At3g21130 Antibody

What is the At3g21130 protein in Arabidopsis thaliana?

At3g21130 is a putative F-box protein containing associated interaction domains in Arabidopsis thaliana. According to ThaleMine database records, it's identified with the locus tag AT3G21130 (locus:2092995) and UniProt accession Q9LJB9 . F-box proteins typically function as part of SCF (Skp, Cullin, F-box) ubiquitin ligase complexes, mediating protein-protein interactions and substrate recognition for targeted protein degradation through the ubiquitin-proteasome pathway. This protein plays potential roles in plant development, hormone signaling, and stress responses.

What are the standard applications for At3g21130 antibodies in plant research?

At3g21130 antibodies are versatile tools in plant molecular biology with several key applications:

These applications help researchers study protein expression patterns, subcellular localization, post-translational modifications, and protein-protein interactions related to At3g21130 function .

How can I determine the specificity of commercial At3g21130 antibodies?

Determining antibody specificity requires multiple validation approaches:

• Primary validation: Western blot analysis using wild-type Arabidopsis extracts alongside At3g21130 knockout/knockdown lines. A specific antibody will show band absence or reduction in mutant lines.

• Secondary validation: Immunoprecipitation followed by mass spectrometry to confirm target protein enrichment.

• Specificity controls: Pre-absorption with recombinant At3g21130 protein should eliminate signal if the antibody is specific.

• Cross-reactivity testing: Testing against closely related F-box proteins to ensure specificity within the protein family.

The antibody should recognize the expected molecular weight protein (~45 kDa) with minimal cross-reactivity to other proteins .

What are the optimal conditions for At3g21130 antibody use in immunoblotting?

For optimal immunoblotting results with At3g21130 antibodies:

• Sample preparation:

  • Extract proteins from Arabidopsis tissues using a buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, and protease inhibitor cocktail

  • Include phosphatase inhibitors if studying phosphorylation states

• Western blot conditions:

  • Separate 20-30 μg total protein on 10-12% SDS-PAGE

  • Transfer to PVDF membrane at 100V for 1 hour or 30V overnight

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

  • Incubate with primary antibody (1:3000-1:5000) overnight at 4°C

  • Wash 3× with TBST, 10 minutes each

  • Incubate with HRP-conjugated secondary antibody (1:10,000) for 1 hour

  • Develop using enhanced chemiluminescence (ECL) detection system

• Controls: Always include positive controls (wild-type extract) and negative controls (At3g21130 knockout line and/or secondary antibody only) .

How can I use At3g21130 antibodies to study protein-protein interactions?

To study protein-protein interactions involving At3g21130:

• Co-immunoprecipitation protocol:

  • Prepare plant tissue lysate in a gentle lysis buffer (25 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% NP-40, 5% glycerol, protease inhibitors)

  • Pre-clear lysate with Protein A/G beads

  • Incubate cleared lysate with At3g21130 antibody (5-10 μg) overnight at 4°C

  • Add Protein A/G beads for 2 hours

  • Wash beads extensively (at least 4 times)

  • Elute bound proteins by boiling in SDS sample buffer

  • Analyze by immunoblotting or mass spectrometry

• Proximity ligation assay (PLA):

  • This technique allows visualization of protein-protein interactions in situ

  • Fix plant tissues and incubate with At3g21130 antibody plus antibody against putative interacting protein

  • Follow with PLA-specific secondary antibodies and detection reagents

This approach has proven successful for studying protein complexes in Arabidopsis, including those involving F-box proteins similar to At3g21130 .

What considerations are important when using At3g21130 antibodies in mutant lines?

When working with mutant lines:

• Mutation type assessment: Determine whether the mutation affects:

  • Protein expression (null mutant)

  • Protein structure (truncation or domain mutation)

  • Post-translational modifications

  • Subcellular localization

• Epitope accessibility: Confirm the antibody's epitope region isn't affected by the mutation

• Expression level variation: Use loading controls appropriate for plant tissues (such as anti-actin antibodies) to normalize protein levels

• Complementation analysis: Include complementation lines (e.g., cpk3-2/CPK3) alongside mutants to confirm phenotype specificity

• Multiple antibody approach: When possible, use antibodies targeting different epitopes to verify results

This methodological approach has been demonstrated in studies of other Arabidopsis proteins, providing a framework for At3g21130 research .

How can I troubleshoot non-specific binding with At3g21130 antibodies?

Non-specific binding is a common challenge with plant antibodies. Implement these troubleshooting strategies:

• Optimize blocking conditions:

  • Test different blocking agents (BSA, casein, commercial blockers)

  • Increase blocking time to 2 hours or overnight

  • Add 0.1-0.5% Tween-20 to reduce hydrophobic interactions

• Adjust antibody conditions:

  • Increase antibody dilution (1:5000 to 1:10,000)

  • Reduce incubation temperature to 4°C

  • Add 5% non-fat milk to antibody dilution buffer

• Implement additional washing steps:

  • Increase wash duration (15-20 minutes per wash)

  • Add higher salt concentration (250-500 mM NaCl) to wash buffer

  • Consider using 0.1% SDS in wash buffer for very sticky antibodies

• Pre-absorb the antibody:

  • Incubate diluted antibody with extract from knockout plants

  • Use recombinant proteins for competitive blocking

• Use validated antibody from reliable sources:

  • Commercial antibodies like CSB-PA864774XA01DOA have been validated for specificity against At3g21130

How should I interpret inconsistent results when using At3g21130 antibodies?

When faced with inconsistent results:

• Systematic evaluation:

  • Document all experimental conditions in detail

  • Verify protein extraction efficiency across samples

  • Check for protein degradation using fresh protease inhibitors

• Technical variables assessment:

  • Antibody lot-to-lot variation (request same lot for critical experiments)

  • Storage conditions (avoid repeated freeze-thaw cycles)

  • Buffer composition differences

• Biological variables consideration:

  • Plant growth conditions (light, temperature, humidity)

  • Developmental stage differences

  • Stress exposure prior to harvest

  • Circadian rhythm effects on protein expression

• Advanced troubleshooting:

  • Peptide competition assays to confirm specificity

  • Multiple antibody comparison (polyclonal vs. monoclonal)

  • Mass spectrometry validation of immunoprecipitated proteins

This approach has been successfully employed in resolving contradictory findings in plant immunological research .

How can I quantify At3g21130 protein levels in response to environmental stimuli?

For accurate protein quantification:

• Experimental design considerations:

  • Include time-course measurements to capture dynamics

  • Maintain strict environmental controls for reproducibility

  • Use biological replicates (minimum n=3) from independent experiments

• Sample preparation optimization:

  • Standardize tissue collection and flash-freezing protocols

  • Use consistent protein extraction method across all samples

  • Include phosphatase inhibitors to preserve post-translational modifications

• Quantification methodology:

  • Use infrared fluorescence-based Western blot systems for wider linear range

  • Include internal loading controls (anti-actin antibodies) on same membrane

  • Employ standard curves using recombinant protein for absolute quantification

• Data analysis protocol:

  • Use image analysis software with background subtraction

  • Normalize target protein signal to loading control

  • Apply appropriate statistical tests (ANOVA with post-hoc tests for multiple conditions)

This approach provides more reliable quantification than traditional chemiluminescence methods, especially when measuring subtle changes in protein expression .

What computational methods can improve At3g21130 antibody experimental design?

Advanced computational approaches enhance antibody-based experiments:

• Epitope prediction and antibody design:

  • In silico approaches can identify optimal epitopes for antibody generation

  • Computational tools predict protein structure to identify accessible regions

  • Molecular docking simulations evaluate potential antibody-antigen interactions

• Experimental validation enhancement:

  • Molecular dynamics simulations assess antibody stability

  • Statistical models optimize experimental design parameters

  • Machine learning algorithms improve signal detection in complex samples

• Recommended computational tools:

  Tool Purpose	Example Tools	Application
  Protein structure prediction	AlphaFold, RoseTTAFold	Identify optimal epitopes
  Molecular docking	GROMACS, HADDOCK	Predict antibody-antigen interactions
  Epitope prediction	BepiPred, IEDB	Design specific antibodies
  Image analysis	ImageJ, CellProfiler	Quantify immunofluorescence signals

These computational methods have revolutionized antibody research, increasing success rates while reducing experimental costs .

How can At3g21130 antibodies be used to study plant immunity pathways?

At3g21130 antibodies can illuminate plant immunity mechanisms:

• Pattern-triggered immunity (PTI) analysis:

  • Track At3g21130 protein levels and modifications following PAMP exposure

  • Combine with phospho-specific antibodies to detect activation-dependent phosphorylation

  • Perform co-immunoprecipitation to identify immunity-specific interaction partners

• Effector-triggered immunity (ETI) investigation:

  • Monitor At3g21130 stability during pathogen infection

  • Examine subcellular relocalization using immunofluorescence microscopy

  • Employ chromatin immunoprecipitation if At3g21130 regulates defense gene expression

• Methodology integration with calcium signaling:

  • As demonstrated with CPK3 studies, combine antibody approaches with:

    • Actin cytoskeleton analysis using anti-actin antibodies

    • Pathogen growth assays to correlate protein function with resistance

    • Complementation studies in knockout lines

Recent research on Arabidopsis calcium-dependent protein kinase 3 (CPK3) provides a methodological framework applicable to studying At3g21130 in immunity contexts .

What are the emerging techniques for single-cell analysis using At3g21130 antibodies?

Cutting-edge single-cell approaches with At3g21130 antibodies:

• Single-cell immunofluorescence:

  • Protoplast isolation followed by immunolabeling

  • Clearing methods for whole-tissue immunofluorescence with cellular resolution

  • Quantitative image analysis of protein abundance at single-cell level

• Mass cytometry (CyTOF):

  • Metal-conjugated antibodies for high-dimensional cellular analysis

  • Simultaneous measurement of multiple proteins in single cells

  • Clustering algorithms to identify cell populations with distinct At3g21130 expression

• Spatial transcriptomics integration:

  • Combine antibody detection with RNA analysis at tissue level

  • Correlate protein localization with gene expression patterns

  • Create comprehensive maps of protein-RNA relationships

These approaches provide unprecedented insights into protein function with cellular and subcellular resolution, revealing heterogeneity masked by traditional bulk methods .

How might computational antibody design improve At3g21130 research?

Computational antibody design represents a transformative approach for At3g21130 research:

• De novo antibody design advantages:

  • Generation of antibodies with predetermined specificity and affinity

  • Precise epitope targeting for specific protein domains or modifications

  • Reduced reliance on animal immunization

  • Faster development timelines

• Current computational platforms:

  • JAM (Joint Antibody Modeling) system can design antibodies with nanomolar affinities

  • MAGE (Monoclonal Antibody GEnerator) generates paired heavy-light chain sequences

  • These platforms could be adapted for plant protein targets like At3g21130

• Implementation methodology:

  • Protein sequence analysis to identify unique epitopes

  • Structure prediction to determine accessibility

  • Machine learning optimization of antibody-antigen interactions

  • Experimental validation through binding assays

These approaches have been successful for viral antigens and could be repurposed for plant proteins, potentially creating highly specific At3g21130 antibodies with superior performance characteristics .

What statistical approaches best analyze At3g21130 antibody-based quantitative data?

Robust statistical analysis enhances antibody-based research reliability:

• Experimental design considerations:

  • Power analysis to determine appropriate sample sizes

  • Randomization and blinding procedures to reduce bias

  • Inclusion of technical and biological replicates

• Recommended statistical models:

  • Mixed-effects models to account for batch effects and biological variation

  • Non-parametric methods for data with non-normal distributions

  • Bayesian approaches for small sample sizes

  • Feature selection strategies when analyzing multiple antibodies simultaneously

• Advanced analysis pipeline:

  • Data normalization using reference genes/proteins

  • Elimination of outliers based on objective criteria

  • Multiple testing correction for high-dimensional datasets

  • Integration of metadata for covariate analysis

These statistical approaches enhance data reliability and interpretation, particularly when analyzing subtle changes in protein abundance or modification states across experimental conditions .