At4g10955 Antibody
Description
Biological Context of Target Protein
At4g10955 is a gene locus in Arabidopsis thaliana encoding a protein with UniProt entry Q680C0. While functional annotations remain incomplete, paralog analysis suggests potential roles in:
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Chloroplast membrane organization
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Stress response pathways
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Protein-protein interaction networks
Comparative studies of Arabidopsis antibodies show such tools enable spatial mapping of low-abundance proteins through techniques like immunogold electron microscopy .
Research Applications
Though no peer-reviewed studies specifically using this antibody were identified, its technical profile suggests utility in:
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Protein localization: Tracking subcellular distribution in plant tissues
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Gene expression studies: Correlating protein levels with environmental stressors
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Mutant validation: Verifying knockout/knockdown lines in genetic studies
A 2023 UCLA study demonstrated antibodies against Arabidopsis proteins achieved 92% specificity in plant tissue arrays when properly validated , underscoring the importance of rigorous testing for reagents like At4g10955 antibody.
Validation Recommendations
For researchers using this antibody, we propose:
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Orthogonal verification: Compare results with RNA-seq data from At4g10955 mutants
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Cross-reactivity testing: Validate against Arabidopsis protein extracts
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Dose-response curves: Establish linear detection range via serial dilution
Product Specs
Composition: 50% Glycerol, 0.01M PBS, pH 7.4
Q&A
The At4g10955 antibody is a specialized tool for studying plant gene function, particularly in Arabidopsis thaliana. Below is a structured FAQ addressing key research considerations, informed by antibody engineering principles from recent computational design studies .
Advanced Research Questions
How to optimize antibody performance for single-cell resolution imaging?
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Phage display mutagenesis for affinity maturation (Kd optimization)
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Structure-guided engineering of CDR regions:
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Validate with super-resolution microscopy (dSTORM/PALM)
What statistical approaches handle batch effects in multi-lab validation studies?
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Apply mixed-effects modeling:
Signal ~ Treatment + (1|Lab) + (1|LotNumber) -
Implement quantile normalization across datasets
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Use reference standard curves with NIST-traceable antigens
