At2g14500 Antibody

What is At2g14500 and why is it significant in Arabidopsis research?

At2g14500 is a gene locus in Arabidopsis thaliana that corresponds to the UniProt accession number Q9ZQR2. This gene encodes a protein of significant interest in plant molecular biology research due to its potential roles in cellular functions. Understanding this protein through antibody-based detection methods provides critical insights into plant developmental processes, stress responses, and metabolic pathways. The antibody targeting this protein serves as an essential research tool for elucidating gene function, protein localization, and expression patterns under various experimental conditions. Researchers studying plant molecular biology often investigate At2g14500 as part of broader research into gene regulation and protein function in model plant systems .

How should I determine the optimal antibody dilution for Western blotting with At2g14500 antibody?

Determining the optimal antibody dilution for Western blotting requires systematic optimization through titration experiments. Begin with a standard dilution range between 1:500 and 1:2000 for primary antibody incubation. Prepare multiple identical Western blot membranes with your protein samples and positive controls containing the At2g14500 protein. Test different antibody concentrations on each membrane while keeping all other conditions constant. The optimal dilution will provide clear, specific bands with minimal background signal.

For this specific antibody (CSB-PA186357XA01DOA), consider the following optimization protocol:

• Prepare a dilution series (1:500, 1:1000, 1:2000, 1:5000)

• Incubate membranes overnight at 4°C with gentle agitation

• Assess signal-to-noise ratio across all dilutions

• Select the highest dilution that maintains strong specific signal

Remember that optimal dilution may vary depending on protein expression levels, protein extraction methods, and detection systems used. Document your optimization process methodically for reproducibility in future experiments .

What controls should I include when using At2g14500 antibody in immunohistochemistry experiments?

For rigorous immunohistochemistry (IHC) experiments with At2g14500 antibody, multiple controls are essential to validate specificity and reliability. Include the following controls in each experimental setup:

• Positive control: Arabidopsis tissue samples known to express the At2g14500 protein. This validates that your antibody and detection system are functioning properly.

• Negative control: Tissue samples from knockout or knockdown lines where At2g14500 expression is eliminated or reduced. This confirms the specificity of your antibody.

• Secondary antibody control: Omit the primary At2g14500 antibody but include all other reagents and procedures. This identifies any non-specific binding from your secondary antibody.

• Isotype control: Use a non-specific antibody of the same isotype and concentration as your At2g14500 antibody. This helps identify non-specific binding due to antibody class.

• Peptide competition control: Pre-incubate your antibody with excess target peptide before application to tissue. Signal reduction confirms antibody specificity.

Document all controls thoroughly in your experimental records and include representative images in publications. This comprehensive control strategy ensures reliable interpretation of immunohistochemistry results with the At2g14500 antibody .

How can I validate the specificity of At2g14500 antibody for my experiments?

Validating antibody specificity is crucial for generating reliable research data. For At2g14500 antibody, implement a multi-faceted validation approach:

• Western blot analysis: The antibody should recognize a protein band of the expected molecular weight in wild-type Arabidopsis extracts, while showing reduced or absent signal in At2g14500 knockout/knockdown lines.

• Immunoprecipitation followed by mass spectrometry: This confirms that the antibody specifically enriches the target protein from complex biological samples.

• Peptide competition assay: Pre-incubating the antibody with excess immunizing peptide should abolish signal in Western blot or immunohistochemistry, confirming specificity.

• Cross-reactivity testing: Test the antibody against protein extracts from related plant species to evaluate specificity across evolutionary boundaries.

• Recombinant protein control: Use purified recombinant At2g14500 protein as a positive control to confirm recognition by the antibody.

How can I optimize the At2g14500 antibody for detecting post-translational modifications?

Detecting post-translational modifications (PTMs) of the At2g14500 protein requires specialized optimization of antibody-based techniques. Standard antibodies against the protein backbone may not distinguish between modified and unmodified forms. Consider the following methodological approach:

• Enrichment strategies: Implement phospho-enrichment (using TiO₂ or IMAC) or ubiquitin enrichment protocols prior to immunoprecipitation with the At2g14500 antibody to increase detection sensitivity.

• Modification-specific detection: Use a two-antibody approach where the At2g14500 antibody captures the protein, followed by probing with antibodies specific to common PTMs (phospho-serine/threonine/tyrosine, ubiquitin, SUMO, etc.).

• Sequential immunoprecipitation: First immunoprecipitate with the At2g14500 antibody, then perform a second immunoprecipitation with PTM-specific antibodies to isolate the modified subpopulation.

• 2D gel electrophoresis: Combined with Western blotting using the At2g14500 antibody, this can separate protein isoforms with different PTMs based on charge variations.

• Mass spectrometry validation: Always confirm detected PTMs using mass spectrometry analysis of immunoprecipitated protein samples.

When analyzing results, compare PTM patterns across different experimental conditions (stress, developmental stages, etc.) to understand the functional significance of these modifications in At2g14500 regulation .

What are the best approaches for quantifying At2g14500 protein expression levels across different developmental stages?

Quantifying At2g14500 protein expression across developmental stages requires robust quantitative approaches. Implement the following methodological strategies:

• Quantitative Western blotting: Use standardized protein loading (verified by total protein staining methods like Ponceau S) and include recombinant protein standards at known concentrations for calibration. Analyze band intensities using software like ImageJ with appropriate background correction.

• ELISA development: Establish a sandwich ELISA using the At2g14500 antibody paired with another antibody recognizing a different epitope. This provides higher throughput quantification compared to Western blotting.

• Multiplexed protein analysis: Combine At2g14500 detection with other developmental markers through multiplexed Western blotting or antibody arrays, enabling correlation of expression patterns.

• Tissue-specific analysis: Microdissect specific tissues or cell types before protein extraction to obtain spatially resolved expression data rather than whole-plant averages.

• Normalization strategy: Normalize At2g14500 signals to multiple housekeeping proteins (not just one) that remain stable across developmental stages.

Present quantitative data in standardized units (ng protein per mg total protein) rather than arbitrary units when possible. This facilitates comparison across studies and laboratories investigating At2g14500 function in plant development .

How can I combine At2g14500 antibody with other techniques for studying protein-protein interactions?

Investigating protein-protein interactions involving At2g14500 requires integrating antibody-based techniques with complementary methodologies. Implement these advanced strategies for comprehensive interaction analysis:

• Co-immunoprecipitation optimization: Use chemical crosslinking (formaldehyde or DSP) prior to cell lysis to stabilize transient interactions. Vary extraction buffer compositions to preserve different interaction types (ionic, hydrophobic, etc.).

• Proximity ligation assay (PLA): Combine the At2g14500 antibody with antibodies against suspected interaction partners. This in situ technique visualizes protein interactions as fluorescent dots when proteins are within 40nm proximity.

• FRET-based immunocytochemistry: Use fluorophore-labeled secondary antibodies with appropriate spectral properties for Förster Resonance Energy Transfer detection, providing evidence of direct physical interaction.

• Antibody-based ChIP-seq: If At2g14500 functions in transcriptional regulation, combine chromatin immunoprecipitation using the antibody with next-generation sequencing to identify genomic binding sites and potential co-factors.

• BioID or APEX2 proximity labeling: Create fusion proteins of At2g14500 with biotin ligase variants, then use the antibody to verify expression and biotinylated proximity proteins.

Data integration is crucial—confirm interactions detected by one method using at least one orthogonal technique. Present interaction data in network diagrams showing confidence levels based on detection methods and reproducibility .

What factors should I consider when designing immunofluorescence experiments with At2g14500 antibody?

Designing effective immunofluorescence experiments with At2g14500 antibody requires careful consideration of multiple technical parameters:

• Fixation optimization: Compare performance of different fixatives (4% paraformaldehyde, methanol, or glutaraldehyde) on antigen preservation. At2g14500 may be sensitive to certain fixation protocols that could mask the epitope.

• Permeabilization protocol: Test a gradient of detergent concentrations (0.1-0.5% Triton X-100) to determine optimal cell permeabilization without disrupting cellular architecture.

• Antibody penetration: For thicker plant tissues, consider extended incubation times (24-48 hours) or vacuum infiltration steps to ensure antibody penetration to all cell layers.

• Antigen retrieval methods: If initial staining is weak, implement heat-induced epitope retrieval (citrate buffer, pH 6.0) or enzymatic retrieval methods to expose masked epitopes.

• Multi-channel imaging strategy: Plan fluorophore combinations carefully to avoid spectral overlap with plant autofluorescence, particularly chlorophyll fluorescence which can interfere with green fluorescent channels.

• Z-stack acquisition: Program confocal microscopy to capture optical sections at appropriate intervals (0.5-1μm) to enable 3D reconstruction of protein localization.

Document each optimization step methodically, creating a standardized protocol that ensures reproducibility. Include subcellular markers (nuclear, ER, Golgi, etc.) to provide context for At2g14500 localization patterns within the cell .

How should I modify protein extraction protocols to optimize At2g14500 detection in different plant tissues?

Optimizing protein extraction for At2g14500 detection requires tissue-specific protocol modifications. Implement these methodological adjustments for different Arabidopsis tissues:

• Leaf tissue protocol:

  • Include 1% PVPP in extraction buffer to remove interfering phenolic compounds

  • Use mechanical disruption with liquid nitrogen followed by brief sonication

  • Include protease inhibitor cocktail with EDTA for metalloproteases inhibition

• Root tissue protocol:

  • Increase detergent concentration (1-2% Triton X-100) due to higher membrane content

  • Extend extraction time (45-60 minutes) with gentle agitation

  • Include phosphatase inhibitors if studying phosphorylation status

• Seed/silique protocol:

  • Implement extended grinding time in liquid nitrogen to break resistant cell walls

  • Include 2-5% SDS in the extraction buffer for more stringent protein solubilization

  • Consider protein precipitation steps (TCA/acetone) to concentrate proteins from these tissues

• Flower tissue protocol:

  • Adjust buffer:tissue ratio to 5:1 due to high water content of floral tissues

  • Include higher concentrations of reducing agents (10mM DTT) to disrupt disulfide bonds

  • Modify centrifugation speeds (15,000g for 20 minutes) for optimal clarification

For all tissues, evaluate extraction efficiency by measuring total protein concentration and performing Western blot analysis with another plant protein antibody as a reference control. Document extraction yields quantitatively to identify optimal protocols for each tissue type .

What experimental design is recommended for studying At2g14500 protein expression under stress conditions?

Designing robust experiments to study At2g14500 protein expression under stress conditions requires careful planning and appropriate controls. Implement this comprehensive experimental design:

• Stress treatment matrix:

  • Include multiple stress intensities (mild, moderate, severe) to capture threshold responses

  • Establish a detailed time course (0, 1, 3, 6, 12, 24, 48 hours) to capture both rapid and delayed responses

  • Test multiple stress types independently and in combination (drought, salt, heat, cold, pathogen)

• Biological replication:

  • Use minimum 3-5 biological replicates per condition

  • Control for plant developmental stage by using age-matched plants

  • Randomize treatment groups to minimize position effects in growth chambers

• Sampling strategy:

  • Harvest tissues at consistent times of day to control for circadian effects

  • Sample the same tissue types/positions across plants to reduce variability

  • Process all samples identically with immediate flash-freezing in liquid nitrogen

• Protein quantification approach:

  • Implement absolute quantification using purified recombinant At2g14500 protein standards

  • Normalize to multiple housekeeping proteins verified to be stable under your stress conditions

  • Consider using fluorescent Western blotting for wider linear dynamic range

• Data analysis plan:

  • Apply appropriate statistical tests for time-course experiments (repeated measures ANOVA)

  • Perform correlation analysis between protein levels and physiological stress indicators

  • Develop mathematical models of protein induction/decay kinetics

How can I resolve inconsistent Western blot results when using At2g14500 antibody?

Inconsistent Western blot results with At2g14500 antibody can stem from multiple sources. Implement this systematic troubleshooting approach:

• Sample preparation variables:

  • Standardize protein extraction buffers and maintain consistent sample:buffer ratios

  • Verify protein stability by minimizing freeze-thaw cycles and adding fresh protease inhibitors

  • Ensure complete denaturation by heating samples at 95°C for 5 minutes in 1× Laemmle buffer

• Gel and transfer parameters:

  • Optimize polyacrylamide percentage based on target protein size (10-12% recommended for mid-size proteins)

  • Calibrate transfer conditions for complete protein transfer (verify with Ponceau S staining)

  • Consider wet transfer over semi-dry for more consistent results with plant proteins

• Antibody-specific variables:

  • Test different antibody lots for consistency using the same positive control

  • Prepare fresh antibody dilutions for each experiment rather than storing diluted antibody

  • Optimize incubation temperature (4°C overnight versus room temperature for 1-3 hours)

• Detection system optimization:

  • Compare chemiluminescence reagents from different manufacturers for sensitivity

  • Optimize exposure times using a series of exposures rather than a single timepoint

  • Consider fluorescent Western blotting for improved quantitative reliability

• Systematic documentation:

  • Maintain a detailed experimental log recording all variables for each blot

  • Photograph all blots with the same settings and include molecular weight markers

  • Report data from multiple independent experiments rather than selecting "representative" blots

Implementing this structured approach will help identify the specific variables causing inconsistency in At2g14500 detection and lead to more reproducible Western blotting results .

What are the common pitfalls in data interpretation when studying protein-protein interactions involving At2g14500?

Data interpretation for protein-protein interactions involving At2g14500 presents several challenges. Avoid these common pitfalls through methodological rigor:

• Overinterpretation of co-immunoprecipitation data:

  • Always verify interactions with reciprocal pulldowns (using antibodies against the putative interacting partner)

  • Distinguish between direct and indirect interactions by including detergent titration series

  • Control for abundant "sticky" proteins that frequently appear in immunoprecipitates nonspecifically

• Artifactual interactions in overexpression systems:

  • Validate interactions at endogenous expression levels where possible

  • Compare interaction stoichiometry against known interacting protein pairs

  • Use competition assays with unlabeled protein to test interaction specificity

• Context-dependent interaction misinterpretation:

  • Test interactions under multiple physiological conditions (developmental stages, stress responses)

  • Consider cellular compartmentalization—proteins may interact only in specific subcellular locations

  • Evaluate temporal dynamics of interactions rather than single timepoints

• Technical artifact misinterpretation:

  • Implement stringent statistical analysis with appropriate thresholds for significance

  • Compare raw data with normalized data to ensure normalization doesn't create artificial patterns

  • Use orthogonal methods to confirm interactions detected by any single technique

• Literature bias:

  • Consider proteins beyond the "usual suspects" in your interaction network analysis

  • Test interactions with proteins from different functional categories

  • Be cautious about confirmation bias when results align with existing hypotheses

Present interaction data with confidence metrics and clearly distinguish between high-confidence direct interactions and lower-confidence associations. This cautious approach prevents overinterpretation while still advancing understanding of At2g14500 function .

How should I analyze conflicting results between At2g14500 antibody detection and transcriptomic data?

Discrepancies between protein abundance (detected by At2g14500 antibody) and mRNA levels (from transcriptomic data) are common in biological systems and require careful analysis. Apply these methodological approaches when facing such conflicts:

• Temporal relationship analysis:

  • Implement detailed time-course studies to determine if protein changes lag behind transcript changes

  • Calculate the time delay between mRNA induction and protein accumulation

  • Consider protein half-life measurements using cycloheximide chase experiments

• Post-transcriptional regulation assessment:

  • Evaluate miRNA regulation potential through computational prediction and experimental validation

  • Analyze 5' and 3' UTR elements that might affect translational efficiency

  • Consider RNA-binding protein interactions that could modify transcript stability

• Post-translational regulation investigation:

  • Assess protein degradation rates under different conditions using inhibitors like MG132

  • Examine potential PTMs that might affect antibody recognition using mass spectrometry

  • Test alternative antibodies recognizing different epitopes of At2g14500

• Technical validation:

  • Verify antibody specificity in the specific experimental context

  • Confirm transcriptomic data with targeted qRT-PCR using multiple reference genes

  • Consider absolute quantification of both mRNA and protein molecules per cell

• Integrative data analysis:

  • Apply mathematical modeling to quantify the relationship between transcript and protein

  • Consider stochastic effects in gene expression, particularly for low-abundance transcripts

  • Implement Bayesian frameworks that incorporate prior knowledge about protein-mRNA relationships

Present conflicting data transparently, acknowledging that such discrepancies often reveal biologically significant regulatory mechanisms rather than experimental errors. This approach transforms apparent conflicts into opportunities for deeper mechanistic understanding of At2g14500 regulation .

What statistical approaches are most appropriate for analyzing quantitative data generated with At2g14500 antibody?

Selecting appropriate statistical methods for At2g14500 protein quantification data requires consideration of experimental design and data properties. Implement these statistical approaches for robust analysis:

• Exploratory data analysis:

  • Assess normality using Shapiro-Wilk test before applying parametric statistics

  • Generate boxplots and Q-Q plots to visualize data distribution

  • Calculate coefficient of variation across biological replicates as quality metric

• Comparative analysis between conditions:

  • For normally distributed data: t-test (two conditions) or ANOVA with post-hoc tests (multiple conditions)

  • For non-normally distributed data: Mann-Whitney U (two conditions) or Kruskal-Wallis with Dunn's test (multiple conditions)

  • For time-course experiments: repeated measures ANOVA or mixed-effects models

• Correlation analysis:

  • Calculate Pearson correlation (parametric) or Spearman correlation (non-parametric) between protein levels and physiological/developmental parameters

  • Implement partial correlation analysis to control for confounding variables

  • Consider time-delayed correlation analysis for dynamic processes

• Advanced modeling approaches:

  • Apply multiple regression to identify predictor variables of At2g14500 protein levels

  • Consider principal component analysis for experiments with multiple related measurements

  • Implement Bayesian hierarchical models for integrating data across experiments

• Power analysis and validation:

  • Determine minimum sample size required for detecting biologically meaningful differences

  • Implement bootstrapping for confidence interval estimation when sample sizes are small

  • Use cross-validation approaches to test predictive models

Present statistical results with appropriate visualizations including scatter plots with error bars, box plots showing data distribution, and correlation plots. Report exact p-values rather than significance thresholds, and include effect sizes (Cohen's d, eta-squared) to contextualize statistical significance with biological relevance .

How can I integrate At2g14500 antibody-based approaches with omics techniques for functional characterization?

Integrating antibody-based detection of At2g14500 with multi-omics approaches creates powerful systems biology insights. Implement these methodological integration strategies:

• Proteomics integration:

  • Combine immunoprecipitation using At2g14500 antibody with mass spectrometry (IP-MS)

  • Compare antibody-based quantification with label-free or TMT-based proteomics data

  • Use targeted proteomics (PRM/MRM) to validate antibody-based findings with peptide-specific quantification

• Transcriptomics correlation:

  • Correlate At2g14500 protein levels with genome-wide expression patterns

  • Identify co-expressed genes through network analysis of matched protein-transcript datasets

  • Implement time-lagged correlation analysis between transcript and protein dynamics

• Epigenomic connections:

  • Combine ChIP-seq data from transcription factors with At2g14500 protein levels

  • Correlate DNA methylation patterns with protein expression across tissues/conditions

  • Integrate chromatin accessibility data to identify regulatory elements affecting expression

• Metabolomic associations:

  • Correlate At2g14500 protein levels with metabolite profiles

  • Apply pathway analysis to position the protein within metabolic networks

  • Use metabolic flux analysis to determine functional impacts of protein abundance changes

• Advanced computational integration:

  • Implement machine learning approaches to integrate multi-omics datasets

  • Develop predictive models of At2g14500 function based on integrated data

  • Apply network analysis to position At2g14500 within functional interaction communities

This multi-layered approach provides a comprehensive functional characterization beyond what any single methodology can achieve. Present integrated findings using multi-dimensional visualizations that highlight connections across biological levels of organization .

What experimental design would you recommend for evaluating At2g14500 function in different Arabidopsis ecotypes?

Evaluating At2g14500 function across different Arabidopsis ecotypes requires a systematic comparative approach. Implement this comprehensive experimental design:

• Ecotype selection strategy:

  • Include geographically diverse ecotypes (Col-0, Ler, Ws, C24, and at least 5-10 additional ecotypes)

  • Select ecotypes with known phenotypic differences relevant to hypothesized At2g14500 function

  • Include ecotypes from extreme environments to capture adaptive variation

• Multi-level phenotyping approach:

  • Quantify At2g14500 protein levels using calibrated Western blotting across all ecotypes

  • Determine subcellular localization patterns using immunofluorescence microscopy

  • Assess protein-protein interaction networks through standardized co-immunoprecipitation

• Genetic analysis integration:

  • Sequence the At2g14500 locus including promoter regions across all ecotypes

  • Correlate protein expression variations with sequence polymorphisms

  • Perform QTL analysis if working with recombinant inbred line populations

• Environmental response characterization:

  • Test protein expression across standardized stress conditions (drought, cold, heat, pathogens)

  • Quantify ecotype-specific differences in protein induction kinetics

  • Correlate protein abundance with ecotype-specific physiological responses

• Functional complementation studies:

  • Express At2g14500 variants from different ecotypes in a reference knockout background

  • Quantify restoration of wild-type phenotypes for each variant

  • Correlate functional differences with protein sequence/expression variations

Present this comparative data in standardized formats that highlight ecotype-specific patterns, potentially using hierarchical clustering and heatmap visualizations. This systematic approach reveals how natural genetic variation impacts At2g14500 function across diverse genetic backgrounds .

What are the most promising emerging techniques that could enhance At2g14500 protein research in the next five years?

Emerging technologies promise to revolutionize At2g14500 protein research in coming years. These methodological innovations will enable deeper functional insights:

• Advanced microscopy approaches:

  • Super-resolution microscopy (PALM/STORM) for nanoscale localization below diffraction limit

  • Expansion microscopy for physical magnification of protein complexes

  • Live-cell single-molecule tracking with photoconvertible fusion proteins or labeled antibody fragments

• Proximity labeling innovations:

  • TurboID and miniTurbo systems for rapid biotin labeling of protein neighborhoods

  • Split-BioID for detecting conditional/dynamic protein interactions

  • Organelle-specific proximity labeling to map compartment-specific interactomes

• CRISPR-based protein visualization and manipulation:

  • CRISPR-mediated endogenous tagging for native-level protein tracking

  • Programmable CasRx-based protein degradation systems

  • Optogenetic control of protein function combined with antibody-based detection

• Mass spectrometry advances:

  • Single-cell proteomics for cell-type specific protein quantification

  • Ion mobility mass spectrometry for structural characterization

  • Top-down proteomics for complete proteoform characterization including PTMs

• Computational and AI integration:

  • Deep learning approaches for predicting protein-protein interactions

  • AlphaFold-based structural prediction integrated with antibody epitope mapping

  • Automated image analysis for high-throughput phenotyping of protein localization

To leverage these techniques effectively, researchers should establish collaborations with technology development laboratories and consider cross-disciplinary training programs. Early adoption of these methods will provide competitive advantages in understanding At2g14500 function within its cellular context .