At3g27283 Antibody

What is At3g27283 and why is it studied in Arabidopsis research?

At3g27283 is a gene/protein in Arabidopsis thaliana (Mouse-ear cress), a model organism widely used in plant molecular biology research. While the specific function of At3g27283 is not detailed in current literature, antibodies against this protein enable researchers to detect and quantify it in various experimental contexts. Arabidopsis thaliana is valued in plant biology due to its small genome, rapid life cycle, and ease of genetic manipulation, making it an excellent system for studying fundamental plant processes .

What applications is the At3g27283 Antibody validated for?

According to the product datasheet, the At3g27283 Antibody has been validated for the following applications:

• ELISA (Enzyme-Linked Immunosorbent Assay): For detection and quantification of At3g27283 protein in solution

• Western Blotting (WB): For detection of At3g27283 protein after separation by gel electrophoresis

When designing experiments, researchers should ensure their methodology aligns with these validated applications. The antibody has been specifically tested to ensure identification of the target antigen .

What are the optimal storage conditions for At3g27283 Antibody?

The At3g27283 Antibody should be stored at -20°C or -80°C upon receipt. It is critical to avoid repeated freeze-thaw cycles as these can degrade antibody quality. The antibody is supplied in liquid form with the following storage buffer:

• Preservative: 0.03% Proclin 300

• Constituents: 50% Glycerol, 0.01M PBS, pH 7.4

For long-term stability, aliquot the antibody into smaller volumes before freezing to minimize freeze-thaw cycles when only portions are needed for experiments .

How can I validate the specificity of At3g27283 Antibody in my experiments?

Validating antibody specificity is crucial for reliable research results. For At3g27283 Antibody, implement the following approaches:

• Genetic Controls:

  • Use knockout/knockdown lines of At3g27283 in Arabidopsis thaliana

  • Compare wild-type plants with transgenic plants overexpressing At3g27283

• Molecular Weight Verification:

  • Confirm the detected band matches the predicted molecular weight of At3g27283

  • Account for potential shifts due to post-translational modifications

• Peptide Competition Assay:

  • Pre-incubate the antibody with excess purified At3g27283 protein or immunizing peptide

  • A specific antibody should show significantly reduced signal

• Orthogonal Detection Methods:

  • Correlate protein detection with mRNA levels using RT-PCR or RNA-seq

  • Use mass spectrometry to confirm the identity of immunoprecipitated protein

A comprehensive validation strategy employs multiple approaches to build confidence in antibody specificity before proceeding with complex experiments. Since this is a polyclonal antibody raised against recombinant Arabidopsis thaliana At3g27283 protein, it may recognize multiple epitopes .

What approaches can optimize At3g27283 Antibody for detecting low-abundance targets?

When working with potentially low-abundance targets like At3g27283 in plant tissues, consider these optimization strategies:

• Sample Preparation:

  • Grind tissues in liquid nitrogen to preserve protein integrity

  • Use optimized extraction buffers with appropriate protease inhibitors

  • Consider subcellular fractionation to concentrate target protein

• Western Blot Enhancement:

  • Increase protein load (30-50 μg per lane)

  • Extend primary antibody incubation (overnight at 4°C)

  • Use high-sensitivity detection systems (chemiluminescence)

• ELISA Optimization:

  • Evaluate different coating buffers (carbonate/bicarbonate at pH 9.6)

  • Titrate antibody concentrations to determine optimal signal-to-noise ratio

  • Extend incubation times while maintaining low background

Since this antibody is affinity-purified, it may provide better specificity for detecting low-abundance targets compared to crude antiserum preparations .

How can I integrate At3g27283 Antibody into advanced proteomic workflows?

Integrating At3g27283 Antibody into advanced proteomic workflows requires careful consideration of several technical aspects:

• Immunoprecipitation for Protein-Protein Interaction Studies:

  • Optimize antibody-to-bead coupling ratios (typically 5-10 μg antibody per 50 μL of protein A/G beads)

  • Determine optimal extraction conditions that preserve protein complexes

  • Implement appropriate controls (IgG control, input sample)

• Mass Spectrometry Integration:

  • Ensure antibody purification method doesn't introduce contaminants

  • Use appropriate elution methods compatible with downstream MS

  • Implement appropriate normalization and quantification strategies

• Data Analysis Considerations:

  • Implement appropriate normalization methods

  • Account for technical variation through replicates

  • Validate findings with independent methodologies

Similar integration approaches have been successful with antibodies targeting other plant proteins, as demonstrated in studies using computational methods like H3-OPT for analyzing antibody structures and their applications .

What is the recommended protocol for using At3g27283 Antibody in Western blotting?

Based on standard practices for polyclonal antibodies like At3g27283 Antibody, the following protocol is recommended:

Sample Preparation:

• Extract total protein from Arabidopsis thaliana tissues using an appropriate buffer (e.g., RIPA buffer with protease inhibitors)

• Determine protein concentration using Bradford or BCA assay

• Prepare samples containing 20-30 μg total protein mixed with Laemmli buffer

• Heat samples at 95°C for 5 minutes

SDS-PAGE and Transfer:

• Load samples onto an SDS-PAGE gel (10-12% acrylamide)

• Include molecular weight markers

• Run gel at 100-120V until adequate separation is achieved

• Transfer proteins to PVDF or nitrocellulose membrane

Immunoblotting:

• Block membrane with 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature

• Incubate with primary At3g27283 Antibody at 1:1000 dilution in blocking buffer overnight at 4°C

• Wash 3 times with TBST, 5-10 minutes each

• Incubate with HRP-conjugated secondary anti-rabbit antibody at 1:5000 dilution for 1 hour

• Wash 3 times with TBST, 5-10 minutes each

• Detect signal using ECL substrate and imaging system

Data Analysis:

• Quantify band intensity using appropriate software

• Normalize to loading control (e.g., GAPDH, actin, or total protein stain)

• Perform statistical analysis on replicate experiments

How can I troubleshoot weak or absent signals when using At3g27283 Antibody?

When encountering weak or absent signals, implement this systematic troubleshooting approach:

Potential Issues and Solutions:

• Insufficient Target Protein:

  • Increase total protein loaded (up to 50-60 μg)

  • Use protein concentration methods (e.g., TCA precipitation)

  • Consider subcellular fractionation if the protein is compartmentalized

• Antibody-Related Issues:

  • Titrate antibody concentration (try 1:500 to 1:2000 dilutions)

  • Extend primary antibody incubation (up to 48 hours at 4°C)

  • Check antibody storage conditions and expiration

  • Assess if multiple freeze-thaw cycles have affected antibody performance

• Technical Issues:

  • Verify transfer efficiency using reversible protein stains

  • Optimize blocking conditions (type, concentration, duration)

  • Test different membrane types (PVDF vs. nitrocellulose)

  • Enhance detection system (use high-sensitivity ECL substrates)

Create a troubleshooting matrix where you change one variable at a time while maintaining all others constant. Document results carefully to identify the critical parameters affecting detection .

What data collection and analysis methods are recommended for quantitative experiments?

For quantitative experiments using At3g27283 Antibody, implement these rigorous methods:

Data Collection Guidelines:

• Experimental Design:

  • Include biological replicates (n ≥ 3)

  • Plan technical replicates within each biological sample

  • Include appropriate positive and negative controls

  • Design experiments to test the linear range of detection

• Western Blot Quantification:

  • Use a digital imaging system with a linear dynamic range

  • Avoid film exposure for quantification (limited dynamic range)

  • Capture multiple exposure times to ensure signals are within linear range

Data Analysis Methods:

• Normalization Strategies:

  • For Western blots: normalize to housekeeping proteins or total protein stains

  • For ELISA: use standard curves with appropriate curve fitting (4PL model recommended)

• Statistical Analysis:

  • Perform appropriate statistical tests based on experimental design

  • Use ANOVA with post-hoc tests for multiple comparisons

  • Report effect sizes along with p-values

This sample data table format demonstrates proper reporting of quantitative Western blot results .

How should I design experiments to study At3g27283 protein expression across plant developmental stages?

When designing experiments to study At3g27283 expression across developmental stages:

• Sampling Strategy:

  • Collect tissues at defined developmental stages (seedling, vegetative, reproductive)

  • Sample specific organs (roots, stems, leaves, flowers, siliques) at each stage

  • Maintain consistent harvesting times to control for circadian effects

• Experimental Controls:

  • Include tissue-specific positive controls (proteins known to be expressed in each tissue)

  • Use genetically modified lines (overexpression, knockdown) as reference points

  • Consider environmentally matched wild-type controls

• Quantification Approach:

  • Implement absolute quantification using recombinant protein standards

  • Track relative changes normalized to stable reference proteins

  • Correlate protein levels with transcript abundance through RT-qPCR

• Validation Methods:

  • Confirm expression patterns with immunolocalization techniques

  • Verify antibody specificity with negative controls for each tissue type

  • Complement protein data with promoter-reporter studies

Similar experimental designs have been used effectively in studies characterizing other Arabidopsis proteins and could be adapted for At3g27283 .

What considerations are important when using the At3g27283 Antibody for co-immunoprecipitation studies?

When designing co-immunoprecipitation (Co-IP) experiments with At3g27283 Antibody:

• Extraction Conditions:

  • Use gentle lysis buffers (avoid strong detergents that may disrupt protein-protein interactions)

  • Optimize salt concentration (typically 100-150 mM NaCl)

  • Include protease and phosphatase inhibitors to preserve native interactions

  • Perform extractions at 4°C to minimize protein degradation

• IP Protocol Optimization:

  • Test different antibody amounts (typically 2-5 μg per mg of total protein)

  • Compare direct antibody coupling to beads versus indirect capture

  • Determine optimal incubation times (4-16 hours at 4°C with gentle rotation)

  • Optimize wash stringency to remove non-specific binders

• Controls:

  • Include IP with non-specific IgG from the same species

  • Use lysate from knockout/knockdown plants as negative controls

  • Consider reverse Co-IP to confirm interactions

  • Include input samples (typically 5-10% of starting material)

• Detection Methods:

  • Western blot with antibodies against suspected interaction partners

  • Mass spectrometry for unbiased identification of co-precipitated proteins

  • Correlate with yeast two-hybrid or other in vitro interaction data

These methodological considerations are similar to those used in advanced antibody research applications as described in H3-OPT antibody structure studies .

How do I interpret conflicting results between protein levels detected by At3g27283 Antibody and gene expression data?

When facing discrepancies between protein levels detected by At3g27283 Antibody and gene expression data:

• Biological Explanations:

  • Post-transcriptional regulation may cause protein and mRNA levels to differ

  • Protein stability and turnover rates affect steady-state protein levels

  • Translational efficiency might vary under different conditions

  • Post-translational modifications could affect antibody recognition

• Technical Considerations:

  • Verify antibody specificity under your experimental conditions

  • Assess whether the antibody recognizes all isoforms or specific variants

  • Check if experimental conditions affect epitope accessibility

  • Ensure RNA and protein samples represent the same biological state

• Analytical Approach:

  • Calculate protein-to-mRNA ratios across conditions to identify regulatory patterns

  • Implement time-course studies to detect temporal delays between transcription and translation

  • Use pharmacological inhibitors of protein synthesis or degradation to assess protein turnover

  • Consider pulse-chase experiments to measure protein half-life

• Validation Strategy:

  • Test multiple primer sets and antibodies targeting different epitopes

  • Implement orthogonal techniques (e.g., mass spectrometry)

  • Use reporter constructs to monitor transcription and translation independently

This approach to resolving data conflicts is consistent with best practices in antibody-based research and structural prediction methods for antibodies .

What statistical methods are appropriate for analyzing data from experiments using At3g27283 Antibody?

When analyzing data from experiments using At3g27283 Antibody:

• Descriptive Statistics:

  • Calculate means, standard deviations, and standard errors

  • Assess data distribution (normality tests like Shapiro-Wilk)

  • Generate box plots or violin plots to visualize data distribution

• Inferential Statistics:

  • For comparing two groups: t-test (parametric) or Mann-Whitney U test (non-parametric)

  • For multiple groups: ANOVA with appropriate post-hoc tests (Tukey, Bonferroni)

  • For non-normal data: Kruskal-Wallis with Dunn's post-hoc test

• Correlation Analysis:

  • Pearson correlation (parametric) or Spearman rank correlation (non-parametric)

  • Regression analysis for predictive relationships

  • Path analysis for complex relationship networks

• Advanced Methods:

  • Mixed-effects models for nested experimental designs

  • Multiple comparison correction methods (FDR, Bonferroni)

  • Power analysis for determining adequate sample size

• Reporting Requirements:

  • Include exact p-values rather than threshold indicators

  • Report effect sizes alongside significance values

  • Clearly state biological and technical replication numbers

  • Specify software and versions used for analysis

These statistical approaches align with best practices described in data table guidelines for biological research .