At1g61320 Antibody

What is At1g61320 and why is it studied in plant research?

At1g61320 is a gene in Arabidopsis thaliana that encodes an FBD-associated F-box protein consisting of 459 amino acids. This protein belongs to the F-box family, which plays crucial roles in protein ubiquitination and degradation pathways in plants. The study of At1g61320 contributes to our understanding of protein turnover regulation, stress responses, and developmental processes in plants. The protein has been classified as "Hard" in the AbClassTM system, indicating challenges in antibody development and detection . Research on At1g61320 helps elucidate plant-specific protein degradation mechanisms that differ from those in animal systems, providing insights into unique aspects of plant cellular regulation.

What types of At1g61320 antibodies are available for research?

Commercially available At1g61320 antibodies typically consist of combinations of mouse monoclonal antibodies targeting different regions of the protein. The main categories include:

Each antibody combination is created using synthetic peptide antigens representing different regions of the At1g61320 protein and demonstrates high sensitivity with ELISA titers around 10,000, corresponding to approximately 1 ng detection sensitivity in Western blot applications .

How specific are At1g61320 antibodies compared to other plant protein antibodies?

The specificity of At1g61320 antibodies must be rigorously validated due to the complex nature of plant proteomes. While monoclonal antibodies generally offer high specificity, researchers should be aware that cross-reactivity with related F-box proteins remains possible. Unlike some well-characterized mammalian targets, plant-specific antibodies like those against At1g61320 often require more extensive validation. Cross-reactivity testing should include negative controls from knockout lines and pre-adsorption tests with the immunizing peptides. Sequence homology analysis against other Arabidopsis F-box proteins is recommended before experimental design . The specificity challenge for At1g61320 antibodies is similar to that observed with other plant protein antibodies, where validation through multiple independent methods is considered best practice.

What are the optimal conditions for using At1g61320 antibodies in Western blotting?

For optimal Western blotting with At1g61320 antibodies, researchers should consider the following protocol:

• Sample preparation: Extract total protein from Arabidopsis tissues using a buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, and protease inhibitor cocktail.

• Protein loading: Load 20-30 μg of total protein per lane for typical tissue samples, or 10-15 μg for enriched samples.

• Antibody concentration: Use primary antibody at dilutions between 1:1000 and 1:5000. Higher concentrations (above 2.5 μg/mL) may increase background without improving signal .

• Incubation conditions: Incubate with primary antibody overnight at 4°C in blocking buffer containing 5% non-fat dry milk or BSA in TBST.

• Detection system: HRP-conjugated secondary antibodies and ECL detection systems are recommended, with signal optimization through titration between 0.62-2.5 μg/mL to find the optimal balance between signal strength and background .

Including positive controls (overexpression lines) and negative controls (knockout mutants) is essential for result validation. The expected molecular weight of At1g61320 is approximately 51 kDa based on its 459 amino acid sequence .

How can I optimize immunoprecipitation experiments using At1g61320 antibodies?

For successful immunoprecipitation (IP) of At1g61320 and its binding partners from plant tissues, follow these methodological steps:

• Tissue preparation: Harvest 2-3 g of fresh Arabidopsis tissue and flash-freeze in liquid nitrogen before grinding to a fine powder.

• Lysis conditions: Extract proteins in a gentle lysis buffer (50 mM HEPES pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% NP-40, 10% glycerol, protease inhibitors) with 30-minute incubation on ice with periodic mixing.

• Pre-clearing: Incubate lysate with protein A/G beads for 1 hour at 4°C to reduce non-specific binding.

• Antibody binding: Use 5-10 μg of At1g61320 antibody per 500 μL of pre-cleared lysate. Titrate antibody concentration if necessary, as concentrations above 2.5 μg/mL may not improve specific binding .

• Complex capture: Add 30-50 μL of protein A/G beads and incubate overnight at 4°C with gentle rotation.

• Washing: Perform 4-5 stringent washes with lysis buffer containing reduced detergent concentrations.

• Elution and analysis: Elute complexes with SDS sample buffer at 95°C for 5 minutes.

To validate IP specificity, perform parallel experiments with unrelated control antibodies of the same isotype. The effectiveness of IP can be assessed by Western blotting of input, unbound, and eluted fractions. Consider crosslinking antibodies to beads to prevent heavy and light chain interference during subsequent analysis .

What are the best practices for immunohistochemistry with At1g61320 antibodies?

For successful immunohistochemistry (IHC) with At1g61320 antibodies in plant tissues, implement these methodological approaches:

• Fixation: Fix fresh tissue samples in 4% paraformaldehyde in PBS for 12-24 hours, followed by dehydration and paraffin embedding.

• Section preparation: Cut 5-8 μm sections and mount on adhesive slides. Deparaffinize and rehydrate sections through an ethanol series.

• Antigen retrieval: Perform heat-mediated antigen retrieval in 10 mM sodium citrate buffer (pH 6.0) for 10-15 minutes.

• Blocking: Block non-specific binding with 5% normal serum from the same species as the secondary antibody and 1% BSA in PBS for 1 hour.

• Primary antibody: Apply At1g61320 antibody at optimized dilutions (typically 1:100 to 1:500) and incubate overnight at 4°C in a humidified chamber.

• Detection: Use fluorophore-conjugated or HRP-conjugated secondary antibodies, with signal amplification systems for low-abundance targets.

To control for specificity, always include a negative control by omitting the primary antibody and use tissue from At1g61320 knockout plants when available. Signal-to-noise ratios can be improved by carefully titrating antibody concentrations within the 0.62-2.5 μg/mL range, as higher concentrations may increase background without improving specific signal detection .

How can I address high background issues when using At1g61320 antibodies?

High background is a common issue when working with plant antibodies, including At1g61320 antibodies. To address this problem:

• Optimize antibody concentration: Titrate the primary antibody concentration, focusing on the 0.62-2.5 μg/mL range. Research indicates that antibody concentrations above 2.5 μg/mL often increase background without improving specific signal .

• Adjust blocking conditions: Experiment with different blocking agents (5% non-fat milk, 3-5% BSA, or commercial blocking reagents) and extend blocking time to 2 hours at room temperature.

• Reduce non-specific binding: Add 0.1-0.3% Triton X-100 or Tween-20 to wash buffers and increase the number and duration of wash steps.

• Modify sample preparation: For Western blots, ensure complete protein denaturation and consider extracting proteins with specialized plant protein extraction buffers containing polyvinylpolypyrrolidone (PVPP) to remove interfering phenolic compounds.

• Assess heterophilic antibody interference: If working with complex samples, heterophilic antibodies may cause interference by binding to murine antibodies used in the assay. Pre-adsorb samples with species-specific IgG not related to the target antibody to reduce this interference .

• Reduce cell density for flow cytometry applications: When performing flow cytometry, reduce cell density at staining to 8 × 10^6 cells/mL instead of 40 × 10^6 cells/mL to improve signal-to-noise ratio for antibodies targeting low-abundance epitopes .

Implementing these strategies systematically can significantly improve signal-to-noise ratio in Western blots, immunoprecipitation, and immunohistochemistry applications.

Why might I observe inconsistent results with At1g61320 antibody across different experimental replicates?

Inconsistent results with At1g61320 antibody across experimental replicates can stem from multiple factors:

• Variability in protein expression: At1g61320 expression may vary with plant developmental stages, environmental conditions, and stress responses. Standardize growth conditions and tissue sampling protocols to minimize biological variability.

• Antibody batch variation: Monoclonal antibody combinations may exhibit batch-to-batch variations. When possible, use the same antibody lot for related experiments or validate new lots against previous ones.

• Sample preparation inconsistencies: Differences in protein extraction efficiency, protein degradation during sample preparation, or variable denaturation can affect epitope availability. Standardize extraction protocols and include protease inhibitors.

• Detection system sensitivity fluctuations: Variable ECL reagent activity or inconsistent secondary antibody performance can impact signal development. Include internal controls on each blot for normalization.

• Sample loading variations: Ensure equal protein loading through careful quantification and verification with loading controls specific to plant samples.

• Epitope accessibility issues: Post-translational modifications or protein-protein interactions may mask epitopes in some experimental conditions. Consider using antibodies targeting different regions of At1g61320 (N-terminal, C-terminal, or middle regions) .

• Heterophilic antibody interference: Sample-specific heterophilic antibodies can interfere with immunoassays by forming complexes with the detection antibodies, leading to unpredictable variations. This interference affects approximately 30% of samples in some contexts .

Implementing rigorous standardization of protocols, including appropriate controls, and maintaining detailed records of experimental conditions can help identify and address sources of variability.

What are the common false positive and false negative scenarios when working with At1g61320 antibodies?

Understanding potential sources of false results is critical for accurate data interpretation when using At1g61320 antibodies:

False Positive Scenarios:

• Cross-reactivity with related F-box proteins: At1g61320 belongs to a large family of F-box proteins with conserved domains. Cross-reactivity testing against closely related proteins is essential.

• Heterophilic antibody interference: Approximately 30% of samples containing heterophilic antibodies can generate false positive results by forming complexes with murine antibodies used in the assay system .

• Non-specific binding to plant components: Plant tissues contain compounds (phenolics, alkaloids) that can non-specifically bind antibodies. Increase washing stringency and use plant-optimized blocking agents.

• Degradation product detection: Proteolytic fragments of larger proteins may be mistakenly identified as At1g61320. Include protease inhibitors during sample preparation.

False Negative Scenarios:

• Epitope masking by post-translational modifications: Phosphorylation, ubiquitination, or other modifications may block antibody binding. Consider using multiple antibodies targeting different regions of At1g61320 .

• Low protein abundance: At1g61320 may be expressed at low levels under certain conditions. Optimize extraction and enrichment protocols or increase sample loading.

• Epitope denaturation during processing: Some fixation methods may alter epitope structure. Optimize fixation conditions and consider antigen retrieval methods.

• Antibody saturation effects: When target proteins are present at high concentrations, the "hook effect" can occur, leading to artificially low signals. Perform dilution series to identify optimal sample concentration ranges .

To mitigate these issues, always include appropriate positive and negative controls, use complementary detection methods when possible, and consider multiple antibodies targeting different regions of the At1g61320 protein.

How can At1g61320 antibodies be used in protein interaction network studies?

At1g61320 antibodies can be powerful tools for mapping protein interaction networks in plants through several methodological approaches:

• Co-immunoprecipitation (Co-IP) coupled with mass spectrometry: Use At1g61320 antibodies to isolate the protein complex from plant extracts, followed by mass spectrometric analysis to identify interacting partners. This approach requires careful optimization of extraction buffers to preserve protein-protein interactions while minimizing non-specific binding.

• Proximity-dependent biotin identification (BioID): Generate fusion proteins with At1g61320 and a biotin ligase, then use the antibodies to verify expression and localization of the fusion protein before streptavidin-based pulldown of biotinylated proximity partners.

• Yeast two-hybrid validation: After identifying potential interactors through Y2H screens, use At1g61320 antibodies to confirm the interactions in planta through co-IP or pull-down assays.

• Biophysical modeling: Apply computational models similar to those used in viral epitope mapping to predict and analyze potential interaction interfaces. These models can help identify key residues involved in protein-protein interactions that might be disrupted by antibody binding .

• Chromatin immunoprecipitation (ChIP): If At1g61320 has nuclear functions, ChIP using specific antibodies can identify DNA binding sites and potential transcriptional regulatory roles.

For quantitative interaction studies, researchers should optimize antibody concentrations between 0.62-2.5 μg/mL, as this range typically provides the best balance between signal specificity and background in co-IP applications . Including appropriate controls such as IgG-only immunoprecipitations and reciprocal Co-IPs with antibodies against predicted interacting partners is essential for validating true interactions.

What role can computational modeling play in optimizing At1g61320 antibody applications?

Computational modeling offers several advantages for optimizing At1g61320 antibody applications:

• Epitope prediction and optimization: Computational tools can predict linear and conformational epitopes on the At1g61320 protein, guiding antibody design and selection. This approach can identify unique regions with minimal similarity to other F-box proteins, reducing cross-reactivity risks.

• Antibody binding simulation: Molecular dynamics simulations can model antibody-antigen interactions, predicting binding affinity and specificity. These models can help select optimal antibody clones or combinations for specific applications.

• Polyclonal response modeling: Biophysical models similar to those developed for viral epitope mapping can predict how combinations of monoclonal antibodies (as in the X-O64788 antibody combinations) will interact with various epitopes on At1g61320 .

• Optimization of experimental conditions: Computational models can simulate the effects of various experimental parameters (pH, salt concentration, detergent types) on antibody-antigen interactions, guiding protocol optimization.

• Cross-reactivity prediction: Sequence and structural homology analyses can identify potential cross-reactive proteins in the Arabidopsis proteome, informing experimental design and control selection.

• Deep mutational scanning simulation: Computational approaches can simulate how mutations in the At1g61320 protein might affect antibody binding, helpful for studying protein variants or homologs .

Research teams have developed software packages like "polyclonal" that use gradient-based optimization to fit biophysical models to experimental data, potentially allowing researchers to predict antibody binding characteristics under various conditions . These computational approaches can significantly reduce the time and resources needed for empirical optimization of antibody applications.

How can At1g61320 antibodies be adapted for single-cell and spatial transcriptomics studies?

Adapting At1g61320 antibodies for cutting-edge single-cell and spatial transcriptomics studies requires specialized approaches:

• Oligo-conjugated antibody preparation: At1g61320 antibodies can be conjugated with DNA oligonucleotides for use in CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) or related methods. Careful titration is essential, with concentrations typically effective between 0.62-2.5 μg/mL to balance signal strength and background .

• Cell preparation optimization: When using oligo-conjugated At1g61320 antibodies for single-cell studies, reducing cell density to approximately 8 × 10^6 cells/mL during staining can significantly improve signal-to-noise ratio compared to higher densities (40 × 10^6 cells/mL) .

• Antibody panel design: In multimodal studies, classify At1g61320 antibodies into appropriate categories based on signal strength and epitope abundance to guide concentration optimization. This classification helps balance sequencing resources across all antibodies in the panel .

• Background reduction strategies: For spatial transcriptomics applications, implement background reduction techniques such as:

  • Adjusting antibody concentrations based on epitope abundance

  • Optimizing blocking conditions specific to plant tissues

  • Reducing staining volume while maintaining adequate antibody concentration

• Signal amplification methods: For low-abundance targets, consider using branched DNA technology or tyramide signal amplification compatible with spatial transcriptomics platforms.

• Validation with complementary methods: Confirm spatial patterns observed with At1g61320 antibodies using fluorescence in situ hybridization (FISH) for the corresponding mRNA or transgenic reporter lines.

When analyzing data from these advanced applications, implement computational methods to distinguish true positive signals from background, especially important for plant tissues which can have high autofluorescence and non-specific binding issues.

How should I quantify and normalize At1g61320 antibody signals in Western blots?

Proper quantification and normalization of At1g61320 signals in Western blots requires systematic methodology:

• Image acquisition guidelines:

  • Capture images using a linear dynamic range detection system (digital imager rather than film)

  • Avoid saturation by optimizing exposure times

  • Acquire multiple exposures to ensure signal is within the linear range

• Background subtraction approaches:

  • Use rolling ball algorithm for uniform background

  • Perform local background subtraction for each lane

  • Include blank lanes for background reference

• Normalization strategies:

  • Normalize to total protein using stain-free technology or Ponceau S staining

  • Use multiple housekeeping proteins rather than a single reference protein

  • Consider using the geometric mean of multiple reference proteins for more stable normalization

• Statistical analysis recommendations:

  • Perform at least three biological replicates

  • Apply appropriate statistical tests based on data distribution

  • Report both raw and normalized data with statistical parameters

• Addressing plant-specific challenges:

  • Account for tissue-specific variations in reference protein expression

  • Consider normalization to multiple reference proteins expressed in different cellular compartments

  • Be aware of developmental stage-specific protein expression patterns

When reporting data, include representative images showing both At1g61320 signal and loading controls, along with quantification methods and statistical analysis details. Researchers should be aware that antibody concentrations between 0.62-2.5 μg/mL typically provide optimal signal-to-noise ratios for quantification .

What statistical approaches are recommended for analyzing immunoprecipitation-mass spectrometry (IP-MS) data with At1g61320 antibodies?

For robust analysis of IP-MS data using At1g61320 antibodies, implement these statistical approaches:

• Filtering criteria for potential interactions:

  • Apply fold-change thresholds comparing At1g61320 IP to control IPs (typically >2-fold enrichment)

  • Implement statistical significance cutoffs (p-value <0.05 or FDR <0.1)

  • Consider protein abundance index (PAI) or normalized spectral abundance factors (NSAF) for quantification

  • Require detection in multiple biological replicates (minimum of 3)

• Control selection and implementation:

  • Use both IgG controls and unrelated antibody controls

  • Include "competing peptide" controls where the antibody is pre-incubated with the immunizing peptide

  • When available, include IP from knockout/knockdown plant lines

• Data normalization approaches:

  • Normalize to bait protein recovery across experiments

  • Apply global normalization methods like median scaling

  • Consider advanced computational normalization using probabilistic models

• Network analysis methods:

  • Apply graph theory approaches to identify protein clusters

  • Implement Significance Analysis of INTeractome (SAINT) algorithm

  • Use MiST (Mass spectrometry interaction STatistics) scoring

• Validation strategies:

  • Confirm key interactions with reciprocal IPs

  • Validate with orthogonal methods (Y2H, BiFC, etc.)

  • Apply biophysical modeling to assess interaction probability

When reporting results, clearly describe all filtering criteria, statistical thresholds, and control experiments. For complex interaction networks, consider visualization tools like Cytoscape with appropriate layout algorithms to highlight biologically relevant protein clusters.

How can I interpret conflicting results between different applications of At1g61320 antibodies?

When faced with conflicting results between different experimental applications of At1g61320 antibodies, systematic troubleshooting and interpretation approaches are essential:

• Epitope accessibility assessment:

  • Different applications (WB, IP, IHC) expose different protein epitopes

  • Denatured proteins in Western blots may reveal epitopes hidden in native conformations

  • Consider using multiple antibodies targeting different regions of At1g61320

• Technical validation approaches:

  • Validate antibody specificity in each application independently

  • Perform peptide competition assays in each experimental context

  • Include genetic controls (knockout/knockdown lines) when available

• Application-specific considerations:

  • Western blot: Evaluate protein extraction methods, denaturation conditions

  • IP: Assess buffer stringency, detergent types, salt concentration

  • IHC/ICC: Compare fixation methods, antigen retrieval techniques

  • Flow cytometry: Evaluate cell preparation, fixation, permeabilization

• Heterophilic antibody interference:

  • Consider that approximately 30% of samples may contain heterophilic antibodies

  • These can cause false positives in some assay formats but not others

  • Implement blocking strategies specific to each application

• Computational integration approaches:

  • Apply Bayesian integration methods to reconcile conflicting datasets

  • Weigh evidence based on technical reproducibility and controls

  • Use biophysical modeling to predict antibody behavior in different conditions

When reporting conflicting results, transparently describe all experimental conditions, validation steps, and potential sources of discrepancy. Consider that true biological complexity, such as post-translational modifications or interaction partners, may explain seemingly contradictory observations across different experimental platforms.

How might emerging antibody technologies enhance At1g61320 research?

Emerging antibody technologies offer promising avenues to advance At1g61320 research:

• Nanobodies and single-domain antibodies:

  • Smaller size allows access to cryptic epitopes on At1g61320

  • Enhanced tissue penetration for in vivo plant imaging

  • Improved stability under variable experimental conditions

  • Potential for direct expression in plants as intrabodies

• Antibody engineering approaches:

  • Humanized or plantized antibodies with reduced immunogenicity

  • Bispecific antibodies for simultaneous targeting of At1g61320 and interaction partners

  • pH-sensitive antibodies for compartment-specific detection

• Integration with synthetic biology:

  • Antibody-based biosensors for real-time monitoring of At1g61320 in living plants

  • PROTAC (PROteolysis TArgeting Chimera) approaches using antibody fragments

  • Optogenetic antibody systems for spatiotemporal control

• Advanced imaging applications:

  • Super-resolution microscopy compatible antibody conjugates

  • Expansion microscopy protocols optimized for plant cell walls

  • Correlative light and electron microscopy using antibodies with dual labels

• Computational design and optimization:

  • AI-driven epitope selection and antibody design

  • Molecular dynamics simulations to predict antibody-antigen interactions

  • Integration with biophysical models similar to those used in viral epitope mapping

These emerging technologies could address current limitations in At1g61320 research, particularly challenges related to specificity, sensitivity, and access to complex plant tissues. Researchers should consider interdisciplinary collaborations to adapt these technologies from mammalian systems to plant research applications.

What are the current technical limitations in At1g61320 antibody research that need to be addressed?

Several technical limitations currently hamper At1g61320 antibody research:

• Specificity challenges:

  • Cross-reactivity with related F-box proteins remains difficult to eliminate completely

  • Limited availability of knockout/knockdown lines for comprehensive validation

  • Plant-specific post-translational modifications may alter epitope recognition

• Sensitivity barriers:

  • Low natural abundance of At1g61320 in many tissues requires signal amplification

  • Plant tissue complexity introduces high background in many applications

  • Limited signal-to-noise ratio in single-cell applications

• Reproducibility issues:

  • Batch-to-batch variability in antibody production

  • Inconsistent effectiveness across different plant growth conditions and developmental stages

  • Heterophilic antibody interference in approximately 30% of complex samples

• Technical adaptation needs:

  • Most antibody protocols are optimized for mammalian systems

  • Plant-specific compounds (phenolics, polysaccharides) interfere with many standard protocols

  • Cell wall barriers limit antibody penetration in many applications

• Quantification challenges:

  • Limited dynamic range in many detection systems

  • Difficulties in absolute quantification of At1g61320 levels

  • Lack of standardized reference materials for cross-laboratory comparisons

Addressing these limitations requires interdisciplinary approaches combining plant biology, immunology, biochemistry, and computational modeling. Development of plant-specific antibody validation guidelines, similar to those established for mammalian research, would significantly advance the field. Computational approaches like those used in viral epitope mapping could help predict and mitigate specificity issues .

How can At1g61320 antibody research contribute to broader understanding of plant protein interaction networks?

At1g61320 antibody research can significantly enhance our understanding of plant protein interaction networks through several approaches:

• F-box protein interaction mapping:

  • At1g61320 as an F-box protein likely participates in E3 ubiquitin ligase complexes

  • Antibody-based co-IP studies can reveal SCF complex components and substrates

  • Comparative analysis with other F-box proteins can illuminate functional specialization

• Temporal dynamics of protein interactions:

  • Time-course studies using At1g61320 antibodies can reveal interaction dynamics

  • Stress-responsive changes in interaction networks

  • Developmental stage-specific interactions during plant growth

• Spatial organization of interaction networks:

  • Cell-type specific interaction patterns revealed through single-cell approaches

  • Subcellular compartmentalization of At1g61320 interactions

  • Tissue-specific interaction networks across plant organs

• Integration with multi-omics data:

  • Correlation of protein interaction data with transcriptomics and metabolomics

  • Validation of predicted interactions from computational network analysis

  • Construction of regulatory networks combining protein-protein and protein-DNA interactions

• Evolutionary perspectives:

  • Comparative studies across plant species using cross-reactive antibodies

  • Conservation and divergence of F-box protein interaction networks

  • Adaptations specific to Arabidopsis versus other plants