AGL65 Antibody

What is AGL65 antibody and what is its molecular target?

AGL antibody targets amylo-1, 6-glucosidase, 4-alpha-glucanotransferase (AGL), an enzyme involved in glycogen metabolism. This antibody recognizes a protein with an observed molecular weight of approximately 165 kDa, which closely matches the calculated molecular weight of 175 kDa for the full-length protein consisting of 1532 amino acids. The antibody demonstrates cross-reactivity with human, mouse, and rat samples, making it valuable for comparative studies across these species . Researchers should note that the specificity of this antibody has been validated through multiple applications including Western blotting and immunohistochemistry, with consistent detection in various tissue types including heart tissue samples from humans, mice, and rats.

What are the validated applications for AGL antibody in research settings?

AGL antibody has been validated for multiple research applications with specific recommended dilution parameters:

For IHC applications, researchers should note that antigen retrieval is optimally performed with TE buffer at pH 9.0, although citrate buffer at pH 6.0 may serve as an alternative method . This antibody has been cited in at least six publications for Western blot applications, demonstrating its reliability in peer-reviewed research. When optimizing experimental conditions, researchers should consider that the optimal dilution is sample-dependent and should be determined empirically for each experimental system to obtain reliable results.

How should pre-experimental validation of antibody specificity be conducted?

Before initiating research with AGL antibody, comprehensive validation of specificity should be performed. First, evaluate pre-immune serum from the host animal as a negative control to establish baseline reactivity. This approach helps identify any potential cross-reactivity with the target tissues or cells that might compromise experimental interpretations . Second, perform cross-reactivity testing with closely related proteins, particularly if working with novel tissue types beyond those already validated (heart tissue from human, mouse, and rat).

Western blot analysis should reveal a specific band at approximately 165 kDa, the observed molecular weight of AGL protein. Multiple dilutions should be tested to identify the optimal concentration that provides the highest signal-to-noise ratio. When preparing validation experiments, include both positive controls (tissues known to express AGL, such as heart tissue) and negative controls (tissues with minimal AGL expression or AGL-knockout samples if available) . Document all validation steps methodically, as this information will be essential for publication and ensuring experimental reproducibility.

What are the critical parameters for optimizing Western blot protocols with AGL antibody?

Optimizing Western blot protocols with AGL antibody requires careful attention to several critical parameters:

• Sample preparation: Given the high molecular weight of AGL (165 kDa), use low percentage (6-8%) SDS-PAGE gels to ensure adequate separation. Extend running time to properly resolve high molecular weight proteins.

• Transfer conditions: For high molecular weight proteins like AGL, employ wet transfer methods rather than semi-dry transfers, preferably at lower voltages (30V) for extended periods (overnight) to ensure complete transfer.

• Blocking conditions: Use 5% non-fat dry milk or BSA in TBST (Tris-buffered saline with 0.1% Tween-20) for 1-2 hours at room temperature to minimize background.

• Antibody dilution: Begin testing with a 1:1000 dilution, then optimize based on signal intensity and background. The recommended range of 1:1000-1:8000 provides flexibility for different sample types .

• Incubation conditions: Incubate with primary antibody overnight at 4°C with gentle agitation to maximize specific binding while minimizing non-specific interactions.

• Detection method: For quantitative analysis, use chemiluminescence detection with a standard curve of known protein concentrations to ensure measurements fall within the linear range of detection.

When troubleshooting, methodically alter one parameter at a time while maintaining careful documentation of all experimental conditions to identify optimal protocols for specific experimental systems.

How should researchers address background issues in immunohistochemistry applications?

Background issues in immunohistochemistry with AGL antibody can significantly impact data quality and interpretation. To address these challenges systematically:

First, optimize antigen retrieval conditions. While TE buffer at pH 9.0 is recommended for AGL antibody, experimental comparison with citrate buffer at pH 6.0 may be necessary to determine which method minimizes background while maintaining specific signal . Second, implement more stringent blocking procedures using 5-10% normal serum from the same species as the secondary antibody for at least 1 hour at room temperature.

If high background persists, employ a titration approach by testing dilutions across the recommended range (1:50-1:500) to identify the optimal concentration that maximizes signal-to-noise ratio . Additionally, evaluate the quality of the serum used in antibody production, as hemolyzed serum can contribute to increased fluorescent or HRP-reactive background. Clear, minimally colored serum (such as that obtained using the Vacutainer system) typically produces lower background in immunostaining experiments .

For advanced troubleshooting, consider using tissue-specific negative controls (ideally AGL-knockout tissues) and pre-absorption controls where the antibody is pre-incubated with excess antigen before application to tissue sections, which should abolish specific staining.

What approaches can resolve discrepancies between predicted and observed molecular weights in Western blot analysis?

Discrepancies between predicted and observed molecular weights are common challenges in antibody-based research. In the case of AGL antibody, the observed molecular weight (165 kDa) differs slightly from the calculated weight (175 kDa) . This difference could be attributed to several factors that researchers should systematically investigate:

• Post-translational modifications: Analyze whether glycosylation, phosphorylation, or other modifications affect electrophoretic mobility. Use enzymatic treatments (e.g., phosphatases or glycosidases) before Western blotting to assess their impact on mobility.

• Protein isoforms: Confirm whether alternative splicing generates different isoforms with altered molecular weights. Employ RT-PCR to identify potential splice variants in your experimental system.

• Sample preparation conditions: Evaluate whether denaturing conditions (temperature, reducing agents) influence protein migration. Compare boiled versus non-boiled samples and reducing versus non-reducing conditions.

• Gel system artifacts: Use gradient gels (4-15%) to verify that the observed molecular weight is consistent across different electrophoresis systems and conditions.

• Reference standards: Always include molecular weight markers that bracket your protein of interest, especially for high molecular weight proteins where migration can be less predictable.

When reporting results, clearly document both predicted and observed molecular weights, and propose rational explanations for any discrepancies based on systematic investigation of these factors.

How can computational approaches enhance antibody specificity and cross-reactivity for AGL detection?

Advanced computational approaches can significantly enhance antibody performance for AGL detection, particularly when working across different species or with challenging samples. Modern antibody design platforms leverage supercomputing capabilities to identify key amino acid substitutions that can restore or enhance antibody potency .

Using computational modeling to predict epitope structures can help researchers select antibodies with epitopes in conserved regions of AGL, improving cross-species reactivity. This approach is particularly valuable when working with AGL across human, mouse, and rat samples, as structural differences in the protein may affect antibody binding .

Machine learning algorithms can be employed to analyze large datasets of antibody-antigen interactions, identifying patterns that predict binding affinity and specificity. For example, LLNL researchers used computational approaches to assess mutated antibodies' ability to bind to viral targets, selecting just 376 promising candidates from a theoretical design space of over 10^17 possibilities . Similar approaches could be applied to optimize AGL antibody binding characteristics.

When implementing these advanced approaches, researchers should validate computational predictions with experimental methods such as surface plasmon resonance or bio-layer interferometry to confirm improved binding kinetics and specificity.

What strategies can maximize reproducibility in longitudinal studies using AGL antibody?

Ensuring reproducibility in longitudinal studies using AGL antibody requires rigorous methodological approaches to minimize batch effects and technical variability:

• Antibody lot management: Purchase sufficient antibody from a single lot for the entire study duration. If multiple lots must be used, perform side-by-side validation to quantify lot-to-lot variation before incorporating new lots into ongoing studies.

• Reference standards: Develop stable, well-characterized positive control samples (e.g., purified AGL protein or standardized cell/tissue lysates) that can be included in every experiment to normalize between runs.

• Storage optimization: Store antibody aliquots at -20°C to maintain stability for up to one year after shipment, avoiding repeated freeze-thaw cycles that can damage antibody function . For critical applications, prepare small working aliquots to minimize freeze-thaw cycles.

• Standardized protocols: Develop detailed standard operating procedures (SOPs) that specify all experimental parameters, including:

  • Sample preparation methods

  • Antibody dilutions and incubation conditions

  • Detection reagents and image acquisition settings

  • Data analysis workflows

• Internal controls for normalization: Implement a system of internal controls (housekeeping proteins, spike-in standards) that allows for normalization across experimental runs.

Additionally, maintain comprehensive records of antibody performance over time, including signal intensity, background levels, and specificity markers. This longitudinal quality control data enables the identification of subtle changes in antibody performance that might impact experimental outcomes.

How should AGL antibody protocols be modified for use with challenging tissue types?

When adapting AGL antibody protocols for challenging tissue types beyond the validated heart tissues, several methodological modifications may be necessary:

For fibrous tissues (e.g., skeletal muscle, liver with fibrosis), extend antigen retrieval times (20-30 minutes) and consider using higher concentrations of proteolytic enzymes (proteinase K or trypsin) for enhanced epitope exposure. Research indicates that liver fibrosis can alter molecular detection parameters, requiring protocol adjustment .

For adipose-rich tissues, implement additional deparaffinization steps and extend washing periods to remove lipids that may trap antibodies and increase background. Consider using detergent-enhanced wash buffers (0.3% Triton X-100) to improve antibody penetration.

For tissues with high endogenous peroxidase activity, such as liver or kidney, include additional blocking steps using 3% hydrogen peroxide for 15-30 minutes before primary antibody incubation, or consider fluorescent secondary antibodies instead of HRP-based detection.

When working with fixation-sensitive epitopes, compare results from frozen sections with formalin-fixed tissues to determine if the AGL epitope recognized by the antibody is fixation-sensitive, and adjust fixation protocols accordingly.

Document all protocol modifications systematically and include appropriate validation controls to ensure that modified protocols maintain specificity while improving detection in challenging samples.

What considerations are important when using AGL antibody in multiplexed immunoassays?

Implementing multiplexed immunoassays with AGL antibody requires careful consideration of several technical factors to ensure valid, interpretable results:

• Antibody compatibility: Since AGL antibody is a rabbit polyclonal (IgG) , pair it with antibodies from different host species (mouse, goat, etc.) to enable clear discrimination using species-specific secondary antibodies. If using multiple rabbit antibodies, consider directly conjugated primary antibodies or sequential detection methods.

• Spectral overlap mitigation: When using fluorescent detection, select fluorophores with minimal spectral overlap and implement appropriate compensation controls. For the high molecular weight AGL (165 kDa) , ensure adequate separation from other target proteins through careful selection of protein extraction and separation methods.

• Cross-reactivity validation: Perform single-staining controls alongside multiplexed assays to confirm that detection of each target is consistent between single and multiplexed formats, particularly important given the polyclonal nature of the AGL antibody.

• Signal amplification balancing: When targets have vastly different expression levels, employ differential signal amplification strategies (e.g., longer exposure for low-abundance targets) while maintaining quantitative relationships.

• Quantitative analysis calibration: Develop calibration curves using purified protein standards to establish the linear range of detection for each antibody in the multiplex panel. This is particularly important for quantitative comparisons between different targets.

When designing multiplexed assays, begin with simpler duplex experiments before progressing to more complex panels, validating each additional antibody to ensure it doesn't compromise detection of existing targets in the panel.