| Code | CSB-RA002121A0HU |
| Size | US$210 |
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| Application | Recommended Dilution |
|---|---|
| WB | 1:500-1:5000 |
| IHC | 1:50-1:200 |
| IP | 1:200-1:1000 |
ARNT, also known as HIF-1-beta, serves as an essential transcriptional partner in cellular responses to hypoxia and environmental toxins. As the obligate dimerization partner for hypoxia-inducible factors and the aryl hydrocarbon receptor, ARNT orchestrates gene expression programs critical to angiogenesis, metabolic adaptation, and xenobiotic metabolism. This central role in oxygen-sensing pathways makes ARNT a compelling target for cardiovascular research and studies exploring tumor microenvironment biology.
This recombinant monoclonal antibody, clone 2F11, offers the reproducibility that demanding experimental workflows require. Generated against a synthetic peptide derived from human ARNT and produced using recombinant technology, this antibody delivers sequence-defined consistency across lots, eliminating the variability that can compromise longitudinal studies or multi-site collaborations. The rabbit IgG format provides strong signal amplification while maintaining specificity for your target.
Validation across multiple platforms demonstrates genuine experimental flexibility. Western blot analysis confirms robust detection of ARNT at the expected 87 kDa molecular weight across diverse human cell lines including HeLa, MCF-7, HepG2, and K562, with recommended dilutions ranging from 1:500 to 1:5000. Immunohistochemistry performance has been validated in paraffin-embedded human prostate cancer tissue using citrate buffer antigen retrieval, with effective staining at 1:50 to 1:200 dilutions. Immunoprecipitation capability has been confirmed in HeLa whole cell lysates, enabling protein interaction studies and complex isolation experiments.
Whether investigating hypoxic signaling in cardiovascular disease models or examining ARNT's role in cellular stress responses, this antibody provides the validated performance and lot-to-lot reliability essential for generating publication-quality data.
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