At2g31440 Antibody

What is At2g31440 and what role does the antibody play in plant research?

At2g31440 is a protein-coding gene in Arabidopsis thaliana (mouse-ear cress) that encodes a gamma-secretase subunit APH1-like protein located on chromosome 2 . The At2g31440 antibody is a research tool designed specifically to detect and study this protein in various experimental contexts. The antibody enables researchers to investigate protein expression, localization, and interactions in plant molecular biology studies, providing critical insights into the function of this protein in plant development and cellular processes.

What are the key specifications of commercially available At2g31440 antibodies?

The commercially available At2g31440 antibody (e.g., CSB-PA811869XA01DOA) is a polyclonal antibody raised in rabbits against recombinant Arabidopsis thaliana At2g31440 protein . It is supplied in liquid form with the following specifications:

This antibody has been specifically designed for research applications and is not intended for diagnostic or therapeutic procedures .

How does the At2g31440 antibody compare to other plant protein detection methods?

The At2g31440 antibody offers several advantages over alternative protein detection methods in plant research. Unlike genetic approaches such as reporter gene assays or transcript analysis, antibodies directly detect the protein of interest, allowing researchers to study post-translational modifications and actual protein levels rather than just gene expression.

Compared to mass spectrometry-based proteomics, antibody-based detection provides better sensitivity for low-abundance proteins and can be used in techniques that preserve spatial information, such as immunohistochemistry. The methodology for antibody-based detection also follows principles similar to those used in studies of receptor occupancy in animal cells, where specificity and titration are critical considerations for obtaining reliable results .

What is the optimal antibody concentration for detecting At2g31440 in Western blot and ELISA applications?

Determining the optimal antibody concentration requires careful titration to balance specific signal with background noise. Based on general antibody titration principles in similar research contexts, concentrations between 0.62 and 2.5 μg/mL typically represent the optimal range for many antibodies, including those targeting plant proteins .

For At2g31440 antibody specifically, researchers should:

• Start with a concentration range of 0.5-5.0 μg/mL and perform a titration experiment

• Compare signal-to-noise ratios at each concentration

• Select the lowest concentration that produces consistent, specific detection

Research on antibody titration indicates that concentrations above 2.5 μg/mL often show minimal improvement in signal while increasing background . For optimal results with At2g31440 antibody, initial experiments should include a titration series to determine the ideal concentration for the specific experimental conditions and sample type.

What sample preparation techniques ensure optimal At2g31440 detection in Arabidopsis tissue samples?

Effective sample preparation is crucial for successful At2g31440 detection in Arabidopsis tissues. The following protocol has been developed based on best practices for plant protein extraction:

• Tissue collection and homogenization:

  • Collect fresh tissue samples (100-200 mg) and flash-freeze in liquid nitrogen

  • Grind thoroughly to a fine powder using a pre-chilled mortar and pestle

  • Maintain cold chain throughout to prevent protein degradation

• Protein extraction buffer composition:

  • 50 mM Tris-HCl (pH 7.5)

  • 150 mM NaCl

  • 1% Triton X-100

  • 0.5% sodium deoxycholate

  • 1 mM EDTA

  • Protease inhibitor cocktail

  • 1 mM DTT or 2-mercaptoethanol

• Extraction process:

  • Add 3-5 volumes of extraction buffer to the ground tissue

  • Vortex and incubate on ice for 30 minutes with intermittent mixing

  • Centrifuge at 15,000 × g for 15 minutes at 4°C

  • Collect supernatant containing soluble proteins

• Sample preparation for immunoblotting:

  • Determine protein concentration using Bradford or BCA assay

  • Add appropriate amount of loading buffer

  • Heat at 70°C for 10 minutes (avoid boiling, which can cause protein aggregation)

  • Load 10-30 μg of total protein per lane

This methodology draws upon principles similar to those used in other antibody-antigen studies, where sample preparation significantly impacts epitope accessibility and detection sensitivity .

How should blocking conditions be optimized when using At2g31440 antibody in immunoassays?

Optimizing blocking conditions is essential for reducing background and improving specificity when using At2g31440 antibody. Based on principles applied in receptor binding studies and antibody optimization research:

• Blocking buffer options:

  • 5% non-fat dry milk in TBS-T (standard, economical option)

  • 3-5% BSA in TBS-T (preferred for phospho-specific applications)

  • Commercial plant-specific blocking buffers (may reduce plant-specific background)

• Optimization strategy:

  • Test multiple blocking agents in parallel experiments

  • Evaluate signal-to-noise ratio with each blocking condition

  • Consider plant-specific autofluorescence when using fluorescent detection systems

• Blocking protocol:

  • Block membrane for 1-2 hours at room temperature or overnight at 4°C

  • Use gentle agitation to ensure even coverage

  • Rinse briefly with TBS-T before primary antibody incubation

• Additional considerations:

  • For high background issues, increase blocking percentage to 5-10%

  • For weak signals, reduce blocking time or percentage

  • Consider adding 0.1-0.5% Tween-20 to reduce hydrophobic interactions

This approach follows similar principles to those used in flow cytometry studies for receptor detection, where blocking and washing conditions significantly impact specific binding .

How can researchers distinguish between specific and non-specific binding when using At2g31440 antibody?

Distinguishing specific from non-specific binding is crucial for accurate data interpretation when using At2g31440 antibody. Based on established antibody validation principles:

• Include appropriate controls:

  • Negative control: samples from At2g31440 knockout plants or tissues where the protein is not expressed

  • Isotype control: non-specific IgG from the same species at the same concentration

  • Peptide competition: pre-incubate antibody with excess target peptide to block specific binding

• Analyze band pattern and molecular weight:

  • At2g31440 protein should appear at its predicted molecular weight

  • Multiple bands may indicate degradation products, isoforms, or non-specific binding

  • Compare observed banding pattern with literature reports for this protein

• Quantitative analysis approach:

  • Calculate signal-to-noise ratio by comparing target band intensity to background

  • Signal from knockout or negative control samples can be used to establish background threshold

  • Specific binding typically shows dose-dependent relationships in titration experiments

• Advanced validation:

  • Use orthogonal methods (e.g., mass spectrometry) to confirm protein identity

  • Employ secondary detection methods such as alternative antibodies targeting different epitopes

This methodology builds upon approaches used in multispecific antibody studies, where distinguishing specific from non-specific binding is essential for accurate interpretation of binding data .

What statistical approaches are recommended for quantifying At2g31440 expression levels across different plant tissues?

Robust statistical analysis is essential for accurately quantifying At2g31440 expression across different plant tissues. Researchers should consider:

• Normalization strategies:

  • Normalize target protein signal to appropriate housekeeping proteins (e.g., actin, tubulin, GAPDH)

  • Consider multiple reference proteins for more robust normalization

  • Account for potential tissue-specific variations in reference protein expression

• Quantification methods:

  • Integrated optical density (IOD) measurements of immunoblot bands

  • Standard curve generation using recombinant At2g31440 protein at known concentrations

  • Relative quantification using fold-change compared to control samples

• Statistical tests and representations:

  • For comparing multiple tissues: One-way ANOVA with appropriate post-hoc tests

  • For comparing treated vs. control: t-test or non-parametric equivalent

  • Represent data as mean ± SD or SEM with individual data points

  • Minimum of 3-5 biological replicates recommended

• Data visualization:

  • Bar graphs with error bars showing expression across tissues

  • Heat maps for large-scale tissue comparisons

  • Box plots to show distribution of expression levels

This approach draws on principles similar to those used in analyzing receptor occupancy data, where quantitative analysis of binding across different conditions requires careful normalization and statistical evaluation .

How can researchers account for epitope masking when interpreting negative results with At2g31440 antibody?

Epitope masking can lead to false-negative results when using At2g31440 antibody. To address this issue:

• Potential causes of epitope masking:

  • Post-translational modifications (phosphorylation, glycosylation)

  • Protein-protein interactions blocking the epitope

  • Conformational changes in protein structure

  • Fixation-induced epitope alterations (in immunohistochemistry)

• Experimental approaches to address masking:

  • Try multiple extraction methods (native vs. denaturing conditions)

  • Test different sample preparation protocols (heat, reducing agents)

  • Use epitope retrieval methods for fixed samples (heat-induced or enzymatic)

  • Consider multiple antibodies targeting different epitopes

• Validation experiments:

  • Overexpression controls: test antibody on samples overexpressing At2g31440

  • Recombinant protein controls: use purified protein as a positive control

  • Correlation with transcript analysis: compare protein detection with RT-PCR results

• Quantitative assessment:

  • Compare detection sensitivity across different protocols

  • Document conditions that improve epitope accessibility

  • Establish minimum detectable concentration threshold

This approach builds on research showing that even with high-affinity antibodies, accessibility of the target epitope significantly impacts detection sensitivity, as demonstrated in studies of antigen density effects on binding .

What strategies can optimize At2g31440 antibody use in co-immunoprecipitation experiments with plant lysates?

Co-immunoprecipitation (Co-IP) with At2g31440 antibody requires specific optimization for plant systems:

• Lysis buffer optimization:

  • Test multiple lysis buffers to balance protein solubilization and complex preservation

  • Recommended starting buffer: 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 0.5-1% NP-40 or Triton X-100, 1 mM EDTA, 5% glycerol, protease inhibitors

  • Adjust detergent concentration based on target compartment (membrane vs. cytosolic)

• Antibody coupling strategies:

  • Direct approach: covalently couple At2g31440 antibody to protein A/G beads

  • Indirect approach: add antibody to lysate, then capture with protein A/G beads

  • Pre-clearing: pass lysate through isotype control beads to reduce non-specific binding

• Washing conditions:

  • Start with 3-5 washes using lysis buffer

  • Consider increasing salt concentration (up to 300 mM) in later washes

  • Include detergent in wash buffer to reduce non-specific interactions

  • Final wash with detergent-free buffer to prepare for mass spectrometry

• Elution methods:

  • Gentle: elute with excess immunizing peptide

  • Standard: boil in SDS sample buffer

  • For mass spectrometry: on-bead digestion or mild elution conditions

This methodology builds on principles used in studying antibody-antigen interactions in complex biological systems, where optimization of binding and washing conditions significantly impacts specificity .

What technical challenges might cause inconsistent At2g31440 antibody performance, and how can they be addressed?

Inconsistent antibody performance can result from multiple technical factors. Based on research in antibody reliability:

• Antibody storage and handling issues:

  • Problem: Repeated freeze-thaw cycles causing antibody degradation

  • Solution: Aliquot antibody upon receipt; store at -80°C; use glycerol-containing buffer

  • Assessment: Test aliquots from different storage conditions side-by-side

• Sample preparation variables:

  • Problem: Inconsistent protein extraction or epitope accessibility

  • Solution: Standardize homogenization protocol; test multiple extraction buffers

  • Assessment: Include positive control samples in each experiment

• Batch-to-batch antibody variation:

  • Problem: Different lots showing variable specificity or sensitivity

  • Solution: Reserve sufficient antibody from a validated lot for critical experiments

  • Assessment: Test new lots against standard samples with known expression patterns

• Environmental factors:

  • Problem: Temperature fluctuations during incubations

  • Solution: Use temperature-controlled incubators; standardize incubation times

  • Assessment: Monitor and record temperature throughout protocols

• Detection system variability:

  • Problem: Inconsistent secondary antibody performance or substrate degradation

  • Solution: Use fresh detection reagents; standardize exposure times

  • Assessment: Include standard curve to normalize between experiments

This approach draws on research showing that technical factors significantly impact antibody performance in multimodal detection systems, where standardization is essential for consistent results .

How can cross-reactivity with related plant proteins be assessed and mitigated when using At2g31440 antibody?

Cross-reactivity assessment and mitigation is essential for specificity:

• Cross-reactivity assessment methods:

  • Sequence analysis: Identify proteins with similar epitope sequences

  • Knockout validation: Test antibody on At2g31440 knockout plants

  • Peptide array analysis: Test binding to related and unrelated peptides

  • Immunoprecipitation followed by mass spectrometry to identify all captured proteins

• Pre-experimental strategies:

  • Pre-absorption: Incubate antibody with related proteins to remove cross-reactive antibodies

  • Affinity purification: Purify antibody against the specific target epitope

  • Bioinformatic screening: Identify potential cross-reactive proteins based on sequence homology

• Experimental controls:

  • Include samples with varying expression of related proteins

  • Use tissue-specific expression patterns to differentiate target from related proteins

  • Compare detection patterns with mRNA expression data

• Data analysis approaches:

  • Examine unexpected band patterns that may indicate cross-reactivity

  • Quantify signal in tissues known to lack At2g31440 expression

  • Document cross-reactivity for accurate data interpretation

This approach builds on research showing that even antibodies with high target affinity can exhibit cross-reactivity that affects data interpretation, particularly in complex biological samples .

How can researchers combine At2g31440 antibody detection with transcriptomic data for comprehensive protein function analysis?

Integrating antibody-based protein detection with transcriptomic data provides powerful insights:

• Experimental design considerations:

  • Collect matched samples for protein and RNA analysis

  • Include time-course measurements to capture dynamic relationships

  • Design experiments with appropriate statistical power for both protein and RNA analysis

• Integration methodologies:

  • Correlation analysis: Calculate Pearson or Spearman correlation between protein and transcript levels

  • Ratio analysis: Examine protein-to-mRNA ratios to identify post-transcriptional regulation

  • Clustering approaches: Group genes/proteins by expression patterns across conditions

• Data normalization strategies:

  • Use appropriate housekeeping controls for each data type

  • Consider global normalization methods for system-wide analyses

  • Apply batch correction when integrating data from different experiments

• Visualization approaches:

  • Scatter plots of protein vs. mRNA levels with correlation statistics

  • Heat maps showing relative changes across conditions

  • Network diagrams connecting correlated transcript-protein pairs

This integration approach builds on principles used in multimodal single-cell analysis, where combining protein and transcript measurements provides deeper biological insights than either measurement alone .

What considerations are important when using At2g31440 antibody in combination with fluorescent protein tagging approaches?

Combining antibody detection with fluorescent protein tagging requires careful experimental design:

• Experimental design considerations:

  • Position of fluorescent tag relative to antibody epitope

  • Potential tag effects on protein localization or function

  • Tag stability and maturation time in plant systems

• Validation approaches:

  • Co-localization analysis between antibody signal and fluorescent tag

  • Functional complementation testing with tagged protein

  • Western blot validation of tagged protein size and expression level

• Data interpretation guidelines:

  • Discrepancies between antibody and tag signal may indicate:

    • Epitope masking by the tag

    • Antibody cross-reactivity with untagged proteins

    • Differential detection sensitivity

    • Processing or degradation of tagged protein

• Quantitative analysis strategies:

  • Calibration of antibody signal using known concentrations of tagged protein

  • Ratiometric analysis of tag signal to antibody signal

  • Controls for autofluorescence and non-specific antibody binding

This approach draws on research principles from multispecific binding studies, where understanding the relationship between different detection modalities improves data interpretation .