At1g26690 Antibody
Description
Definition and Target
The At1g26690 antibody is a polyclonal antibody developed against the protein encoded by the At1g26690 gene in Arabidopsis thaliana. This gene corresponds to UniProt accession Q9LQY3, though its precise biological function remains uncharacterized in publicly available literature. Antibodies like this are typically used to localize the protein, quantify its expression, or study its interactions in cellular pathways .
Protein Localization Studies
Antibodies like At1g26690 are critical for subcellular localization experiments. For example, studies on Arabidopsis p24 proteins (e.g., p24δ5) utilized similar antibodies to track endoplasmic reticulum (ER) export mechanisms, revealing roles in glycosylphosphatidylinositol (GPI)-anchored protein transport . While direct data on At1g26690 is limited, such methodologies highlight potential applications for this antibody.
Functional Genomics
Knockout mutants of Arabidopsis genes (e.g., p24δ3δ4δ5δ6) often rely on antibodies to confirm protein absence and assess compensatory mechanisms . The At1g26690 antibody could similarly validate gene-editing outcomes or explore redundancy in uncharacterized pathways.
Comparative Analysis of Arabidopsis Antibodies
The following table contextualizes At1g26690 among related Arabidopsis antibodies from Cusabio:
| Antibody Target | Product Code | UniProt ID | Applications |
|---|---|---|---|
| At1g26690 | CSB-PA787573XA01DOA | Q9LQY3 | WB, IHC |
| PI4KG4 | CSB-PA255692XA01DOA | Q9ZPY9 | Enzyme activity assays |
| PP2C5 | CSB-PA893557XA01DOA | Q9XEE8 | Signal transduction studies |
These antibodies collectively enable multifaceted research into plant protein networks .
Challenges and Future Directions
Despite its availability, peer-reviewed studies explicitly using the At1g26690 antibody are absent in indexed literature. This gap underscores the need for:
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Functional characterization of the At1g26690 protein, potentially through knockout phenotyping or interactome mapping.
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Method optimization for advanced techniques like cryo-electron microscopy or single-cell sequencing, which require high-affinity antibodies.
Insights from analogous research, such as CD38 monoclonal antibodies repurposed for kidney disease , suggest that plant biology could similarly benefit from expanding antibody toolkits.
Product Specs
Constituents: 50% Glycerol, 0.01M Phosphate Buffered Saline (PBS), pH 7.4
Target Background
Q&A
The following FAQs address key methodological and analytical considerations for researchers working with AT1g26690 antibodies, synthesized from peer-reviewed studies on angiotensin receptor type 1 (AT1R) antibody validation and experimental applications. Content is organized by research complexity and informed by reproducibility challenges identified in multiple studies .
What experimental designs resolve contradictory staining patterns between studies?
Advanced troubleshooting strategy:
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Conduct antibody cross-comparison studies using standardized protocols across multiple platforms (Fig. 4a demonstrates platform-dependent variability)
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Address batch variability through multi-lot testing with statistical power analysis (α=0.01, β=0.8)
Which cellular models best characterize AT1g26690 antibody binding dynamics?
Model selection criteria:
| Model Type | Strengths | Limitations |
|---|---|---|
| HEK293-AT1R Transfectants | Controlled receptor density | Overexpression artifacts |
| Primary Podocytes | Native physiological context | Low transfection efficiency |
| AT1R Knockout Lines | Specificity controls | Requires genotyping validation |
For functional studies, combine label-free DMR technology with calcium flux assays to differentiate orthosteric vs allosteric effects .
How to differentiate between agonistic vs pathogenic antibody effects?
Mechanistic analysis framework:
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Binding characterization:
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Functional profiling:
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In vivo correlation:
What statistical methods resolve tissue-specific antibody cross-reactivity?
Advanced analytical approach:
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Apply mixed-effects models to account for:
Where $u_j$ = random tissue effect, $ε_{ij}$ = residual error -
Implement machine learning-based pattern recognition for:
How to optimize antibody concentrations for dual staining protocols?
Experimental optimization workflow:
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Checkerboard titration: Vary primary antibody concentrations (0.1-10 μg/mL)
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Cross-talk assessment: Use spectral unmixing controls
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Validation matrix:
Parameter Optimal Range Signal:Noise Ratio ≥5:1 Cross-Reactivity ≤15% Inter-Observer Variability κ≥0.8
