At4g15610 Antibody

What is the At4g15610 gene in Arabidopsis and why are antibodies against it important?

At4g15610 is a gene identifier in the model plant organism Arabidopsis thaliana. Antibodies against this protein are essential tools for studying its expression, localization, and function within plant tissues. The At4g15610 protein can be studied as part of the broader effort to understand protein dynamics in Arabidopsis roots. Antibodies enable visualization of protein localization at subcellular, cellular, and tissue levels, which leads to better understanding of protein functions, roles in cell dynamics, protein-protein interactions, and regulatory networks . These insights are particularly valuable in the post-genomics era where integrative systems biology approaches are increasingly employed to model multi-cellular systems.

What database resources are available for At4g15610 identification?

Several database identifiers exist for At4g15610 to facilitate cross-referencing across different biological databases:

These identifiers allow researchers to access comprehensive information about protein interactions, pathway involvement, and expression data across multiple databases . When designing experiments using At4g15610 antibodies, consulting these databases can provide valuable context about protein function and expression patterns.

How should researchers select between peptide and recombinant protein-based At4g15610 antibodies?

When choosing an At4g15610 antibody, consider the approach used to generate it. Research has demonstrated that recombinant protein-based antibodies generally show higher success rates than peptide-based antibodies for Arabidopsis proteins. In a comprehensive study of 94 Arabidopsis antibodies, the success rate with peptide antibodies was very low, while recombinant protein approaches achieved approximately 55% detection rates .

For At4g15610 specifically, researchers should:

• Review documentation to determine if the antibody was generated using peptides or recombinant proteins

• Select recombinant protein-generated antibodies when possible

• Verify that antigenic regions were selected with less than 40% sequence similarity to other proteins to minimize cross-reactivity

• Confirm that affinity purification has been performed, as this significantly improves detection rates

What are the recommended validation procedures for At4g15610 antibodies?

Proper validation of At4g15610 antibodies is critical for ensuring experimental reliability. Based on established practices for Arabidopsis antibodies, researchers should implement the following validation protocol:

• Initial quality control: Perform dot blots against recombinant protein to verify detection sensitivity (picogram range indicates good titer)

• Specificity testing: Test against corresponding mutant backgrounds whenever possible, both by:

  • Western blot analysis to confirm band size and absence in mutants

  • In situ immunolocalization to verify spatial patterns and absence in mutants

• Cross-reactivity assessment: Examine potential cross-reactivity with closely related proteins, particularly important for protein families

• Signal verification: Compare localization patterns with published data or fluorescent protein fusion studies when available

A properly validated At4g15610 antibody should show no detectable signal in corresponding mutant backgrounds, similar to validation performed for other Arabidopsis antibodies such as PIN1, PIN2, PIN3, and others .

How can researchers optimize antibody purification to improve At4g15610 detection?

Antibody purification significantly impacts detection success for Arabidopsis proteins, including At4g15610. Research demonstrates that generic purification methods (Caprylic acid precipitation, Protein A/G purification) often yield inadequate results, while affinity purification dramatically improves detection rates .

For optimal At4g15610 antibody performance:

• Affinity purification protocol:

  • Express and purify recombinant At4g15610 protein (or antigenic region)

  • Couple purified protein to an appropriate matrix

  • Pass crude antiserum through the column

  • Elute and collect specific antibodies

  • Verify purification quality by SDS-PAGE or dot blot analysis

• Concentration optimization:

  • Test multiple antibody concentrations (typically 1:100 to 1:5000 dilutions)

  • Determine optimal signal-to-noise ratio for your specific application

Without affinity purification, even high-titer antisera often fail to detect signals in applications like immunolocalization studies . The dramatic improvement in detection observed after affinity purification (from very few to 38 out of 70 antibodies showing high confidence signals) underscores the importance of this step for successful At4g15610 antibody applications.

What technical approaches can resolve low signal detection issues with At4g15610 antibody?

When working with At4g15610 antibody, researchers may encounter low signal detection issues. Based on experiences with similar Arabidopsis antibodies, several technical approaches can address this challenge:

• Signal amplification methods: Employ enhancement techniques such as tyramide signal amplification or polymer-based detection systems

• Fixation optimization: Test multiple fixation protocols, as protein epitope accessibility can be fixative-dependent:

  • Aldehyde-based fixatives (paraformaldehyde, glutaraldehyde)

  • Alcohol-based fixatives (methanol, ethanol)

  • Combined approaches with different fixation times

• Epitope retrieval: Apply antigen retrieval techniques, particularly for formalin-fixed samples:

  • Heat-induced epitope retrieval

  • Enzymatic retrieval methods

  • pH-modified buffer systems

• Permeabilization adjustment: Optimize detergent type and concentration for improved antibody access to target sites

• Blocking optimization: Test different blocking reagents (BSA, normal sera, commercial blockers) to reduce background while preserving specific signals

It's important to note that failure to detect signals may also indicate genuinely low protein abundance below detection thresholds, and not necessarily antibody failure .

How can At4g15610 antibody be effectively used in co-localization studies?

For co-localization studies involving At4g15610, researchers should combine the antibody with established subcellular markers. Based on successful approaches with other Arabidopsis proteins, the following protocol is recommended:

• Selection of compatible markers: Choose from validated subcellular markers that are compatible with At4g15610 antibody:

  • BiP (endoplasmic reticulum)

  • γ-cop (Golgi apparatus)

  • PM-ATPase (plasma membrane)

  • MDH (mitochondria)

  • CATALASE (peroxisome)

  • AtBIM1/AtbHLH046 (nucleus)

  • GNOM (endosome)

• Multi-labeling protocol:

  • Use primary antibodies from different host species

  • Apply fluorophore-conjugated secondary antibodies with non-overlapping spectra

  • Include appropriate controls for antibody cross-reactivity

  • Capture images using sequential scanning to prevent bleed-through

• Colocalization analysis:

  • Calculate Pearson's correlation coefficient or Manders' overlap coefficient

  • Perform intensity correlation analysis

  • Use appropriate software (ImageJ with JACoP or similar plugins) for quantification

Subcellular markers are extremely valuable tools for applications including colocalization and fractionation studies .

What strategies can address epitope recognition challenges in At4g15610 antibody studies?

Epitope recognition challenges are common with plant protein antibodies, including those against At4g15610. Research on Arabidopsis antibodies has identified several factors that affect epitope recognition and potential solutions:

• Discontinuous epitope issues: Prediction methods primarily identify continuous epitopes (individual stretches of amino acids), while epitopes are often discontinuous (involving distant subsequences brought together by tertiary structure). This limitation significantly impacts peptide antibody success rates .

  Solution: Use longer recombinant protein fragments rather than short peptides, increasing the probability of capturing native conformational epitopes.

• Protein folding differences: Synthetic peptides or recombinant proteins may not fold correctly, generating antibodies that fail to recognize native protein structures .

  Solutions:

  • Express proteins in eukaryotic systems when possible

  • Include post-translational modifications where relevant

  • Employ partial denaturation strategies during immunization

• Cross-reactivity with related proteins: For protein families, obtaining specific antibodies can be challenging.

  Solutions:

  • Use bioinformatic analysis to identify unique antigenic regions with <40% similarity to other proteins

  • When unique regions cannot be identified, develop family-specific antibodies and combine with genetic approaches for specificity confirmation

  • Employ sliding window approaches to obtain smaller regions with reduced sequence similarity

Understanding these challenges is crucial for interpreting unexpected results with At4g15610 antibodies and developing appropriate experimental strategies.

How can researchers reconcile discrepancies between antibody detection and fluorescent protein fusion localization of At4g15610?

When discrepancies arise between At4g15610 antibody localization and fluorescent protein fusion approaches, researchers should implement a systematic analytical approach:

• Evaluation of potential artifacts:

  • Antibody artifacts: Cross-reactivity, fixation artifacts, non-specific binding

  • Fluorescent protein artifacts: Fusion-induced mislocalization, overexpression effects, altered trafficking

• Complementary approaches:

  • Cell fractionation followed by western blot analysis

  • Super-resolution microscopy for improved spatial resolution

  • Proximity labeling methods (BioID, APEX)

  • Correlative light and electron microscopy for ultrastructural confirmation

• Temporal considerations:

  • Analysis of protein dynamics through time-course experiments

  • Evaluation of different developmental stages or conditions

• Quantitative comparison:

  • Quantification of signal distribution across subcellular compartments

  • Statistical analysis of colocalization with established markers

• Genetic approaches:

  • Analysis in various mutant backgrounds

  • Complementation studies with antibody detection in parallel

These complementary approaches provide a more complete understanding of protein localization and can help resolve apparent discrepancies between different detection methods.

How might machine learning approaches improve At4g15610 antibody design and performance?

Recent advances in protein prediction and design could significantly enhance At4g15610 antibody development. The DyAb model, which leverages pre-trained protein language models with a pair-wise representation approach, demonstrates how machine learning can improve antibody design even with limited training data .

For At4g15610 antibody optimization, researchers could implement the following machine learning strategy:

• Sequence-based antibody design:

  • Utilize pre-trained protein language models like AntiBERTy or LBSTER

  • Apply relative embedding computation between sequence pairs

  • Train models on available binding data, even with datasets as small as 100 labeled points

  • Employ genetic algorithms to sample design space and improve predicted binding affinity

• Property prediction in low-data regimes:

  • Apply convolutional neural networks to predict differences in properties between variants

  • Achieve high correlation with experimental measurements (Spearman rank correlation up to 0.85)

  • Generate novel antibody candidates with improved binding rates

• Implementation framework:

  • Start with existing At4g15610 antibody sequence

  • Identify beneficial point mutations through small-scale experiments

  • Use machine learning models to predict optimal combinations of mutations

  • Test predicted high-performance variants experimentally

This approach could lead to At4g15610 antibodies with significantly improved sensitivity, specificity, and versatility for plant biology applications.

What are the considerations for using At4g15610 antibodies in multi-omics integration studies?

As plant biology moves toward multi-omics integration, At4g15610 antibodies can serve as valuable tools within a broader experimental framework:

• Proteomics integration:

  • Use antibodies for protein quantification alongside mass spectrometry data

  • Apply for immunoprecipitation followed by protein complex analysis

  • Correlate protein levels with transcriptomic data for gene-protein expression relationships

• Spatial biology applications:

  • Implement in spatial transcriptomics studies to correlate transcript and protein localization

  • Apply in tissue-specific protein extraction and analysis

  • Develop multiplex immunostaining approaches for spatial protein interaction networks

• Temporal dynamics:

  • Deploy in time-series experiments to track protein expression changes

  • Correlate with metabolomic changes during development or stress responses

  • Monitor post-translational modifications in response to environmental stimuli

• Cross-species comparative studies:

  • Test antibody cross-reactivity with related species

  • Analyze evolutionary conservation of protein localization and function

  • Develop standardized protocols for comparative plant biology

At4g15610 antibodies can provide critical data points within integrated datasets, particularly for understanding protein localization in the context of gene expression, metabolic changes, and developmental processes.