At4g22170 Antibody

What is At4g22170 and why is it significant in plant research?

At4g22170 is a gene encoding a putative F-box protein in Arabidopsis thaliana, a widely used model organism in plant biology. F-box proteins are key components of SCF (Skp1-Cullin-F-box) ubiquitin ligase complexes, which regulate protein degradation through the ubiquitin-proteasome pathway. Homologs of this protein have been identified in multiple plant species, including Asparagus officinalis (garden asparagus) and Arabidopsis lyrata .

The significance of At4g22170 stems from the critical role F-box proteins play in:

• Plant hormone signaling pathways

• Developmental regulation processes

• Stress response mechanisms

• Plant immunity and pathogen defense

• Cell cycle control and differentiation

Understanding At4g22170 function through antibody-based approaches contributes to our knowledge of protein degradation pathways in plants and their role in adapting to environmental challenges.

What techniques should be used to validate the specificity of At4g22170 antibodies?

Validating At4g22170 antibody specificity requires a multi-faceted approach:

• Western blot validation:

  • Test against recombinant At4g22170 protein

  • Compare wild-type versus At4g22170 knockout/knockdown plants

  • Verify the expected molecular weight (typically using 6-10% SDS-PAGE gels)

  • Perform peptide competition assays to confirm epitope specificity

• Immunoprecipitation validation:

  • Perform mass spectrometry analysis of immunoprecipitated proteins

  • Conduct reciprocal co-IP experiments with known interaction partners

  • Compare results across multiple antibodies targeting different epitopes

• Immunohistochemistry validation:

  • Compare staining patterns with subcellular localization data

  • Test antibody in tissues with known expression patterns

  • Include appropriate negative controls (primary antibody omission, pre-immune serum)

• Cross-reactivity assessment:

  • Test against closely related F-box proteins

  • Evaluate reactivity across different plant species

  • Document any unexpected cross-reactivity with other proteins

Similar to other plant antibodies, each lot should be quality control tested using multiple applications to ensure consistent performance .

What are the optimal sample preparation methods for At4g22170 detection?

Effective sample preparation is critical for successful At4g22170 detection:

• Protein extraction:

  • Use fresh plant material whenever possible

  • Include protease inhibitor cocktails to prevent degradation

  • Add phosphatase inhibitors if studying phosphorylation states

  • Consider detergent selection based on subcellular localization (0.1-1% NP-40, Triton X-100, or CHAPS)

  • Maintain cold temperatures throughout extraction

  • Consider adding proteasome inhibitors (e.g., MG132) to stabilize F-box proteins, which can have rapid turnover rates

• For Western blotting:

  • Run samples on appropriate percentage gels (typically 6-12% for F-box proteins)

  • Transfer to PVDF or nitrocellulose membranes

  • Block with 3-5% BSA or non-fat milk in TBST

  • Include positive and negative controls

• For immunohistochemistry/immunofluorescence:

  • Test multiple fixation methods (4% paraformaldehyde is often suitable)

  • Optimize permeabilization conditions for plant tissues

  • Consider antigen retrieval methods if necessary

  • Control for plant tissue autofluorescence

• For immunoprecipitation:

  • Optimize lysis conditions to maintain protein-protein interactions

  • Pre-clear lysates to reduce non-specific binding

  • Use gentle washing conditions to preserve interactions

What are the common applications for At4g22170 antibodies in plant research?

At4g22170 antibodies serve multiple experimental purposes:

• Western Blotting:

  • Detection of At4g22170 protein expression levels

  • Assessment of protein stability under different conditions

  • Evaluation of post-translational modifications

  • Recommended dilution: 1:1000-1:5000

• Immunoprecipitation:

  • Isolation of At4g22170 protein complexes

  • Identification of interaction partners

  • Study of dynamic changes in protein associations

  • Typical usage: 5 μg antibody per gram of fresh tissue

• Immunohistochemistry/Immunocytochemistry:

  • Visualization of subcellular localization

  • Tissue-specific expression patterns

  • Co-localization with interaction partners

  • Recommended dilution: 1:100

• ChIP (Chromatin Immunoprecipitation):

  • If At4g22170 associates with chromatin-bound proteins

  • Investigation of potential DNA-binding activities

  • Study of transcriptional regulation mechanisms

• ELISA:

  • Quantitative measurement of At4g22170 levels

  • High-throughput screening applications

  • Antibody specificity testing

Each application requires specific optimization for maximum sensitivity and specificity, with particular attention to extraction methods that preserve F-box protein integrity.

How can researchers distinguish between specific and non-specific binding in At4g22170 antibody experiments?

Distinguishing specific from non-specific binding requires rigorous controls:

• Essential experimental controls:

  • Primary antibody omission: Reveals secondary antibody background

  • Isotype control: Non-specific primary antibody of same isotype

  • Peptide competition: Pre-incubation with immunizing peptide should abolish specific signal

  • Genetic controls: Reduced or absent signal in At4g22170 knockout/knockdown plants

  • Multiple antibodies: Different antibodies against At4g22170 should show similar patterns

• Western blot-specific approaches:

  • Expected molecular weight verification (F-box proteins typically 40-60 kDa)

  • Antibody titration to determine optimal concentration

  • Detection of signal reduction in knockdown lines

  • Documentation of any cross-reactivity with protein markers, as seen with other plant antibodies

• Immunolocalization considerations:

  • Comparison with fluorescent protein-tagged At4g22170

  • Subcellular fractionation followed by Western blotting

  • Co-localization with known compartment markers

  • Careful optimization of antigen retrieval conditions for each antibody

• Quantitative assessment:

  • Signal-to-noise ratio calculation

  • Comparison between technical and biological replicates

  • Statistical analysis of staining patterns or band intensities

Similarly to what was observed with other antibodies, different extraction and experimental conditions may be needed to avoid cross-reactivity issues, such as using specific gel percentages and longer run times .

What approaches are recommended for studying At4g22170 protein-protein interactions?

Investigating At4g22170 interactions requires specialized techniques:

• Co-immunoprecipitation (Co-IP):

  • Use At4g22170 antibodies to capture protein complexes

  • Include appropriate controls (IgG, pre-immune serum)

  • Try both native and crosslinked conditions

  • Analyze by Western blot or mass spectrometry

  • Consider epitope masking in protein complexes

• Proximity-based methods:

  • Proximity Ligation Assay (PLA) for in situ detection

  • BioID or TurboID for proximity labeling

  • FRET or BiFC for direct interaction assessment

  • Each method provides complementary information about spatial relationships

• Affinity purification coupled with mass spectrometry:

  • Use antibodies for immunoprecipitation followed by MS/MS analysis

  • Apply appropriate statistical methods for identifying significant interactors

  • Validate key interactions through reciprocal approaches

• Functional validation:

  • Genetic interaction studies (double mutants, suppressor screens)

  • In vitro binding assays with recombinant proteins

  • Structure-function analysis of interaction domains

• Data analysis and integration:

  • Network analysis incorporating multiple interaction datasets

  • GO term enrichment of interacting partners

  • Integration with transcriptomic data

How do post-translational modifications affect At4g22170 antibody recognition?

Post-translational modifications (PTMs) significantly impact antibody recognition:

• Common PTMs in F-box proteins:

  • Phosphorylation: Regulates protein function and interactions

  • Ubiquitination: F-box proteins themselves can be ubiquitinated

  • SUMOylation: Affects protein stability and localization

  • Glycosylation: Less common but possible in plant proteins

• Effects on antibody binding:

  • PTMs may mask or expose epitopes

  • Modifications can alter protein conformation

  • Some antibodies may specifically recognize modified forms

  • Different subcellular pools may have distinct modification patterns

• Experimental strategies:

  • Use phosphatase treatment to determine phosphorylation effects

  • Apply deubiquitinating enzymes to assess ubiquitination impact

  • Compare detection in different extraction conditions

  • Employ modification-specific antibodies when available

  • Consider the impact of glycosylation on antibody recognition, similar to effects seen with other proteins

• Technical approaches:

  • Compare multiple antibodies targeting different epitopes

  • Combine with mass spectrometry to map modifications

  • Use phospho-mimetic or phospho-null mutants in validation

  • Develop protocols that preserve or remove specific modifications

Understanding PTM effects is particularly important for F-box proteins, which often undergo modifications that regulate their stability and function within the SCF complex.

What are the challenges in developing quantitative assays using At4g22170 antibodies?

Developing quantitative assays presents several challenges:

• Technical considerations:

  • Dynamic range limitations of detection methods

  • Antibody affinity and specificity variations

  • Normalization strategy selection

  • Signal linearity across concentration ranges

  • Potential epitope masking in protein complexes

• Biological variability factors:

  • Expression level differences across tissues and conditions

  • Developmental stage variations

  • Stress-induced changes in protein abundance

  • Post-translational modification heterogeneity

  • Protein degradation rate fluctuations

• Quantification approaches:

  • Western blot densitometry with appropriate loading controls

  • ELISA-based quantification with standard curves

  • Flow cytometry for single-cell level analysis

  • Mass spectrometry with isotope-labeled standards

  • Image analysis for immunofluorescence quantification

• Statistical considerations:

  • Appropriate experimental design with sufficient replication

  • Proper statistical tests based on data distribution

  • Multiple testing correction for high-throughput analyses

  • Power analysis for sample size determination

  • Correlation with orthogonal measurement techniques

• Validation strategies:

  • Comparison with fluorescent protein-tagged versions

  • Absolute quantification using recombinant protein standards

  • Cross-validation with RNA expression data

  • Biological replication across conditions

As with other protein quantification methods, accurate results depend upon suitable experimental designs, normalization procedures that eliminate systematic bias, and appropriate statistical analyses .

How can researchers integrate At4g22170 antibody data with other -omics approaches?

Multi-omics integration enhances antibody-derived data:

• Transcriptomics integration:

  • Correlation between protein and mRNA levels

  • Analysis of discordant expression patterns

  • Integration with stress response transcriptome data

  • Identification of co-regulated gene networks

• Proteomics complementation:

  • Validation of antibody-detected proteins by mass spectrometry

  • Global protein interaction network mapping

  • Post-translational modification profiling

  • Protein turnover rate determination

• Metabolomics connections:

  • Association between At4g22170 expression and metabolite changes

  • Pathway analysis incorporating enzymatic activities

  • Correlation with stress-responsive metabolites

  • Identification of regulated metabolic networks

• Phenomics correlations:

  • Linking protein expression to phenotypic changes

  • High-throughput phenotyping data integration

  • Stress response phenotype correlation

  • Development of predictive models

• Computational approaches:

  • Network analysis incorporating protein interaction data

  • Machine learning for pattern recognition across datasets

  • Pathway enrichment analysis

  • Integration of data from multiple experimental conditions

This multi-layered approach provides context for understanding At4g22170 function within the broader cellular machinery, similar to how researchers have integrated data for other stress response proteins in Arabidopsis .

What are the optimal conditions for immunoprecipitation of At4g22170?

Optimizing immunoprecipitation requires careful consideration of multiple factors:

• Lysis buffer optimization:

  • Buffer composition: Typically Tris or HEPES-based (pH 7.4-8.0)

  • Salt concentration: Usually 100-150 mM NaCl (adjust based on interaction strength)

  • Detergent selection: 0.1-1% NP-40, Triton X-100, or CHAPS

  • Protease inhibitors: Complete cocktail plus 1 mM PMSF

  • Phosphatase inhibitors: If studying phosphorylation states

  • Proteasome inhibitors: Consider adding MG132 (10 μM) to stabilize F-box proteins

• Antibody binding conditions:

  • Antibody amount: 5 μg antibody per gram of fresh tissue

  • Incubation time: 2-4 hours or overnight at 4°C

  • Pre-binding to beads vs. direct addition to lysate

  • Consider crosslinking antibody to beads to prevent contamination

• Washing optimization:

  • Buffer stringency: Balance between specificity and maintaining interactions

  • Number of washes: Typically 3-5 times

  • Wash duration: 5-10 minutes per wash

  • Temperature: Usually 4°C to preserve interactions

• Elution methods:

  • Denaturing: SDS buffer with heating (95°C, 5 minutes)

  • Native: Peptide competition or pH shift

  • On-bead digestion for mass spectrometry applications

• Controls:

  • Input sample (5-10% of starting material)

  • IgG or pre-immune serum control

  • Knockout/knockdown tissue when available

  • Reciprocal IP with interaction partners

This approach is similar to that used for other plant proteins, where careful optimization of extraction conditions is critical for maintaining protein integrity and interactions .

How should researchers design western blot experiments to detect At4g22170?

Effective western blot design requires attention to detail:

• Sample preparation:

  • Extract proteins using appropriate buffers with protease inhibitors

  • Determine protein concentration (Bradford or BCA assay)

  • Prepare samples in Laemmli buffer with reducing agent

  • Include positive controls (tissues with known expression)

  • Use knockout/knockdown samples as negative controls

• Gel electrophoresis:

  • Use 6-10% SDS-PAGE for optimal separation of F-box proteins

  • Run gel longer to avoid cross-reactivity issues at lower molecular weights

  • Include appropriate molecular weight markers

  • Load equal amounts of protein (15-30 μg per lane)

• Transfer conditions:

  • Select appropriate membrane (PVDF often preferred for plant proteins)

  • Optimize transfer time and voltage (typically 100V for 60-90 minutes)

  • Verify transfer efficiency with Ponceau S staining

• Antibody incubation:

  • Block with 3-5% BSA or non-fat milk in TBST (1-2 hours)

  • Dilute primary antibody 1:1000-1:5000 in blocking buffer

  • Incubate overnight at 4°C or 2 hours at room temperature

  • Wash thoroughly (3-5 times, 5-10 minutes each)

  • Incubate with HRP-conjugated secondary antibody (1:5000-1:10000)

• Detection and analysis:

  • Use ECL or other chemiluminescent substrates

  • Capture images with digital systems

  • Quantify using appropriate software

  • Normalize to loading controls (tubulin, actin)

• Special considerations:

  • Be aware of potential cross-reactivity with protein markers

  • Test different antibody concentrations to optimize signal-to-noise ratio

  • Consider membrane stripping for reprobing if necessary

This approach aligns with best practices for plant protein detection as seen in multiple research contexts .

What approaches are recommended for studying At4g22170 localization in plant cells?

Subcellular localization studies require specialized techniques:

• Immunofluorescence microscopy:

  • Fixation optimization (4% paraformaldehyde or other fixatives)

  • Permeabilization methods appropriate for plant cell walls

  • Antigen retrieval if necessary (buffer pH optimization critical)

  • Primary antibody dilution (typically 1:100)

  • Secondary antibody selection (fluorophore choice based on microscopy setup)

  • Counterstaining with organelle markers

  • Controls for autofluorescence and non-specific binding

• Biochemical fractionation:

  • Careful isolation of subcellular compartments

  • Western blot analysis of fractions

  • Enrichment assessment using compartment markers

  • Comparison across different tissues or conditions

• Fluorescent protein fusion approaches:

  • C- or N-terminal GFP/YFP/RFP fusions

  • Transient expression systems (protoplasts, Agrobacterium)

  • Stable transgenic lines with native promoters

  • Live cell imaging capabilities

  • Validation with antibody-based detection

• Advanced imaging techniques:

  • Confocal microscopy for 3D localization

  • Super-resolution microscopy for detailed analysis

  • FRET for protein-protein interaction assessment

  • Correlative light and electron microscopy

• Controls and validation:

  • Multiple antibodies targeting different epitopes

  • Comparison with known localization patterns

  • Co-localization with compartment markers

  • Genetic validation (knockout/knockdown plants)

Similar to approaches used for other plant proteins , these methods provide complementary information about At4g22170 localization and dynamics in plant cells.

What strategies can improve reproducibility in At4g22170 antibody experiments?

Enhancing reproducibility requires systematic approaches:

• Antibody validation and documentation:

  • Thorough characterization of each antibody lot

  • Detailed documentation of validation experiments

  • Use of multiple antibodies when possible

  • Adherence to established antibody reporting standards

• Experimental standardization:

  • Detailed protocols with all parameters specified

  • Consistent sample preparation methods

  • Standard operating procedures for key techniques

  • Regular calibration of equipment

• Appropriate controls:

  • Positive and negative controls in each experiment

  • Internal standards for quantification

  • Knockout/knockdown validation

  • Technical and biological replicates

• Data collection and analysis:

  • Blinded analysis where applicable

  • Consistent normalization methods

  • Appropriate statistical approaches

  • Clear reporting of all data transformations

• Plant growth and treatment standardization:

  • Controlled growth conditions (light, temperature, humidity)

  • Consistent developmental staging

  • Standardized stress treatments

  • Detailed recording of all environmental parameters

• Reporting practices:

  • Complete methods documentation

  • Raw data availability

  • Full disclosure of failed experiments

  • Sharing of detailed protocols

These approaches align with best practices in plant research and help ensure that findings related to At4g22170 are robust and reproducible across different laboratories .

How can researchers integrate At4g22170 antibody studies with genetic approaches?

Combining antibody-based and genetic approaches provides powerful insights:

• Genetic validation strategies:

  • T-DNA insertion lines or CRISPR-generated knockouts

  • RNAi-mediated knockdown lines

  • Overexpression lines with native or constitutive promoters

  • Complementation with wild-type or mutant versions

  • Site-directed mutagenesis of key residues

• Protein variant analysis:

  • Epitope-tagged versions for detection without antibodies

  • Phospho-mimetic or phospho-null mutants

  • Domain deletion or substitution variants

  • Structure-guided mutations of interaction surfaces

  • Development of transgenic Arabidopsis lines expressing these variants

• Comparative approaches:

  • Analysis across multiple genetic backgrounds

  • Evolutionary conservation studies with orthologs

  • Comparison of protein expression with mRNA levels

  • Cross-species functional complementation

• Functional genomics integration:

  • Correlation with transcriptome data under stress conditions

  • Analysis in various signaling pathway mutants

  • Phenotypic characterization linked to protein expression

  • Genetic interaction studies (suppressors, enhancers)

• Advanced genetic tools:

  • Inducible expression systems

  • Tissue-specific promoters

  • Cell-type specific expression analysis

  • Developmental stage-specific regulation

This integrated approach provides a comprehensive understanding of At4g22170 function and regulation, similar to strategies used for other plant F-box proteins and stress response factors .

What are common issues when using At4g22170 antibodies and how can they be resolved?

Common challenges and solutions include:

• Weak or no signal in Western blot:

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

  • Reduce antibody dilution (try 1:500 if 1:1000 fails)

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

  • Try different extraction buffers to improve protein solubilization

  • Check protein transfer efficiency

  • Use more sensitive detection systems

  • Verify that epitope is not masked by fixation or denaturation

• High background:

  • Increase blocking time (2-3 hours)

  • Try different blocking agents (BSA vs. milk)

  • Increase washing duration and number of washes

  • Filter all buffers to remove particulates

  • Pre-absorb antibody with non-specific proteins

  • Reduce secondary antibody concentration

  • Use higher quality secondary antibodies

• Multiple bands or unexpected band size:

  • Check for protein degradation (add protease inhibitors)

  • Consider post-translational modifications

  • Test for isoforms or splice variants

  • Verify antibody specificity with peptide competition

  • Be aware of potential cross-reactivity with markers

• Inconsistent results:

  • Standardize protein extraction protocol

  • Maintain consistent incubation times and temperatures

  • Use the same antibody lot when possible

  • Prepare fresh reagents regularly

  • Document all experimental conditions thoroughly

• Immunolocalization difficulties:

  • Optimize fixation conditions

  • Test different antigen retrieval methods and pH conditions

  • Control for tissue autofluorescence

  • Use thin sections for better antibody penetration

  • Include detergents in wash buffers to reduce non-specific binding

These troubleshooting approaches are consistent with best practices for plant antibody experiments as seen in multiple research contexts .

How should researchers interpret conflicting results from different antibody-based techniques?

When facing conflicting results, consider:

• Technique-specific limitations:

  • Western blot detects denatured proteins, potentially revealing normally hidden epitopes

  • IP recognizes native conformations, possibly missing buried epitopes

  • IHC/IF results depend on fixation and permeabilization conditions

  • Different buffers affect epitope accessibility

• Systematic investigation approach:

  • Compare results using multiple antibodies targeting different epitopes

  • Validate with complementary non-antibody methods

  • Conduct epitope mapping to understand antibody recognition

  • Consider post-translational modifications that may affect detection

• Protein state considerations:

  • Protein complexes may mask epitopes in some techniques

  • Conformational changes under different conditions

  • Compartment-specific modifications

  • Technique-induced alterations to protein structure

• Biological interpretations:

  • Different protein pools with distinct properties

  • Context-dependent interactions affecting recognition

  • Developmental or stress-induced changes in protein properties

  • Tissue-specific processing or modification

As demonstrated in search result , antibodies may work in some applications but not others: "mAb CU-28-24 does not recognize rRBD by immunoblotting, which is likely due to epitope destruction under the denaturing conditions of SDS-PAGE." This illustrates how technique-specific conditions can significantly affect antibody recognition.

What statistical approaches are recommended for analyzing At4g22170 antibody data?

Appropriate statistical analysis enhances data reliability:

• For Western blot quantification:

  • Normalization to loading controls (actin, tubulin)

  • Multiple biological and technical replicates (minimum n=3)

  • Parametric tests (t-test, ANOVA) for normally distributed data

  • Non-parametric alternatives (Mann-Whitney, Kruskal-Wallis) for non-normal data

  • Multiple testing correction for comparisons across conditions

• For immunolocalization quantification:

  • Randomized field selection

  • Blinded analysis to prevent bias

  • Colocalization coefficients (Pearson's, Mander's)

  • Statistical comparison of localization patterns

  • Mixed-effects models to account for within-sample correlation

• For immunoprecipitation-mass spectrometry:

  • Appropriate controls for background binding

  • Statistical models for enrichment calculation

  • Multiple testing correction for large datasets

  • Volcano plots for visualizing significant interactions

  • Network analysis for interpreting interaction data

• Reporting standards:

  • Clear description of normalization methods

  • Presentation of all data points (not just means)

  • Appropriate error bars (SD vs. SEM based on question)

  • Effect size estimation

  • P-value reporting with appropriate significance thresholds

Similar to approaches used in other plant studies , statistical analysis should include: "Significant differences between the samples indicated by different lowercase letters; samples that are significantly different from each other have no letter in common. P < 0.05 was regarded significant analyzed by the Kruskal-Wallis test, followed by post hoc comparison."

How can researchers effectively document and share At4g22170 antibody validation data?

Comprehensive documentation enhances reproducibility:

• Antibody characterization information:

  • Immunogen sequence and design rationale

  • Host animal and production method

  • Purification approach

  • Lot number and source

  • Storage conditions and stability data

• Validation experiment details:

  • Western blot showing specificity (with molecular weight markers)

  • Immunoprecipitation efficiency data

  • Peptide competition results

  • Knockout/knockdown validation

  • Cross-reactivity assessment with related proteins

• Application-specific protocols:

  • Detailed buffer compositions

  • Incubation times and temperatures

  • Dilution factors for each application

  • Critical steps and precautions

  • Troubleshooting guidance

• Sharing mechanisms:

  • Publication in peer-reviewed journals

  • Deposition in antibody validation databases

  • Protocol sharing on platforms like protocols.io

  • Inclusion of raw data in repositories

  • Comprehensive methods sections in publications

• Documentation format:

  • Structured reporting following established guidelines

  • Images of key validation experiments

  • Tables of optimal conditions for each application

  • Quantitative metrics of antibody performance

  • Known limitations and cross-reactivity

This approach follows best practices seen in other plant antibody documentation and enhances the value of At4g22170 antibodies as research tools.

What considerations are important when comparing At4g22170 expression across different experimental conditions?

When comparing expression across conditions:

• Experimental design considerations:

  • Include appropriate controls for each condition

  • Maintain consistent sample preparation methods

  • Process all samples in parallel when possible

  • Include time course analysis for dynamic processes

  • Consider biological and technical replication needs

• Normalization approaches:

  • Use consistent loading controls across all conditions

  • Validate stability of reference proteins under experimental conditions

  • Consider multiple normalization strategies

  • Report both raw and normalized data

  • Document all normalization calculations

• Technical variables to control:

  • Protein extraction efficiency differences

  • Gel loading and transfer variations

  • Exposure time consistency for detection

  • Batch effects in antibody incubations

  • Image acquisition parameters

• Biological factors to consider:

  • Developmental stage differences

  • Tissue-specific expression patterns

  • Circadian or diurnal regulation

  • Environmental condition standardization

  • Stress response timing and magnitude

• Interpretation frameworks:

  • Correlation with transcript levels

  • Functional significance of observed changes

  • Comparison with related proteins

  • Integration with phenotypic data

  • Pathway and network context