At4g29360 Antibody

What is At4g29360 Antibody and what organism does it target?

At4g29360 Antibody is a polyclonal antibody raised in rabbits that targets the At4g29360 protein from Arabidopsis thaliana (Mouse-ear cress), a model organism widely used in plant molecular biology research . The antibody is generated using recombinant Arabidopsis thaliana At4g29360 protein as the immunogen, making it specifically reactive to this target protein . The antibody is purified using antigen affinity methods, which helps ensure high specificity for the target protein while minimizing cross-reactivity with other proteins . This antibody represents an important tool for researchers studying gene expression and protein function in plant biology, particularly for those focusing on Arabidopsis as a model system.

What are the validated applications for At4g29360 Antibody?

The At4g29360 Antibody has been experimentally validated for two primary applications: Enzyme-Linked Immunosorbent Assay (ELISA) and Western Blotting (WB) . ELISA applications utilize this antibody for quantitative detection of the target protein in solution, while Western Blotting allows for size-based separation and identification of the target protein in complex samples. The antibody's effectiveness in these applications has been verified through rigorous quality control procedures during manufacturing to ensure consistent performance . When designing experiments utilizing this antibody, researchers should note that while these applications are validated, optimization may still be required for specific experimental conditions and sample types.

What are the optimal storage conditions for maintaining At4g29360 Antibody activity?

To maintain optimal activity of the At4g29360 Antibody, proper storage conditions are crucial. Upon receipt, the antibody should be stored at either -20°C or -80°C . For long-term storage, -80°C is generally preferred to minimize protein degradation. Repeated freeze-thaw cycles should be strictly avoided as they can significantly compromise antibody activity through structural damage to the immunoglobulin proteins . The antibody is supplied in a stabilizing buffer containing 50% glycerol, 0.01M PBS at pH 7.4, and 0.03% Proclin 300 as a preservative, which helps maintain its functionality during storage . For working aliquots, it's recommended to prepare small volumes to minimize freeze-thaw cycles, and these aliquots can typically be stored at 4°C for up to two weeks during active experimentation periods.

How should researchers prepare controls when working with At4g29360 Antibody?

When designing experiments with At4g29360 Antibody, appropriate controls are essential for result validation. Researchers should implement multiple control types:

• Positive Control: Include samples known to express At4g29360 protein (wild-type Arabidopsis thaliana tissue) .

• Negative Control: Utilize samples where the protein is absent or substantially reduced (knockout/knockdown lines or non-plant tissues) .

• Antibody Controls:

  • Primary antibody only (no secondary antibody)

  • Secondary antibody only (no primary antibody)

  • No antibody control

• Blocking Control: Test the efficiency of your blocking solution by comparing blocked versus non-blocked samples.

These controls help distinguish true positive signals from potential artifacts like non-specific binding or background noise. Additionally, conducting concentration gradient experiments with recombinant At4g29360 protein can establish detection limits and optimal working dilutions for specific experimental conditions. This methodical approach ensures experimental rigor and supports valid data interpretation when working with this antibody.

How does the polyclonal nature of At4g29360 Antibody influence experimental design and interpretation?

The polyclonal nature of At4g29360 Antibody has significant implications for both experimental design and data interpretation. Unlike monoclonal antibodies that recognize a single epitope, this polyclonal antibody contains a heterogeneous mixture of immunoglobulins that recognize multiple epitopes on the At4g29360 protein . This characteristic offers both advantages and challenges that researchers must consider.

For experimental design, the multi-epitope recognition typically provides:

• Enhanced sensitivity through signal amplification

• Greater tolerance to minor protein denaturation or modification

• Better performance across diverse applications

• Potentially higher background compared to monoclonal antibodies

• Batch-to-batch variability requiring validation between lots

• Possible cross-reactivity with structurally similar proteins

What considerations are important when evaluating At4g29360 Antibody specificity in the context of protein structural modifications?

Evaluating At4g29360 Antibody specificity requires careful consideration of potential protein structural modifications that may affect epitope recognition. Post-translational modifications (PTMs), protein folding states, and experimental conditions can all influence antibody-antigen interactions.

Since the At4g29360 Antibody was raised against recombinant protein , researchers should consider:

• Post-translational modification differences: The recombinant immunogen may lack PTMs present in native At4g29360, potentially affecting antibody recognition. Researchers should verify whether their experimental conditions might induce PTMs like phosphorylation, glycosylation, or ubiquitination that could mask or create epitopes.

• Protein conformation sensitivity: The antibody may preferentially recognize certain conformational states, particularly important when comparing results between native and denaturing conditions . This is particularly relevant when comparing results between applications like immunoprecipitation (native conditions) versus Western blotting (denaturing conditions).

• Cross-reactivity assessment: Sequence homology analysis should be performed to identify potential cross-reactive proteins. This is especially important given that immunoglobulin binding can be influenced by V-gene allelic polymorphisms, which can significantly affect antibody-antigen interactions as demonstrated in recent structural biology studies .

• Validation methodology: Multiple orthogonal techniques should be employed to confirm specificity, ideally including knockout/knockdown controls where the target protein is absent or reduced.

Researchers should document these considerations in their experimental protocols and publish comprehensive antibody validation data to strengthen the reliability of their findings.

How can researchers optimize At4g29360 Antibody for novel applications beyond ELISA and Western Blot?

Optimizing At4g29360 Antibody for novel applications requires methodical validation and adaptation strategies. While this antibody is validated for ELISA and Western Blot , researchers can potentially extend its utility to other techniques through careful optimization:

For Immunohistochemistry/Immunofluorescence:

• Begin with fixation method optimization (4% paraformaldehyde, methanol, or acetone)

• Test variable antibody concentrations (typically starting at 1:100-1:500 dilutions)

• Optimize antigen retrieval methods (heat-induced, enzymatic, or pH-based)

• Evaluate different blocking reagents (BSA, normal serum, or commercial blockers)

• Compare detection systems (direct fluorophore conjugates vs. secondary antibody amplification)

For Chromatin Immunoprecipitation (ChIP):

• Test crosslinking conditions (formaldehyde concentration and incubation time)

• Optimize sonication parameters for consistent chromatin fragmentation

• Validate antibody binding under ChIP buffer conditions

• Perform sequential ChIP with known interacting partners

For Flow Cytometry:

• Develop permeabilization protocols compatible with plant cell walls

• Test fixation impact on epitope accessibility

• Optimize signal amplification methods if needed

When adapting this antibody to novel applications, researchers should:

• Always run application-specific controls

• Perform stepwise optimization changing only one variable at a time

• Document detailed protocols for reproducibility

• Validate results with orthogonal methods when possible

This systematic approach maximizes the likelihood of successfully extending the utility of At4g29360 Antibody beyond its validated applications.

How might allelic polymorphisms in Arabidopsis thaliana affect At4g29360 Antibody binding and experimental reproducibility?

Allelic polymorphisms in Arabidopsis thaliana can significantly impact At4g29360 Antibody binding and experimental reproducibility. Recent research in immunology has demonstrated that V-gene allelic polymorphisms can be critical determinants for antibody-antigen interactions . This understanding has important implications for plant research using antibodies against polymorphic targets.

The impact of allelic variation can manifest in several ways:

• Binding affinity differences: Even minor amino acid substitutions in epitope regions can dramatically alter antibody recognition. Recent structural biology studies have shown that paratope allelic polymorphisms on both heavy and light chains can completely abolish antibody binding .

• Ecotype-dependent variation: Different Arabidopsis ecotypes (Columbia, Landsberg, Wassilewskija, etc.) may contain At4g29360 sequence variations that affect epitope structure and accessibility.

• Signal intensity inconsistencies: Researchers working with different Arabidopsis accessions may observe unexplained variations in signal intensity that could be attributed to allelic differences rather than expression level changes.

To address these challenges, researchers should:

• Sequence verification: Confirm the At4g29360 sequence in their particular Arabidopsis ecotype or strain.

• Multi-ecotype validation: Test antibody performance across different Arabidopsis accessions when possible.

• Epitope mapping: Identify which regions of At4g29360 are recognized by the antibody to predict potential impacts of known polymorphisms.

• Consistent germplasm: Maintain consistent ecotype usage throughout a research project.

• Documentation: Clearly report the Arabidopsis accession used in publications to improve reproducibility.

This consideration is particularly important for comparative studies across different Arabidopsis strains or when attempting to reproduce published results using different ecotypes.

What optimal dilution ranges and incubation conditions are recommended for At4g29360 Antibody in different applications?

Determining optimal dilution ranges and incubation conditions for At4g29360 Antibody requires systematic optimization for each application. While the manufacturer provides general recommendations, researchers should perform application-specific titrations to determine optimal working conditions:

Table 1: Recommended Starting Dilution Ranges by Application

For optimal results, researchers should:

• Perform antibody titration using a dilution series to identify the concentration that provides the best signal-to-noise ratio for each specific application.

• Optimize incubation times - longer incubations at 4°C often provide cleaner results than shorter incubations at room temperature.

• Test different blocking agents - while BSA and non-fat milk are standard, plant-specific blocking agents may reduce background in some applications.

• Adjust buffer conditions - varying salt concentration and detergent levels can improve specificity, particularly in applications where the antibody hasn't been extensively validated.

• Document optimal conditions - maintain detailed records of optimization experiments to ensure reproducibility across studies.

These recommendations serve as starting points that should be refined based on specific experimental conditions, sample types, and detection methods being employed.

What troubleshooting approaches are recommended for addressing non-specific binding with At4g29360 Antibody?

When encountering non-specific binding issues with At4g29360 Antibody, a systematic troubleshooting approach should be implemented. Non-specific binding can manifest as multiple bands in Western blots, diffuse staining in immunohistochemistry, or high background noise across applications. The following methodological interventions can help resolve these issues:

For Western Blotting:

• Optimize blocking: Test different blocking agents (BSA, casein, non-fat milk) at various concentrations (3-5%) and incubation times.

• Increase washing stringency: Extend wash steps and increase Tween-20 concentration (0.05-0.1%) in wash buffers.

• Adjust antibody dilution: Further dilute the antibody beyond standard recommendations.

• Modify buffer salt concentration: Increasing NaCl concentration (150-500 mM) can reduce electrostatic non-specific interactions.

• Pre-absorb the antibody: Incubate with non-target tissue lysate to remove cross-reactive antibodies.

For ELISA:

• Optimize coating concentration and buffer: Test different antigen coating concentrations and buffer pH values.

• Evaluate blocking efficiency: Compare different blocking agents for their ability to reduce non-specific binding.

• Test additives: Include low concentrations of detergents or carrier proteins in antibody diluent.

• Adjust incubation temperature: Compare room temperature versus 4°C incubations.

When implementing these troubleshooting approaches, researchers should:

• Change only one variable at a time

• Include appropriate controls in each experiment

• Document all modifications to standard protocols

• Consider lot-to-lot variability when troubleshooting

By methodically testing these interventions, researchers can identify and address the specific factors contributing to non-specific binding with At4g29360 Antibody, ultimately improving experimental outcomes and data reliability.

How can researchers effectively incorporate At4g29360 Antibody into complex experimental workflows such as co-immunoprecipitation studies?

Incorporating At4g29360 Antibody into complex experimental workflows like co-immunoprecipitation (Co-IP) requires careful optimization and methodological considerations. While not explicitly validated for immunoprecipitation , researchers can adapt this antibody for such applications by following these systematic approaches:

Antibody Immobilization Options:

• Direct Coupling: Covalently attach purified antibody to activated agarose or magnetic beads using chemical crosslinkers.

• Indirect Capture: Utilize Protein A/G beads to capture the antibody-antigen complex, leveraging the rabbit IgG origin of the antibody .

• Pre-clearing Strategy: Remove non-specific binding proteins by pre-incubating lysates with beads alone before adding antibody.

Co-IP Protocol Optimization:

• Lysis buffer selection: Test multiple buffer compositions to balance extraction efficiency with preservation of protein-protein interactions:

  • Standard RIPA buffer (higher stringency)

  • NP-40/Triton X-100 buffer (gentler, preserves more interactions)

  • Plant-specific buffers containing protease inhibitors, DTT, and EDTA

• Crosslinking considerations: For transient or weak interactions, consider membrane-permeable crosslinkers (DSP, formaldehyde) prior to lysis.

• Antibody amount titration: Test 1-10 μg antibody per mg of protein lysate to determine optimal concentration.

• Incubation parameters: Compare short (2 hours) versus long (overnight) incubations at 4°C with gentle rotation.

• Washing stringency gradient: Implement sequential washes with increasing stringency to reduce background while preserving specific interactions.

Validation Approaches:

• Perform reverse Co-IP with antibodies against suspected interacting partners

• Include knockout/knockdown controls to verify specificity

• Compare results with tagged-protein approaches when available

• Validate key interactions with orthogonal methods (Y2H, BiFc, etc.)

By carefully optimizing these parameters and documenting the protocol systematically, researchers can effectively incorporate At4g29360 Antibody into co-immunoprecipitation workflows to study protein-protein interactions involving the At4g29360 protein in Arabidopsis thaliana.

What are the best practices for quantitative analysis of At4g29360 protein using this antibody?

Quantitative analysis of At4g29360 protein requires rigorous methodological approaches to ensure accurate and reproducible results. When using At4g29360 Antibody for quantitative applications, researchers should implement these best practices:

For Western Blot Quantification:

• Standard curve implementation: Create a dilution series of recombinant At4g29360 protein to establish a standard curve relating band intensity to protein quantity.

• Loading control optimization: Select appropriate loading controls based on experimental conditions (e.g., housekeeping proteins like actin or tubulin) and verify their stability under your experimental conditions.

• Technical replication: Perform at least three technical replicates for each biological sample.

• Image acquisition parameters: Ensure all images are captured within the linear dynamic range of the detection system, avoiding saturation.

• Normalization methodology: Apply consistent normalization across samples using validated housekeeping proteins or total protein staining methods like Ponceau S.

For Quantitative ELISA:

• Standard curve design: Prepare recombinant At4g29360 protein standards covering a wide concentration range (typically 7-8 points with 2-fold dilutions).

• Optimization of coating conditions: Test different coating buffers and concentrations to maximize consistent antigen binding.

• Four-parameter logistic regression: Use this statistical model rather than linear regression for accurate interpolation of unknown samples.

• Inter-assay calibration: Include common calibrator samples across multiple plates to adjust for plate-to-plate variation.

Data Analysis Recommendations:

• Software selection: Use specialized quantitative analysis software capable of background subtraction and normalization.

• Statistical approach: Apply appropriate statistical tests based on sample distribution and experimental design.

• Biological significance thresholds: Establish minimum fold-change thresholds for biological significance beyond statistical significance.

• Visualization methods: Present data with appropriate error bars representing biological variability.

By implementing these methodological approaches, researchers can generate robust quantitative data regarding At4g29360 protein expression levels across different experimental conditions.

How can computational approaches enhance experimental design when working with At4g29360 Antibody?

Computational approaches can significantly enhance experimental design and interpretation when working with At4g29360 Antibody. Recent advances in deep learning and bioinformatics offer powerful tools that researchers can leverage to optimize antibody-based experiments and improve data analysis:

Epitope Prediction and Analysis:

• In silico epitope mapping: Utilize algorithms like BepiPred, IEDB, and Ellipro to predict potential linear and conformational epitopes on At4g29360 protein. This helps identify which regions of the protein are likely recognized by the antibody .

• Structural analysis: If structural data is available, use molecular visualization tools to assess epitope accessibility in different protein conformations.

• Cross-reactivity prediction: Employ BLAST and protein alignment tools to identify potential cross-reactive proteins with similar epitope sequences.

Experimental Design Optimization:

• Condition prediction models: Implement machine learning algorithms trained on antibody performance data to predict optimal experimental conditions (dilutions, buffers, etc.) .

• Statistical power analysis: Use computational tools to determine minimum sample sizes needed for detecting biologically significant differences in expression.

• Experimental variable prioritization: Apply design of experiments (DOE) methodology to efficiently identify key variables affecting antibody performance.

Advanced Data Analysis:

• Automated image analysis: Implement computer vision algorithms for consistent quantification of immunostaining or Western blot results.

• Multi-dimensional data integration: Combine antibody-based data with transcriptomics or proteomics datasets for comprehensive pathway analysis.

• Machine learning for pattern recognition: Apply supervised learning techniques to identify subtle patterns in complex immunostaining results.

Recent developments in deep learning-based antibody design demonstrate how computational approaches can improve antibody characteristics . While these methods were developed for therapeutic antibodies, the underlying principles can be adapted to enhance research antibody applications. Researchers can leverage these computational tools to better understand At4g29360 Antibody characteristics, optimize experimental conditions, and extract more reliable and meaningful data from their experiments.