At1g24062 Antibody

What is At1g24062 Antibody and what is its target protein in Arabidopsis thaliana?

At1g24062 Antibody (product code CSB-PA647282XA01DOA) is a research reagent designed to detect and bind to the protein encoded by the At1g24062 gene in Arabidopsis thaliana (Mouse-ear cress) . This antibody is part of a larger collection of plant-specific antibodies developed for functional studies in plant systems. When conducting experiments with this antibody, researchers should be aware that proper validation is essential as the success rate of plant antibodies can vary significantly depending on the production method and validation approach .

How should researchers validate the specificity of At1g24062 Antibody before experimental use?

Validation should follow a multi-step approach:

• Western blot analysis with positive and negative controls:

  • Test against wild-type plant extracts (positive control)

  • Test against At1g24062 knockout/mutant lines (negative control)

  • Verify detection of a single band of expected molecular weight

• Immunolocalization comparison:

  • Perform parallel tests in wild-type and mutant backgrounds

  • Confirm absence of signal in tissues lacking the target protein

• Affinity purification assessment:

  • Compare crude antiserum vs. affinity-purified antibody

  • Document significant improvement in detection specificity after purification

Research indicates that affinity purification can dramatically improve detection rates, with studies showing improvement from minimal detection to approximately 55% detection confidence after purification .

What are the common pitfalls in interpreting At1g24062 Antibody experimental results?

Several key issues can affect result interpretation:

Studies have shown that even widely-used commercial antibodies can display significant non-specificity issues. For instance, research on AT1R antibodies demonstrated identical immunostaining patterns in both wild-type and AT1R knockout mice, highlighting the critical importance of proper controls .

How should researchers design experiments to maximize the reliability of At1g24062 Antibody data?

A comprehensive experimental design should include:

• Control samples hierarchy:

  • Positive control (known expression of At1g24062)

  • Negative control (knockout/mutant line)

  • Technical controls (secondary antibody only, pre-immune serum)

• Multiple detection methods:

  • Western blot for protein size verification

  • Immunolocalization for spatial distribution

  • Co-localization with established markers if subcellular distribution is studied

• Quantification approaches:

  • Signal intensity normalization to appropriate loading controls

  • Statistical analysis of replicate experiments (minimum n=3)

  • Consideration of biological variability between plant samples

Experimental design should be documented comprehensively to ensure replicability, with particular attention to antibody dilution, incubation conditions, and detection methods .

What are the optimal protocols for using At1g24062 Antibody in different applications?

Application-specific recommendations:

For Western Blot:

• Recommended dilution: 1:1000-1:5000 (requires optimization)

• Blocking agent: 5% non-fat milk or BSA in TBST

• Incubation: Overnight at 4°C for primary antibody

For Immunofluorescence:

• Recommended dilution: 1:400

• Membrane permeabilization: 0.5% Triton-X100, 10 min at room temperature

• Blocking: 5% fish gelatin

• Primary antibody incubation: Overnight at 4°C

• Secondary antibody: Anti-rabbit IgG conjugated with fluorophore (1:600)

For ChIP-qPCR:

• Recommended amount: 2.5 μg per 100 μg of chromatin

• Cross-linking: Formaldehyde (1% final concentration)

• Sonication: Optimize to achieve 200-500 bp DNA fragments

• DNA purification: Phenol-chloroform extraction or commercial kits

How can researchers troubleshoot weak or absent signals when using At1g24062 Antibody?

Systematic troubleshooting approach:

• Antibody viability check:

  • Dot blot against recombinant protein or synthetic peptide

  • Verify antibody hasn't degraded during storage

• Target abundance assessment:

  • Enrich target protein through subcellular fractionation

  • Increase protein loading for Western blots

  • For tissues with low expression, consider using more sensitive detection methods

• Protocol optimization:

  • Test different fixation methods for immunohistochemistry

  • Adjust antibody concentration (titration experiments)

  • Extend incubation times or modify temperature

  • Try different antigen retrieval methods

Studies have shown that affinity purification significantly improved detection rates from negligible levels to approximately 55% for recombinant protein antibodies in plant systems .

How can At1g24062 Antibody be effectively used in multi-parameter flow cytometry experiments?

For successful multi-parameter analysis:

• Essential controls:

  • Single stain controls for each fluorophore used

  • Fluorescence minus one (FMO) controls

  • Compensation beads AND single-stained cells for proper compensation matrix creation

• Antibody panel design considerations:

  • Spectral overlap minimization

  • Expression level matching (bright fluorophores for dim antigens)

  • Titration of antibodies to determine optimal concentration

• Data analysis approach:

  • Proper gating strategy based on controls

  • Compensation adjustment if needed

  • Statistical analysis accounting for multiple parameters

Research has demonstrated that using compensation matrices from previous experiments can lead to significant data misinterpretation, emphasizing the need for experiment-specific controls .

What approaches should be used to reconcile conflicting data between At1g24062 Antibody localization and other experimental evidence?

When antibody data conflicts with other methods:

• Systematic validation approach:

  • Cross-validate with epitope-tagged proteins

  • Compare with subcellular fractionation results

  • Reconcile with transcriptomic data or GFP fusion localization

• Consider technical limitations:

  • Antibody may recognize specific protein conformations or post-translational modifications

  • Epitope accessibility may vary in different experimental conditions

  • Sample preparation may alter protein localization or antigenicity

• Biological interpretation:

  • Protein may have dynamic localization depending on developmental stage or stress conditions

  • Different isoforms may exist with varying subcellular distributions

  • Potential cross-reactivity with structurally similar proteins

Research has shown that even with careful bioinformatic analysis to identify antigenic regions with less than 40% sequence similarity to other proteins, cross-reactivity can still occur and must be experimentally verified .

How can researchers utilize At1g24062 Antibody in combination with emerging technologies for advanced plant cell biology studies?

Integration with cutting-edge approaches:

• Proximity labeling proteomics:

  • Combine antibody-based protein purification with BioID or APEX2 systems

  • Identify interaction partners in native cellular contexts

  • Map protein neighborhoods within subcellular compartments

• Super-resolution microscopy applications:

  • Direct STORM using fluorophore-conjugated primary antibodies

  • dSTORM with conventional immunofluorescence protocols

  • Expansion microscopy for improved resolution of protein localization

• Single-cell proteomics integration:

  • Antibody-based sorting of specific cell populations

  • Correlation of protein expression with single-cell transcriptomics

  • Spatial mapping of protein expression in tissue contexts

Recent advances in antibody design using computational approaches demonstrate how machine learning can improve antibody specificity and binding characteristics, which might be applied to plant antibodies in the future .

What are the comparative advantages of different antibody production methods for plant proteins like At1g24062?

Comprehensive comparison of production methods:

Studies demonstrate that peptide-based approaches for plant antibodies showed surprisingly poor results with only 1 out of 24 antibodies working satisfactorily, while recombinant protein approaches achieved better success rates, particularly after affinity purification .

How can researchers develop improved validation strategies for At1g24062 Antibody and similar plant antibodies?

Advanced validation framework:

• Genetic validation approaches:

  • Testing in multiple knockout/mutant lines

  • Complementation studies with tagged proteins

  • CRISPR-edited epitope verification

• Orthogonal method verification:

  • Correlation with mass spectrometry data

  • Comparison with RNA-seq expression patterns

  • Integration with protein interaction databases

• Quantitative assessment metrics:

  • Signal-to-noise ratio quantification

  • Titration curves for sensitivity determination

  • Cross-reactivity profiling against protein arrays

Research on AT1R antibodies demonstrated that even widely-used commercial antibodies failed specificity tests when rigorously evaluated against knockout models, highlighting the need for more stringent validation standards .

What are the emerging computational approaches for predicting potential cross-reactivity in plant antibodies like At1g24062 Antibody?

Advanced prediction methods:

• Structural epitope mapping:

  • 3D structure prediction of target proteins

  • Molecular dynamics simulations of antibody-antigen interactions

  • Identification of conformational epitopes beyond primary sequence

• Machine learning applications:

  • Training models on validated antibody-antigen interactions

  • Identification of potential cross-reactive proteins based on structural similarities

  • Optimization of epitope selection for improved specificity

• Integrative bioinformatic pipelines:

  • Combination of sequence similarity, structural properties, and expression patterns

  • Proteome-wide screening for potential cross-reactants

  • Evolutionary conservation analysis for epitope stability assessment

Recent research demonstrates how protein language models like AntiBERTy, ESM-2, and LBSTER can be used to improve antibody design, suggesting potential applications for enhancing plant antibody specificity and performance .

How might emerging technologies improve the specificity and utility of plant antibodies like At1g24062 Antibody?

Next-generation approaches:

• Synthetic antibody engineering:

  • Phage display selection against plant protein targets

  • Yeast surface display for affinity maturation

  • Nanobody development for improved tissue penetration

• AI-assisted antibody design:

  • Deep learning models for epitope optimization

  • Structure-guided binding site prediction

  • Computational screening of antibody candidates before production

• Alternative binding scaffolds:

  • Aptamer development for plant protein detection

  • Affibody molecules as antibody alternatives

  • Synthetic binding proteins with enhanced stability in plant extracts

Research on antibody design using deep learning models like DyAb shows promise for generating high-affinity binders, suggesting similar approaches could be applied to plant antibodies to improve their performance .

What research questions about At1g24062 protein function could be addressed using improved antibody resources?

Key research avenues:

• Developmental regulation studies:

  • Protein expression changes during plant development

  • Tissue-specific and cell-type-specific localization

  • Dynamic changes in response to environmental stimuli

• Protein interaction network mapping:

  • Co-immunoprecipitation coupled with mass spectrometry

  • Proximity-dependent labeling in native contexts

  • Temporal changes in protein complexes during stress response

• Post-translational modification profiling:

  • Development of modification-specific antibodies

  • Quantification of modified protein fractions

  • Correlation of modifications with protein function and localization

Research approaches used for other plant proteins, like PIN proteins, AXR1, TIR1, and membrane trafficking proteins, could serve as models for studying At1g24062 protein function .