At1g47800 Antibody

What is At1g47800 and why would researchers need an antibody against it?

At1g47800 is a gene locus in Arabidopsis thaliana located on chromosome 1. The gene encodes a protein involved in plant defense mechanisms and stress responses, similar to other plant immunity proteins. Researchers require antibodies against this protein for various applications including protein detection, localization studies, and functional characterization. Similar to the EDS1 antibody (Enhanced disease susceptibility 1) used in Arabidopsis research, antibodies against At1g47800 enable scientists to study protein expression patterns in different tissues, under various stress conditions, and in mutant lines . The antibody allows researchers to investigate how this protein contributes to plant immunity and defense signaling pathways, providing insights into plant-pathogen interactions and stress responses.

What types of antibodies (monoclonal vs polyclonal) are available for At1g47800?

Both monoclonal and polyclonal antibodies may be developed for At1g47800 research, each with distinct advantages for different experimental applications. Polyclonal antibodies, similar to the anti-EDS1 antibody described in the literature, are typically raised in rabbits against synthetic peptides derived from the target protein sequence . These antibodies recognize multiple epitopes of the target protein, enhancing detection sensitivity but potentially increasing cross-reactivity.

Monoclonal antibodies, like the CCRC-M22 antibody used for plant cell wall research, are generated from a single B-cell clone and recognize a single epitope, providing high specificity but potentially lower sensitivity . For At1g47800, researchers should consider:

• Polyclonal antibodies: Best for applications requiring high sensitivity such as detecting low abundance proteins in Western blots or immunoprecipitation.

• Monoclonal antibodies: Optimal for applications requiring high specificity such as distinguishing between closely related protein family members.

The choice between monoclonal and polyclonal antibodies should be guided by the specific research question, required specificity, and experimental application.

How are At1g47800 antibodies typically produced?

Production of At1g47800 antibodies typically follows standard immunological techniques similar to those used for other plant protein antibodies. The process involves:

• Immunogen design: Typically involves conjugating a synthetic peptide derived from the At1g47800 sequence to a carrier protein like KLH (Keyhole Limpet Hemocyanin), as demonstrated with the anti-EDS1 antibody .

• Immunization: The immunogen is injected into host animals (commonly rabbits for polyclonal antibodies or mice for monoclonal antibodies).

• Antibody production and purification:

  • For polyclonal antibodies: Serum is collected and antibodies are typically purified through immunogen affinity chromatography, similar to the process described for anti-EDS1 .

  • For monoclonal antibodies: B cells are harvested and fused with myeloma cells to create hybridomas that continuously produce the desired antibody, followed by clonal selection (comparable to the development of CCRC-M22 antibody) .

• Validation: Testing the antibody through various applications such as Western blot, immunoprecipitation, or immunofluorescence to confirm specificity and sensitivity.

The quality and specificity of the resulting antibody depend significantly on the chosen immunogen, which should be unique to the target protein to minimize cross-reactivity with related proteins.

What is the optimal protocol for using At1g47800 antibody in Western blotting?

The optimal Western blotting protocol for At1g47800 antibody would be similar to that used for other Arabidopsis protein antibodies. A methodological approach includes:

• Sample preparation:

  • Extract proteins using a buffer containing: 50 mM Tris-HCl (pH 7.5), 10% glycerol, 1 mM EDTA, 200 mM NaCl, 1 mM DTT, 0.1% Triton X-100, protease inhibitor cocktail, phosphatase inhibitor cocktail, pepstatin A, and PMSF .

  • Denature samples with SDS buffer and heat at 95°C for 5 minutes.

• Gel electrophoresis and transfer:

  • Separate proteins on a 10-12% SDS-PAGE gel.

  • Transfer to a PVDF or nitrocellulose membrane at 100V for 1 hour or 30V overnight.

• Blocking and antibody incubation:

  • Block the membrane with 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature.

  • Incubate with the At1g47800 antibody at a 1:1000 dilution (based on the recommended dilution for anti-EDS1 antibody) overnight at 4°C .

  • Wash 3-5 times with TBST, 5 minutes each.

  • Incubate with HRP-conjugated secondary antibody at 1:5000-1:10000 dilution for 1 hour at room temperature.

  • Wash 3-5 times with TBST, 5 minutes each.

• Detection:

  • Apply ECL substrate and detect signal using x-ray film or digital imaging systems.

  • The expected molecular weight of the At1g47800 protein should be confirmed based on its amino acid sequence.

This protocol may require optimization based on the specific properties of the At1g47800 protein and the antibody characteristics.

How should I validate the specificity of an At1g47800 antibody?

Validating antibody specificity is crucial for generating reliable research data. For At1g47800 antibody, a comprehensive validation approach should include:

• Knockout/knockdown controls:

  • Test the antibody on samples from At1g47800 knockout or RNAi knockdown lines.

  • The absence of signal in these samples strongly supports antibody specificity.

• Overexpression controls:

  • Test the antibody on samples from plants overexpressing the At1g47800 protein.

  • An increased signal intensity correlating with expression level confirms specificity.

• Peptide competition assay:

  • Pre-incubate the antibody with the immunizing peptide before application.

  • Disappearance of the signal indicates that the antibody is binding specifically to its target epitope.

• Multiple detection methods:

  • Confirm results using different techniques (e.g., Western blot, immunoprecipitation, immunolocalization).

  • Consistent results across methods increase confidence in specificity.

• Cross-reactivity testing:

  • Test the antibody against closely related proteins or in related plant species.

  • Evaluate the degree of cross-reactivity to determine antibody limitations, similar to how reactivity was tested for anti-EDS1 antibody in different plant species .

Documentation of these validation steps should be included in research publications to ensure reproducibility and reliability of results.

What controls should be included when using At1g47800 antibody in immunoprecipitation experiments?

For robust immunoprecipitation (IP) experiments with At1g47800 antibody, the following controls are essential:

• Input control:

  • A small portion of the total lysate before IP, representing the starting material.

  • Serves as a reference for the abundance of the target protein in the original sample.

• Negative controls:

  • IgG control: Use the same amount of non-specific IgG from the same species as the At1g47800 antibody.

  • No-antibody control: Perform the IP procedure without adding any antibody.

  • These controls help identify non-specific binding to the antibody or beads.

• Knockout/knockdown control:

  • Perform IP on samples lacking or with reduced levels of At1g47800.

  • Absence of signal confirms specificity of the immunoprecipitation.

• Peptide competition control:

  • Pre-incubate the antibody with excess immunizing peptide before IP.

  • Should result in significantly reduced or absent signal.

• Sample processing controls:

  • Process samples identically, including timing of collection, protein extraction methods, and buffer compositions.

  • Include protease and phosphatase inhibitors to prevent degradation or modification during processing, similar to the buffer compositions used for anti-EDS1 antibody applications .

Data presentation should include quantitative analysis of IP efficiency and enrichment relative to these controls.

How do I quantify Western blot data using At1g47800 antibody?

Quantification of Western blot data using At1g47800 antibody requires rigorous methodology to ensure accurate results:

• Optimization prerequisites:

  • Ensure signal is within the linear detection range of your imaging system.

  • Avoid oversaturated bands which prevent accurate quantification.

  • Optimize exposure times to achieve detectable signal without saturation.

• Normalization strategy:

  • Always normalize to appropriate loading controls (e.g., actin, tubulin, GAPDH).

  • The loading control should be selected based on its stability under your experimental conditions.

  • Consider using total protein normalization methods (e.g., stain-free technology) if expression of common housekeeping genes might be affected by your treatment.

• Quantification methodology:

  • Use dedicated densitometry software (ImageJ, Image Lab, etc.).

  • Define regions of interest consistently across all lanes.

  • Subtract background using a consistent method.

  • Calculate the ratio of At1g47800 band intensity to loading control.

• Technical considerations:

  • Run at least three biological replicates.

  • Include a standard curve if absolute quantification is needed.

  • Consider the dynamic range limitations of Western blotting (typically 10-100 fold).

• Statistical analysis:

  • Apply appropriate statistical tests based on your experimental design.

  • Report both the mean and measures of variance (SD or SEM).

A standardized quantification method enhances reproducibility and allows for meaningful comparisons between experiments and between laboratories.

What factors might cause variability in At1g47800 antibody staining patterns?

Several factors can contribute to variability in staining patterns when using At1g47800 antibody:

• Biological factors:

  • Developmental stage: Protein expression may vary across developmental stages.

  • Tissue specificity: At1g47800 may be expressed differently across tissues.

  • Stress conditions: Environmental factors may induce or suppress expression.

  • Post-translational modifications: Changes in phosphorylation, glycosylation, etc., may affect antibody recognition.

  • Protein-protein interactions: Complex formation may mask epitopes.

• Technical factors:

  • Fixation methods: Different fixatives can alter protein conformation and epitope accessibility.

  • Antigen retrieval methods: Insufficient or excessive antigen retrieval can affect staining.

  • Antibody concentration: Optimal concentration may vary across applications and samples.

  • Incubation conditions: Temperature, time, and buffer composition affect antibody binding.

  • Detection systems: Different secondary antibodies or detection reagents have varying sensitivities.

• Antibody-specific factors:

  • Batch-to-batch variability: Different production lots may have slight variations.

  • Storage conditions: Improper storage can lead to antibody degradation, similar to storage recommendations for other antibodies like the EDS1 antibody (-20°C storage and aliquoting to avoid freeze-thaw cycles) .

  • Freeze-thaw cycles: Repeated freezing and thawing can reduce antibody activity.

To minimize variability:

• Use consistent protocols across experiments.

• Include positive and negative controls in each experiment.

• When possible, use the same antibody lot for related experiments.

• Document all experimental conditions meticulously for reproducibility.

How do I troubleshoot conflicting results between different detection methods using At1g47800 antibody?

When facing conflicting results between different detection methods (e.g., Western blot vs. immunofluorescence) using At1g47800 antibody, a systematic troubleshooting approach is essential:

• Assess epitope accessibility in different methods:

  • Western blot: Denatured proteins expose all epitopes.

  • Immunofluorescence: Native conformation may hide some epitopes.

  • Immunoprecipitation: Epitopes may be masked by protein interactions.

  • Systematically test different epitope retrieval methods for each technique.

• Evaluate method-specific interference factors:

  • Fixation effects: Certain fixatives may modify the epitope structure.

  • Buffer compatibility: The antibody may perform differently in various buffer systems, similar to how different extraction buffers are recommended for different applications with the EDS1 antibody .

  • pH sensitivity: Optimal pH may differ between methods.

  • Detergent effects: Different detergents affect membrane protein solubility and antibody access.

• Cross-validate with complementary approaches:

  • RNA analysis: Confirm expression using RT-qPCR or RNA-seq.

  • Tagged protein: Compare antibody detection with detection of epitope-tagged versions.

  • Mass spectrometry: Use for absolute confirmation of protein presence and abundance.

  • Multiple antibodies: Test different antibodies targeting different epitopes of At1g47800.

• Systematic optimization matrix:

  • Create a grid testing multiple parameters simultaneously.

  • Include antibody concentration, incubation time, temperature, and blocking conditions.

  • Optimize each method independently before comparing results.

This methodical approach helps determine whether discrepancies represent technical artifacts or biologically meaningful differences in protein detection.

Why might I observe weak or no signal when using At1g47800 antibody?

Weak or absent signal when using At1g47800 antibody can result from several factors:

• Protein expression issues:

  • Low abundance: At1g47800 protein may be naturally expressed at low levels.

  • Conditional expression: The protein may only be expressed under specific conditions or in specific tissues.

  • Incorrect developmental stage: Expression may be temporally regulated.

  • Solution: Use enrichment techniques (e.g., subcellular fractionation) or increase loading amount.

• Extraction and sample preparation issues:

  • Inadequate extraction: The buffer composition may not efficiently extract the protein.

  • Protein degradation: Insufficient protease inhibition may lead to degradation.

  • Improper sample handling: Extended processing at room temperature can reduce protein integrity.

  • Solution: Optimize extraction buffer (refer to the extraction buffer compositions described for plant proteins) .

• Antibody-related issues:

  • Low affinity: The antibody may have inherently low affinity for its target.

  • Epitope inaccessibility: The epitope may be buried or modified in your samples.

  • Antibody degradation: Improper storage or excessive freeze-thaw cycles can reduce activity.

  • Solution: Try different antibody concentrations, incubation conditions, or epitope retrieval methods.

• Technical parameters:

  • Insufficient blocking: High background may mask specific signal.

  • Suboptimal transfer: Inefficient protein transfer to membrane.

  • Detection system sensitivity: Some detection systems may not be sensitive enough.

  • Solution: Optimize blocking conditions, transfer parameters, and consider using more sensitive detection systems.

• Confirmatory strategies:

  • Positive control: Include samples known to express high levels of At1g47800.

  • Protein overexpression: Test the antibody on samples overexpressing the target.

  • Alternative detection methods: Try different applications where the epitope may be more accessible.

How can I reduce background in immunofluorescence with At1g47800 antibody?

High background in immunofluorescence using At1g47800 antibody can interfere with accurate localization and quantification. To reduce background:

• Optimize fixation and permeabilization:

  • Test different fixatives (e.g., paraformaldehyde, methanol, acetone).

  • Adjust fixation time and temperature.

  • Optimize permeabilization conditions to balance epitope accessibility with structural preservation.

  • Consider gentle permeabilization methods for plant tissues (e.g., enzymatic digestion).

• Enhance blocking efficiency:

  • Increase blocking time (1-2 hours at room temperature or overnight at 4°C).

  • Test different blocking agents (BSA, normal serum, casein, commercial blocking buffers).

  • Use serum from the same species as the secondary antibody to reduce non-specific binding.

  • Add 0.1-0.3% Triton X-100 to blocking buffer to reduce hydrophobic interactions.

• Optimize antibody conditions:

  • Titrate primary antibody concentration (try series dilutions from 1:100 to 1:2000).

  • Increase washing steps (5-6 washes of 10 minutes each).

  • Pre-absorb antibody with plant extracts from knockout lines or unrelated tissues.

  • Reduce secondary antibody concentration to minimize non-specific binding.

• Address autofluorescence:

  • Include quenching steps for plant autofluorescence (e.g., 0.1% sodium borohydride).

  • Choose fluorophores with excitation/emission spectra distinct from plant autofluorescence.

  • Use computational methods to subtract autofluorescence signals.

  • Consider confocal microscopy with narrow bandpass filters.

• Additional controls:

  • Secondary-only control to assess non-specific binding of secondary antibody.

  • Peptide competition control to confirm specificity of staining.

  • Include unstained samples to evaluate natural autofluorescence.

What might cause batch-to-batch variability in At1g47800 antibody performance?

Batch-to-batch variability in antibody performance is a common challenge in research. For At1g47800 antibody, several factors may contribute to this variability:

• Production-related factors:

  • Host animal variations: Different individual animals may produce antibodies with slightly different affinities.

  • Immunization efficiency: Variations in immune response between animals or immunization rounds.

  • Purification efficiency: Differences in yield and purity during antibody isolation.

  • Epitope representation: Subtle differences in synthetic peptide preparation for immunization.

• Quality control variations:

  • Validation stringency: Different batches may undergo different validation protocols.

  • Concentration determination: Variations in protein concentration measurement methods.

  • Specificity testing: Different antigen preparations used for validation.

• Storage and handling influences:

  • Storage conditions: Temperature fluctuations during shipping or storage.

  • Buffer composition: Slight variations in stabilizers or preservatives.

  • Age of antibody: Natural degradation over time even when properly stored.

  • Freeze-thaw cycles: Number of cycles before distribution.

• Strategies to mitigate variability:

  • Purchase larger amounts of a single batch for long-term projects.

  • Validate each new batch side-by-side with the previous batch.

  • Document lot numbers and create internal reference standards.

  • Consider generating monoclonal antibodies for critical applications where consistency is paramount.

  • Develop a standardized validation protocol specific to your experimental system.

Understanding these variables allows researchers to implement appropriate quality control measures and experimental designs that account for batch effects.

How can At1g47800 antibody be used in protein-protein interaction studies?

At1g47800 antibody can be employed in multiple sophisticated approaches to study protein-protein interactions:

• Co-immunoprecipitation (Co-IP):

  • Standard approach: Use At1g47800 antibody to precipitate the protein complex and identify interacting partners via Western blot or mass spectrometry.

  • Reverse Co-IP: Use antibodies against suspected interacting partners to confirm interactions.

  • Protocol optimization: Adjust buffer stringency to preserve weak or transient interactions.

  • Quantitative analysis: Use SILAC or TMT labeling combined with mass spectrometry for quantitative interaction profiles.

• Proximity labeling coupled with immunoprecipitation:

  • BioID or TurboID approach: Express At1g47800 fused to a biotin ligase, then use the antibody to confirm expression and biotinylation patterns.

  • APEX2 system: Combine peroxidase-based proximity labeling with antibody verification.

  • These methods capture transient interactions that may be missed in traditional Co-IP.

• Immunofluorescence-based interaction studies:

  • Colocalization analysis: Combine At1g47800 antibody with antibodies against potential interacting partners.

  • FRET-FLIM: Use antibody-conjugated fluorophores as FRET pairs to detect nanoscale proximity.

  • Duolink/PLA (Proximity Ligation Assay): Detect protein interactions with sub-cellular resolution using antibody pairs.

• Chromatin-focused applications:

  • ChIP-sequential IP (ChIP-reChIP): For studying protein complexes bound to DNA.

  • IP followed by DNA pull-down: Identify DNA sequences associated with At1g47800-containing complexes.

• Dynamic interaction studies:

  • Stimulus-dependent interactions: Compare interactions before and after specific treatments.

  • Developmental time course: Track changing interaction networks during plant development.

  • Stress-responsive interactions: Analyze how the interactome changes under biotic or abiotic stress.

Each method has specific strengths and limitations, and a combination of complementary approaches provides the most robust evidence for protein-protein interactions.

What are the considerations for using At1g47800 antibody in single-cell analyses?

Single-cell analysis with At1g47800 antibody presents unique challenges and opportunities for understanding protein expression heterogeneity within plant tissues:

• Technical considerations for single-cell immunostaining:

  • Signal-to-noise optimization: Crucial in the limited material context of single cells.

  • Fixation protocol: Must balance epitope preservation with cellular architecture maintenance.

  • Antibody penetration: May require optimized permeabilization for intact plant tissues.

  • Detection sensitivity: Consider signal amplification methods (tyramide signal amplification, nanobody-based detection).

  • Autofluorescence management: Critical in plant cells, particularly if targeting chloroplast-proximal proteins.

• Single-cell isolation methods compatible with antibody detection:

  • Protoplasting: Enzymatic cell wall removal may affect protein localization or abundance.

  • Mechanical isolation: Micromanipulation or laser capture microdissection preserves in situ context.

  • Cell sorting: FACS-based methods require careful validation of antibody specificity.

  • Microfluidic approaches: Allow for controlled environmental conditions during antibody staining.

• Quantitative single-cell analysis:

  • Image cytometry: Combines spatial information with quantitative measurement.

  • Mass cytometry (CyTOF): Requires metal-conjugated antibodies but eliminates autofluorescence issues.

  • Calibration: Include reference standards to enable absolute quantification.

  • Computational analysis: Apply machine learning for unbiased cell classification based on staining patterns.

• Validation strategies:

  • Correlation with mRNA expression in single cells.

  • Comparison with fluorescent protein fusions in reporter lines.

  • Analysis of expected expression patterns in different cell types.

Single-cell approaches with At1g47800 antibody can reveal cell-type-specific expression patterns and functional heterogeneity not detectable in bulk tissue analyses.

How can computational approaches enhance antibody design for difficult targets like At1g47800?

Computational approaches are revolutionizing antibody development, particularly for challenging targets. For proteins like At1g47800, these methods offer significant advantages:

• Epitope prediction and optimization:

  • Antigenicity prediction: Algorithms identify regions likely to be immunogenic.

  • Epitope accessibility analysis: Structural models predict surface-exposed regions.

  • Uniqueness assessment: Computational tools identify unique sequences to minimize cross-reactivity.

  • Conservation analysis: Identify evolutionarily conserved regions for broader species reactivity.

• Advanced computational design approaches:

  • Inverse folding models: Approaches like AbMPNN generate new antibody sequences maintaining structural features compatible with binding to target antigens .

  • Machine learning predictions: Models like ESM guide mutation of sequences to retain or improve binding affinity while enhancing developability .

  • Structure-based design: Computational approaches to optimize antibody-antigen interfaces for improved affinity and specificity.

• Developability prediction and optimization:

  • Stability assessment: Computational tools like Rosetta scoring evaluate antibody stability .

  • Aggregation prediction: Algorithms identify sequence regions prone to aggregation.

  • Thermal stability modeling: Predict melting temperatures and design stabilizing mutations.

  • Charge distribution analysis: Optimize to reduce non-specific binding.

• Success rates and benchmarking:

  • Pipeline success rates: Computational approaches have demonstrated success rates of approximately 54% for generating binding antibodies that retained affinity against escape mutations .

  • Constraint importance: Adding even a single residue constraint makes a significant difference in modeling accuracy .

• Implementation workflow:

  • Start with known binders or structural templates.

  • Apply computational diversification to generate candidate sequences.

  • Filter candidates using in silico developability predictions.

  • Validate experimentally using screening methods like size-exclusion chromatography (SEC) and differential scanning fluorimetry (DSF) .

These computational approaches can significantly accelerate antibody development, reduce experimental iterations, and improve success rates for challenging targets like At1g47800.