pre-incubated with immunizing peptide)

Procedural Controls:

• Internal reference standards (housekeeping proteins like β-actin)

• Technical replicates (minimum triplicate)

• Biological replicates (different samples, minimum n=3)

• Cross-validation using different detection methods (e.g., IF vs. WB)

Quantitative Controls:

• Standard curve using recombinant PDE11A (for absolute quantification)

• Dilution series to confirm antibody linearity

• Multiple exposure times (for Western blots)

These controls should be properly documented and included in methods sections of publications to facilitate reproducibility.

How can researchers improve signal-to-noise ratio when using HRP-conjugated PDE11A antibodies?

To enhance signal-to-noise ratio with HRP-conjugated PDE11A antibodies:

Optimization Strategies:

• Antibody titration: Systematically test dilutions to find the optimal concentration that maximizes specific signal while minimizing background

• Blocking optimization: Test different blocking agents (BSA, non-fat milk, normal serum, commercial blockers) at various concentrations (3-5%)

• Buffer optimization: Adjust salt concentration in wash buffers to reduce non-specific interactions

• Incubation conditions: Compare room temperature vs. 4°C incubation with adjusted timing

Enhanced Detection Approaches:

• Substrate selection: Compare chemiluminescent substrates with different sensitivities (standard ECL vs. enhanced ECL)

• Signal amplification: Consider tyramide signal amplification for tissues with low PDE11A expression

• Development time optimization: Monitor signal development to prevent overdevelopment and background buildup

Background Reduction Techniques:

• Extended washing: Increase number and duration of washes with gentle agitation

• Detergent adjustment: Optimize Tween-20 or Triton X-100 concentration in wash buffers

• Secondary antibody cross-adsorption: Use highly cross-adsorbed secondary antibodies to minimize species cross-reactivity

• Fresh reagents: Use freshly prepared buffers and substrates to ensure optimal performance

Tissue-Specific Considerations:

• Autofluorescence quenching: For IF applications, consider Sudan Black B treatment

• Endogenous enzyme blocking: Thorough peroxidase and alkaline phosphatase blocking for IHC applications

• Tissue pre-treatment: Optimize antigen retrieval methods specific to tissue type

A systematic optimization approach, documenting each parameter's effect on signal-to-noise ratio, will yield the most reliable results.

How can PDE11A antibodies be utilized in co-localization studies to understand its cellular function?

Co-localization studies with PDE11A antibodies can provide valuable insights into its cellular function and interactions:

Methodological Approach:

• Dual immunofluorescence: Use PDE11A antibody alongside markers for:

  • Cellular compartments (nucleus, mitochondria, endoplasmic reticulum)

  • Signaling pathway components (adenylyl cyclase, protein kinase A, cGMP-dependent protein kinase)

  • Cell type-specific markers (especially in heterogeneous tissues like brain)

• Proximity ligation assay (PLA): For detecting protein-protein interactions between PDE11A and potential binding partners with spatial resolution below 40nm

• super-resolution microscopy: Techniques like STED or STORM can provide nanoscale resolution of PDE11A localization

• Live-cell imaging: For temporal dynamics, combine with fluorescently tagged cAMP/cGMP sensors to correlate PDE11A localization with cyclic nucleotide levels

Analysis and Quantification:

• Calculate Pearson's or Mander's correlation coefficients to quantify co-localization

• Perform line-scan analysis across cellular compartments

• Use 3D reconstruction for volumetric co-localization analysis

• Apply automated image analysis algorithms for unbiased quantification

Biological Relevance in Glioblastoma Research:
Since PDE11A is overexpressed in glioblastoma , co-localization studies could investigate:

• PDE11A interaction with growth factor receptors in cancer cell membranes

• Nuclear translocation patterns during cell cycle progression

• Association with invasion/migration machinery in tumor cells

• Changes in PDE11A localization in response to treatment

These approaches can connect PDE11A's subcellular distribution to its functional role in normal and pathological conditions.

What methodological approaches are recommended for studying the effects of PDE11A inhibition in cell culture models?

To study PDE11A inhibition effects in cell culture:

Inhibition Strategies:

• Pharmacological inhibition: Use PDE11A inhibitors with known IC₅₀ values:

  • IBMX (non-selective, IC₅₀: 49.8 μM)

  • Zaprinast (IC₅₀: 12.0 μM)

  • Dipyridamole (IC₅₀: 0.37 μM)

  • Include dose-response studies and time-course analyses

• Genetic inhibition: Use validated siRNA sequences:

  • sense 5′-ACUAUCGGAUGGUUCUAUATT−3′

  • anti-sense 5′-UAUAGAACCAUCCGAUAGUTT−3′

  • Control siRNA: sense 5′-UUCUCCGAACGUGUCACGUTT−3′ and anti-sense 5′-ACGUGACACGUUCGGAGAATT−3′

  • Consider inducible shRNA systems for temporal control of knockdown

Functional Readouts:

• Cyclic nucleotide measurements:

  • ELISA-based cAMP/cGMP quantification

  • FRET-based real-time cyclic nucleotide sensors

  • Correlation with PDE11A expression levels by Western blotting

• Cellular phenotypes (based on GBM findings ):

  • Proliferation assays (MTT, BrdU incorporation)

  • Cell cycle analysis (flow cytometry with PI staining)

  • Migration/invasion assays (transwell, wound healing)

  • Apoptosis assessment (Annexin V/PI staining)

Experimental Design Table:

This comprehensive approach allows researchers to connect PDE11A inhibition with specific cellular outcomes.

How can researchers integrate PDE11A antibody data with genetic and transcriptomic analyses for comprehensive biomarker studies?

Integration of antibody-based protein data with genetic and transcriptomic analyses creates a multilevel biomarker approach:

Data Integration Framework:

• Protein-mRNA correlation analysis:

  • Quantify PDE11A protein levels using validated antibodies in Western blot or IHC

  • Measure corresponding mRNA using qRT-PCR with primers spanning exons (as in search result )

  • Calculate correlation coefficients between protein and mRNA levels

  • Investigate discordance cases that might indicate post-transcriptional regulation

• Multi-omics integration approaches:

  • Combine PDE11A antibody-based proteomics with:

    • RNA-seq data (gene expression)

    • DNA methylation status of PDE11A promoter

    • Copy number variations affecting PDE11A locus

    • miRNA profiles targeting PDE11A mRNA

  • Apply dimensionality reduction techniques (PCA, t-SNE) to visualize integrated datasets

• Prognostic value enhancement:

  • Develop multivariate models incorporating:

    • PDE11A protein expression (antibody-based)

    • PDE11A mRNA levels

    • Genetic alterations

    • Clinical parameters

  • Use machine learning approaches to identify the most predictive combination of markers

  • Validate findings across independent patient cohorts

Methodological Considerations:

• Ensure antibody specificity through rigorous validation

• Use consistent sampling procedures across omics platforms

• Apply appropriate normalization methods for cross-platform comparison

• Implement statistical approaches for handling multi-dimensional data

Applied Example for Glioblastoma Research:
Based on search result , PDE11A's potential as a GBM biomarker could be enhanced by:

• Correlating antibody-detected PDE11A overexpression with transcript levels

• Integrating with REMBRANDT survival data and other genomic datasets

• Developing a composite biomarker panel including PDE11A protein/mRNA and other GBM markers

• Comparing protein expression in patient-derived xenografts with corresponding genomic profiles

This integrated approach provides more robust biomarker identification than single-platform analyses.

What experimental design is recommended for studying PDE11A expression changes during disease progression using antibody-based methods?

For studying PDE11A expression changes during disease progression:

Longitudinal Sample Collection Strategy:

• Time-point selection: Define clinically relevant stages of disease progression

• Sampling consistency: Use standardized collection and processing protocols

• Patient stratification: Group samples by disease subtype, treatment response, outcome

• Control samples: Include matched non-diseased tissues when possible

Antibody-Based Detection Methods:

• Tissue microarrays (TMAs): For high-throughput analysis of multiple patient samples and timepoints

• Whole section IHC: For spatial distribution analysis of PDE11A in heterogeneous tissues

• Multiplex IHC/IF: Co-staining with disease stage markers and cell-type specific markers

• Quantitative Western blotting: For precise quantification of PDE11A protein levels

Validation and Controls:

• Technical validation: Include positive and negative controls on each TMA/slide

• Biological validation: Correlate with multiple disease markers

• Replicate analysis: Independent scoring by multiple trained observers

• Quantification standards: Include calibration samples of known PDE11A concentration

Data Analysis Framework:

Applied Example for Glioblastoma:
Based on search result , researchers could:

• Analyze PDE11A expression in tissue samples representing:

  • Low-grade glioma

  • High-grade glioma/GBM at diagnosis

  • Recurrent GBM

• Correlate expression patterns with patient survival data

• Investigate whether PDE11A expression changes precede or follow clinical progression

• Determine if PDE11A expression changes correlate with treatment response

This longitudinal approach provides deeper insights than single-timepoint analyses and may reveal PDE11A's role in disease mechanisms.

What future research directions are most promising for PDE11A antibody applications?

Based on the current state of PDE11A research, several promising future directions emerge:

• Therapeutic targeting validation: As mentioned in search result , PDE11A could be a "therapeutic target for glioma." Future studies should use antibodies to validate target engagement of PDE11A inhibitors and correlate with therapeutic outcomes.

• Biomarker development: Expand on the finding that "PDE11A could be a putative diagnostic marker" for glioma by developing standardized antibody-based diagnostic assays with clinically validated cutoff values.

• Isoform-specific functions: Develop and validate isoform-specific antibodies to distinguish between the multiple PDE11A isoforms (78, 65, and 56 kDa proteins) and determine their differential roles in normal physiology and disease.

• Structure-function studies: Use domain-specific antibodies to investigate the functional significance of PDE11A's structural elements, particularly the GAF domain that constitutes a potential allosteric binding site for cGMP or other small ligands .

• Single-cell analysis: Apply PDE11A antibodies in single-cell proteomics approaches to understand cellular heterogeneity in PDE11A expression within tissues and tumors.

• Non-canonical functions: Investigate potential non-enzymatic roles of PDE11A beyond cAMP/cGMP hydrolysis using antibody-based interaction studies.

These directions would significantly advance our understanding of PDE11A biology and its potential applications in research and medicine.

How should researchers interpret conflicting data between PDE11A antibody results and gene expression studies?

When faced with discrepancies between antibody-based protein detection and gene expression data:

Systematic Troubleshooting Approach:

• Verify antibody specificity:

  • Confirm epitope conservation across isoforms

  • Check for cross-reactivity with related proteins

  • Validate using knockout/knockdown controls

• Consider post-transcriptional regulation:

  • Investigate miRNA-mediated suppression

  • Assess protein stability and half-life

  • Examine translational efficiency

• Evaluate technical factors:

  • Sample preparation differences

  • Detection sensitivity limits

  • Antibody lot variation

  • Primer design and specificity for gene expression studies

• Explore biological explanations:

  • Tissue-specific post-translational modifications affecting epitope recognition

  • Alternative splicing creating variants not detected by certain antibodies

  • Subcellular localization changes affecting extraction efficiency

Methodology for Resolution:

• Use multiple antibodies targeting different epitopes

• Apply complementary protein detection methods (mass spectrometry)

• Design transcript-specific primers targeting different exons

• Perform time-course studies to identify temporal discordance

• Isolate different cellular compartments to check for localization-dependent expression