At2g03955 Antibody

What is At2g03955 Antibody and what organism is it specific to?

At2g03955 Antibody (product code CSB-PA652773XA01DOA) is a research antibody specifically developed against the At2g03955 protein found in Arabidopsis thaliana, commonly known as Mouse-ear cress. This antibody recognizes the protein encoded by the At2g03955 gene locus and is available in both concentrated (0.1ml) and standard (2ml) preparations . This antibody should not be confused with other Arabidopsis antibodies targeting similar gene loci such as At2g03932 (CSB-PA651996XA01DOA) or At2g03937 (CSB-PA684161XA01DOA).

What are the common applications for At2g03955 Antibody in plant research?

While specific application data for At2g03955 is limited in the provided search results, antibodies for Arabidopsis proteins are typically employed in various immunological techniques including:

• Western blotting for protein expression analysis

• Immunoprecipitation for protein-protein interaction studies

• Immunohistochemistry for localization studies

• Flow cytometry for quantitative analysis (similar to methods described for AGTR-2 antibodies)

• ChIP (Chromatin Immunoprecipitation) for DNA-protein interaction studies

The optimal dilution ratios should be determined empirically for each application, as is standard practice with research antibodies.

How should At2g03955 Antibody be stored and handled to maintain optimal activity?

While specific storage information for At2g03955 Antibody is not provided in the search results, research-grade antibodies typically require careful handling to maintain functionality. Based on standard protocols for similar research antibodies:

Proper storage and handling significantly affect experimental reproducibility and reliability in immunological assays.

What controls should be implemented when using At2g03955 Antibody for immunological assays?

When conducting experiments with At2g03955 Antibody, rigorous control measures are essential for data validation:

Positive Controls:

• Known samples expressing the At2g03955 protein

• Recombinant At2g03955 protein (if available)

Negative Controls:

• Samples from knockout or knockdown At2g03955 Arabidopsis lines

• Wild-type samples of non-Arabidopsis plant species

• Isotype control antibodies (similar to approaches used with AGTR-2 antibody protocols)

Technical Controls:

• Secondary antibody-only controls to assess non-specific binding

• Blocking peptide competition assays to confirm specificity

• Gradient dilution series to determine optimal antibody concentration

Implementation of these controls helps distinguish specific signals from background noise and validates experimental findings.

How can cross-reactivity issues with At2g03955 Antibody be assessed and mitigated?

Cross-reactivity represents a significant challenge in plant antibody research due to protein sequence similarities across gene families:

• Sequence Homology Analysis:

  • Compare the At2g03955 protein sequence with related proteins in Arabidopsis and other species

  • Identify regions of high similarity that might contribute to cross-reactivity

• Experimental Verification:

  • Perform Western blot analysis using extracts from:

    • Wild-type Arabidopsis

    • At2g03955 knockout/knockdown lines

    • Related Arabidopsis varieties

  • Compare banding patterns to identify non-specific interactions

• Absorption Controls:

  • Pre-incubate antibody with purified target protein

  • Compare immunostaining patterns before and after absorption

• Dilution Optimization:

  • Test multiple antibody dilutions to identify the optimal signal-to-noise ratio

  • Document the dilution series results for protocol optimization

What are the optimal protein extraction methods when working with At2g03955 in Arabidopsis?

Efficient protein extraction is critical for successful immunological detection of plant proteins:

The extraction method should be optimized based on the subcellular localization and biochemical properties of the At2g03955 protein.

How should experiments be designed to study At2g03955 protein expression patterns across different developmental stages?

A comprehensive experimental design for developmental expression studies would include:

• Tissue Sampling Strategy:

  • Collect multiple tissue types (roots, stems, leaves, flowers, siliques)

  • Sample at defined developmental stages (seedling, vegetative, reproductive)

  • Include biological replicates (minimum n=3) for statistical validity

• Quantitative Analysis Methods:

  • Western blotting with densitometry measurement

  • qRT-PCR for transcript level correlation

  • Immunohistochemistry for spatial localization

  • Consider flow cytometry for single-cell analysis similar to approaches used with other antibodies

• Data Normalization:

  • Use constitutively expressed proteins (actin, tubulin, GAPDH) as loading controls

  • Implement tissue-specific reference genes for transcript analysis

  • Employ standard curves with recombinant protein (if available)

• Statistical Analysis:

  • Apply appropriate statistical tests (ANOVA, t-tests) to expression data

  • Calculate confidence intervals for biological replicates

  • Perform correlation analysis between protein and transcript levels

What approaches can resolve contradictory immunoblotting results with At2g03955 Antibody?

When facing inconsistent immunoblotting results, a systematic troubleshooting approach is essential:

• Antibody Validation:

  • Verify antibody specificity through epitope mapping

  • Test different antibody lots for consistency

  • Consider testing alternative antibodies targeting different epitopes

• Sample Preparation Variables:

  • Evaluate different protein extraction methods

  • Test multiple sample buffer compositions

  • Assess the impact of reducing/non-reducing conditions

  • Investigate protein degradation through time-course experiments

• Technical Parameters:

  • Optimize transfer conditions (buffer composition, time, voltage)

  • Test different membrane types (PVDF vs. nitrocellulose)

  • Vary blocking reagents (BSA vs. non-fat milk)

  • Adjust incubation times and temperatures

• Data Reconciliation:

  Discrepancy Type	Investigation Approach
  Unexpected band size	Assess post-translational modifications, splice variants
  Variable signal intensity	Standardize protein loading, exposure times
  Inconsistent results between experiments	Implement stricter protocol controls
  Signal in negative controls	Increase stringency of washing steps, blocking conditions

How can At2g03955 Antibody be used effectively in co-immunoprecipitation studies to identify interaction partners?

Co-immunoprecipitation (Co-IP) represents a powerful approach for identifying protein-protein interactions:

• Sample Preparation:

  • Use mild lysis buffers to preserve protein-protein interactions

  • Include reversible crosslinking agents if interactions are transient

  • Pre-clear lysates to reduce non-specific binding

• Immunoprecipitation Strategy:

  • Compare direct IP (antibody-protein) vs. indirect methods (antibody-bead conjugation)

  • Test different bead types (Protein A/G, magnetic vs. agarose)

  • Optimize antibody concentrations and incubation conditions

  • Include appropriate controls (IgG control, knockout lines)

• Interaction Validation:

  • Confirm results with reciprocal Co-IP experiments

  • Verify interactions through orthogonal methods (yeast two-hybrid, BiFC)

  • Assess the effects of environmental conditions on interaction dynamics

• Mass Spectrometry Analysis:

  • Implement stringent filtering to eliminate common contaminants

  • Require multiple peptide identification for confident protein assignment

  • Use quantitative approaches to distinguish specific from non-specific interactions

  • Compare results against known interactome databases

How should quantitative data from At2g03955 Antibody experiments be analyzed and presented?

Proper quantitative analysis ensures reproducibility and statistical validity:

• Signal Quantification:

  • Use appropriate software (ImageJ, ImageQuant) for densitometry

  • Apply background subtraction methods consistently

  • Normalize signals to loading controls or total protein stains

  • Consider using housekeeping gene products like actin or GAPDH for normalization

• Statistical Approaches:

  Analysis Type	Recommended Method
  Group comparisons	ANOVA with post-hoc tests
  Pairwise comparisons	Student's t-test or non-parametric alternatives
  Correlation analysis	Pearson's or Spearman's coefficients
  Time-course data	Repeated measures ANOVA

• Data Visualization:

  • Present original immunoblot images alongside quantified data

  • Use box plots or violin plots for distribution visualization

  • Indicate statistical significance levels clearly

  • Include error bars representing standard deviation or SEM

• Reproducibility Considerations:

  • Report the number of independent biological replicates

  • Describe technical replication strategy

  • Document any excluded data points with justification

What considerations are important when comparing At2g03955 expression data across different Arabidopsis ecotypes?

Cross-ecotype comparisons require careful experimental design and data interpretation:

• Genetic Variation Assessment:

  • Compare At2g03955 gene sequences across ecotypes

  • Identify SNPs or other variations that might affect antibody binding

  • Consider potential splice variants or post-translational modifications

• Experimental Standardization:

  • Grow all ecotypes under identical controlled conditions

  • Harvest tissues at equivalent developmental stages

  • Process all samples simultaneously using standardized protocols

  • Include universal controls across all experiments

• Data Normalization Strategies:

  • Use conserved reference genes for transcript analysis

  • Implement multiple normalization controls for protein quantification

  • Consider normalization to total protein content

• Interpretation Frameworks:

  • Contextualize expression differences with phenotypic variations

  • Correlate expression patterns with known ecotype-specific traits

  • Consider evolutionary and adaptive significance of expression differences

How can researchers integrate immunological data with transcriptomic findings for comprehensive At2g03955 expression analysis?

Multi-omics integration provides deeper insights into gene function and regulation:

• Correlation Analysis:

  • Calculate correlation coefficients between protein abundance and transcript levels

  • Identify conditions where protein/mRNA correlations diverge

  • Investigate potential post-transcriptional regulatory mechanisms

• Temporal Dynamics:

  • Compare the timing of transcript induction versus protein accumulation

  • Analyze protein turnover rates in relation to transcript stability

  • Develop mathematical models to describe transcript-to-protein relationships

• Pathway Integration:

  • Map expression data onto known biological pathways

  • Identify co-regulated genes and proteins

  • Infer regulatory relationships based on expression patterns

• Visualization Approaches:

  Data Type	Visualization Method
  Co-expression networks	Force-directed graphs
  Multi-condition comparisons	Heatmaps with hierarchical clustering
  Time-series data	Line plots with confidence intervals
  Multi-omics integration	Circos plots or multi-layer networks

How can At2g03955 Antibody be adapted for high-throughput screening applications?

Scaling immunological methods for high-throughput screening requires systematic optimization:

• Assay Miniaturization:

  • Adapt protocols to 96-well or 384-well formats

  • Optimize reagent volumes and incubation times

  • Develop automated sample handling procedures

  • Implement parallel processing workflows

• Detection Methods:

  • Consider fluorescence-based detection for improved sensitivity

  • Explore multiplexing with additional antibodies

  • Implement automated image acquisition and analysis

  • Develop quantitative readouts suitable for large datasets

• Quality Control:

  • Include positive and negative controls in every plate

  • Calculate Z-factors to assess assay robustness

  • Implement drift correction across multiple plates

  • Develop standard operating procedures for consistency

• Data Management:

  • Create structured databases for result storage

  • Implement automated analysis pipelines

  • Develop visualization tools for rapid result interpretation

  • Ensure compliance with data sharing standards

What considerations are important when using At2g03955 Antibody in chromatin immunoprecipitation (ChIP) experiments?

ChIP applications require specific adaptations for plant chromatin:

• Chromatin Preparation:

  • Optimize crosslinking conditions for plant tissues

  • Develop efficient nuclei isolation protocols

  • Determine optimal sonication parameters for desired fragment sizes

  • Verify chromatin quality through DNA purification and sizing

• Immunoprecipitation Optimization:

  • Test antibody binding capacity to crosslinked epitopes

  • Optimize antibody:chromatin ratios

  • Compare different bead types and blocking conditions

  • Include appropriate controls (input, IgG, non-target regions)

• Data Analysis:

  • Implement normalization strategies for ChIP-qPCR

  • Develop appropriate analytical workflows for ChIP-seq

  • Use peak calling algorithms optimized for plant genomes

  • Validate binding sites through motif analysis

• Functional Interpretation:

  • Correlate binding sites with gene expression data

  • Map binding sites relative to transcription start sites

  • Identify co-occurring transcription factor binding motifs

  • Integrate with epigenetic data (DNA methylation, histone modifications)

How can researchers effectively use At2g03955 Antibody in plant developmental studies using immunohistochemistry?

Immunohistochemistry in plant tissues presents unique challenges:

• Tissue Preparation:

  • Optimize fixation conditions for different plant tissues

  • Develop sectioning protocols that preserve antigenicity

  • Consider whole-mount approaches for intact organ imaging

  • Test different embedding media for optimal morphology preservation

• Antigen Retrieval:

  • Evaluate heat-induced versus enzymatic antigen retrieval

  • Optimize buffer compositions for plant cell wall penetration

  • Determine optimal retrieval durations for different tissues

  • Assess the impact of retrieval methods on tissue morphology

• Signal Development:

  • Compare chromogenic versus fluorescent detection systems

  • Implement counterstaining strategies for tissue orientation

  • Optimize signal amplification methods for low-abundance targets

  • Consider multiplexing with cellular markers for co-localization studies

• Quantitative Analysis:

  • Develop image analysis pipelines for signal quantification

  • Implement cell-type specific measurement strategies

  • Use z-stack imaging for three-dimensional expression analysis

  • Apply statistical methods for comparing expression across tissues

How might At2g03955 Antibody be utilized in single-cell proteomic approaches?

Emerging single-cell technologies offer new opportunities for protein-level analysis:

• Sample Preparation Considerations:

  • Adapt protoplast isolation protocols for single-cell applications

  • Optimize fixation methods that maintain antigenicity

  • Develop microfluidic approaches for cell isolation and processing

  • Implement barcode-based strategies for multiplexed analysis

• Detection Technologies:

  • Explore mass cytometry (CyTOF) applications for plant cells

  • Adapt flow cytometry protocols similar to those used with other antibodies

  • Consider microwell-based imaging approaches

  • Investigate proximity ligation assays for interaction studies

• Data Analysis Frameworks:

  • Implement dimensionality reduction techniques for visualization

  • Develop clustering algorithms for cell-type identification

  • Create trajectory analysis methods for developmental studies

  • Design statistical approaches for handling technical variation

• Validation Strategies:

  • Correlate single-cell findings with bulk tissue measurements

  • Implement orthogonal validation through RNA-protein co-detection

  • Use genetic models to confirm specificity of detected signals

  • Develop computational approaches to account for technical artifacts

What considerations are important when designing CRISPR-based gene editing experiments to validate At2g03955 Antibody specificity?

CRISPR-Cas9 gene editing provides powerful validation approaches:

• Target Design:

  • Select gRNA sites that cause complete protein disruption

  • Consider generating epitope-specific mutations to confirm antibody binding site

  • Design strategies for creating tagged versions of the endogenous protein

  • Implement multiplexed editing for gene family studies

• Validation Strategy:

  Editing Approach	Validation Method
  Complete knockout	Western blot signal absence
  Epitope modification	Altered antibody binding pattern
  Domain deletion	Size shift in detected protein
  Endogenous tagging	Co-localization with tag-specific antibodies

• Off-target Assessment:

  • Computationally predict potential off-target sites

  • Sequence top predicted off-target regions

  • Assess expression changes in related gene family members

  • Perform whole-genome sequencing for comprehensive analysis

• Phenotypic Characterization:

  • Correlate molecular validation with phenotypic changes

  • Implement complementation studies to confirm specificity

  • Design rescue experiments with modified protein versions

  • Consider tissue-specific or inducible systems for lethal modifications