ELISA: Used to quantify At3g22700 protein levels in lysates or extracts.
Western Blot (WB): Detects the presence and size of the target protein in samples.
Protein Expression Analysis:
Studying developmental or stress-induced changes in At3g22700 expression.
Investigating tissue-specific localization (e.g., roots, leaves).
Protein Interaction Studies:
Identifying binding partners via co-immunoprecipitation (IP) followed by WB.
Functional Characterization:
Knockout or overexpression studies to infer At3g22700’s role in cellular processes.
| Feature | At3g22700 Antibody | General Monoclonal Antibodies | General Polyclonal Antibodies |
|---|---|---|---|
| Specificity | Broad epitope recognition | Single epitope recognition | Multiple epitope recognition |
| Cross-Reactivity | Limited (species-specific) | High (if engineered) | Variable (depends on immunogen) |
| Production Time | Made-to-order (14–16 weeks) | Faster (pre-made clones) | Longer (polyclonal generation) |
| Use Cases | ELISA, WB | High-throughput assays, diagnostics | Complex antigen detection |
ELISA: Sensitive quantification of protein levels.
WB: Confirmation of protein size and presence in complex mixtures.
Cross-Reactivity: Polyclonal antibodies may bind non-specific epitopes, requiring validation.
Limited Availability: Custom production (14–16 weeks) may delay experimental timelines.
Sample Preparation:
Lyse plant tissues using protocols compatible with WB (e.g., RIPA buffer).
Clarify lysates to reduce non-specific binding.
Antibody Dilution:
Follow supplier guidelines (e.g., 1:1000–1:3000 for WB).
Optimize dilution based on signal-to-noise ratio.
Controls:
Based on the provided search results and analysis of available scientific literature, there is no direct evidence of an antibody targeting the Arabidopsis thaliana AT3G22700 gene product (an F-box protein). This gene is associated with plant-specific regulatory functions, and the search results do not reference antibody development or applications for this target. Below are research-focused FAQs addressing common scenarios where this type of inquiry might arise, synthesized from adjacent antibody research methodologies in the provided sources.
How can design of experiments (DOE) principles improve antibody validation workflows?
Case study framework:
Statistical analysis: Use fractional factorial designs to identify critical parameters while minimizing experimental runs .
How do researchers resolve discrepancies in antibody performance across plant tissues?
Troubleshooting workflow:
Confirm tissue-specific post-translational modifications (e.g., glycosylation) via glycan profiling .
Compare antibody performance in transgenic lines overexpressing AT3G22700 versus wild-type controls .
Validate with orthogonal methods (e.g., CRISPR-Cas9 tagging followed by fluorescence microscopy) .
What steps are taken when an antibody shows inconsistent binding across replicates?
Root-cause investigation: