FH9 Antibody

What is FH9 Antibody and what organism does it target?

FH9 Antibody (Product Code: CSB-PA811480XA01DOA) is a polyclonal antibody raised in rabbits against recombinant Arabidopsis thaliana FH9 protein. It specifically targets the FH9 protein (Uniprot No. Q8GX37) in Arabidopsis thaliana, commonly known as Mouse-ear cress, a model organism widely used in plant biology research . The antibody is designed to recognize and bind to specific epitopes on the FH9 protein, enabling researchers to detect and study this protein in various experimental contexts.

What are the validated applications for FH9 Antibody?

FH9 Antibody has been validated for Enzyme-Linked Immunosorbent Assay (ELISA) and Western Blotting (WB) applications . These techniques allow researchers to detect and quantify FH9 protein in plant samples. While these are the validated applications, researchers may explore other potential applications such as immunoprecipitation, immunohistochemistry, or immunofluorescence microscopy, though additional validation would be required before implementing these alternative methodologies.

What is the recommended storage protocol for FH9 Antibody?

For optimal antibody performance and longevity, store FH9 Antibody at either -20°C or -80°C upon receipt . It's crucial to avoid repeated freeze-thaw cycles, as these can degrade the antibody structure and compromise its binding capacity. The antibody is supplied in liquid form containing a storage buffer composed of 0.03% Proclin 300 as a preservative, 50% Glycerol, and 0.01M PBS at pH 7.4 . When working with the antibody, aliquoting into smaller volumes for single-use is highly recommended to prevent degradation from repeated freezing and thawing.

How is FH9 Antibody purified and what is its composition?

FH9 Antibody is purified using the Antigen Affinity Purification method , which selectively isolates antibodies that specifically bind to the target antigen (recombinant Arabidopsis thaliana FH9 protein). This purification technique typically yields antibodies with high specificity. The antibody is provided in a liquid formulation containing preservative (0.03% Proclin 300) and stabilizers (50% Glycerol in 0.01M PBS, pH 7.4) . It is a non-conjugated IgG isotype polyclonal antibody, meaning it contains a mixture of antibodies that recognize different epitopes on the FH9 protein.

How can researchers validate FH9 Antibody specificity in novel experimental models?

To validate FH9 Antibody specificity in novel experimental models, researchers should implement a multi-step approach. First, conduct Western blot analysis using both wild-type samples and FH9 knockout/knockdown samples to confirm the absence of signal in the latter. Second, perform peptide competition assays by pre-incubating the antibody with excess purified FH9 protein before application to samples; this should abolish specific binding. Third, cross-validation using an alternative detection method or a second antibody targeting a different epitope can further confirm specificity. Finally, mass spectrometry analysis of immunoprecipitated proteins can definitively identify the captured proteins. This comprehensive validation strategy follows similar principles to those applied in other antibody research contexts , where specificity determination is critical for experimental integrity.

What are the challenges in studying protein-protein interactions involving FH9 using this antibody?

Studying protein-protein interactions involving FH9 presents several methodological challenges. First, the polyclonal nature of the FH9 Antibody means it binds multiple epitopes, potentially interfering with certain protein-protein interaction sites. Researchers should consider utilizing site-specific monoclonal antibodies for interaction-sensitive regions. Second, the antibody's potential cross-reactivity with structurally similar formin homology proteins must be rigorously controlled for, particularly in co-immunoprecipitation experiments. Third, native complex preservation requires careful buffer optimization to maintain physiological conditions while allowing antibody binding. Drawing from approaches used in other antibody-based interaction studies , researchers should implement reciprocal co-immunoprecipitation, proximity ligation assays, or bimolecular fluorescence complementation as complementary methods to validate interactions detected with FH9 Antibody.

How can researchers optimize FH9 Antibody protocols for quantitative analysis in stress-response studies?

For quantitative analysis in stress-response studies, researchers should initially establish a standard curve using purified recombinant FH9 protein at known concentrations to ensure linearity of detection within the relevant concentration range. Implementation of multiplex approaches where FH9 and stable reference proteins are simultaneously detected can enhance normalization precision. Statistical power calculations should determine appropriate biological and technical replicate numbers (minimum n=4 biological replicates recommended). As observed in comparable antibody research , standardization of sample processing timing is crucial when studying stress responses, as protein modifications and degradation can rapidly alter epitope accessibility. Additionally, researchers should implement spike-in controls with known quantities of recombinant FH9 to assess recovery efficiency across different stress conditions, as cellular stress may affect extraction efficiency.

What optimization strategies are recommended for Western blotting with FH9 Antibody?

Optimizing Western blotting with FH9 Antibody requires systematic evaluation of multiple parameters. Begin with a titration experiment testing antibody dilutions ranging from 1:500 to 1:5000 to determine optimal signal-to-noise ratio. The blocking solution composition significantly impacts background; compare 5% BSA versus 5% non-fat dry milk in TBST to identify which produces cleaner results with this particular antibody. Membrane transfer efficiency for the ~100 kDa FH9 protein is maximized using wet transfer at low voltage (30V) overnight at 4°C rather than rapid high-voltage transfers. Similar to approaches used for other research antibodies , incubation temperature and duration should be evaluated (4°C overnight versus room temperature for 1-2 hours) to determine which provides optimal specific binding while minimizing background. For challenging samples, addition of 0.1% SDS to the antibody dilution buffer may improve specificity by reducing non-specific interactions.

What are the recommended protocols for immunolocalization of FH9 protein in plant tissues?

While not explicitly validated for immunolocalization, researchers attempting this application with FH9 Antibody should consider the following protocol framework. Fixation should be performed using 4% paraformaldehyde in PBS for 2 hours, as stronger fixatives may mask the epitope. Antigen retrieval using citrate buffer (pH 6.0) at 95°C for 10 minutes often improves accessibility of plant proteins. For primary antibody incubation, use FH9 Antibody at 1:100 to 1:200 dilution in 1% BSA/PBST buffer overnight at 4°C. Drawing from methodologies employed in comparable plant antibody studies, include appropriate negative controls (primary antibody omission, pre-immune serum, and peptide competition controls) to verify specificity. Sequential double-labeling with cytoskeletal markers can provide valuable co-localization data, particularly given FH9's potential role in actin organization. To minimize autofluorescence, which is common in plant tissues, implement a 0.1% sodium borohydride treatment step prior to blocking.

How can researchers troubleshoot weak or absent signals when using FH9 Antibody?

When troubleshooting weak or absent signals with FH9 Antibody, researchers should systematically evaluate each experimental component. First, verify antibody integrity by testing with a positive control (recombinant FH9 protein). Second, consider epitope accessibility issues by testing alternative extraction methods; for plant tissues, RIPA buffer supplemented with 1% plant-specific protease inhibitor cocktail often yields better results than gentler lysis buffers. Third, evaluate whether post-translational modifications might be masking the epitope by treating samples with appropriate phosphatases or deglycosylation enzymes prior to analysis. Fourth, drawing from approaches used with other research antibodies , enhance detection sensitivity by implementing signal amplification systems such as biotin-streptavidin or tyramide signal amplification. Finally, consider that expression levels may be naturally low, necessitating enrichment by immunoprecipitation prior to detection.

What controls are essential when using FH9 Antibody in comparative expression studies?

Designing rigorous comparative expression studies with FH9 Antibody requires implementation of several critical controls. First, include both positive controls (samples with confirmed FH9 expression) and negative controls (FH9 knockout/knockdown lines where available) to establish detection range boundaries. Second, incorporate loading controls that remain stable across your experimental conditions; for Arabidopsis studies, ACTIN2 or UBQ10 are recommended references. Third, implement method controls including primary antibody omission and isotype controls to distinguish specific from non-specific signals. Fourth, as demonstrated in similar antibody research contexts , include cross-reactivity controls by testing the antibody against recombinant proteins from the same family (other formin homology proteins) to confirm specificity. Fifth, for quantitative studies, include a standard curve using recombinant FH9 protein at known concentrations to enable accurate quantification. These controls collectively ensure the validity of comparative data and facilitate meaningful interpretation.

How should researchers approach experimental design for studying FH9 protein modifications?

Studying FH9 protein modifications requires a carefully structured experimental approach. First, establish baseline detection parameters using non-treated samples to identify the unmodified FH9 protein migration pattern. For phosphorylation studies, implement a dual detection strategy using both FH9 Antibody and phospho-specific antibodies (if available) or phospho-protein stains. When analyzing potential phosphorylation, include lambda phosphatase-treated control samples to confirm shifts are due to phosphorylation. For ubiquitination studies, immunoprecipitate with FH9 Antibody followed by ubiquitin-specific antibody detection, with proteasome inhibitor (MG132) treated and untreated samples as controls. Drawing from methodologies used in other protein modification studies , researchers should consider targeted mass spectrometry approaches to precisely identify modification sites. Additionally, time-course experiments following specific treatments can reveal the dynamics of these modifications, providing insight into regulatory mechanisms.

What statistical considerations are important when analyzing data from FH9 Antibody experiments?

Statistical analysis of FH9 Antibody experimental data requires careful consideration of several factors. First, determine appropriate sample sizes through power analysis; preliminary data suggests a minimum of 4-6 biological replicates is necessary to detect a 30% difference in FH9 protein levels with 80% power at α=0.05. Second, assess data normality using Shapiro-Wilk tests before selecting parametric or non-parametric statistical methods. Third, implement appropriate normalization strategies; for Western blot densitometry, normalize FH9 signals to consistent loading controls, and for ELISA, use standard curves with 4 or 5-parameter logistic regression models. Fourth, when comparing multiple experimental conditions, apply ANOVA with appropriate post-hoc tests (Tukey's HSD for balanced designs, Scheffé's method for unbalanced designs) with correction for multiple comparisons. As observed in similar antibody-based research contexts , controlling for batch effects through randomized experimental design and inclusion of inter-assay calibrators significantly improves data reliability and reproducibility.