OR8H2 Antibody

What is OR8H2 and what cellular functions does it perform?

OR8H2 (Olfactory receptor 8H2) is a member of the G-protein coupled receptor 1 family with a molecular weight of approximately 35 kDa. It functions primarily as an odorant receptor in the olfactory system . OR8H2 is also known as Olfactory receptor OR11-171 . The protein is a multi-pass membrane protein localized to the cell membrane . In cellular signaling pathways, OR8H2 is involved in olfactory transduction mechanisms through G-protein coupled receptor activity, though its specific ligand profile and downstream signaling cascade remain areas of active investigation.

What applications are OR8H2 antibodies validated for?

Based on the available data, OR8H2 antibodies have been validated for multiple applications with varying degrees of optimization:

Validation has been primarily conducted using A549 cells for immunofluorescence applications, with scientific validation images available as supporting evidence . When planning experiments, researchers should perform preliminary titration experiments to determine optimal antibody concentration for their specific experimental conditions .

What is the recommended storage protocol for OR8H2 antibodies?

For optimal performance and longevity, OR8H2 antibodies should be stored according to the following protocol:

• Long-term storage: -20°C for up to one year

• Short-term storage (frequent use): 4°C for up to one month

• Avoid repeated freeze/thaw cycles as this can degrade antibody quality and affect experimental reproducibility

• Most formulations contain 50% glycerol to prevent freeze damage, 0.02% sodium azide as a preservative, and may include BSA for stability

• Upon receipt, it is recommended to aliquot the antibody into smaller volumes to minimize freeze/thaw cycles

This storage protocol maintains antibody integrity and ensures consistent experimental results across multiple studies.

How should researchers validate OR8H2 antibody specificity in their experimental systems?

Proper validation of OR8H2 antibody specificity is essential for reliable experimental results. A comprehensive validation strategy should include:

• Peptide competition assay: Pre-incubate the antibody with the immunizing peptide before application. Loss of signal confirms specificity, as demonstrated in the immunofluorescence analysis of A549 cells where the antibody signal was blocked after pre-incubation with the synthesized peptide .

• Positive and negative controls: Use cell lines or tissues known to express OR8H2 (positive control) and those that don't (negative control).

• Secondary antibody-only controls: To rule out non-specific binding of the secondary antibody.

• Cross-reactivity testing: If working with multiple species, confirm specificity across target species.

• Molecular weight verification: Confirm that the detected band matches the expected molecular weight (calculated 35 kDa for OR8H2), keeping in mind that post-translational modifications may alter the observed molecular weight .

This systematic approach ensures that experimental observations genuinely reflect OR8H2 expression patterns rather than artifacts.

What methodological considerations should be addressed when using OR8H2 antibodies for cross-species applications?

When applying OR8H2 antibodies across different species, several critical methodological considerations must be addressed:

• Epitope conservation analysis: Perform sequence alignment of the immunogen region (amino acids 87-136 or 243-272, depending on the antibody) across target species to predict cross-reactivity potential .

• Validation hierarchy: Establish a tiered validation approach where:

  • Primary validation is performed in human samples (highest homology to immunogen)

  • Secondary validation in mouse/rat samples with appropriate controls

  • Tertiary validation in any additional species with stringent controls

• Antibody dilution optimization: Cross-species applications typically require optimization of antibody concentration for each species, as binding kinetics may vary with epitope differences .

• Signal verification strategies: Employ multiple detection methods (IF, WB, ELISA) to confirm signal specificity across species, as some applications may be more prone to non-specific binding in certain species .

• Blocking optimization: Species-specific blocking agents may be required to minimize background in cross-species applications.

This methodological framework enables reliable cross-species OR8H2 detection while minimizing false positives.

What explains the discrepancy between calculated (35 kDa) and observed (72 kDa) molecular weights for OR8H2?

The discrepancy between the calculated molecular weight of approximately 35 kDa and the observed molecular weight of 72 kDa for OR8H2 can be explained by several biological and technical factors:

• Post-translational modifications:

  • Glycosylation: As a membrane receptor, OR8H2 may undergo extensive glycosylation

  • Phosphorylation: Multiple phosphorylation sites can significantly increase apparent molecular weight

  • Ubiquitination: Addition of ubiquitin molecules (8.5 kDa each) can substantially increase molecular weight

• Protein complexes and dimerization:

  • G-protein coupled receptors often form homodimers or heterodimers that may not fully dissociate under standard SDS-PAGE conditions

  • Stable protein-protein interactions can result in higher molecular weight bands

• Technical considerations:

  • Incomplete denaturation: Insufficient SDS or reducing agent can lead to protein folding that affects migration

  • Post-extraction modifications: Sample processing can introduce artifacts that alter electrophoretic mobility

To address this discrepancy experimentally, researchers should:

• Perform enzymatic deglycosylation assays

• Use gradient gels with appropriate molecular weight markers

• Employ additional validation methods beyond western blotting

This insight helps researchers properly interpret OR8H2 western blot results and avoid misidentification of target proteins.

How can computational approaches be integrated with OR8H2 antibody selection for enhanced experimental specificity?

Integrating computational approaches with OR8H2 antibody selection represents an advanced strategy to enhance experimental specificity:

• Epitope prediction and optimization:

  • Computational models can identify optimal antigenic determinants within OR8H2

  • Analysis of the protein's tertiary structure helps select epitopes with high surface accessibility

  • Algorithms can predict epitope conservation across species for cross-reactivity assessment

• Specificity profile customization:

  • Computational tools enable design of antibodies with predefined binding profiles

  • Models can be trained on phage display experiment data to predict antibody-antigen interactions

  • Energy functions can be optimized to either minimize or maximize binding to specific epitopes

• Cross-reactivity mitigation:

  • Sequence alignment algorithms identify regions unique to OR8H2 versus related olfactory receptors

  • Homology modeling predicts potential cross-reactive epitopes

  • Computational screening against proteome databases reduces off-target binding

• Implementation methodology:

  • Begin with in silico epitope mapping of OR8H2

  • Select antibodies targeting computationally validated epitopes

  • Validate with orthogonal experimental approaches

  • Iteratively refine computational models with experimental feedback

This integrated approach has been demonstrated to successfully design antibodies with customized specificity profiles, including those with specific high affinity for particular target ligands or cross-specificity for multiple target ligands .

What are the critical factors for optimization when using OR8H2 antibodies in multicolor immunofluorescence experiments?

Optimizing OR8H2 antibodies for multicolor immunofluorescence experiments requires systematic attention to several critical factors:

• Protocol optimization specific to OR8H2:

  • Fixation method: OR8H2 epitope accessibility may vary between paraformaldehyde, methanol, or acetone fixation

  • Permeabilization: As a membrane protein, OR8H2 detection requires balanced permeabilization to maintain membrane integrity while allowing antibody access

  • Antigen retrieval: Heat-induced or enzymatic retrieval methods should be systematically tested

  • Blocking parameters: Duration, temperature, and blocking agent composition significantly impact signal-to-noise ratio

• Antibody panel design considerations:

  • Host species compatibility: Select primary antibodies from different host species to avoid cross-reactivity

  • Fluorophore selection: Choose fluorophores with minimal spectral overlap

  • Signal intensity balancing: Titrate each antibody individually before combining

  • Sequential staining strategy: Consider sequential rather than simultaneous staining if cross-reactivity occurs

• Controls and validation:

  • Single-color controls: Essential for spectral compensation

  • Fluorescence minus one (FMO) controls: Critical for establishing gating boundaries

  • Peptide competition: Pre-incubation with the immunizing peptide (87-136 amino acid region) provides definitive specificity control

• Image acquisition parameters:

  • Sequential scanning: Minimizes bleed-through between channels

  • Exposure standardization: Maintain consistent exposure settings across experimental conditions

  • Resolution optimization: Balance between magnification, numerical aperture, and pixel density

This methodological framework has been validated in A549 cells, where OR8H2 antibody produced specific immunofluorescence signals that were effectively blocked by pre-incubation with the immunizing peptide .

How should researchers approach troubleshooting inconsistent results with OR8H2 antibodies across different experimental platforms?

When encountering inconsistent results with OR8H2 antibodies across different experimental platforms, researchers should implement a systematic troubleshooting framework:

• Antibody validation hierarchy:

  • Confirm antibody integrity through simple binding assays

  • Verify immunogen region specificity through epitope mapping

  • Establish concordance between detection methods (IF vs. WB vs. ELISA)

• Platform-specific optimization matrix:

• Sample preparation standardization:

  • Implement consistent cell lysis protocols across all experiments

  • Standardize protein quantification methods

  • Document and control cell confluence and passage number

  • Consider the impact of cell activation state on OR8H2 expression

• Cross-validation strategy:

  • Employ alternative antibodies targeting different OR8H2 epitopes

  • Correlate protein detection with mRNA expression analysis

  • When possible, implement genetic approaches (siRNA knockdown, CRISPR knockout) as validation tools

This comprehensive troubleshooting approach addresses variables across the entire experimental workflow rather than focusing only on antibody parameters, leading to more consistent and reproducible OR8H2 detection.

What is the optimal experimental design for measuring OR8H2 expression in primary olfactory neurons?

Designing experiments to measure OR8H2 expression in primary olfactory neurons requires specialized methodologies that address the unique challenges of these cells:

• Sample preparation protocol:

  • Tissue collection: Fresh nasal epithelium should be rapidly processed to preserve membrane protein integrity

  • Enzymatic dissociation: Optimized papain or collagenase treatment (10-20 min, 37°C) followed by gentle mechanical trituration

  • Purification: Consider using magnetic-activated cell sorting (MACS) with olfactory marker protein antibodies

• Detection strategy optimization:

  • Immunofluorescence parameters: Use 1:100-1:200 dilution of OR8H2 antibody with overnight incubation at 4°C

  • Co-localization markers: Include antibodies against olfactory marker protein (OMP) to confirm neuronal identity

  • Super-resolution microscopy: Consider techniques like STORM or STED for precise subcellular localization

• Expression analysis methods:

  • Single-cell approach: Flow cytometry with careful gating strategy

  • Population-level: Quantitative western blotting with digital image analysis

  • Spatial distribution: RNAscope in situ hybridization paired with OR8H2 immunostaining

• Validation controls specific to olfactory neurons:

  • Functional validation: Consider calcium imaging with odorant stimulation

  • Specificity controls: Pre-adsorption with immunizing peptide is essential as olfactory neurons express multiple receptor types

  • Knockout validation: When available, OR8H2 knockout tissue provides the definitive negative control

This comprehensive experimental design addresses the challenges specific to olfactory neurons while leveraging the validated applications of OR8H2 antibodies in immunofluorescence and western blot formats.

How can researchers design quantitative ELISA assays for OR8H2 protein with maximum sensitivity and specificity?

Developing a quantitative ELISA for OR8H2 requires careful optimization to achieve high sensitivity and specificity:

• ELISA format selection and rationale:

  • Sandwich ELISA: Recommended for maximum specificity using two antibodies targeting different OR8H2 epitopes

  • Direct ELISA: Suitable for purified OR8H2 protein but prone to higher background

  • Competitive ELISA: Consider for samples with potential interfering substances

• Protocol optimization parameters:

• Sensitivity enhancement strategies:

  • Signal amplification: Consider biotin-streptavidin systems

  • Substrate selection: Chemiluminescent substrates provide 10-100× higher sensitivity than colorimetric

  • Sample concentration: Membrane protein fractionation before analysis

• Validation and quality control:

  • Standard curve preparation: Recombinant OR8H2 protein in serial dilution

  • Inter-assay calibration: Include common calibrator across plates

  • Minimum detection limit determination: Calculate based on standard deviation of blank plus 3SD

• Data analysis methodology:

  • Four-parameter logistic regression for standard curve fitting

  • Sample interpolation with attention to dilution factors

  • Normalization to total protein content when appropriate

This methodological framework has been adapted from validated ELISA protocols for OR8H2, which recommend detection antibody dilutions of 1:20000 , and incorporates membrane protein-specific considerations.

What approaches should be used to investigate potential post-translational modifications of OR8H2?

Investigating post-translational modifications (PTMs) of OR8H2 requires a multi-faceted approach:

• Prediction and identification strategy:

  • In silico prediction: Use algorithms to identify potential glycosylation, phosphorylation, and ubiquitination sites

  • Mass spectrometry: Employ LC-MS/MS with appropriate enrichment strategies for each PTM type

  • Mobility shift assays: Compare electrophoretic mobility before and after specific PTM-removing enzymes

• PTM-specific experimental approaches:

• Functional impact assessment:

  • Site-directed mutagenesis: Mutate putative PTM sites and assess receptor localization and function

  • Pharmacological manipulation: Use inhibitors of specific PTM-mediating enzymes

  • Temporal analysis: Monitor PTM changes in response to receptor activation

• PTM mapping visualization:

  • Create topological maps of identified PTMs on the OR8H2 sequence

  • Structural modeling to assess how PTMs affect receptor conformation

  • Correlation analysis between PTM patterns and receptor function

This comprehensive approach addresses the significant discrepancy between calculated (35 kDa) and observed (72 kDa) molecular weights of OR8H2 , potentially resulting from extensive post-translational modifications typical of membrane-bound G-protein coupled receptors.

How might computational antibody design advance OR8H2 antibody specificity beyond current limitations?

Computational antibody design represents a frontier approach to overcome current limitations in OR8H2 antibody specificity:

• Advanced modeling techniques applicable to OR8H2 antibodies:

  • Structure-based computational design: Using molecular dynamics simulations to optimize antibody-antigen interfaces

  • Machine learning approaches: Training models on existing antibody-antigen interaction data to predict optimal binding configurations

  • Energy function optimization: Computational minimization or maximization of binding energies for specific OR8H2 epitopes

• Implementation methodology for OR8H2-specific antibodies:

  • Epitope-specific binding mode identification: Computational models can identify distinct binding modes associated with particular ligands, enabling the design of highly specific antibodies

  • Disentanglement of binding modes: Even when ligands are chemically similar, computational approaches can distinguish distinct binding modes

  • Cross-specificity engineering: Design antibodies with predetermined cross-reactivity profiles for related olfactory receptors when desired

• Experimental validation pipeline:

  • Phage display selection: Generate experimental data on antibody-antigen interactions to train computational models

  • High-throughput sequencing analysis: Identify sequence-function relationships

  • Iterative design-build-test cycles: Refine computational models based on experimental feedback

• Practical applications for OR8H2 research:

  • Custom antibodies distinguishing between highly similar olfactory receptors

  • Antibodies targeting specific conformational states of OR8H2

  • Antibodies with predictable cross-species reactivity profiles

This emerging approach has demonstrated success in designing antibodies with customized specificity profiles, including those with specific high affinity for particular target ligands and those with controlled cross-specificity for multiple target ligands .

What role might OR8H2 play in non-olfactory tissues, and how can researchers best investigate these functions?

Investigating OR8H2 expression and function in non-olfactory tissues represents an emerging research direction:

• Evidence-based experimental approach for tissue distribution analysis:

  • Comprehensive tissue screening: Systematic RT-qPCR analysis across tissue panels

  • Protein-level confirmation: Western blot and immunohistochemistry with rigorous controls

  • Single-cell resolution: scRNA-seq to identify specific cell types expressing OR8H2

• Functional characterization methodology:

  • Receptor signaling analysis: Calcium imaging, cAMP assays, and β-arrestin recruitment

  • Genetic manipulation: CRISPR/Cas9 knockout or knockdown approaches

  • Ligand screening: Modified yeast display systems or high-throughput screening platforms

• Physiological context investigation:

  • Tissue-specific interactome mapping: Proximity labeling approaches (BioID, APEX)

  • Signaling pathway delineation: Phosphoproteomics before/after potential ligand exposure

  • Phenotypic analysis: Behavioral, metabolic, or physiological assessment of genetic models

• Technical considerations for non-olfactory tissues:

  • Expression level adjustment: OR8H2 likely expresses at lower levels in non-olfactory tissues, requiring sensitive detection methods

  • Epitope accessibility: Different tissue fixation and preparation protocols may be necessary

  • Antibody dilution optimization: Consider using more concentrated antibody solutions (1:100-1:200) than the standard 1:1000 for WB

This research direction builds upon emerging evidence that olfactory receptors may serve diverse functions outside the nasal epithelium, potentially in chemosensation, cellular migration, or other signaling processes.

How can researchers integrate latest findings from computational antibody design to optimize OR8H2 detection in complex biological samples?

Integrating computational antibody design with experimental detection of OR8H2 in complex samples requires a sophisticated methodological framework:

• Computational-experimental integration workflow:

  • Initial computational modeling: Identify optimal epitopes and binding configurations specific to OR8H2

  • Virtual screening: In silico assessment of antibody candidates against OR8H2 and potential cross-reactive targets

  • Experimental validation: Phage display or yeast display systems to confirm binding predictions

  • Refinement cycle: Machine learning models improved through experimental feedback

• Application to complex sample analysis:

• Practical implementation strategy:

  • Begin with biophysics-informed modeling to identify optimal antibody-antigen interactions

  • Design antibodies with customized specificity profiles based on computational predictions

  • Validate experimentally through orthogonal approaches

  • Iterate design based on experimental outcomes

• Advantages over traditional approaches:

  • Reduced experimental bias: Computational models can minimize artifacts from selection experiments

  • Enhanced specificity: Design antibodies that discriminate between very similar epitopes

  • Predictable cross-reactivity: Engineer desired cross-species reactivity profiles with precision

  • Resource efficiency: Focus experimental efforts on computationally promising candidates

This integrative approach leverages recent advances in computational antibody design to overcome traditional limitations in antibody specificity and sensitivity, particularly relevant for detecting membrane proteins like OR8H2 in complex biological matrices.