The locus At1g80170 is annotated in the Arabidopsis genome as encoding a DUF247 domain-containing protein with unknown molecular function.
No peer-reviewed studies, antibody development projects, or commercial products related to this gene were identified in the reviewed sources.
Genes like At1g80170 with poorly characterized roles rarely attract antibody development efforts unless linked to critical phenotypes.
For comparison, antibodies for well-studied Arabidopsis proteins (e.g., QRT2/At3g07970 , ADPG1/At2g41800 ) are documented due to their roles in plant development.
Antibody specificity requires robust antigen design (e.g., peptide sequences with low homology to other proteins) .
Commercial viability drives antibody production toward high-demand targets (e.g., human disease-related proteins) .
If research on At1g80170 is critical, consider:
Antigen Design:
Use tools like AntiJen or IEDB to identify immunogenic epitopes in the At1g80170 protein sequence.
Custom Antibody Development:
Functional Characterization:
Conduct knockout studies or transcriptomic analyses to elucidate At1g80170’s role, which could justify antibody development.
No publications in PubMed, PMC, or Frontiers journals reference At1g80170 antibodies.
Commercial antibody databases (e.g., CiteAb, Antibodypedia) show no listings for this target.
To advance work on At1g80170:
Validate gene expression patterns via RNA-seq or GFP tagging.
Submit protein sequence to structural prediction tools (AlphaFold, RoseTTAFold) to identify functional domains.
Explore CRISPR-Cas9 knockout lines for phenotypic screening.
At1g80170 is a gene from Arabidopsis thaliana with UniProt number Q94AJ5. The polyclonal antibody against this protein (CSB-PA850085XA01DOA-0.2) is primarily designed for plant research applications. Based on the product specifications, this antibody has been validated for the following applications:
| Application | Validated | Notes |
|---|---|---|
| ELISA | Yes | For quantitative protein detection |
| Western Blot | Yes | For protein expression analysis |
The antibody is generated using recombinant Arabidopsis thaliana At1g80170 protein as the immunogen and is affinity-purified from rabbit hosts. This makes it particularly suitable for studying protein expression, localization, and function in plant systems .
When designing experiments with the At1g80170 antibody, proper controls are essential for result validation. The commercial antibody product includes:
200μg of antigens (to be used as positive control)
1ml of pre-immune serum (to be used as negative control)
For methodologically sound experimental design, implement the following controls:
Positive controls:
Use the provided antigen at a known concentration
Include wild-type Arabidopsis samples known to express At1g80170
Negative controls:
Apply pre-immune serum to establish background levels
Test At1g80170 knockout/knockdown samples when available
Include secondary antibody-only controls
This approach mirrors established methods for antibody validation in research settings, similar to those used with other research antibodies .
To maintain antibody functionality and specificity, the At1g80170 antibody should be stored under the following conditions:
| Storage Temperature | Recommendation |
|---|---|
| -20°C | Suitable |
| -80°C | Suitable |
For optimal performance, follow these methodological guidelines:
Aliquot the antibody upon receipt to minimize freeze-thaw cycles
When retrieving from storage, thaw the antibody on ice
Centrifuge briefly before opening to collect solution at the bottom of the vial
Follow similar handling procedures to those established for other research-grade antibodies
Proper storage significantly impacts experimental reproducibility, as antibody deterioration can lead to decreased sensitivity and increased background .
Antibody specificity validation is crucial for ensuring reliable results. Implement the following methodological approach:
Western blot analysis:
Confirm detection of a band at the expected molecular weight
Compare wild-type and At1g80170 knockout samples
Perform peptide competition assays by pre-incubating with excess immunizing antigen
Cross-reactivity assessment:
Test the antibody against related Arabidopsis proteins
Evaluate potential cross-reactivity with proteins from other plant species
Orthogonal detection methods:
Compare protein expression with mRNA levels using RT-qPCR
Utilize GFP-tagged At1g80170 to confirm localization patterns
This validation approach parallels methods used for other research antibodies in the field of plant biology, ensuring experimental rigor .
Determining optimal antibody concentration is critical for obtaining clear, specific signals while minimizing background. Implement this methodological approach:
Initial titration experiment:
Prepare dilution series (e.g., 1:500, 1:1000, 1:2000, 1:5000)
Use identical protein samples loaded in equal amounts
Process all blots simultaneously with identical protocols
Optimization parameters to consider:
Blocking agent (BSA vs. non-fat milk)
Incubation time and temperature (2h at room temperature vs. overnight at 4°C)
Washing buffers and protocols
Signal-to-noise evaluation:
Assess both signal intensity and background levels
Document optimal conditions for reproducibility
This approach to antibody optimization is consistent with established practices in immunoblotting techniques, similar to those used with other research antibodies .
Understanding the characteristics of polyclonal vs. monoclonal antibodies informs appropriate selection for specific research applications:
| Characteristic | Polyclonal At1g80170 Antibody | Monoclonal Alternatives |
|---|---|---|
| Epitope Recognition | Multiple epitopes | Single epitope |
| Sensitivity | Generally higher | May be less sensitive |
| Specificity | May show cross-reactivity | Usually more specific |
| Batch Consistency | May vary between lots | More consistent |
| Robustness | More tolerant to antigen modifications | May lose recognition if epitope is altered |
For research applications:
Western Blot applications:
Polyclonal antibodies often provide stronger signals due to recognition of multiple epitopes
Better tolerance to denaturing conditions in SDS-PAGE
May require more stringent blocking to control background
ELISA applications:
Polyclonal antibodies can function well as both capture and detection antibodies
Recognition of multiple epitopes can increase sensitivity
This comparison follows similar principles to those discussed in antibody selection for other research applications .
Detecting low abundance proteins in plant tissues presents several challenges requiring specialized approaches:
Plant-specific extraction challenges:
Cell wall interference with protein extraction
High levels of interfering compounds (phenolics, polysaccharides)
Abundance of RuBisCO and storage proteins masking signals
Methodological solutions:
Implement tissue-specific or subcellular fractionation
Use specialized extraction buffers with chaotropic agents
Include multiple protease inhibitors to prevent degradation
Consider immunoprecipitation to concentrate the target protein
Signal enhancement strategies:
Use high-sensitivity detection methods (e.g., enhanced chemiluminescence)
Optimize exposure times and imaging parameters
Consider signal amplification methods like tyramide signal amplification for immunohistochemistry
These methodological approaches are based on established practices in plant protein research and mirror techniques used for other low-abundance proteins .
Post-translational modifications (PTMs) can affect antibody recognition and protein function. Consider these methodological approaches:
PTM analysis considerations:
Phosphorylation, glycosylation, and other modifications may alter protein mobility on gels
Multiple bands on Western blots may indicate modified forms of At1g80170
PTMs can affect epitope accessibility and antibody binding
Experimental approaches:
Compare samples treated with phosphatases or glycosidases
Use PTM-specific enrichment strategies before immunodetection
Consider employing PTM-specific antibodies in parallel (e.g., phospho-specific antibodies)
Verify PTMs using mass spectrometry approaches
Controls for PTM studies:
Include samples with induced or inhibited modifications
Compare against recombinant protein without modifications
Use appropriate molecular weight markers that account for PTM-induced shifts
This approach parallels established methods for studying protein modifications in other research contexts .
While the product information specifically mentions ELISA and Western blot applications, polyclonal antibodies are often suitable for immunofluorescence with proper optimization. Consider these methodological approaches:
Fixation and permeabilization optimization:
Test different fixatives (e.g., paraformaldehyde, methanol)
Optimize permeabilization protocols for plant tissues
Consider antigen retrieval methods if necessary
Signal-to-noise optimization:
Implement extended blocking steps (1-2 hours)
Test different blocking agents (BSA, normal serum, casein)
Optimize primary antibody dilution and incubation time
Use highly cross-adsorbed secondary antibodies
Controls for immunofluorescence:
Include secondary antibody-only controls
Test pre-immune serum at the same concentration
Use tissues from At1g80170 knockout plants if available
This approach follows similar principles to those used in immunofluorescence studies with other antibodies, as exemplified in the literature .
Integrating antibody-based detection with other omics approaches provides comprehensive insights into protein function:
Integrative methodological approaches:
Correlate protein expression (Western blot/ELISA) with transcriptomics data
Use the antibody for pulldown experiments followed by mass spectrometry
Combine with chromatin immunoprecipitation if At1g80170 has DNA-binding properties
Multi-omics data integration:
Compare protein levels detected by the antibody with RNA-seq expression data
Correlate protein changes with metabolomic alterations
Integrate with phosphoproteomics or other PTM-omics approaches
Computational analysis approaches:
This multi-omics integration follows principles similar to those emerging in advanced antibody research applications, including computational modeling approaches for antibody binding .