At4g37220 Antibody

What is At4g37220 and why is it significant in plant research?

At4g37220 is a gene locus in Arabidopsis thaliana (Mouse-ear cress) that encodes a specific protein of research interest. The antibody targeting this protein is significant because it allows researchers to detect, quantify, and characterize the expression patterns of this protein in various experimental conditions. Arabidopsis thaliana serves as a model organism in plant biology, and studying specific proteins like At4g37220 contributes to our understanding of plant molecular biology, development, and stress responses . The antibody provides a critical tool for visualizing and analyzing the protein's presence, abundance, and localization within plant tissues or cells.

What are the key specifications of commercially available At4g37220 antibodies?

Commercially available At4g37220 antibodies typically have the following specifications:

The antibody is specifically designed for research use only and should not be used for diagnostic or therapeutic procedures .

How do At4g37220 antibodies compare to other plant protein antibodies in terms of validation standards?

At4g37220 antibodies, like other research antibodies, should undergo rigorous validation to ensure their specificity and reliability. According to modern antibody validation standards, antibodies targeting plant proteins should be validated using genetic approaches such as knockout controls, which is considered more reliable than orthogonal approaches.

Research indicates that antibodies validated using genetic approaches (testing on knockout or knockdown samples) demonstrate higher reliability in applications like Western blotting, with approximately 89% of genetically validated antibodies successfully detecting their intended targets compared to 80% of those validated by orthogonal approaches . For plant proteins specifically, validation becomes crucial as protein families often contain highly homologous members. Though no specific validation data is provided for At4g37220 antibodies in the search results, researchers should examine the manufacturer's validation data and potentially conduct their own validation experiments to ensure specificity in their experimental system.

What are the recommended applications for At4g37220 antibody in plant research?

The At4g37220 antibody is primarily recommended for ELISA and Western Blotting applications in plant research . For Western Blotting, the recommended working dilution is 0.1-0.2 μg/ml, allowing researchers to detect the target protein in plant tissue lysates. When using the antibody in ELISA applications, a concentration range of 0.5-2.0 μg/ml is typically recommended .

For optimal results in immunohistochemistry or immunofluorescence applications, researchers may need to optimize conditions through preliminary experiments. When designing experiments with At4g37220 antibody, researchers should consider including appropriate positive and negative controls to validate antibody performance. Positive controls might include recombinant At4g37220 protein, while negative controls might involve tissues where the protein is not expressed or knockout plant lines.

How should researchers optimize Western blot protocols for At4g37220 antibody?

Optimizing Western blot protocols for At4g37220 antibody requires careful consideration of several factors:

• Sample preparation: Prepare plant tissue lysates using appropriate extraction buffers containing protease inhibitors to prevent protein degradation. For Arabidopsis samples, grinding tissue in liquid nitrogen followed by extraction in a buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1% Triton X-100, and protease inhibitor cocktail is often effective.

• Protein loading: Load 20-50 μg of total protein per well, depending on the abundance of your target protein.

• Gel separation: Use 10-12% SDS-PAGE gels for optimal separation of the target protein.

• Transfer conditions: Transfer proteins to PVDF or nitrocellulose membranes at 100V for 1 hour or 30V overnight at 4°C.

• Blocking: Block membranes with 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature.

• Primary antibody incubation: Dilute At4g37220 antibody to 0.1-0.2 μg/ml in blocking buffer and incubate overnight at 4°C .

• Washing: Wash membranes thoroughly with TBST (3-5 times, 5 minutes each).

• Secondary antibody: Use an appropriate anti-rabbit secondary antibody conjugated to HRP at the manufacturer's recommended dilution.

• Detection: Use enhanced chemiluminescence (ECL) for detection, with exposure times optimized based on signal strength.

• Controls: Include positive controls (recombinant At4g37220 protein) and negative controls (lysates from At4g37220 knockout plants) .

Research shows that antibody validation approaches using genetic controls (such as knockout lines) significantly improve reliability in Western blotting applications, with up to 89% of genetically validated antibodies successfully detecting their intended targets .

What are effective troubleshooting strategies for weak or non-specific signals when using At4g37220 antibody?

When encountering weak or non-specific signals with At4g37220 antibody, consider these evidence-based troubleshooting strategies:

For weak signals:

• Increase antibody concentration: Try increasing the primary antibody concentration incrementally (e.g., from 0.1 μg/ml to 0.2 μg/ml) .

• Extend incubation time: Increase primary antibody incubation from overnight to 24-48 hours at 4°C.

• Enhance protein extraction: Use more effective extraction buffers or increase starting material.

• Optimize detection method: Use a more sensitive detection system or increase exposure time.

• Reduce washing stringency: Decrease salt concentration in wash buffers or reduce washing time.

For non-specific signals:

• Increase blocking: Extend blocking time or increase blocking agent concentration (e.g., from 5% to 7% BSA).

• Optimize antibody dilution: Test a series of dilutions to find the optimal signal-to-noise ratio .

• Increase washing stringency: Add more detergent (0.1-0.3% Tween-20) or increase salt concentration in wash buffers.

• Pre-absorb antibody: Incubate with negative control lysates to reduce non-specific binding.

• Use alternative blocking agents: Try different blockers (milk, BSA, commercial blockers) to reduce background.

Approximately 9/65 (13.8%) of targeted proteins in a comprehensive antibody validation study showed patterns of antibodies that were specific but non-selective, detecting both the cognate protein and unrelated proteins . This highlights the importance of thorough validation and optimization for each experimental system.

How can researchers validate the specificity of At4g37220 antibody in their experimental system?

Researchers can rigorously validate At4g37220 antibody specificity through these methods:

• Genetic knockout validation: The gold standard for antibody validation involves testing the antibody on samples from At4g37220 knockout Arabidopsis plants. A specific antibody will show signal in wild-type plants but no signal in knockout plants . This approach is considered superior to orthogonal methods, with research showing that 89% of antibodies validated using genetic approaches successfully detect their intended targets .

• RNA interference (RNAi): If knockout lines aren't available, RNAi-mediated knockdown of At4g37220 can provide validation. The antibody signal should decrease proportionally to the reduction in target protein expression.

• Recombinant protein controls: Testing the antibody against purified recombinant At4g37220 protein alongside plant lysates can confirm specific binding .

• Peptide competition assay: Pre-incubating the antibody with excess purified At4g37220 peptide (used as immunogen) should abolish specific signals in Western blot or immunostaining.

• Cross-reactivity testing: Test the antibody against closely related plant proteins to ensure specificity within the protein family.

• Multiple antibody approach: Compare results using different antibodies against the same target or different epitopes of At4g37220.

Recent studies have highlighted that antibodies validated using genetic approaches (like knockout controls) demonstrate significantly higher reliability than those validated by orthogonal methods alone .

What are the considerations for using At4g37220 antibody in co-immunoprecipitation experiments?

When designing co-immunoprecipitation (Co-IP) experiments with At4g37220 antibody, researchers should consider:

• Buffer optimization: Use non-denaturing lysis buffers that preserve protein-protein interactions while efficiently extracting the target protein. For plant samples, a buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 0.5% NP-40, and protease inhibitors is often effective.

• Antibody binding conditions: Determine optimal antibody concentration and incubation conditions. Start with 2-5 μg of antibody per 500 μg of protein lysate, incubating overnight at 4°C with gentle rotation.

• Pre-clearing lysates: Pre-clear lysates with protein A/G beads to reduce non-specific binding.

• Controls: Include negative controls (IgG from the same species as the antibody) and input samples (5-10% of the lysate used for IP).

• Washing stringency: Optimize wash conditions to remove non-specific interactors while preserving specific interactions. Typically, 4-5 washes with decreasing salt concentrations are effective.

• Elution conditions: Choose between native elution (competitive peptide) or denaturing elution (SDS buffer), depending on downstream applications.

• Validation: Confirm successful IP by Western blotting for the target protein using a portion of the IP sample .

• Cross-linking consideration: For weak or transient interactions, consider using chemical cross-linkers before lysis.

Research indicates that all antibodies should be tested by immunoprecipitation on non-denaturing cell lysates, with Western blot using a successfully validated antibody to evaluate the immunocapture efficiency .

How does the performance of At4g37220 polyclonal antibody compare to monoclonal alternatives in different applications?

While specific comparative data for At4g37220 antibodies is not provided in the search results, general principles regarding polyclonal versus monoclonal antibodies can be applied:

The current At4g37220 antibody is described as polyclonal with a lead time of 14-16 weeks . Research has shown that both polyclonal and monoclonal antibodies can be effective, but their performance depends on the specific application and target. A comprehensive study of antibody performance found that selecting the right antibody validation strategy is more important than whether an antibody is monoclonal or polyclonal .

How should researchers quantify and normalize Western blot data when using At4g37220 antibody?

For accurate quantification and normalization of Western blot data when using At4g37220 antibody, researchers should follow these evidence-based practices:

• Image acquisition: Capture images using a digital imaging system with a linear dynamic range. Avoid film exposure, which can be non-linear and difficult to quantify accurately.

• Avoid saturation: Ensure signals are within the linear range of detection by performing preliminary experiments with different exposure times or protein amounts.

• Quantification software: Use appropriate software (ImageJ, Image Lab, etc.) to measure band intensities. Define identical regions of interest (ROIs) for each band and subtract background from an adjacent area.

• Loading controls: Always include appropriate loading controls:

  • For total protein normalization: Use housekeeping proteins such as actin, tubulin, or GAPDH for Arabidopsis samples

  • Consider total protein stains (Ponceau S, SYPRO Ruby) as an alternative normalization method

• Normalization calculation: Calculate the relative expression as:
  $\text{Relative Expression} = \frac{\text{Target Protein Band Intensity}}{\text{Loading Control Band Intensity}}$

• Technical replicates: Perform at least three technical replicates per biological sample.

• Biological replicates: Include a minimum of three biological replicates for statistical validity.

• Statistical analysis: Apply appropriate statistical tests (t-test, ANOVA) to determine significant differences between experimental groups.

• Data presentation: Present data as mean ± standard deviation/error with appropriate statistical significance indicators.

Research on antibody validation has shown that proper quantification and normalization are essential for reliable interpretation of Western blot data, particularly when comparing protein expression levels across different conditions or genotypes .

What methodologies are recommended for optimizing immunohistochemistry protocols with At4g37220 antibody?

For optimizing immunohistochemistry (IHC) protocols with At4g37220 antibody in plant tissues, researchers should consider these methodological approaches:

• Tissue fixation optimization:

  • Test different fixatives (4% paraformaldehyde, glutaraldehyde, or combinations)

  • Optimize fixation time (4-24 hours) and temperature (4°C or room temperature)

  • For plant tissues, vacuum infiltration of fixatives improves penetration

• Antigen retrieval methods:

  • Heat-mediated antigen retrieval using citrate buffer (pH 6.0) is often effective

  • Test different temperatures (90-100°C) and incubation times (10-30 minutes)

  • Enzymatic retrieval using proteases may be necessary for some plant tissues

• Blocking optimization:

  • Test different blocking agents (3-5% BSA, normal serum, commercial blockers)

  • Optimize blocking time (1-2 hours) and temperature

• Antibody dilution series:

  • Perform a titration of antibody concentrations (starting with 5.0 μg/ml as suggested )

  • Test both overnight incubation at 4°C and extended incubation times

• Detection system comparison:

  • Compare direct vs. indirect detection methods

  • For fluorescent detection, select fluorophores with minimal spectral overlap with plant autofluorescence

  • For chromogenic detection, optimize development times

• Controls:

  • Include positive control tissues (known to express At4g37220)

  • Include negative controls (primary antibody omission, isotype controls)

  • When possible, include genetic controls (At4g37220 knockout plants)

• Counterstaining optimization:

  • Select counterstains that don't interfere with primary signal

  • For plant tissues, consider using counterstains that highlight cell walls or other structures

Research shows that approximately 38% of antibodies recommended by manufacturers based on orthogonal validation strategies are confirmed to work in immunofluorescence applications when tested using knockout cell lines , highlighting the importance of thorough optimization and validation.

What are the key considerations for interpreting subcellular localization data obtained using At4g37220 antibody?

When interpreting subcellular localization data obtained using At4g37220 antibody in immunofluorescence or immunohistochemistry experiments, researchers should consider these critical factors:

Studies have shown that antibody validation using genetic approaches significantly increases confidence in subcellular localization data, with proper controls being essential for accurate interpretation .

How can researchers employ At4g37220 antibody in studying protein-protein interactions in plant signaling networks?

Researchers can leverage At4g37220 antibody to study protein-protein interactions in plant signaling networks through several advanced approaches:

• Co-immunoprecipitation (Co-IP):

  • Use At4g37220 antibody to pull down the target protein along with its interacting partners

  • Analyze the precipitated complex using mass spectrometry to identify novel interactors

  • Confirm interactions by reverse Co-IP with antibodies against identified partners

  • For transient or weak interactions, consider using chemical crosslinking before immunoprecipitation

• Proximity-dependent labeling:

  • Combine At4g37220 antibody with proximity labeling techniques like BioID or APEX

  • Use the antibody to validate the expression and localization of fusion proteins

  • Identify proteins in close proximity to At4g37220 in their native cellular environment

• Immunofluorescence co-localization:

  • Perform dual immunofluorescence with At4g37220 antibody and antibodies against suspected interacting proteins

  • Quantify co-localization using statistical methods (Pearson's correlation, Manders' overlap)

  • Use super-resolution microscopy for more precise co-localization analysis

• Protein complex analysis:

  • Use At4g37220 antibody to detect the target protein in native gel electrophoresis

  • Identify shifts in complex size or composition under different conditions

  • Combine with second-dimension SDS-PAGE to identify components of protein complexes

• In situ proximity ligation assay (PLA):

  • Combine At4g37220 antibody with antibodies against potential interactors

  • Detect protein-protein interactions with single-molecule sensitivity in fixed tissues

  • Quantify interaction events spatially and temporally across different conditions

When designing these experiments, researchers should always include appropriate controls and validate antibody specificity using genetic approaches, as studies have shown that antibodies validated using genetic controls demonstrate significantly higher reliability in complex applications .

What considerations are important when using At4g37220 antibody for chromatin immunoprecipitation (ChIP) experiments?

When adapting At4g37220 antibody for chromatin immunoprecipitation (ChIP) experiments, researchers should consider these specialized parameters:

• Antibody suitability assessment:

  • Verify that the antibody recognizes its native, folded epitope in the chromatin context

  • Perform preliminary IP experiments to confirm the antibody can effectively immunoprecipitate the target protein

  • Consider using epitope-tagged versions of At4g37220 and well-characterized tag antibodies as alternatives

• Crosslinking optimization:

  • For plant tissues, optimize formaldehyde concentration (typically 1-3%) and crosslinking time (10-15 minutes)

  • Consider dual crosslinking with DSG (disuccinimidyl glutarate) followed by formaldehyde for improved efficiency

  • Ensure proper quenching of crosslinking with glycine

• Chromatin preparation:

  • Optimize sonication conditions to achieve chromatin fragments of 200-500 bp

  • Verify fragmentation by agarose gel electrophoresis

  • For plant tissues, additional nuclei isolation steps may be necessary before sonication

• Antibody concentration determination:

  • Titrate antibody amounts (typically 2-10 μg per reaction)

  • Include IgG controls at equivalent concentrations

  • Consider including a spike-in of control chromatin for normalization

• Washing stringency optimization:

  • Test different washing buffers with increasing stringency

  • Monitor signal-to-noise ratio with known positive and negative genomic regions

• Controls and validation:

  • Include input chromatin controls (5-10% of starting material)

  • Use IgG negative controls

  • Include positive controls (genomic regions known to be bound)

  • Validate results with ChIP-qPCR before proceeding to ChIP-seq

  • When possible, include chromatin from At4g37220 knockout plants as biological negative controls

• Data analysis considerations:

  • Normalize to input and IgG controls

  • Use appropriate peak calling algorithms

  • Validate enrichment at candidate regions by ChIP-qPCR

Although no specific data on using At4g37220 antibody for ChIP is provided in the search results, research has shown that proper antibody validation is critical for ChIP experiments, with antibodies validated through genetic approaches showing higher reliability .

How can researchers integrate At4g37220 antibody-based techniques with omics approaches for comprehensive protein function analysis?

Researchers can integrate At4g37220 antibody-based techniques with omics approaches to achieve a comprehensive understanding of protein function through these advanced strategies:

• Antibody-based proteomics integration:

  • Use At4g37220 antibody for immunoprecipitation followed by mass spectrometry (IP-MS)

  • Compare interactome data across different conditions, tissues, or developmental stages

  • Integrate with global proteomics data to contextualize function within broader networks

  • Validate key interactions using targeted approaches (Co-IP, BiFC, FRET)

• Transcriptomics correlation:

  • Correlate protein expression levels (quantified by Western blot) with transcript levels (RNA-seq)

  • Identify post-transcriptional regulatory mechanisms affecting At4g37220

  • Use antibody-validated localization data to correlate with tissue-specific transcriptomes

• Chromatin dynamics analysis:

  • Combine ChIP-seq using At4g37220 antibody with RNA-seq or ATAC-seq

  • Correlate binding sites with gene expression changes and chromatin accessibility

  • Integrate with histone modification data to understand chromatin context

• Systems biology approaches:

  • Use antibody-validated protein quantification across conditions for systems modeling

  • Integrate protein expression/modification data with metabolomics datasets

  • Develop predictive models incorporating antibody-validated protein interactions

• Spatial omics integration:

  • Combine immunohistochemistry using At4g37220 antibody with spatial transcriptomics

  • Correlate protein localization patterns with spatially resolved omics data

  • Integrate with cell-type-specific omics datasets

• Single-cell analysis:

  • Adapt antibody for single-cell protein detection (flow cytometry, mass cytometry)

  • Correlate with single-cell transcriptomics or epigenomics data

  • Develop multimodal single-cell analyses incorporating antibody-based protein detection

• Machine learning implementation:

  • Use antibody-validated datasets as training data for machine learning models

  • Develop predictive models for protein function or interaction partners

  • Apply active learning strategies to iteratively improve predictions and guide experimental design

Research has shown that active learning approaches can significantly improve experimental efficiency in library-on-library settings for antibody-antigen binding prediction, reducing the number of required antigen mutant variants by up to 35% . This highlights the potential for integrating computational approaches with antibody-based experimental techniques.