ARI1 Antibody

What is the optimal storage protocol for ARI1 antibodies to maintain activity?

ARI1 antibodies should be stored following specific temperature and handling protocols to maintain their efficacy. For long-term storage (12 months), keep antibodies at -20 to -70°C as supplied. After reconstitution, antibodies remain stable for approximately 1 month at 2-8°C under sterile conditions, or 6 months at -20 to -70°C under sterile conditions . To prevent activity loss, avoid repeated freeze-thaw cycles by using a manual defrost freezer and aliquoting reconstituted antibodies into single-use volumes before freezing .

How do I determine the appropriate antibody dilution for my specific experiment?

Optimal dilutions should be determined empirically for each application and experimental system. Begin with manufacturer-recommended dilution ranges (typically 1:100 to 1:1000 for Western blots), then perform a dilution series experiment. For Western blot applications with ARI1 antibodies, start with 1 μg/mL as demonstrated in published protocols . Create a standard curve with multiple dilutions, assess signal-to-noise ratio, and select the dilution that provides clear specific bands with minimal background. Document your optimization process for reproducibility.

What are the validated applications for commercially available ARI1 antibodies?

Commercial ARI1 antibodies have been validated for various applications including:

Additional protocols are typically available in the technical information sections provided by manufacturers .

How can I validate ARID1A antibody specificity for mutation status assessment?

To validate ARID1A antibody specificity for mutation status assessment, implement a multi-step approach. First, perform comparative testing of multiple antibodies (e.g., EPR13501, D2A8U, and HPA005456) against samples with known mutation status determined by next-generation sequencing . Use cell line models with confirmed ARID1A status as positive and negative controls. Establish a modified immunoreactive scoring system and determine the optimal cut-point for mutation status identification through Receiver Operating Characteristic (ROC) analysis . For highest accuracy, the EPR13501 antibody has demonstrated 100% concordance between ARID1A mutation status and immunohistochemistry results in gynecological tumors . Include both wild-type and mutant samples in each experiment to confirm consistent performance.

What strategies can improve antibody affinity for emerging viral variants?

Enhancing antibody affinity for emerging viral variants involves both computational and experimental approaches. Implement machine learning models like AbRFC that predict binding affinity changes due to mutations (ΔΔG) . Systematically sample non-deleterious mutations identified by these models through iterative wet lab screening with small design libraries (under 100 designs per round) . This approach has successfully yielded antibodies with >1000-fold improved affinity against SARS-CoV-2 Omicron subvariants . Alternatively, consider AI-driven methods like RFdiffusion, which has been fine-tuned to design human-like antibodies with optimal binding properties . Focus on complementarity-determining regions (CDRs) and consider both directed evolution and structure-guided design approaches to address epitope changes in viral variants.

How does ARIH1 antibody staining correlate with immune signaling activation?

ARIH1 antibody staining intensity correlates with activation of specific immune signaling pathways. In immunohistochemical analyses, strong ARIH1 staining positively correlates with enhanced type I interferon (IFN) expression and proinflammatory cytokine production in response to viral infection or cytoplasmic DNA . When interpreting ARIH1 staining results, consider that ARIH1 functions by catalyzing mono-ISGylation and oligomerization of cGAS, thereby promoting antiviral immunity . Decreased ARIH1 staining in knockout models corresponds with impaired expression of IFNB, ISG56, CCL5, and IP10 in response to HSV-1 infection . This correlation provides a valuable biomarker for assessing innate immune activation status in tissue samples and experimental models.

What controls should be included when using ARI1 antibodies in immunohistochemistry?

When using ARI1 antibodies for immunohistochemistry, implement a comprehensive control strategy:

For ARID1A antibodies specifically, include samples with known mutation status confirmed by sequencing to establish accurate scoring thresholds .

How do I troubleshoot inconsistent results when using ARI1 antibodies in Western blots?

Inconsistent Western blot results with ARI1 antibodies can be addressed through systematic troubleshooting:

• Sample Preparation Issues: Ensure complete cell lysis using appropriate buffer systems. For ARIH1 detection, use RIPA buffer supplemented with protease inhibitors and phosphatase inhibitors if examining phosphorylation status.

• Protein Degradation: Confirm sample integrity by checking housekeeping proteins. Process samples quickly and maintain cold conditions throughout.

• Transfer Efficiency: Verify complete transfer using reversible staining of membranes. Adjust transfer conditions for high molecular weight proteins (ARIH1 ~100 kDa, ASK1 ~154 kDa) .

• Antibody Specificity: Test alternative antibody clones and lots. Validate using positive controls (e.g., lysates of Raji human Burkitt's lymphoma cell line for ASK1) .

• Detection Sensitivity: Optimize enhanced chemiluminescence reagents and exposure times. Consider signal amplification for low-abundance targets.

• Buffer Compatibility: Use recommended buffer systems (e.g., Immunoblot Buffer Group 2 for ASK1 detection) .

• Reduction Conditions: Perform experiments under appropriate reducing conditions as specified in protocols .

Document all optimization steps methodically to ensure reproducibility.

What are the critical factors in designing experiments to assess ARIH1's role in antiviral immunity?

When designing experiments to assess ARIH1's role in antiviral immunity, several critical factors must be considered:

• Model Selection: Choose appropriate cellular models (e.g., THP-1 cells for human studies) or mouse models (Lyz2-Cre; Arih1 fl/fl mice) depending on the research question .

• Stimulus Specificity: Use specific stimuli to activate distinct pathways - HSV-1 or cytoplasmic DNA for cGAS-STING pathway, SeV for RIG-I pathway, and poly(I:C) for TLR3 pathway .

• Temporal Considerations: Design time-course experiments to capture the kinetics of ARIH1-mediated responses, as antiviral signaling occurs in temporally distinct waves.

• Readout Selection: Measure multiple endpoints including:

  • Gene expression (IFNB, ISG56, CCL5, IP10) by qRT-PCR

  • Protein levels by Western blot

  • Pathway activation (phosphorylation of signaling components)

  • Functional outcomes (viral replication, cell survival)

• Genetic Manipulation: Implement both loss-of-function (shRNA, CRISPR/Cas9) and gain-of-function (overexpression) approaches to comprehensively assess ARIH1 function .

• In vivo Validation: Extend findings to mouse models using conditional knockout strategies (Lyz2-Cre; Arih1 fl/fl) to examine tissue-specific effects .

• Mechanistic Dissection: Investigate specific biochemical mechanisms, such as ARIH1-mediated ISGylation of cGAS at K187 .

How should I interpret discrepancies between ARID1A antibody staining and sequencing results?

When facing discrepancies between ARID1A antibody staining and sequencing results, implement this systematic analytical approach:

• Assess Mutation Type: Not all mutations lead to protein loss. Some mutations may affect protein function without altering expression or antibody epitope recognition . Examine whether identified mutations are predicted to be deleterious.

• Evaluate Epitope Location: Determine if the antibody's epitope region is affected by the detected mutation. Different antibodies (e.g., EPR13501, D2A8U, or HPA005456) recognize different epitopes and may yield varying results .

• Consider Technical Factors:

  • Sequencing depth (aim for >250X coverage)

  • Tumor heterogeneity and sample representation

  • Fixation artifacts affecting IHC detection

  • Sensitivity of the scoring system used (e.g., modified immunoreactive score)

• Apply Statistical Analysis: Use ROC analysis to determine optimal cut-points for antibody positivity that correlate with mutation status .

• Resolve Discrepancies: For critical applications, confirm results with alternative antibodies and sequencing methods. EPR13501 antibody has shown 100% concordance with sequencing results and may be preferable for validation .

What methodological approaches can distinguish between specific and non-specific antibody binding in tissue samples?

To distinguish between specific and non-specific antibody binding in tissue samples, employ these methodological approaches:

• Sequential Dilution Analysis: Perform titration series of the primary antibody. Specific binding maintains the staining pattern at increasing dilutions while non-specific binding diminishes.

• Competitive Inhibition: Pre-incubate the antibody with purified target protein or immunizing peptide. Specific staining should be reduced or eliminated.

• Genetic Controls: Compare staining in wild-type versus knockout/knockdown tissues (e.g., Arih1 fl/fl mice) . Specific staining should be absent or significantly reduced in knockout samples.

• Multiple Antibody Validation: Use multiple antibodies targeting different epitopes of the same protein. Concordant results support specificity .

• Orthogonal Technique Correlation: Compare IHC results with other detection methods:

  • mRNA expression (in situ hybridization or RT-PCR)

  • Protein expression (Western blot or mass spectrometry)

  • Mutation status (next-generation sequencing)

• Quantitative Image Analysis: Apply digital pathology algorithms to standardize interpretation and reduce subjective assessment. Establish clear scoring systems like the modified immunoreactive score used for ARID1A evaluation .

• Cross-Species Reactivity: If the epitope is conserved, consistent staining patterns across species support specificity.

How can AI-driven antibody design be applied to develop novel ARI1 targeting strategies?

AI-driven antibody design can revolutionize ARI1 targeting strategies through several innovative approaches:

• Structure-Guided Generation: Utilize RFdiffusion, a machine learning model fine-tuned for designing human-like antibodies, to generate novel binding interfaces specific to ARI1 proteins . This approach can design antibody loops—the intricate, flexible regions responsible for antibody binding—customized for optimal ARI1 interaction.

• Affinity Optimization: Apply models like AbRFC to predict binding affinity changes due to mutations (ΔΔG) and systematically sample non-deleterious mutations . This iterative approach can enhance binding affinity by >1000-fold through limited screening rounds (<100 designs per round).

• Epitope Diversification: Generate antibodies targeting multiple distinct epitopes on ARI1 proteins to:

  • Improve detection specificity

  • Enable sandwich assays for quantification

  • Target functionally critical domains

• Species Cross-Reactivity Engineering: Design antibodies that recognize conserved epitopes across species, enabling translational research from animal models to human applications.

• Functional Modulation: Design antibodies that not only bind ARI1 but also modulate its function, such as inhibiting or enhancing ARIH1's E3 ubiquitin ligase activity .

• Therapeutic Development: Apply RFdiffusion techniques to design therapeutic antibodies targeting ARIH1 for modulating antiviral immunity or autoimmune responses .

Future implementations will likely combine computational prediction with high-throughput experimental validation to accelerate development timelines.

What are the methodological considerations for studying ARIH1's role in autoimmunity using antibody-based approaches?

When studying ARIH1's role in autoimmunity using antibody-based approaches, implement these methodological considerations:

• Model Selection: Choose appropriate autoimmune models where ARIH1 may play a role. Evidence suggests ARIH1 deletion in myeloid cells alleviates autoimmune phenotypes caused by TREX1 deficiency .

• Cell-Type Specificity: Use cell type-specific markers in combination with ARIH1 antibodies to determine which populations express ARIH1 in autoimmune contexts. Consider using conditional knockout models (Lyz2-Cre; Arih1 fl/fl mice) to study cell-type specific contributions .

• Pathway Interaction Analysis: Investigate ARIH1's interaction with key autoimmunity pathways:

  • cGAS-STING pathway activation through K187 ISGylation

  • Type I interferon production

  • Inflammasome activation

• Temporal Dynamics: Establish the temporal relationship between ARIH1 expression/activity and disease progression through longitudinal sampling.

• Therapeutic Targeting Assessment: Use neutralizing antibodies against ARIH1 or its downstream mediators to evaluate therapeutic potential.

• Biomarker Development: Validate ARIH1 antibodies for use as biomarkers of autoimmune disease activity through correlation with clinical parameters.

• Post-Translational Modification Analysis: Develop and validate antibodies specific to modified forms of ARIH1 (e.g., phosphorylated, ubiquitinated) to understand regulatory mechanisms in autoimmunity.

• Multiplexing Strategies: Combine ARIH1 antibody staining with markers of inflammation (cytokines, immune cell infiltration) to create comprehensive tissue signatures of autoimmune pathology.

How can researchers integrate antibody-based detection with genomic and proteomic approaches for comprehensive ARI1 analysis?

Integrating antibody-based detection with genomic and proteomic approaches enables comprehensive ARI1 analysis through a multi-omics workflow:

• Sequential Validation Pipeline:

  • Begin with genomic profiling through targeted sequencing of ARID1A/ARIH1 with high coverage (>250X)

  • Confirm protein expression using optimized IHC with validated antibodies like EPR13501 for ARID1A

  • Perform quantitative proteomic analysis to identify interaction partners and post-translational modifications

• Correlation Analysis Framework:

  • Establish concordance between mutation status and protein expression using statistical methods like ROC analysis

  • Develop integrated scoring systems that combine genomic, transcriptomic, and proteomic data

  • Apply machine learning algorithms to identify patterns across multi-omics datasets

• Functional Validation Approaches:

  • Use antibodies to track protein dynamics following genetic manipulation

  • Combine ChIP-seq (for ARID1A) or ubiquitylome analysis (for ARIH1) with antibody-based detection methods

  • Implement CRISPR screens with antibody-based readouts to identify functional pathways

• Spatial Analysis Integration:

  • Pair single-cell transcriptomics with multiplexed immunofluorescence

  • Apply spatial proteomics techniques alongside antibody staining

  • Correlate tissue morphology with molecular profiles

• Translational Research Framework:

  • Develop companion diagnostic approaches pairing antibody-based assays with genetic testing

  • Establish biomarker signatures combining protein expression patterns with genetic alterations

  • Create patient stratification algorithms based on integrated data for personalized treatment approaches