At1g21900 (UniProt: Q8RWM6) is a protein expressed in Arabidopsis thaliana (mouse-ear cress), a model organism widely used in plant molecular biology research. While the specific details of its function are not fully characterized in the provided search results, researchers typically investigate this protein to understand specific plant cellular pathways, developmental processes, or stress responses. The At1g21900 antibody allows for detection and quantification of this protein in various experimental contexts, enabling researchers to correlate protein expression with specific plant phenotypes or conditions .
The At1g21900 antibody has been specifically validated for Enzyme-Linked Immunosorbent Assay (ELISA) and Western Blot (WB) applications. These techniques allow researchers to detect and quantify the At1g21900 protein in plant tissue samples . ELISA provides quantitative data on protein concentration, while Western blotting allows visualization of protein expression levels and molecular weight confirmation. When designing experiments, researchers should consider that this antibody has been specifically tested with these methods, and validation would be required before using it in other applications such as immunohistochemistry or immunoprecipitation.
For maximum stability and activity retention, the At1g21900 antibody should be stored at either -20°C or -80°C upon receipt. The manufacturer explicitly warns against repeated freeze-thaw cycles, which can damage antibody structure and reduce binding efficacy . The antibody is supplied in liquid form in a storage buffer containing 0.03% Proclin 300 as a preservative, 50% glycerol, and 0.01M PBS at pH 7.4 . The glycerol component helps prevent complete freezing and reduces damage from freeze-thaw cycles, but proper aliquoting upon receipt is still recommended for antibodies that will be used multiple times.
When designing experiments with the At1g21900 antibody, multiple controls should be incorporated to ensure reliable and interpretable results. These include:
Positive control: Samples with confirmed At1g21900 expression, such as specific Arabidopsis thaliana tissues or cell types where the protein is known to be expressed
Negative control: Samples lacking At1g21900 expression, which could include knockout lines or tissues where the protein is not expressed
Isotype control: Using a non-specific antibody of the same isotype (IgG) and host species (rabbit) to identify non-specific binding
Loading control: When performing Western blots, include detection of a housekeeping protein (like actin or tubulin) to normalize protein loading
Blocking peptide control: Competition assay with the immunogen used to generate the antibody (recombinant At1g21900 protein)
These controls help distinguish specific signal from background and validate antibody specificity, which is particularly important when working with polyclonal antibodies that may exhibit batch-to-batch variation .
While the specific manufacturer-recommended dilution for the At1g21900 antibody is not provided in the search results, general principles of antibody titration can be applied. Research on antibody titration suggests that most antibodies reach their saturation plateau between 0.62-2.5 μg/mL, with concentrations above 2.5 μg/mL often showing minimal gain in sensitivity while increasing background signal .
For initial experiments with At1g21900 antibody, researchers should consider the following approach:
| Application | Starting Dilution Range | Notes |
|---|---|---|
| Western Blot | 1:500 - 1:2000 | Begin with 1:1000 and adjust based on signal-to-noise ratio |
| ELISA | 1:1000 - 1:5000 | Higher dilutions typically work well for ELISA |
| Optimization | Sequential 4-fold dilutions | Test 4-fold serial dilutions to find linear response range |
It is important to note that antibodies used at concentrations at or below 0.62 μg/mL typically show linear response to dilution, making signal interpretation more straightforward . Titration experiments should be conducted to determine the optimal concentration that provides maximum specific signal with minimal background.
High background is a common challenge when using antibodies in plant tissues due to endogenous peroxidases, phenolic compounds, and other plant components that can interfere with detection systems. To minimize background signal when using At1g21900 antibody:
Optimize antibody concentration: Research indicates that antibodies used at high concentrations (≥2.5 μg/mL) often contribute to increased background without proportional increases in specific signal . Consider using lower concentrations (0.625-2.5 μg/mL) as a starting point.
Optimize blocking conditions: Plant tissues may require specific blocking agents. Consider testing 3-5% BSA, 5% non-fat dry milk, or commercial blocking buffers specifically formulated for plant tissues.
Add detergents: Include 0.1-0.3% Triton X-100 or Tween-20 in wash buffers to reduce non-specific hydrophobic interactions.
Pre-absorb the antibody: Incubate the diluted antibody with ground plant tissue from negative control samples to remove antibodies that bind non-specifically to plant components.
Modify extraction buffers: Include protease inhibitors and compounds that sequester plant phenolics and other interfering substances.
Studies show that reducing background signal can significantly improve data quality, with optimized antibody panels showing up to 43% lower background signal compared to non-optimized panels .
For researchers seeking to analyze At1g21900 alongside other proteins or markers, multiplexed detection strategies can be employed:
Sequential immunoblotting: After detecting At1g21900, the membrane can be stripped and reprobed with antibodies against other proteins of interest.
Dual-color Western blotting: Using At1g21900 antibody alongside another primary antibody from a different host species, followed by species-specific secondary antibodies conjugated to different fluorophores.
Oligo-conjugated antibody approaches: The At1g21900 antibody could potentially be conjugated to DNA oligonucleotides for use in advanced methods like single-cell proteogenomics, though this would require custom modification.
Validating antibody specificity is crucial for ensuring reliable research results. For At1g21900 antibody, consider the following validation approaches:
Genetic validation: Compare antibody signal between wild-type plants and At1g21900 knockout or knockdown lines.
Recombinant protein competition: Pre-incubate the antibody with excess purified recombinant At1g21900 protein (the immunogen) before application to samples; specific binding should be significantly reduced.
Mass spectrometry correlation: Perform immunoprecipitation with the At1g21900 antibody followed by mass spectrometry to confirm the identity of pulled-down proteins.
Expression pattern correlation: Compare protein detection pattern with known mRNA expression data for At1g21900.
Alternative antibody comparison: When available, compare results with another antibody raised against a different epitope of the same protein.
Research on autoantibody screening has demonstrated the importance of validating binding specificity to avoid false positive results, particularly when working with complex samples .
Batch-to-batch variations are common challenges with polyclonal antibodies like the At1g21900 antibody. To address data inconsistencies:
Internal standards: Include a standard positive control sample across all experimental batches to normalize signals.
Lot testing: When receiving a new antibody lot, perform side-by-side comparison with the previous lot to establish a conversion factor if necessary.
Absolute quantification: Consider developing a standard curve using recombinant At1g21900 protein for quantitative applications.
Data normalization: Implement robust normalization strategies, such as:
| Normalization Method | Application | Advantage |
|---|---|---|
| Housekeeping protein | Western blot | Corrects for loading differences |
| Total protein normalization | Multiple methods | Avoids bias from single reference protein |
| Relative quantification | ELISA | Allows comparison across batches |
| Statistical batch correction | Large datasets | Computational removal of batch effects |
Consistent experimental conditions: Maintain identical sample preparation, incubation times, temperatures, and detection methods across experimental batches.
Researchers should document antibody lot numbers and experimental conditions to facilitate troubleshooting of any observed inconsistencies .
While the At1g21900 antibody is primarily validated for ELISA and Western blot applications , researchers interested in chromatin interactions might adapt it for Chromatin Immunoprecipitation (ChIP) studies with appropriate validation. This approach would be particularly relevant if At1g21900 functions as a transcription factor or chromatin-associated protein.
For ChIP optimization with At1g21900 antibody:
Cross-validation: First confirm antibody specificity via Western blot under ChIP buffer conditions.
Crosslinking optimization: Test both formaldehyde concentration (0.5-1.5%) and crosslinking times (5-20 minutes) to preserve protein-DNA interactions without overcrosslinking.
Chromatin fragmentation: Optimize sonication to achieve DNA fragments of 200-500bp for most applications.
Antibody concentration: ChIP typically requires higher antibody concentrations than Western blot; start with 2-5 μg per reaction.
PCR validation: Design primers for regions suspected to interact with At1g21900, similar to the approach used in the validation of low nucleosome density regions in Arabidopsis (as shown in search result ).
When designing primers for ChIP-qPCR validation, consider the approach demonstrated for other Arabidopsis genes, where primers were designed to span regions of 100-200bp with similar melting temperatures .
For researchers investigating protein interaction networks involving At1g21900, several approaches can be considered:
Co-immunoprecipitation (Co-IP): The At1g21900 antibody could be used to pull down protein complexes containing At1g21900 and its interaction partners.
Proximity labeling: Techniques like BioID or APEX could be used alongside the antibody for validation of interaction partners.
Yeast two-hybrid validation: Antibody detection can confirm interactions identified through other methods.
Important methodological considerations include:
Buffer optimization: Plant proteins often require specialized extraction buffers to maintain interactions while minimizing interference from plant components.
Crosslinking: Consider whether chemical crosslinking is necessary to stabilize transient interactions.
Detergent selection: Test different detergents (NP-40, Triton X-100, Digitonin) at various concentrations to solubilize membrane-associated complexes without disrupting interactions.
Plant growth conditions: Interaction partners may vary depending on developmental stage, tissue type, or stress conditions.
By carefully optimizing conditions for the specific characteristics of plant tissues and the At1g21900 protein, researchers can maximize the utility of this antibody in interaction studies, even beyond its validated applications .
When working with At1g21900 antibody, researchers may encounter several common issues:
Weak or no signal:
Increase antibody concentration incrementally
Extend primary antibody incubation time or temperature
Verify protein expression in the sample
Check protein transfer efficiency (for Western blot)
Test alternative extraction buffers to improve protein solubilization
High background:
Decrease antibody concentration, as research shows concentrations above 2.5 μg/mL often increase background without improving specific signal
Increase blocking time or concentration
Add additional washing steps with increased detergent concentration
Pre-absorb antibody with negative control tissue
Filter secondary antibody to remove aggregates
Multiple bands in Western blot:
Verify if bands represent different isoforms, post-translational modifications, or degradation products
Improve protease inhibition during sample preparation
Optimize reducing conditions
Test freshly prepared samples
Inconsistent results:
Standardize protein extraction and handling procedures
Aliquot antibody to avoid repeated freeze-thaw cycles
Document and maintain consistent incubation times and temperatures
Studies on antibody optimization suggest that most issues can be resolved through systematic titration and condition optimization, with significant improvements possible through methodical troubleshooting .
Different plant tissues and growth conditions can affect protein extraction efficiency and antibody accessibility. To adapt protocols:
Tissue-specific extraction buffers:
Leaves/soft tissues: Standard extraction buffers (e.g., RIPA) with 1-2% plant protease inhibitor cocktail
Roots: Consider adding PVP (polyvinylpyrrolidone) to bind phenolic compounds
Seeds/siliques: More aggressive extraction methods may be necessary, including grinding in liquid nitrogen with mortar and pestle
Growth condition adaptations:
Stress conditions: Include phosphatase inhibitors if studying stress responses
Developmental stages: Adjust extraction buffer:tissue ratio based on water content
Light/dark cycles: Consider timing of harvest relative to circadian cycles
Protein detection optimization:
Young tissues: Often require less antibody due to higher protein extraction efficiency
Mature/senescent tissues: May require longer extraction times and higher antibody concentrations
Specialized structures: May benefit from tissue-specific extraction methods
By systematically adapting protocols to account for tissue-specific challenges, researchers can optimize At1g21900 detection across diverse experimental conditions. Documentation of successful adaptations will benefit the broader research community working with Arabidopsis antibodies.