At4g08039 Antibody

What is At4g08039 Antibody and what is its target in Arabidopsis thaliana?

At4g08039 Antibody (product code CSB-PA648857XA01DOA) is a research-grade antibody that targets the protein encoded by the At4g08039 gene in Arabidopsis thaliana (Mouse-ear cress) . This protein (UniProt accession Q2V3K9) is part of the Arabidopsis proteome. The antibody is typically produced using synthetic peptides or recombinant protein fragments as immunogens to generate specificity against this target. The At4g08039 gene is located on chromosome 4 of the Arabidopsis genome, and the antibody enables detection and study of its protein product in various experimental contexts.

How should At4g08039 Antibody be stored and handled for optimal performance?

For maximum stability and activity retention, At4g08039 Antibody should be stored at -20°C when not in use. Avoid repeated freeze-thaw cycles by aliquoting the antibody into smaller volumes upon first thawing. The standard antibody solution comes in 2ml or 0.1ml sizes , which should be aliquoted based on your experimental needs.

Working dilutions should be prepared fresh before each experiment and kept at 4°C during the experimental procedure. When handling the antibody, use sterile techniques and wear appropriate personal protective equipment. For long-term storage (beyond 6 months), storing aliquots at -80°C may provide better stability, though this should be validated for your specific lot.

Researchers should monitor antibody performance over time, as even properly stored antibodies can experience diminished activity. Consider including positive controls in your experiments to track stability over the antibody's lifetime.

What are the standard protocols for using At4g08039 Antibody in Western blot experiments?

Standard Western blot protocols for At4g08039 Antibody typically involve the following methodological steps:

• Sample Preparation: Extract proteins from Arabidopsis tissues using appropriate buffer systems that preserve protein integrity while minimizing proteolytic degradation. For plant tissues, inclusion of protease inhibitors, PVPP, and reducing agents is often necessary.

• Protein Separation: Separate proteins using SDS-PAGE (10-12% gels are commonly effective). Load 20-50 μg of total protein per lane, alongside molecular weight markers.

• Transfer: Transfer proteins to PVDF or nitrocellulose membranes (0.45 μm pore size) using standard transfer conditions (100V for 1 hour or 30V overnight).

• Blocking: Block membranes in 5% non-fat dry milk or BSA in TBS-T for 1 hour at room temperature.

• Primary Antibody Incubation: Dilute At4g08039 Antibody (typically 1:500 to 1:2000, though optimal dilution should be determined empirically) in blocking buffer and incubate overnight at 4°C.

• Washing: Wash membranes 3-5 times with TBS-T, 5-10 minutes per wash.

• Secondary Antibody Incubation: Incubate with appropriate HRP-conjugated secondary antibody (typically 1:5000 to 1:10000) for 1 hour at room temperature.

• Detection: Develop using enhanced chemiluminescence and image using appropriate detection systems.

When working with plant proteins, additional steps to remove interfering compounds may be necessary. Performance can be enhanced by optimizing extraction buffer composition specifically for Arabidopsis thaliana tissues.

How can the selectivity of At4g08039 Antibody be validated experimentally?

Validating antibody selectivity is critical for ensuring reliable research outcomes. Based on advanced experimental procedures, researchers should implement a multi-step validation approach:

• Peptide Competition Assay: Pre-incubate the antibody with excess immunizing peptide (if available) before applying to your experimental system. Specific binding should be significantly reduced or eliminated.

• Genetic Knockout Controls: Where available, use Arabidopsis knockout lines for At4g08039 gene as negative controls. Absence of signal in these lines provides strong evidence for antibody specificity.

• Multiplexed Testing: Challenge the antibody against a panel of closely related proteins or protein extracts from different tissues to assess cross-reactivity patterns . For most rigorous validation, consider testing against a library of related proteins.

• Orthogonal Detection Methods: Confirm protein expression and localization using complementary techniques such as mass spectrometry or fluorescently-tagged protein constructs.

• Structural Prediction Analysis: Utilize computational tools like AlphaFold 2 to predict protein structure and potential epitopes, supporting wet lab validation data . This computational approach can help identify potential cross-reactivity with structurally similar proteins.

• Multiple Antibody Comparison: When possible, compare results from different antibodies targeting the same protein but recognizing different epitopes.

Researchers at SciLifeLab developed a pipeline to test over 400 antibodies against multiple targets, which demonstrated the importance of comprehensive validation against similar family members to ensure selectivity . Such methodical approaches are necessary for antibodies targeting plant proteins, where cross-reactivity is a common challenge.

What computational tools can be used to predict the binding affinity and selectivity of At4g08039 Antibody?

Several computational approaches can supplement experimental validation of At4g08039 Antibody:

• AlphaFold 2: This AI-powered tool can predict protein structures with high accuracy, allowing researchers to identify potential epitopes and assess structural similarities with related proteins that might lead to cross-reactivity . AlphaFold 2 has been successfully used to support wet lab antibody validation data.

• Virtual Lab Approach: Advanced AI agents can be employed to design and optimize antibody binding. Similar to approaches used for SARS-CoV-2 nanobodies, computational workflows can predict binding interfaces between antibodies and their target proteins .

• ESM (Evolutionary Scale Modeling): This deep learning approach uses protein language models to predict functional effects of mutations and assess conservation of epitope regions.

• Rosetta Suite: These computational tools allow for modeling antibody-antigen interactions and can predict binding energy changes upon mutation.

• Molecular Dynamics Simulations: MD simulations can assess the stability of antibody-antigen complexes and identify key interaction residues.

Implementing a multi-tool approach provides the most robust predictions. For example, a workflow could begin with AlphaFold 2 structure prediction, followed by epitope mapping with ESM, and binding affinity calculations with Rosetta . The results should always be experimentally validated, as computational predictions provide guidance rather than definitive answers.

How does gene expression variation affect At4g08039 Antibody performance in different Arabidopsis ecotypes?

Gene expression variation across Arabidopsis ecotypes can significantly impact At4g08039 Antibody performance through several mechanisms:

• Sequence Polymorphisms: Natural variations in the At4g08039 gene sequence between ecotypes may alter epitope structures, potentially reducing antibody binding affinity or eliminating recognition entirely.

• Expression Level Differences: Regulatory variations between ecotypes can lead to different expression levels of the target protein, affecting signal intensity in experiments without necessarily indicating antibody performance issues.

• Post-translational Modification Differences: Ecotype-specific differences in post-translational modifications may mask or modify epitopes, altering antibody recognition patterns.

• Protein Interaction Partners: Differences in protein complexes or interaction partners between ecotypes may affect epitope accessibility.

To systematically address these variations:

• Preliminary Testing: Before extensive experiments, validate antibody performance across the specific ecotypes of interest.

• Calibration Curves: Develop ecotype-specific calibration curves using purified protein or quantitative standards.

• Epitope Mapping: Identify the specific epitope recognized by the antibody and assess conservation across ecotypes using sequence analysis.

• Multi-method Confirmation: Use complementary techniques like RT-qPCR to confirm protein expression patterns observed with antibody-based methods.

Researchers working with Arabidopsis often need to optimize experimental conditions specifically for their ecotype of interest, as protocols optimized for the Columbia-0 reference ecotype may not perform identically in other genetic backgrounds .

How to troubleshoot non-specific binding with At4g08039 Antibody?

Non-specific binding is a common challenge when working with plant antibodies. To systematically troubleshoot this issue with At4g08039 Antibody:

• Optimize Blocking Conditions:

  • Test different blocking agents (BSA, non-fat milk, commercial blocking buffers)

  • Increase blocking time (2-3 hours at room temperature or overnight at 4°C)

  • Adjust blocking buffer concentration (3-5% is typically effective)

• Modify Antibody Dilution and Incubation:

  • Test a range of antibody dilutions (1:500 to 1:5000)

  • Reduce incubation temperature (4°C rather than room temperature)

  • Extend incubation time (overnight rather than 1-2 hours)

• Adjust Washing Steps:

  • Increase number of washes (5-6 washes of 10 minutes each)

  • Add detergent to wash buffer (0.1-0.3% Tween-20 or 0.05% Triton X-100)

  • Use higher salt concentration in wash buffer (up to 500 mM NaCl)

• Sample Preparation Modifications:

  • Include additional clearing steps for plant extracts

  • Pre-absorb antibody with plant extract from knockout lines

  • Add protein extraction additives that remove plant-specific interfering compounds

• Validation Controls:

  • Include peptide competition controls

  • Run parallel experiments with tissues known to lack target expression

  • Add gradient loading series to distinguish specific from non-specific signals

For particularly challenging cases, consider using alternative detection methods or antibody purification techniques to improve specificity. Documentation of troubleshooting steps is essential for reproducibility and method optimization.

What are the optimal fixation methods for immunohistochemistry with At4g08039 Antibody?

Optimal fixation for immunohistochemistry with At4g08039 Antibody in Arabidopsis tissues requires balancing epitope preservation with structural integrity:

• Paraformaldehyde Fixation:

  • 4% paraformaldehyde in PBS (pH 7.2-7.4) is generally effective

  • Fixation time: 1-2 hours at room temperature or overnight at 4°C

  • After fixation, thoroughly wash samples with PBS (3-4 times, 15 minutes each)

• Alternative Fixation Methods:

  • Acetone fixation (10 minutes at -20°C) may preserve certain epitopes better

  • Ethanol:acetic acid (3:1) can be effective for preserving nucleic acid-protein interactions

  • Combination fixation with initial aldehydes followed by alcohol dehydration

• Permeabilization Considerations:

  • After fixation, permeabilize with 0.1-0.3% Triton X-100 in PBS (10-20 minutes)

  • For thicker tissues, consider longer permeabilization or vacuum infiltration

  • Test enzyme-based cell wall digestion (1-2% cellulase, 0.5% macerozyme) for enhanced antibody penetration

• Antigen Retrieval Options:

  • Heat-induced epitope retrieval (citrate buffer, pH 6.0, 95°C for 10-20 minutes)

  • Enzymatic retrieval (proteinase K, 10-20 μg/ml, 10 minutes at 37°C)

  • Test multiple retrieval methods to determine optimal conditions

• Experimental Optimization:

  • Run parallel samples with different fixation protocols

  • Document tissue morphology preservation alongside signal intensity

  • Consider the subcellular localization of your target protein when selecting fixation method

The optimal fixation method should be determined empirically for each specific application, as the chemical environment of Arabidopsis tissues can vary significantly based on developmental stage, tissue type, and growth conditions .

How to track and analyze At4g08039 Antibody experimental data in research databases?

Effectively tracking and analyzing At4g08039 Antibody experimental data requires systematic documentation and integration with research databases:

• Electronic Laboratory Notebook Documentation:

  • Record complete experimental parameters, including antibody lot number, dilution, incubation conditions

  • Document all controls used and their results

  • Maintain consistent naming conventions for samples and experimental conditions

• Data Tracking in Analytics Platforms:

  • Implement tracking variables in Google Analytics 4 (GA4) or similar platforms

  • Create JavaScript variables to capture experimental metadata

  • Use history change triggers to track data access patterns across team members

• Data Standardization for Database Integration:

  • Use consistent metadata formats (JSON or XML) for experiment description

  • Adopt standard ontologies for experimental conditions and techniques

  • Include persistent identifiers for antibodies (catalog numbers, RRID)

• Multi-dimensional Data Analysis:

  • Create comparison tables of antibody performance across conditions

  • Develop visualization templates for standardized presentation of results

  • Implement quantitative scoring systems for antibody performance metrics

When tracking antibody performance data, researchers should document both successful and failed experiments to build a comprehensive understanding of optimal conditions . This approach allows for more effective troubleshooting and experimental refinement over time.

What are the essential controls when using At4g08039 Antibody in Arabidopsis research?

Implementing rigorous controls is essential for generating reliable data with At4g08039 Antibody:

• Primary Controls:

  • Positive Control: Include samples known to express the target protein (preferably overexpression lines if available)

  • Negative Control: Use genetic knockout/knockdown lines for At4g08039 gene

  • No Primary Antibody Control: Omit primary antibody but include all other reagents

• Secondary Controls:

  • Isotype Control: Use unrelated antibody of the same isotype and concentration

  • Pre-immune Serum Control: If available, use serum collected before immunization

  • Peptide Competition/Blocking: Pre-incubate antibody with immunizing peptide

• Processing Controls:

  • Loading Control: Include detection of constitutively expressed proteins (e.g., actin, tubulin)

  • Transfer Efficiency Control: Use stained membranes to verify protein transfer

  • Secondary Antibody Only: Test for non-specific binding of secondary antibody

• Experimental Validation Controls:

  • Dilution Series: Run samples at multiple concentrations to verify signal linearity

  • Technical Replicates: Process identical samples in parallel to assess reproducibility

  • Biological Replicates: Use independent plant samples to account for biological variation

• Control Matrix for Experimental Design:

This systematic approach to controls helps distinguish genuine biological signals from technical artifacts, ensuring experimental reliability and reproducibility in Arabidopsis research with At4g08039 Antibody.

How does growth media composition affect At4g08039 protein expression and antibody detection?

Growth media composition significantly influences At4g08039 protein expression and subsequent antibody detection in Arabidopsis thaliana:

• Media Components and Protein Expression:

  • Nutrient availability directly impacts gene expression and protein production

  • Peat-based media typically result in different protein expression profiles compared to peat-free alternatives

  • Media containing high nitrogen may alter expression of At4g08039 protein compared to low-nitrogen conditions

• Media Effects on Tissue Development:

  • Plants grown on different media develop varying tissue compositions and cell wall structures

  • These structural differences can affect protein extraction efficiency and epitope accessibility

  • Coir-based media, which contain virtually no nitrate, can dramatically alter protein expression patterns compared to standard peat-based media

• Methodological Considerations:

  • Protein extraction buffers may need optimization based on growth media composition

  • Additional clearing steps may be necessary for plants grown on media with high organic content

  • Autoclaving of growth media generally does not affect physiological parameters but may impact certain protein expression profiles

• Optimized Experimental Design:

  • Maintain consistent growth media composition across experiments to ensure reproducibility

  • Document media composition and preparation methods in experimental reports

  • Consider media effects when comparing results across different studies

  • When possible, use the same batch of growing media for experimental series

• Comparative Analysis of Media Effects:

  • Different growing media show variable effects on biomass accumulation and protein expression

  • Plants grown on peat-free media often show reduced seed yield but may maintain comparable vegetative growth

  • Media selection should be based on the specific research questions and growth phase of interest

For optimal experimental consistency, researchers should standardize growing conditions whenever possible, as Arabidopsis performance is highly dependent on media composition. Current laboratory protocols are typically optimized for peat-based media, which may need to be considered when interpreting antibody detection results from plants grown on alternative media .

How can computational modeling enhance At4g08039 Antibody applications?

Computational modeling offers powerful approaches to enhance At4g08039 Antibody applications in Arabidopsis research:

• Structure-Based Epitope Prediction:

  • AlphaFold 2 can generate high-quality structural models of At4g08039 protein

  • These models enable identification of surface-accessible epitopes for antibody binding

  • Structural comparisons with related proteins can predict potential cross-reactivity issues

• AI-Assisted Antibody Design:

  • Virtual Lab approaches using AI agents can improve antibody specificity through computational design

  • Multi-phase computational workflows similar to those used for nanobody design can optimize binding properties

  • The integration of multiple computational tools (ESM, AlphaFold-Multimer, Rosetta) provides complementary insights into antibody-antigen interactions

• Experimental-Computational Integration:

  • Computational predictions can guide experimental validation efforts

  • Structural modeling can help interpret unexpected experimental results

  • Machine learning algorithms can predict antibody performance across different experimental conditions

• Future Methodological Advances:

  • AI-designed antibody variants with enhanced specificity for plant proteins

  • Computational prediction of optimal fixation and extraction methods based on protein structure

  • Integration of antibody performance data with plant protein interaction networks

• Implementation Steps for Computational Enhancement:

  • Begin with sequence analysis and homology assessment

  • Generate structural models of target and related proteins

  • Predict epitopes and potential binding interfaces

  • Design validation experiments based on computational predictions

  • Iterate between computational and experimental approaches

The rapidly evolving field of computational biology offers increasingly sophisticated tools for antibody research. As demonstrated in the development of nanobodies against SARS-CoV-2 variants, computational workflows can significantly accelerate antibody optimization and specificity enhancement .

What are the emerging techniques for validating plant antibodies like At4g08039 Antibody?

Emerging techniques for validating plant antibodies represent significant methodological advances:

• Multiplexed Validation Pipelines:

  • High-throughput systems testing antibodies against hundreds of related proteins simultaneously

  • Integration of multiple detection methods within single experimental platforms

  • Automated analysis of cross-reactivity patterns across protein families

• CRISPR-Based Validation:

  • Generation of precise genetic knockouts as definitive negative controls

  • Creation of epitope-tagged endogenous proteins for parallel validation

  • Development of inducible expression systems for controlled validation experiments

• Advanced Microscopy Integration:

  • Super-resolution microscopy combined with antibody detection

  • Correlative light and electron microscopy for ultrastructural validation

  • Live-cell imaging with complementary fluorescent tag validation

• Proteomics-Enhanced Validation:

  • Immunoprecipitation coupled with mass spectrometry to identify binding partners

  • Parallel reaction monitoring to quantify specific protein targets

  • Protein correlation profiling to validate antibody specificity

• Computational-Experimental Hybrid Approaches:

  • AlphaFold 2 structure prediction to support wet lab antibody validation

  • Machine learning analysis of antibody binding patterns

  • Integrated data analysis platforms combining multiple validation metrics

These emerging techniques represent significant methodological advances over traditional antibody validation approaches. The integration of computational tools and experimental validation, as demonstrated in GPCR receptor antibody validation studies, provides a more comprehensive assessment of antibody performance . Researchers working with plant-specific antibodies like At4g08039 Antibody can adapt these approaches to address the unique challenges of plant proteomics research.