HLA-DPA1 Antibody

What is HLA-DPA1 and what is its biological significance in immune response?

HLA-DPA1 belongs to the HLA class II alpha chain paralogues. It forms a heterodimer with the HLA-DPB1 chain, creating a functional MHC class II molecule anchored in the cell membrane. This molecule plays a central role in the immune system by presenting peptides derived from extracellular proteins to CD4+ T cells, thus initiating adaptive immune responses .

The HLA-DPA1 protein is approximately 33-35 kDa in size, and its gene contains 5 exons. Exon one encodes the leader peptide, exons 2 and 3 encode the two extracellular domains, and exon 4 encodes the transmembrane domain and cytoplasmic tail. Both the alpha chain (DPA) and beta chain (DPB) contain polymorphisms that determine peptide binding specificities, potentially resulting in up to four different molecular configurations . This variability contributes to the diverse range of peptides that can be presented to T cells and influences individual immune responses, including responses to vaccines and susceptibility to certain diseases.

What research applications are most suitable for HLA-DPA1 antibodies?

HLA-DPA1 antibodies are valuable tools in multiple research applications, particularly in immunology, transplantation, and vaccinology studies. Based on the available product information, these antibodies can be applied in:

• Western Blotting (WB): For detecting and quantifying HLA-DPA1 protein in cell or tissue lysates

• Immunofluorescence (IF): For visualizing HLA-DPA1 expression patterns in cells

• Immunohistochemistry (IHC): For both paraffin-embedded and frozen tissue sections

• Immunocytochemistry (ICC): For cellular localization studies

• Immunopeptidomics: For identification and characterization of neoantigens through high-performance liquid chromatography coupled to tandem mass spectrometry

Researchers should note that different HLA-DPA1 antibodies exhibit varying levels of cross-reactivity, with some showing reactivity to both human and mouse samples while others are human-specific. Application-specific validation is essential for optimal results .

How should researchers select the appropriate HLA-DPA1 antibody format for their experiments?

When selecting an HLA-DPA1 antibody, researchers should consider multiple factors based on their experimental design:

• Antibody format: Polyclonal antibodies provide broader epitope recognition but potentially lower specificity, while monoclonal antibodies offer higher specificity for particular epitopes. The search results indicate multiple formats are available, including rabbit polyclonal and mouse monoclonal (clone 1E3) antibodies .

• Target region: Different antibodies target specific regions of the HLA-DPA1 protein:

  • Full-length (AA 1-260)

  • N-terminal region

  • Central region

  • C-terminal region (AA 231-259)

• Application compatibility: Verify that the antibody has been validated for your specific application. For example, some antibodies are validated for WB, ELISA, IF, IHC, and ICC, while others might have a more limited application range .

• Host species: Consider the host species (rabbit or mouse) to avoid cross-reactivity issues in your experimental system, particularly for multi-color immunostaining protocols.

• Purification method: Antibodies purified by antigen-affinity chromatography generally provide higher specificity, as seen in the described products .

Select an antibody that aligns with your specific research question and technical requirements to maximize experimental success.

What is the relationship between HLA-DPA1 alleles and immune response to hepatitis B vaccination?

Research has demonstrated significant associations between HLA-DPA1 allelic variants and immune response to hepatitis B vaccination. A comprehensive study involving 281 Taiwanese Han individuals (aged 17-29 years) revealed that HLA-DPA1 alleles strongly influence the long-term immunological response to neonatal hepatitis B vaccination (HBVac) .

The study identified two key DPA1 alleles with opposing effects:

• DPA1*01: Associated with improved long-term protection

  • Individuals carrying this allele had a significantly reduced odds ratio (OR = 0.36, 95% CI = 0.17–0.76, p = 0.0076) for lack of HBV protective immunity

  • This suggests a protective effect that enhances vaccine response sustainability

• DPA1*02: Associated with reduced long-term protection

  • Carriers showed an increased odds ratio (OR = 2.39, 95% CI = 1.17–4.87, p = 0.016) for lack of HBV protective immunity

  • This indicates a potential risk factor for faster waning of vaccine-induced protection

These findings suggest that HLA-DPA1 typing could help identify individuals who might require modified vaccination schedules or additional booster doses. The study employed sequence-specific oligonucleotide methodology for HLA typing, providing a methodological framework for similar investigations .

How does HLA-DPA1 interact with other HLA loci in modulating immune responses to vaccines?

HLA-DPA1 functions within a complex immunogenetic network, interacting with other HLA loci to modulate vaccine responses. Research examining hepatitis B vaccination responses found that while HLA-DPA1 alleles were primarily associated with long-term immunity from neonatal vaccination, other HLA loci exhibited distinct contributions to booster vaccination responses .

Key interactions observed in research include:

• HLA-DPB1 influence on booster response: The DPB105 allele was associated with non-responsiveness to the first booster (OR = 2.11, 95% CI = 1.13–3.93, p = 0.019), while all DPB103 carriers responded successfully to the first booster (p = 0.0045) .

• HLA-DQB1 impact on booster efficacy: The DQB1*02 allele showed strong association with non-responsiveness to the first booster (OR = 3.73, 95% CI = 1.43–9.71, p = 0.0070) .

These findings suggest a complementary but distinct role for different HLA class II loci in primary versus recall immune responses. Methodologically, researchers investigating such interactions should employ comprehensive HLA typing covering multiple loci and analyze potential haplotype effects through logistic regression and Fisher's exact test approaches, with appropriate statistical corrections for multiple comparisons .

What methodological approaches are optimal for HLA-DPA1 antibody validation in immunopeptidomics research?

Immunopeptidomics, which identifies and characterizes neoantigens through mass spectrometry, has become an important application area for HLA-DPA1 antibodies . For optimal antibody validation in this context, researchers should implement a multi-step validation approach:

• Specificity assessment: Confirm antibody specificity using cells with known HLA-DPA1 expression profiles. The Raji cell line is recommended as a positive control for HLA-DPA1 antibody validation .

• Application-specific validation: For immunopeptidomics applications:

  • Verify antibody performance in immunoprecipitation (IP) assays

  • Confirm compatibility with high-performance liquid chromatography coupled to tandem mass spectrometry workflows

  • Validate antibody effectiveness in complex protein mixtures

• Technical considerations:

  • Store concentrated antibody solutions according to manufacturer recommendations

  • Centrifuge briefly before opening vials to collect all material

  • Validate optimal antibody concentrations for each application

  • Assess potential cross-reactivity with other HLA class II molecules

• Quality control measures:

  • Include appropriate isotype controls

  • Implement antigen competition assays to confirm binding specificity

  • Consider orthogonal validation using genetic manipulation (knockdown/knockout)

Researchers should note that antibodies targeting different regions of HLA-DPA1 may exhibit varying effectiveness in immunopeptidomics applications, making careful selection based on the target epitope region essential .

How can researchers address variability in HLA-DPA1 detection across different experimental systems?

Variability in HLA-DPA1 detection across experimental systems represents a significant challenge. To address this, researchers should implement the following methodological approaches:

• Standardized sample preparation protocols:

  • For cell lines: Standardize culture conditions and activation states

  • For tissues: Optimize fixation protocols based on the specific antibody requirements

  • For blood samples: Implement consistent processing timeframes and isolation techniques

• Technical standardization:

  • For Western blotting: Standardize protein extraction methods, loading controls, and transfer conditions

  • For immunohistochemistry: Optimize antigen retrieval, blocking solutions, and detection systems

  • For flow cytometry: Establish consistent staining protocols, compensation, and gating strategies

• Controls and normalization:

  • Include consistent positive controls (e.g., Raji cells as recommended)

  • Implement negative controls lacking HLA-DPA1 expression

  • Consider internal normalization with housekeeping proteins or invariant HLA molecules

• Antibody-specific considerations:

  • Validate antibody performance across different lots

  • Determine optimal antibody concentrations for each application

  • Consider the impact of polymorphic regions on epitope accessibility

Researchers should be aware that HLA-DPA1 expression can be dynamically regulated by cytokines and inflammatory signals, potentially introducing biological variability that must be accounted for in experimental designs.

How should researchers interpret HLA-DPA1 genetic data in vaccine response studies?

When interpreting HLA-DPA1 genetic data in vaccine response studies, researchers should implement a systematic analytical framework:

• Allele-specific analysis: Assess the frequency and distribution of specific HLA-DPA1 alleles in responder versus non-responder populations. Research indicates that DPA101 and DPA102 alleles have opposing effects on hepatitis B vaccine response, with odds ratios of 0.36 and 2.39 respectively .

• Statistical rigor: Apply appropriate statistical methodologies:

  • Logistic regression analysis for estimating odds ratios

  • Fisher's exact test for low-frequency alleles

  • False discovery rate-based measures (q-values) for multiple comparisons correction

  • Consider significance thresholds (e.g., q-value < 0.1 as used in cited research)

• Stratification approaches: Categorize subjects based on response criteria such as:

  • Group A: Protective antibody levels without booster (≥ 10 mIU/mL)

  • Group B: High response to first booster (> 1000 mIU/mL)

  • Group C: Non-response to first booster (< 10 mIU/mL)

  • Group D: Non-response to multiple boosters

• Ethnicity considerations: Account for ethnic differences in HLA distributions and potential population-specific effects. The cited research focused on Taiwanese Han individuals, and findings may vary in other populations .

• Longitudinal perspectives: Evaluate both short-term and long-term immunological outcomes, as HLA-DPA1 appears to influence long-term protection more prominently than initial response .

This analytical framework provides a comprehensive approach to interpreting genetic associations while avoiding overgeneralization of findings across populations or vaccines.

What technical challenges exist in HLA-DPA1 antibody-based detection systems and how can they be overcome?

Several technical challenges exist in HLA-DPA1 antibody-based detection systems. Understanding and addressing these challenges is crucial for obtaining reliable results:

• Polymorphism interference: HLA-DPA1 exhibits genetic polymorphism that may affect antibody binding.

  • Solution: Select antibodies targeting conserved regions when studying diverse populations, or use multiple antibodies targeting different epitopes to ensure comprehensive detection.

  • Methodological approach: Validate antibody reactivity using cells with known HLA-DPA1 variants.

• Cross-reactivity issues: Some antibodies may cross-react with other HLA class II molecules due to structural similarities.

  • Solution: Perform extensive validation using appropriate controls, including cells lacking HLA-DPA1 expression.

  • Methodological approach: Implement competition assays with purified proteins to confirm specificity.

• Variable expression levels: HLA-DPA1 expression can be dynamically regulated by cell activation state and cytokine exposure.

  • Solution: Standardize cell culture conditions and activation states.

  • Methodological approach: Include positive controls with known expression levels such as Raji cells .

• Detection sensitivity limitations: Low abundance of HLA-DPA1 in certain cell types may challenge detection.

  • Solution: Optimize signal amplification methods appropriate to the detection system.

  • Methodological approach: For immunohistochemistry, implement tyramide signal amplification; for Western blotting, use enhanced chemiluminescence systems.

• Post-translational modification interference: Glycosylation and other modifications may mask epitopes.

  • Solution: Consider enzymatic treatments to remove interfering modifications when necessary.

  • Methodological approach: Compare native and deglycosylated samples to assess impact on detection.

Addressing these challenges requires rigorous validation protocols and appropriate controls to ensure reliable and reproducible results across different experimental systems.

How can HLA-DPA1 typing inform personalized vaccination strategies?

HLA-DPA1 typing has significant potential to inform personalized vaccination strategies, particularly for vaccines with variable population response rates. Research findings suggest several translational applications:

• Risk stratification for vaccine non-response: HLA-DPA1 allelic variants demonstrate significant associations with vaccine response durability. Specifically:

  • Individuals carrying DPA1*01 showed better long-term protection (OR = 0.36 for lack of protection)

  • DPA1*02 carriers exhibited increased risk of inadequate long-term protection (OR = 2.39)

• Booster scheduling optimization: HLA typing could inform individualized booster vaccination schedules:

  • Standard schedules for favorable HLA profiles

  • Accelerated or more frequent boosters for those with unfavorable profiles

  • Monitoring protocols stratified by HLA-associated risk

• Vaccine formulation considerations: For individuals with high-risk HLA profiles:

  • Consider higher antigen doses

  • Evaluate alternative adjuvant systems

  • Explore different delivery platforms

• Implementation methodology:

  • Prospective HLA typing of vaccine recipients

  • Development of risk algorithms incorporating HLA data with other factors

  • Longitudinal antibody monitoring stratified by HLA profile

  • Data integration into electronic health records for clinical decision support

• Cost-effectiveness considerations: Targeted application of HLA typing in:

  • Vaccines with known variable efficacy

  • High-risk populations

  • Expensive vaccination regimens where optimized protocols would provide significant benefit

The study design used in the cited research provides a methodological framework for investigating HLA associations with vaccine responses, potentially enabling the development of personalized vaccination protocols based on genetic profiles .

What role does HLA-DPA1 play in transplantation immunology research?

HLA-DPA1 serves important functions in transplantation immunology research, influencing graft outcomes and immunosuppression strategies:

• Donor-recipient matching considerations:

  • While HLA-A, B, and DR matching receives primary attention in transplantation, HLA-DP matching also influences outcomes

  • HLA-DPA1 mismatches can trigger alloimmune responses through both direct and indirect presentation pathways

  • The polymorphic nature of HLA-DPA1 contributes to alloreactivity potential

• Methodological approaches for HLA-DPA1 assessment in transplantation:

  • High-resolution typing methods using sequence-specific oligonucleotides as described in research

  • Flow cytometric crossmatch using HLA-DPA1 antibodies to detect pre-existing donor-specific antibodies

  • Solid-phase assays for antibody detection and specification

• Post-transplant monitoring applications:

  • Detection of de novo donor-specific antibodies against HLA-DPA1

  • Monitoring for class II-mediated rejection processes

  • Assessment of immunosuppression adequacy

• Research directions:

  • Investigation of HLA-DPA1 epitope mapping for more precise risk assessment

  • Evaluation of permissible versus non-permissible mismatches

  • Development of epitope-based matching algorithms incorporating HLA-DPA1

Researchers studying HLA-DPA1 in transplantation should implement comprehensive typing protocols and correlate findings with clinical outcomes to advance the field toward more personalized transplantation medicine.

What emerging technologies will enhance HLA-DPA1 research in the coming years?

Several emerging technologies show promise for advancing HLA-DPA1 research:

• Single-cell technologies: Single-cell RNA sequencing and proteomics will enable more precise characterization of HLA-DPA1 expression patterns across diverse cell populations and disease states, providing insight into cell-specific roles in immune regulation.

• CRISPR-based approaches: Gene editing technologies will facilitate functional studies through precise modification of HLA-DPA1 alleles, allowing direct assessment of allele-specific effects on antigen presentation and immune responses.

• Structural biology advances: Cryo-electron microscopy and advanced crystallography techniques will provide detailed insights into HLA-DPA1/DPB1 heterodimer structure and peptide binding properties, informing epitope prediction algorithms.

• Mass cytometry and imaging mass cytometry: These technologies will enable simultaneous detection of HLA-DPA1 expression alongside dozens of other markers at single-cell resolution or within tissue contexts, revealing complex cellular relationships.

• Computational immunology: Machine learning approaches will improve prediction of T cell epitopes presented by different HLA-DPA1 variants, enhancing vaccine design and understanding of autoimmune disease mechanisms.

• Long-read sequencing: These technologies will improve phasing of HLA haplotypes, allowing better understanding of extended HLA haplotypes including HLA-DPA1 and their collective impact on immune responses.

Implementation of these technologies will require interdisciplinary collaboration and methodological standardization but promises to significantly advance our understanding of HLA-DPA1's role in health and disease.

How can researchers integrate HLA-DPA1 findings with broader immunogenomic data?

Integrating HLA-DPA1 findings with broader immunogenomic data requires systematic methodological approaches:

• Multi-omics data integration frameworks:

  • Combine HLA typing with transcriptomics, proteomics, and metabolomics data

  • Implement computational methods for dimension reduction and pattern recognition

  • Develop visualization tools for complex immunogenetic interactions

• Network analysis approaches:

  • Map interactions between HLA-DPA1 and other immune-related genes

  • Identify regulatory networks affecting HLA-DPA1 expression

  • Construct pathway models incorporating HLA-DPA1 function

• Population immunogenetics perspectives:

  • Analyze HLA-DPA1 in the context of extended haplotypes

  • Consider linkage disequilibrium patterns across populations

  • Evaluate evolutionary pressures on HLA-DPA1 diversity

• Clinical data integration:

  • Correlate HLA-DPA1 profiles with clinical outcomes

  • Develop risk assessment algorithms incorporating HLA-DPA1 data

  • Implement machine learning approaches for predictive modeling

• Methodological standards:

  • Establish minimum reporting requirements for HLA-DPA1 studies

  • Develop data sharing platforms with standardized formats

  • Create reference datasets for benchmarking analysis pipelines