The At1g63150 gene product belongs to a family of proteins implicated in transcriptional regulation. Experimental data from chromatin immunoprecipitation (ChIP) studies demonstrate its involvement in leaf development processes through interactions with MYB-domain transcription factors . Specifically:
Validated in ChIP-PCR experiments showing DNA-protein binding activity
Associated with pathways regulating cell proliferation and organ size determination
Co-expressed with genes involved in auxin signaling and cell cycle regulation
Notably, the antibody helped identify binding patterns at promoter regions of developmental regulators like ANT and CYCD3;1 .
While commercial documentation confirms basic reactivity , independent validation data remain limited compared to mammalian antibody standards. Key considerations:
Used successfully in chromatin studies showing differential binding during developmental stages
No published cross-reactivity tests with orthologous proteins from other plant species
Comparative analysis with other Arabidopsis antibodies reveals:
Similar technical validation protocols to PCMP-H44 and POT5 antibodies
Shared applications in studying plant-specific chromatin modifiers
Current applications focus on:
Mapping protein-DNA interactions in developmental biology
Characterizing auxin-responsive gene networks
Comparative studies with other plant model systems
Critical research gaps include:
Structural characterization of the target epitope
In planta functional knockout correlation studies
Cross-species reactivity profiling
Here’s a structured collection of FAQs tailored for academic researchers working with the At1g63150 Antibody, incorporating methodological insights and experimental design considerations:
Workflow:
Case Study: If GFP-tagged At1g63150 shows nuclear localization, but antibody staining indicates cytoplasmic presence:
Integration Strategy:
Use public Arabidopsis single-cell datasets (e.g., Arabidopsis Root Atlas) to identify cell types with high At1g63150 expression.
Correlate with antibody-based protein detection using spatial transcriptomics .
Apply computational models predicting antibody binding efficiency based on transcript abundance .
Advanced Technique:
Computational Pipeline: