At2g18780 Antibody

What protein does At2g18780 code for in Arabidopsis thaliana?

At2g18780 is a gene locus on chromosome 2 of Arabidopsis thaliana. This gene encodes a protein involved in plant cellular processes. When selecting antibodies for this protein, researchers should consider:

• Expected molecular weight (varies depending on post-translational modifications)

• Cellular localization pattern

• Expression levels across different tissues and developmental stages

• Relationship to homologous proteins that may cross-react with antibodies

When planning experiments, compare antibody specifications with published genomic and proteomic data to ensure alignment with expected protein characteristics.

How can I validate the specificity of At2g18780 antibodies?

Antibody validation is critical given widespread concerns about antibody specificity. Research has shown that many commercially available antibodies exhibit nonspecific binding patterns, making validation essential . Recommended validation approaches include:

• Western blot analysis comparing:

  • Wild-type plants

  • At2g18780 knockout or knockdown mutants

  • Tissues with differential expression patterns

• Immunohistochemistry controls:

  • Omission of primary antibody

  • Pre-immune serum controls

  • Peptide competition assays

  • Comparison with fluorescent protein fusion localization

• Additional validation methods:

  • RNA interference to confirm specificity

  • Heterologous expression systems

  • Multiple antibodies targeting different epitopes

Validation across different experimental techniques is recommended, as antibodies may perform differently depending on the application .

What applications are suitable for At2g18780 antibodies?

At2g18780 antibodies can be used in various research applications:

Each application requires specific optimization. When using global antibodies that recognize multiple homologs, additional controls may be needed to distinguish specific signals .

How should I store and handle At2g18780 antibodies?

Proper storage and handling significantly impact antibody performance:

• Store lyophilized antibodies at -20°C until reconstitution

• Once reconstituted, make small aliquots to avoid repeated freeze-thaw cycles

• Store reconstituted antibodies at -20°C for long-term storage

• Spin tubes briefly before opening to collect all material

• Track antibody lot numbers, as performance may vary between batches

• Maintain a validation record for each antibody lot

• Consider adding preservatives for antibodies stored at 4°C

Improper storage can lead to degradation, aggregation, and loss of specificity, potentially explaining inconsistent experimental results.

How do I troubleshoot cross-reactivity issues with At2g18780 antibodies?

Cross-reactivity is a common challenge, particularly with plant proteins that may have multiple isoforms. Studies have shown that antibodies often recognize multiple immunoreactive bands, including in knockout organisms lacking the target protein :

• Analytical approaches:

  • Mass spectrometry identification of detected bands

  • Preabsorption with potential cross-reactive proteins

  • Epitope mapping to identify shared sequence regions

• Experimental solutions:

  • Use antibodies raised against unique protein regions

  • Implement knockout/knockdown controls in every experiment

  • Compare results from multiple antibodies targeting different epitopes

• Data analysis strategies:

  • Document all observed bands/signals

  • Compare with predicted molecular weights

  • Consider post-translational modifications affecting mobility

Research on other plant antibodies demonstrates that identical immunoreactive patterns can be present in both wild-type and knockout mice, highlighting the critical importance of proper controls .

What factors affect At2g18780 antibody performance in immunolocalization?

Several factors influence immunolocalization results:

• Fixation methods significantly impact epitope preservation:

  • Aldehyde fixatives (formaldehyde, glutaraldehyde) preserve structure but may mask epitopes

  • Organic solvents (methanol, acetone) may better preserve some epitopes but disrupt membranes

  • Empirical testing of multiple fixation protocols is recommended

• Tissue-specific considerations:

  • Plant cell walls require optimized permeabilization

  • Autofluorescence varies across tissues and can interfere with detection

  • Background staining patterns differ between tissue types

• Antibody-specific factors:

  • Different antibodies against the same protein can show dramatically different staining patterns

  • Monoclonal versus polyclonal antibodies have different specificity profiles

  • Optimal antibody concentration varies by tissue type and preparation method

Studies examining antibodies raised against different domains of proteins show that immunostaining patterns can differ significantly between antibodies targeting the same protein .

How can I design experiments to distinguish between At2g18780 and related protein isoforms?

Distinguishing between closely related proteins requires careful experimental design:

• Epitope selection strategy:

  • Target unique protein regions for antibody production

  • Analyze sequence homology to identify distinguishing peptides

  • Consider raising antibodies against post-translational modifications unique to specific isoforms

• Genetic approaches:

  • Use isoform-specific knockout/knockdown lines

  • Perform complementation with specific isoforms

  • Create tagged versions of individual isoforms

• Biochemical methods:

  • 2D gel electrophoresis to separate based on both size and charge

  • Ion exchange chromatography prior to immunoblotting

  • Isoform-specific enzymatic assays when applicable

• Advanced detection methods:

  • Mass spectrometry for isoform-specific peptide identification

  • Proximity ligation assays for in situ detection

  • Single-molecule imaging to detect co-localization patterns

Combining multiple approaches provides the most robust isoform discrimination strategy.

How do post-translational modifications affect At2g18780 antibody recognition?

Post-translational modifications (PTMs) can significantly alter antibody binding:

• Common PTMs affecting antibody recognition:

  • Phosphorylation

  • Glycosylation

  • Ubiquitination

  • Sumoylation

  • Proteolytic processing

• Experimental considerations:

  • Use phosphatase treatment to assess phosphorylation effects

  • Compare reducing vs. non-reducing conditions for disulfide-dependent epitopes

  • Consider modification-specific antibodies for studying specific PTM states

• Analytical approaches:

  • Include PTM prediction in experimental design

  • Use mass spectrometry to identify actual modifications

  • Consider multiple antibodies targeting different protein regions

PTMs can explain why antibodies recognize bands of unexpected molecular weights in immunoblotting experiments.

What are the optimal conditions for At2g18780 antibody in Western blotting?

Optimizing Western blot conditions:

• Sample preparation:

  • Include protease inhibitors to prevent degradation

  • Denature samples completely (95°C for 5 minutes in sample buffer)

  • Load appropriate protein amounts (typically 10-30 μg total protein)

• Gel electrophoresis:

  • Select appropriate gel percentage based on protein size

  • Include molecular weight markers

  • Consider gradient gels for better resolution

• Transfer conditions:

  • Optimize transfer time and voltage for the protein size

  • Verify transfer efficiency with reversible staining

  • Choose appropriate membrane (PVDF or nitrocellulose)

• Antibody incubation:

  • Determine optimal antibody dilution (typically 1:10,000 for plant antibodies)

  • Optimize blocking conditions (typically 5% non-fat dry milk or BSA)

  • Consider overnight primary antibody incubation at 4°C for better results

Expected molecular weight should be confirmed against protein databases and may vary depending on post-translational modifications.

How should I prepare plant samples for optimal At2g18780 detection?

Sample preparation significantly impacts detection quality:

• Tissue selection and harvesting:

  • Consider developmental stage and expression timing

  • Harvest at consistent times to control for circadian variation

  • Flash-freeze samples immediately after collection

• Extraction buffer optimization:

  • Test different detergent types and concentrations

  • Include appropriate protease inhibitors

  • Consider phosphatase inhibitors if studying phosphorylated forms

• Homogenization methods:

  • Cryogenic grinding for tough plant tissues

  • Gentle methods for preserving protein complexes

  • Sonication for difficult-to-extract proteins

• Fractionation considerations:

  • Separate subcellular fractions if studying localization

  • Consider detergent solubility when analyzing membrane proteins

  • Include controls for fraction purity

For experimental reproducibility, all samples should be processed using identical protocols and within the same experimental session when possible.

What controls are essential when performing co-immunoprecipitation with At2g18780 antibodies?

Essential controls for co-immunoprecipitation (co-IP) experiments:

• Input controls:

  • Total lysate sample before IP (typically 5-10%)

  • Quantitative analysis of target protein in input

• Negative controls:

  • Non-immune IgG or pre-immune serum

  • Lysate from knockout/knockdown plants

  • Competitive elution with immunizing peptide

• Specificity controls:

  • Reverse IP with antibodies against suspected interaction partners

  • Size-exclusion chromatography to confirm complex formation

  • Chemical crosslinking to stabilize transient interactions

• Technical controls:

  • No-antibody beads to assess non-specific binding

  • Denaturing elution to ensure complete recovery

  • Quantification of unbound fraction

These controls help distinguish genuine interactions from technical artifacts that frequently complicate co-IP experiments.

How can I address inconsistent results with At2g18780 antibodies?

When facing inconsistent antibody results:

• Systematic evaluation of variables:

  • Antibody lot variation (request validation data from suppliers)

  • Sample preparation inconsistencies

  • Protocol drift over time

  • Equipment calibration issues

• Experimental design modifications:

  • Increase biological and technical replicates

  • Implement standardized positive and negative controls

  • Document all protocol details meticulously

• Analytical approaches:

  • Quantitative analysis with appropriate normalization

  • Statistical assessment of variability

  • Blinded analysis to reduce bias

• Alternative approaches:

  • Try different antibodies targeting the same protein

  • Complement with non-antibody techniques (e.g., fluorescent protein tagging)

  • Consider mass spectrometry for unbiased protein identification

Studies have shown that different antibodies against the same protein can yield variable results, emphasizing the importance of comprehensive validation .

How do I interpret contradictory localization data for At2g18780?

When different studies show conflicting localization patterns:

• Methodological analysis:

  • Compare fixation and permeabilization methods

  • Assess antibody specificity under each condition

  • Consider resolution limitations of imaging techniques

• Biological explanations:

  • Dynamic protein localization depending on cell state

  • Developmental regulation of localization

  • Stimulus-dependent protein translocation

  • Isoform-specific localization patterns

• Integration strategies:

  • Combine multiple detection methods

  • Use live-cell imaging to capture dynamic changes

  • Employ super-resolution microscopy for detailed localization

Research on other antibodies has shown that immunostaining patterns can differ dramatically between antibodies targeting different domains of the same protein , requiring careful interpretation of results.

What data should be included when publishing results using At2g18780 antibodies?

To ensure reproducibility and reliability, publications should include:

• Antibody validation data:

  • Specificity controls (especially knockout/knockdown validation)

  • Expected versus observed molecular weights

  • Cross-reactivity assessment

  • Batch/lot information

• Detailed methodology:

  • Complete sample preparation protocol

  • Buffer compositions

  • Incubation times and temperatures

  • Image acquisition settings

• Quantification approach:

  • Raw images before processing

  • Quantification methods with statistical analysis

  • Normalization procedures

  • Sample size and power calculations

• Controls and replicates:

  • All control experiments performed

  • Number of biological and technical replicates

  • Any outliers and how they were handled

Thorough reporting ensures that results can be properly evaluated and reproduced by other researchers in the field.

How can new methodologies enhance At2g18780 antibody research?

Emerging techniques offer new opportunities:

• Proximity labeling approaches:

  • BioID or TurboID fusion proteins for in vivo interaction mapping

  • APEX2 for ultrastructural localization

  • Split-protein complementation for direct interaction validation

• Advanced imaging methods:

  • Super-resolution microscopy for precise localization

  • Light-sheet microscopy for 3D tissue imaging

  • FRET/FLIM for in vivo interaction studies

• Single-cell approaches:

  • Single-cell proteomics

  • In situ protein detection in specific cell types

  • Spatial transcriptomics to correlate protein and mRNA localization

• Computational tools:

  • Machine learning for image analysis

  • Protein structure prediction to identify accessible epitopes

  • Network analysis of protein interactions

These emerging approaches can complement traditional antibody-based methods to provide more comprehensive understanding of protein function.

How should At2g18780 antibody data be integrated with other omics approaches?

Integrative approaches enhance research value:

• Multi-omics integration strategies:

  • Correlate protein levels with transcriptomics data

  • Integrate phosphoproteomics for functional state analysis

  • Combine with metabolomics for pathway analysis

• Data integration tools:

  • Pathway mapping algorithms

  • Protein-protein interaction networks

  • Gene ontology enrichment analysis

• Validation approaches:

  • Genetic manipulation to confirm functional predictions

  • Heterologous expression systems

  • In vitro biochemical assays