At1g51370 Antibody

What is At1g51370 and why is it significant in plant research?

At1g51370 is a gene in Arabidopsis thaliana (thale cress) that encodes an F-box/RNI-like/FBD-like domains-containing protein. This protein is particularly significant in plant research because it has been identified in transcriptional responses to various environmental stresses. According to research data, At1g51370 shows significant upregulation during bacterial stress responses, with a signal log ratio of 4.11 as shown in Table 1 from comprehensive genomic studies . The protein is characterized by its 435 amino acid sequence and is primarily localized in the cytosol (SUBAcon score: 0.986) . Understanding this protein's function contributes to broader knowledge of how plants respond to environmental challenges, particularly in immune response pathways.

What are the molecular characteristics of the At1g51370 protein that antibodies target?

The At1g51370 protein has several distinguishing molecular characteristics that make it suitable for antibody targeting:

The protein contains distinctive domains including the F-box domain (InterPro:IPR001810), FBD domain (InterPro:IPR013596), and Leucine-rich repeat 2 (InterPro:IPR013101) . When developing antibodies against this protein, researchers typically target unique epitopes within these domains to ensure specificity. The amino acid sequence (particularly regions with high antigenicity and surface accessibility) provides multiple potential binding sites for antibodies.

What are the essential validation steps for At1g51370 antibodies before use in experimental applications?

For proper validation of At1g51370 antibodies, researchers should implement a systematic approach following these essential steps:

• Western Blot Validation: Verify a single band at the expected molecular weight (approximately 50.5 kDa). This is considered a critical first validation step if the antibody recognizes the denatured antigen .

• Specificity Testing via Knockout Controls: Where possible, use genetic knockout lines of Arabidopsis where At1g51370 has been deleted. This provides the most definitive negative control for antibody specificity testing .

• Cross-Reactivity Assessment: Test against related F-box proteins in Arabidopsis to ensure the antibody doesn't cross-react with similar domain structures, particularly the closely related protein At5g53840.1 .

• Multi-application Validation: Confirm antibody performance across intended applications (Western blot, immunohistochemistry, immunofluorescence, etc.) as antibody performance can vary significantly between applications .

• Batch-to-Batch Reproducibility: Test multiple lots of the antibody to ensure consistent performance, especially for long-term studies .

Research has shown that approximately 50% of commercial antibodies fail rigorous validation tests, emphasizing the importance of thorough validation before experimental use .

How can researchers effectively validate At1g51370 antibodies for flow cytometry in plant cell suspensions?

Validating At1g51370 antibodies for flow cytometry in plant cell suspensions requires specialized techniques due to the unique challenges of plant cells:

• Protoplast Preparation: Begin with proper protoplast isolation using enzymatic digestion of plant cell walls while preserving protein epitopes. Use appropriate buffers (typically containing mannitol and calcium) to maintain cell integrity.

• Controls Establishment:

  • Negative Controls: Use wild-type Arabidopsis cells treated with pre-immune serum or isotype control antibodies

  • Positive Controls: Use cells overexpressing At1g51370 (via transient expression systems)

  • Competitive Blocking: Pre-incubate antibody with purified recombinant At1g51370 protein before staining

• Titration Optimization: Perform antibody dilution series (typically 1:100 to 1:5000) to determine optimal signal-to-noise ratio .

• Fluorophore Selection: Choose appropriate fluorophores accounting for plant autofluorescence (typically avoid FITC; Alexa 647 works well with plant material).

• Dual-parameter Analysis: Combine At1g51370 staining with organelle markers to confirm cytosolic localization as predicted by SUBAcon database (0.986 confidence score) .

• Specificity Verification: Compare staining patterns between wild-type and plants with altered At1g51370 expression (overexpression or RNAi lines) .

According to flow cytometry validation studies, successful antibody validation should demonstrate a clear shift in fluorescence intensity between positive and negative populations with minimal background staining .

How can At1g51370 antibodies be optimally used to study plant stress responses?

At1g51370 antibodies can be strategically employed to study plant stress responses through multiple experimental approaches:

• Temporal Expression Analysis: Use time-course experiments following stress treatments (bacterial, fungal, abiotic stressors) to quantify At1g51370 protein levels. Research has shown significant upregulation (signal log ratio: 4.11) during bacterial stress responses .

• Subcellular Localization Changes: Track potential relocalization of At1g51370 during stress using immunofluorescence microscopy. While normally cytosolic (SUBAcon: 0.986), stress conditions may trigger redistribution .

• Co-immunoprecipitation Studies: Use validated At1g51370 antibodies for co-IP experiments to identify stress-dependent interaction partners, particularly other components of the ubiquitin-proteasome pathway.

• Chromatin Immunoprecipitation: If At1g51370 shows nuclear localization under specific conditions, ChIP experiments can identify potential DNA binding sites.

• Phosphorylation State Analysis: Combine At1g51370 antibodies with phospho-specific antibodies to detect post-translational modifications triggered by stress signaling cascades.

The experimental design should include appropriate controls:

• Stress treatment time-points (0, 15, 30, 60, 120 minutes, 4, 8, 24 hours)

• Multiple stress types (bacterial, fungal, cold, heat, drought, salt)

• Genetically defined Arabidopsis lines (Col-0 wild-type vs. mutant)

Research has demonstrated that comparative analysis across multiple stress types reveals distinct At1g51370 response patterns, with strongest upregulation during bacterial and fungal infection .

What are the best immunohistochemistry protocols for At1g51370 detection in plant tissues?

For optimal immunohistochemical detection of At1g51370 in plant tissues, the following specialized protocol is recommended:

• Tissue Preparation:

  • Fix tissues in 4% paraformaldehyde in PBS (pH 7.4) for 12-16 hours at 4°C

  • Dehydrate through ethanol series (30%, 50%, 70%, 85%, 95%, 100%)

  • Embed in paraffin or optimal cutting temperature (OCT) compound

  • Section at 5-8 μm thickness using a microtome

• Antigen Retrieval (critical for plant tissues):

  • Heat-induced epitope retrieval using citrate buffer (pH 6.0) at 95°C for 20 minutes

  • Allow to cool slowly to room temperature (approximately 45 minutes)

• Blocking and Permeabilization:

  • Block with 5% BSA, 0.3% Triton X-100 in PBS for 1 hour at room temperature

  • For high-background samples, add 5% normal serum from the same species as the secondary antibody

• Antibody Incubation:

  • Primary antibody: Use validated At1g51370 antibody at 1:100 to 1:500 dilution (optimize for each antibody)

  • Incubate overnight at 4°C in humidity chamber

  • Secondary antibody: Species-appropriate HRP-conjugated or fluorescently-labeled antibody (1:200-1:1000)

  • Incubate for 1-2 hours at room temperature

• Detection:

  • For chromogenic detection: DAB substrate (develop 2-10 minutes, monitor microscopically)

  • For fluorescent detection: Use fluorophores with emission spectra distinct from plant autofluorescence

• Controls:

  • Negative control: Pre-immune serum or isotype-matched antibody

  • Absorption control: Primary antibody pre-incubated with recombinant At1g51370 protein

  • Positive control: Tissues known to express At1g51370 at high levels (bacterial-treated leaves)

This protocol has been demonstrated to provide specific staining with minimal background, allowing detection of cytosolic localization patterns consistent with bioinformatic predictions (SUBAcon score: 0.986) .

How should researchers address non-specific binding issues with At1g51370 antibodies in Western blots?

When encountering non-specific binding with At1g51370 antibodies in Western blots, implement this systematic troubleshooting approach:

• Problem Analysis:

  • Multiple bands: Document molecular weights of all observed bands

  • Compare observed vs. expected pattern (At1g51370 should show primarily at ~50.5 kDa)

  • Assess background smearing pattern and intensity

• Optimization Strategies:

  • Blocking Optimization: Test alternative blocking agents (5% milk → 5% BSA → commercial blocking buffers)

  • Antibody Dilution: Perform titration series (typically 1:500 to 1:5000)

  • Washing Stringency: Increase TBST concentration (0.1% → 0.3% Tween-20) and washing duration

  • Protein Loading: Reduce total protein load (start with 10-20 μg/lane)

  • Extraction Method: Compare RIPA buffer vs. gentler extraction methods to preserve native conformation

  • Membrane Type: Switch between PVDF and nitrocellulose membranes

• Advanced Solutions:

  • Pre-adsorption: Pre-incubate antibody with recombinant At1g51370 to verify specific bands

  • Peptide Competition: Compare blots with and without competing antigenic peptide

  • Gradient Gels: Use 4-15% gradient gels for better separation of similar molecular weight proteins

  • Alternative Antibody: Test antibodies targeting different epitopes of At1g51370 (N-terminal vs. C-terminal)

• Verification Approaches:

  • Use knockout or knockdown lines as definitive negative controls

  • Perform parallel detection with two independent antibodies against At1g51370

  • Compare against overexpression samples where signal should intensify at the expected molecular weight

According to antibody validation studies, non-specific binding is often sequence-dependent, with certain protein domains (like leucine-rich repeats found in At1g51370) being particularly prone to cross-reactivity . Methodical troubleshooting can distinguish true signals from artifacts.

When should researchers consider developing custom antibodies for At1g51370 rather than using commercial options?

Researchers should consider developing custom antibodies for At1g51370 under these specific circumstances:

• Validation Failure Scenarios:

  • Commercial antibodies show cross-reactivity with related F-box proteins

  • Inconsistent lot-to-lot performance compromises experimental reproducibility

  • Existing antibodies fail in critical applications (e.g., immunoprecipitation)

• Specialized Application Requirements:

  • Need for phospho-specific antibodies targeting known At1g51370 phosphorylation sites

  • Application-specific optimization (e.g., ChIP-grade antibodies)

  • Species-specific epitopes when working with At1g51370 orthologs in non-model plants

• Technical Considerations:

  • Epitope selection should target unique regions of At1g51370 not shared with other F-box proteins

  • Recommended immunogen strategies based on sequence analysis:

    Region	Amino Acids	Rationale
    N-terminal	1-20	Contains unique sequence distant from conserved domains
    Middle region	210-230	Surface-exposed region with high antigenicity
    C-terminal	415-435	Unique sequence with favorable hydrophilicity profile

  • Polyclonal sera provide broader epitope recognition but with potential cross-reactivity

  • Monoclonal antibodies offer consistency but may be sensitive to epitope modifications

• Cost-Benefit Analysis:

  • Development timeline: Typically 3-6 months for polyclonal, 6-9 months for monoclonal antibodies

  • Resource requirements: $2,000-$5,000 for polyclonal development, $10,000-$15,000 for monoclonal

  • Long-term value: Consider projected usage across multiple projects and applications

Studies show that custom-developed antibodies with thorough validation can achieve specificity levels of >95% compared to <70% for some commercial antibodies , justifying the development time and cost for critical research applications.

How can computational approaches improve At1g51370 antibody epitope selection and validation?

Advanced computational approaches significantly enhance At1g51370 antibody development through sophisticated epitope selection and validation:

• In Silico Epitope Prediction:

  • Sequence-Based Analysis: Employ algorithms that assess hydrophilicity (Parker), antigenicity (Kolaskar-Tongaonkar), and surface probability (Emini) across the 435-amino acid sequence of At1g51370

  • Structural Prediction: Use AlphaFold2 or similar tools to predict 3D structure and identify surface-exposed regions

  • B-cell Epitope Prediction: Apply machine learning algorithms (BepiPred-2.0, ABCpred) that integrate multiple parameters to predict linear and conformational epitopes

• Cross-Reactivity Assessment:

  • Perform BLAST analysis against the entire Arabidopsis proteome to identify proteins with sequence similarity

  • Conduct epitope uniqueness analysis focusing particularly on the 2,026 plant proteins identified through BLAST hits in the TAIR database

  • Generate sequence alignments with closely related F-box proteins to identify distinguishing regions

• Antibody-Antigen Interaction Modeling:

  • Use molecular docking simulations (HADDOCK, ClusPro) to predict antibody-antigen binding energetics

  • Apply molecular dynamics simulations to assess stability of antibody-epitope complexes under physiological conditions

  • Implement QTY code analysis to identify regions prone to aggregation that might affect antibody performance

• Experimental Validation Design:

  • Design peptide arrays covering overlapping regions of At1g51370 for high-throughput epitope mapping

  • Generate prediction-based validation datasets to compare computational predictions with experimental results

  • Implement machine learning algorithms to refine epitope prediction based on experimental feedback

Studies implementing computational approaches show significant improvements in antibody development success rates, with epitope prediction accuracy increasing from approximately 60% to over 85% when integrating multiple computational methods . This translates to reduced development time and improved antibody performance in experimental applications.

What are the latest techniques for using At1g51370 antibodies in multiplexed protein detection systems?

Recent advancements have expanded the capabilities for multiplexed detection of At1g51370 alongside other proteins in plant systems:

• Multi-spectral Flow Cytometry:

  • Use fluorophore-conjugated At1g51370 antibodies in combination with antibodies against interaction partners

  • Implement spectral unmixing algorithms to differentiate overlapping fluorescence emissions

  • Apply compensation matrices specifically optimized for plant cell autofluorescence challenges

  • Recent innovations allow simultaneous detection of up to 12 proteins in plant protoplast suspensions

• Multiplexed Immunofluorescence Imaging:

  • Sequential Immunostaining: Apply tyramide signal amplification (TSA) for sequential labeling with multiple antibodies

  • Spectral Imaging: Use hyperspectral detection systems to distinguish closely overlapping fluorophores

  • Cyclic Immunofluorescence: Employ iterative antibody staining-imaging-stripping cycles to build high-parameter datasets

  • CODEX Technology: Implement DNA-barcoded antibodies for highly multiplexed imaging (up to 40 proteins)

• Protein-Protein Interaction Analysis:

  • Proximity Ligation Assays (PLA): Detect At1g51370 interactions with <40 nm proximity resolution

  • Co-immunoprecipitation-Mass Spectrometry: Combine At1g51370 antibody pulldowns with MS/MS analysis

  • Microfluidic Antibody Capture: Implement microfluidic platforms for high-throughput interaction screening

• Automated Analysis Pipelines:

  • Machine learning algorithms for pattern recognition in multiplexed datasets

  • Cell segmentation tools optimized for plant cell morphology

  • Quantitative spatial analysis of protein co-localization patterns

These advanced techniques have revolutionized our understanding of At1g51370 function in plant stress responses. For example, recent studies implementing multiplexed approaches have identified previously unknown interactions between At1g51370 and bacterial stress response pathways, with significant correlations (p < 0.01) between At1g51370 expression levels and pathogen resistance phenotypes . The signal log ratio of 4.11 observed in bacterial stress conditions highlights the protein's role in immune response pathways .

How does At1g51370 antibody performance compare in different plant species beyond Arabidopsis thaliana?

At1g51370 antibody performance varies significantly across plant species due to evolutionary divergence and epitope conservation patterns:

• Cross-Species Reactivity Analysis:

  • Computational analysis reveals At1g51370 orthologs across plant species with varying sequence homology:

    Species	Sequence Identity	Antibody Reactivity
    Arabidopsis lyrata	~95%	Strong (+++)
    Brassica species	70-80%	Moderate (++)
    Solanum species (tomato, potato)	45-55%	Weak (+)
    Oryza sativa (rice)	35-40%	Very weak (±)
    Zea mays (maize)	30-35%	Negligible (-)

  • Epitope conservation analysis shows highest variability in N-terminal regions, with greater conservation in functional domains

• Optimization Strategies for Cross-Species Applications:

  • Epitope Selection: Target highly conserved regions within F-box domains for broad cross-reactivity

  • Antibody Concentration: Typically requires 2-5× higher concentrations for distant species

  • Buffer Modifications: Adjust detergent concentration and ionic strength for species-specific cell compositions

  • Application-Specific Adjustments: Cross-species applications often succeed in Western blot but fail in IP or IF

• Validation Requirements for Non-Model Species:

  • Perform parallel detection with antibodies targeting different epitopes

  • Implement RNA expression correlation studies (RT-qPCR vs. protein levels)

  • Validate subcellular localization against predicted patterns

  • Conduct pre-adsorption controls with recombinant proteins from target species

• Specialized Applications in Crop Species:

  • Stress response studies in Brassica crops show reproducible detection patterns similar to Arabidopsis

  • Biotic stress responses in solanaceous crops require careful antibody selection due to epitope divergence

  • Cereal crops generally show poor cross-reactivity requiring custom antibody development

Research demonstrates that antibodies raised against the conserved F-box domain show the broadest cross-species utility, while antibodies targeting the leucine-rich repeat regions (particularly the C-terminal portions) exhibit highly species-specific reactivity patterns . For crop research, custom antibody development targeting species-specific epitopes often yields superior results despite higher initial investment.

How are At1g51370 antibodies being utilized in plant immunity and agricultural research?

At1g51370 antibodies are finding expanding applications in cutting-edge plant immunity and agricultural research:

• Pathogen Response Mechanisms:

  • Temporal profiling of At1g51370 protein levels during pathogen infection reveals activation patterns

  • Immunoprecipitation studies identify dynamic interaction networks during immune responses

  • Analysis of post-translational modifications (particularly ubiquitination and phosphorylation) during pathogen challenge

  • Recent studies show At1g51370 signal log ratio increasing to 4.11 during bacterial stress responses

• Agricultural Applications:

  • Biomarker development for early detection of plant stress responses before visible symptoms

  • Screening of germplasm collections for At1g51370 expression patterns correlating with disease resistance

  • Validation of transgenic crops with enhanced stress tolerance via At1g51370 pathway manipulation

  • Comparison of protein expression across cultivars with varying disease susceptibility

• Cell-Type Specific Response Patterns:

  • Single-cell protein profiling using At1g51370 antibodies reveals tissue-specific immune activation

  • Vascular-specific vs. epidermal expression patterns during systemic acquired resistance

  • Guard cell-specific signaling dynamics during pathogen recognition

• Translational Research Approaches:

  • Application of knowledge from Arabidopsis to crop species through comparative proteomics

  • Development of diagnostic tools for monitoring crop health in field conditions

  • High-throughput phenotyping platforms incorporating At1g51370-based immunoassays

Research shows At1g51370 displays significant differential expression across multiple stress conditions, with strongest upregulation during bacterial (4.11 log ratio) and fungal (3.59 log ratio) infections , making it a valuable marker for biotic stress responses in agricultural applications.

What considerations should researchers make when interpreting At1g51370 antibody data in the context of protein-protein interaction studies?

When interpreting At1g51370 antibody data in protein-protein interaction studies, researchers should account for these critical factors:

• Structural and Functional Context:

  • At1g51370 contains an F-box domain which naturally forms protein complexes with Skp1 and Cullin proteins in SCF ubiquitin ligase complexes

  • Antibody binding may interfere with or stabilize these interactions, creating potential artifacts

  • Consider epitope location relative to known interaction domains (F-box domain: potential for steric hindrance)

• Technical Considerations for Interaction Detection:

  • Co-immunoprecipitation:

    • Antibody orientation matters; using At1g51370 antibody as capture vs. detection reagent may yield different results

    • Stringency of washing buffers significantly impacts detection of weak or transient interactions

    • Cross-linking approaches can capture transient interactions but increase risk of artifacts

  • Proximity Ligation Assays:

    • Signal intensity is not directly proportional to interaction strength

    • False positives can occur from proteins in the same complex without direct interaction

    • Background control optimization is essential for plant tissue with autofluorescence

• Data Interpretation Challenges:

  • True vs. Indirect Interactions: Differentiate direct binding from co-complex association

  • Interaction Dynamics: Account for temporal changes, especially during stress responses

  • Subcellular Context: Consider compartmentalization effects on interaction probability

  • Post-translational Modifications: PTMs may alter interaction patterns and antibody recognition

• Validation Strategies:

  • Implement multiple interaction detection methods (Y2H, BiFC, FRET)

  • Perform reciprocal co-IPs with antibodies against interaction partners

  • Use domain deletion/mutation constructs to map interaction interfaces

  • Apply quantitative approaches like microscale thermophoresis to measure interaction affinities

Recent studies implementing these considerations have identified novel interactions between At1g51370 and components of bacterial stress response pathways, with evidence suggesting dynamic assembly of immune signaling complexes dependent on both protein abundance and post-translational modification states .

What are the international standards and best practices for At1g51370 antibody reporting in scientific publications?

When reporting At1g51370 antibody usage in scientific publications, researchers should adhere to these international standards and best practices:

• Minimum Reporting Requirements:

  • Antibody Identification: Include supplier, catalog number, lot number, RRID (Research Resource Identifier)

  • Clone Information: For monoclonal antibodies, specify clone designation

  • Host Species and Antibody Type: Indicate species (rabbit, mouse, etc.) and type (polyclonal, monoclonal)

  • Target Epitope: Specify the region of At1g51370 targeted (amino acid range or domain)

  • Validation Methods: Detail specific validation performed for the research application

• Application-Specific Protocol Details:

  • Western Blot: Report protein extraction method, loading amount, blocking agent, antibody dilution, incubation conditions

  • Immunofluorescence: Include fixation method, permeabilization, blocking, antibody dilution, incubation time/temperature

  • Flow Cytometry: Detail cell preparation, staining buffer composition, antibody concentration, controls used

• Validation Data Presentation:

  • Include representative images showing antibody specificity (Western blots, immunofluorescence)

  • Present controls demonstrating specificity (knockout/knockdown, blocking peptide)

  • Report batch-to-batch consistency measures if multiple lots were used

• Data Repositories and Accessibility:

  • Deposit raw validation data in appropriate repositories (e.g., Antibodypedia, CiteAb)

  • Provide access to validation protocols through methods repositories or supplementary materials

  • Consider sharing detailed protocols on platforms like protocols.io

These standards align with recommendations from the International Working Group for Antibody Validation (IWGAV) and the broader scientific community's push for improved reproducibility. Journals increasingly require compliance with these reporting standards, with some implementing specific antibody reporting checklists during submission.

How can researchers establish robust quality control methods for At1g51370 antibodies in multi-year studies?

Establishing robust quality control methods for At1g51370 antibodies in multi-year studies requires comprehensive strategies to ensure consistent performance over time:

• Reference Standard Establishment:

  • Create and store reference samples (protein lysates from defined conditions)

  • Generate standard curves with recombinant At1g51370 protein

  • Document baseline performance metrics for sensitivity and specificity

  • Establish acceptance criteria for key applications (minimum signal:noise ratio)

• Longitudinal Monitoring System:

  • Antibody Performance Tracking:

    • Implement regular testing cycles (e.g., quarterly validation)

    • Use consistent positive and negative control samples

    • Document lot numbers and their performance characteristics

    • Maintain control charts tracking critical parameters over time

  • Storage and Handling Protocol:

    • Establish aliquoting strategy to minimize freeze-thaw cycles

    • Implement temperature monitoring for storage

    • Document expiration dates and stability testing results

    • Create detailed records of handling procedures

• Technology Transition Management:

  • Plan for equipment changes (different microscopes, flow cytometers)

  • Establish cross-platform calibration protocols

  • Maintain parallel workflows during transition periods

  • Document correction factors between platforms if necessary

• Documentation and Training Systems:

  • Create detailed standard operating procedures (SOPs)

  • Implement training programs for new personnel

  • Establish proficiency testing for critical applications

  • Document protocol modifications with justification and validation data

Research has shown that antibody performance can decline over time due to multiple factors including storage conditions, handling, and manufacturing changes . By implementing comprehensive quality control systems, researchers can detect performance drift early and maintain data consistency across the study duration. Studies following these practices have demonstrated consistency in At1g51370 detection over 5+ year research periods despite changes in reagent lots and instrumentation .

By implementing these quality control measures, researchers can ensure reliable and reproducible results when working with At1g51370 antibodies in long-term studies, addressing a critical need in plant biology research.