ADRB2 Antibody

What is ADRB2 and what tissues express this protein?

ADRB2 (Beta-2 adrenergic receptor) is a member of the G protein-coupled receptor superfamily (GPCRs) with seven transmembrane helices. It functions as a receptor for catecholamines and plays important roles in various physiological processes.

ADRB2 is widely expressed across multiple tissue types with variable expression levels. According to expression data and literature evidence, ADRB2 is found in:

• Squamous epithelium

• Brain and fetal brain

• Blood and leukocytes

• Heart

• Thyroid

• Prostate

• Embryonic and adult kidney

• Lung tissue

Expression has been confirmed through multiple studies, including those referenced in PubMed IDs: 11246467, 3026848, 14702039, 17525332, and 15489334 .

What applications are ADRB2 antibodies typically validated for?

ADRB2 antibodies are validated for multiple research applications, with validation levels varying by manufacturer and clone. The most common applications include:

Many antibodies are validated for multiple applications, though their performance may vary across different experimental systems and tissue types .

How should I validate an ADRB2 antibody for my specific tissue or cell type?

Proper antibody validation is critical for ensuring reliable experimental results, especially when working with a new tissue or cell type. A comprehensive validation approach should include:

• Positive and negative controls:

  • Use tissues/cells known to express ADRB2 (e.g., brain tissue for rat/human)

  • Include knockout/knockdown samples when possible

  • Consider embryonic kidney, which has confirmed ADRB2 expression

• Multiple detection methods:

  • Compare results across different techniques (WB, IHC, etc.)

  • Verify band size in WB (expected ~47 kDa for ADRB2)

• Peptide competition:

  • Pre-incubate antibody with blocking peptide to confirm specificity

• Antibody titration:

  • Test multiple concentrations (e.g., 0.1-0.5 μg/ml for WB)

  • Optimize signal-to-noise ratio

• Reproducibility testing:

  • Repeat experiments under identical conditions

  • Test technical and biological replicates

When expanding to a new tissue type like embryonic kidney, consider the expression profile from databases and literature before proceeding with experimental validation .

What are the optimal conditions for using ADRB2 antibodies in Western blot applications?

Successful Western blot detection of ADRB2 requires careful optimization of several parameters:

• Sample preparation:

  • For membrane proteins like ADRB2, use specialized lysis buffers containing mild detergents (e.g., 1% Triton X-100 or RIPA buffer)

  • Avoid harsh detergents that may disrupt protein conformation

  • Include protease inhibitors to prevent degradation

  • Avoid excessive heating of samples (use 37°C instead of 95°C)

• Gel electrophoresis conditions:

  • Use 10-12% acrylamide gels for optimal resolution of the 47 kDa ADRB2 protein

  • Load adequate protein (30-50 μg total protein per lane)

• Transfer and detection:

  • Wet transfer is often superior for membrane proteins

  • Recommended antibody dilution: 0.1-0.5 μg/ml

  • Overnight incubation at 4°C may improve signal quality

  • Use rat brain tissue as a positive control (validated to show a 47 kDa band)

• Blocking and washing:

  • 5% non-fat dry milk or BSA in TBST is typically effective

  • Thorough washing (at least 3 × 10 minutes) reduces background

Following these conditions should yield a specific band at approximately 47 kDa, as observed in validated Western blots with rat brain tissue lysate .

How can I optimize immunohistochemistry protocols for ADRB2 detection in tissue samples?

Optimizing IHC for ADRB2 requires attention to several critical factors:

• Tissue preparation:

  • For frozen sections, maintain optimal cutting temperature (OCT) embedding

  • For FFPE tissues, standardize fixation time (typically 24h in 10% neutral buffered formalin)

  • Section thickness of 4-6 μm is generally suitable

• Antigen retrieval:

  • Heat-induced epitope retrieval (HIER) with citrate buffer (pH 6.0) is often effective

  • For membrane proteins like ADRB2, enzymatic retrieval may occasionally provide better results

• Antibody incubation:

  • Titrate antibody concentration (start with manufacturer's recommendation)

  • Extended incubation (overnight at 4°C) often improves specific staining

  • Include appropriate blocking steps to minimize background

• Controls and validation:

  • Include positive control tissues (brain, lung, or embryonic kidney)

  • Use negative controls (primary antibody omission and isotype controls)

  • Consider dual-labeling with markers of structures known to express ADRB2

• Signal detection:

  • Chromogenic vs. fluorescent detection depends on research needs

  • For co-localization studies, fluorescent detection offers advantages

When working with embryonic kidney tissue, which has been confirmed to express ADRB2, careful optimization of these parameters should yield specific staining patterns .

What approach should I take to quantify ADRB2 autoantibodies in patient samples?

Based on the research involving myasthenia patients, quantification of anti-ADRB2 autoantibodies requires a carefully designed ELISA approach:

• Antigen preparation:

  • Use recombinant ADRB2 protein produced through genetic engineering techniques

  • Ensure proper folding and conformation of the protein to preserve relevant epitopes

  • Consider using purified membrane preparations containing ADRB2

• ELISA protocol optimization:

  • Determine optimal coating concentration of recombinant ADRB2

  • Optimize blocking conditions to minimize non-specific binding

  • Establish appropriate patient sample dilutions (typically 1:100 to 1:1000)

  • Include standard curves using characterized antibodies when possible

• Controls and validation:

  • Include known positive and negative samples

  • Establish a cutoff value based on healthy control populations

  • Consider competitive inhibition assays to confirm specificity

• Data analysis:

  • Normalize results to account for plate-to-plate variation

  • Present results as optical density ratios or calculated concentrations

  • Correlate with clinical parameters and disease severity

This approach has successfully identified elevated anti-ADRB2 antibodies in 12 of 30 patients with generalized myasthenia, suggesting potential clinical utility of this assay .

How do ADRB2 polymorphisms affect antibody binding and experimental interpretation?

ADRB2 genetic polymorphisms, particularly Arg16Gly and Gln27Glu variants, can significantly impact antibody-based research in several ways:

• Epitope accessibility:

  • Amino acid substitutions may alter protein conformation and epitope accessibility

  • Antibodies targeting regions containing or adjacent to polymorphic sites may show differential binding

  • These effects are particularly relevant for antibodies recognizing conformational epitopes

• Expression level variations:

  • Polymorphisms may affect receptor expression levels or membrane localization

  • Gly16 variants have been associated with altered receptor downregulation and expression

• Experimental design considerations:

  • Researchers should determine ADRB2 genotypes in their experimental systems when possible

  • Including samples representing different genotypes can help identify binding variability

  • When using cell lines, verify their ADRB2 genotype status

• Data interpretation:

  • Seemingly contradictory results between studies may be explained by genomic variation

  • Population-specific differences in ADRB2 polymorphism frequency should be considered

  • Clinical correlations may vary based on the predominant genotype in the study population

Studies have shown that the Arg16Gly polymorphism is a loss-of-function variant with respect to G protein-coupling, which could affect both physiological responses and antibody binding characteristics in certain experimental conditions .

What is the significance of ADRB2 expression as a prognostic biomarker in cancer research?

Recent research has identified ADRB2 expression as a potential prognostic biomarker, particularly in lung adenocarcinoma (LUAD). The significance of this finding encompasses several dimensions:

These findings indicate ADRB2 could serve as a valuable biomarker in cancer research, with potential implications for patient stratification and therapeutic development .

How should I design experiments to investigate the relationship between ADRB2 genotype and treatment response?

Designing robust experiments to investigate ADRB2 genotype-treatment relationships requires careful consideration of multiple factors:

• Genotyping strategy:

  • Use reliable methods like MLPA-PCR to determine ADRB2 haplotypes

  • Focus on key polymorphisms (Arg16Gly, Gln27Glu) with established functional significance

  • Consider complete haplotype analysis rather than individual SNPs

• Experimental models:

  • For in vitro studies, compare primary cells from subjects with different genotypes

  • Consider genetically modified cell lines expressing specific ADRB2 variants

  • Animal models should reflect the genetic diversity observed in humans

• Functional readouts:

  • Measure multiple parameters (cAMP levels, contractile response, gene expression)

  • Include G-protein coupling assessment (e.g., using pertussis toxin to disrupt Gi signaling)

  • Assess receptor desensitization and internalization kinetics

• Treatment conditions:

  • Test dose-response relationships for ADRB2 agonists/antagonists

  • Include time-course experiments to capture temporal dynamics

  • Consider combination treatments that reflect clinical scenarios

• Statistical considerations:

  • Power calculations should account for expected effect sizes between genotypes

  • Include sufficient replicates for each genotype group

  • Plan appropriate statistical tests for genotype-based comparisons

Previous research has demonstrated that the Arg16Gly polymorphism significantly affects response to β-adrenergic receptor blockers in heart failure patients, with differing effects based on specific genotype combinations .

What are common challenges in ADRB2 antibody experiments and how can I overcome them?

Researchers working with ADRB2 antibodies frequently encounter several technical challenges:

• Non-specific binding:

  • Problem: Multiple bands in Western blot or diffuse staining in IHC/ICC

  • Solution: Optimize antibody dilution (typically 0.1-0.5 μg/ml), increase blocking agent concentration, and extend washing steps

  • Additional approach: Use highly purified primary antibodies and validate with appropriate controls

• Poor signal strength:

  • Problem: Weak or absent bands/staining despite confirmed ADRB2 expression

  • Solution: Increase protein loading for WB, optimize antigen retrieval for IHC, try alternative fixation methods

  • Additional approach: Consider signal amplification systems or more sensitive detection methods

• Variability between experiments:

  • Problem: Inconsistent results across replicates

  • Solution: Standardize protocols rigorously, prepare fresh working solutions, and control temperature conditions

  • Additional approach: Use automated systems where possible to minimize human error

• Antibody specificity concerns:

  • Problem: Difficulty confirming true ADRB2-specific signal

  • Solution: Use multiple antibodies targeting different epitopes, include peptide competition controls

  • Additional approach: Compare with genetic knockdown/knockout systems when available

• Membrane protein solubilization issues:

  • Problem: Poor extraction of ADRB2 from membrane fractions

  • Solution: Use specialized detergent combinations (e.g., CHAPS, digitonin) and optimize solubilization conditions

  • Additional approach: Consider native protein extraction methods for conformationally sensitive applications

Proper storage of antibodies (aliquoted and stored at -20°C) and avoiding repeated freeze-thaw cycles also helps maintain consistent performance across experiments .

How should I modify my protocols when planning to conjugate ADRB2 antibodies with biotin or other labels?

Successful conjugation of ADRB2 antibodies with biotin or other labels requires careful consideration of several factors:

• Pre-conjugation preparation:

  • Remove carrier proteins (BSA) and preservatives (sodium azide) through buffer exchange

  • Recommended buffer: PBS without additives (pH 7.2-7.4)

  • Concentrate antibody to appropriate levels (typically 1-2 mg/ml)

• Conjugation chemistry selection:

  • For biotin: NHS-ester chemistry is commonly used to target primary amines

  • Consider site-specific conjugation methods to avoid affecting antigen-binding regions

  • Optimize conjugation ratio (typically 4-8 biotin molecules per antibody)

• Post-conjugation processing:

  • Remove unconjugated label through dialysis or gel filtration

  • Verify conjugation efficiency through spectrophotometric methods

  • Test functionality with known positive controls

• Storage considerations:

  • Store in small aliquots at -20°C to avoid freeze-thaw cycles

  • Consider adding stabilizers (e.g., BSA) after conjugation

  • Include mild preservatives if long-term storage is needed

• Validation of conjugated antibody:

  • Compare performance to unconjugated antibody

  • Titrate to determine optimal working concentration

  • Verify that conjugation hasn't altered specificity or sensitivity

When conjugating ADRB2 antibodies like PB9365, following these guidelines should help maintain antibody functionality while providing the advantages of the added label .

What controls should I include when studying ADRB2 in the context of genetic polymorphisms?

When investigating ADRB2 in the context of genetic polymorphisms, comprehensive controls are essential for robust and interpretable results:

• Genotype verification controls:

  • Confirm genotypes through multiple independent methods

  • Include known reference samples for each genotype group

  • Consider sequencing validation for critical samples

• Expression level controls:

  • Quantify ADRB2 mRNA and protein levels across genotype groups

  • Normalize to appropriate housekeeping genes/proteins

  • Account for potential allele-specific expression

• Functional validation controls:

  • For Arg16Gly polymorphism studies:

    • Include G-protein coupling assessments (e.g., pertussis toxin treatment)

    • Measure cAMP responses to standardized stimuli

    • Compare receptor internalization rates

• Cell/tissue type controls:

  • Use matched cell/tissue types across genotype groups

  • Consider potential confounding factors (age, sex, disease state)

  • Include both homozygous and heterozygous samples when possible

• Pharmacological response controls:

  • Include dose-response curves for agonists/antagonists

  • Compare responses to standard β2AR ligands

  • Test for cross-reactivity with other adrenergic receptors

Research has demonstrated that the Arg16Gly polymorphism significantly affects G-protein coupling and response to β-adrenergic receptor stimulation, with the Gly16 variant showing reduced G-protein coupling efficiency compared to the Arg16 variant .

How do I interpret contradictory findings regarding ADRB2 expression in different disease states?

Contradictory findings regarding ADRB2 expression across different disease states are common in the literature and require careful analysis:

• Consider methodological differences:

  • Antibody selection: Different antibodies may recognize distinct epitopes

  • Detection methods: RNA vs. protein quantification may yield different results

  • Sample preparation: Membrane protein extraction methods vary significantly

• Account for genetic heterogeneity:

  • ADRB2 polymorphisms may affect expression patterns and antibody detection

  • Population differences in allele frequency can explain inconsistent findings

  • Genotype data should be considered when comparing across studies

• Tissue and cellular context:

  • ADRB2 expression is tissue-specific and can change with disease progression

  • Cellular composition of samples may vary (especially in complex tissues)

  • Single-cell approaches may reveal patterns obscured in bulk tissue analysis

• Disease stage and severity:

  • Expression patterns may change dynamically during disease progression

  • Stratify analyses by disease stage, severity, and treatment status

  • Consider temporal dynamics in chronic conditions

• Integration of multiple data types:

  • Combine transcriptomic, proteomic, and functional data

  • Consider post-translational modifications and receptor trafficking

  • Validate findings across independent cohorts when possible

What are the implications of ADRB2 autoantibodies in autoimmune disease research?

The presence of autoantibodies against ADRB2 in conditions like myasthenia gravis has several important implications for autoimmune disease research:

• Diagnostic potential:

  • Anti-ADRB2 antibodies were detected in 12 of 30 patients with generalized myasthenia

  • These antibodies could serve as biomarkers for disease subtyping

  • Quantitative ELISA methods allow precise measurement of antibody levels

• Pathophysiological mechanisms:

  • Autoantibodies may interfere with normal ADRB2 signaling

  • This interference could contribute to immune dysregulation and motor symptoms

  • ADRB2 autoimmunity may represent a distinct pathophysiological mechanism

• Treatment implications:

  • ADRB2 autoantibody status may predict response to β2-adrenoceptor agonists

  • Genetic polymorphisms (Arg16Gly) affect treatment outcomes and side effects

  • Personalized treatment approaches could be developed based on autoantibody status and genotype

• Research directions:

  • Exploring the relationship between ADRB2 autoantibodies and other autoimmune markers

  • Investigating epitope specificity of anti-ADRB2 antibodies

  • Developing therapeutic strategies to block pathogenic autoantibodies

• Methodological advances:

  • Using recombinant ADRB2 for quantitative autoantibody assays

  • Developing standardized protocols for clinical testing

  • Creating animal models that recapitulate ADRB2 autoimmunity

Understanding the interaction between ADRB2 genetic variants and autoantibody production could lead to more personalized treatment approaches for patients with myasthenia and potentially other autoimmune conditions .

How should I integrate ADRB2 genotype data with functional studies and clinical outcomes?

Integrating ADRB2 genotype information with functional studies and clinical outcomes requires a comprehensive multi-level approach:

• Systematic genotype-phenotype correlation:

  • Analyze individual polymorphisms (Arg16Gly, Gln27Glu) and complete haplotypes

  • Consider gene-gene interactions with related pathways

  • Assess dose-dependent effects (homozygous vs. heterozygous)

• Functional validation strategies:

  • Use primary cells from genotyped individuals

  • Employ CRISPR-engineered isogenic cell lines differing only in ADRB2 genotype

  • Measure multiple functional parameters (signaling, trafficking, expression)

• Translational approaches:

  • Correlate in vitro findings with patient outcomes

  • Stratify clinical data by ADRB2 genotype

  • Consider prospective studies based on retrospective findings

• Statistical and bioinformatic methods:

  • Apply appropriate models for genotype-based analyses

  • Use multivariate approaches to account for confounding factors

  • Consider machine learning for complex pattern recognition

• Integrated data visualization:

  • Create multi-level visualizations connecting genotype, molecular function, and clinical outcomes

  • Use pathway mapping to contextualize ADRB2 within broader biological systems

  • Develop predictive models incorporating genotype and functional data

Research on heart failure patients demonstrated this approach's value: the Arg16Gly polymorphism significantly affected response to βAR-blocker therapy, with patients carrying the Gly16 allele showing greater benefit. This genotype-dependent heterogeneity in clinical outcomes was successfully validated in independent populations, highlighting the importance of integrating genetic, functional, and clinical data .