IFNA10 Antibody

What is IFNA10 and what distinguishes it from other type I interferons?

Interferon alpha-10 (IFNA10), also known as IFN-α-10, interferon alpha-6L, interferon alpha-C, or LeIF C, is one of 13 distinct IFN-α subtypes within the type I interferon family. While type I interferons share over 95% amino acid sequence homology, IFNA10 has unique structural and functional properties that distinguish it from other subtypes .

Methodologically, researchers can differentiate IFNA10 from other IFN-α subtypes through:

• Phylogenetic analysis of amino acid sequences

• Specific antibody detection using subtype-specific epitopes

• Functional assessments measuring differential activation of downstream pathways

IFNA10 binds to the common type I interferon receptor composed of IFNAR1 (125 kDa) and IFNAR2 (100 kDa) subunits, triggering JAK-STAT signaling pathways that induce antiviral and immunomodulatory effects .

How do researchers validate the specificity of anti-IFNA10 antibodies?

Validation of anti-IFNA10 antibody specificity requires multiple complementary approaches:

• Cross-reactivity assessment: Testing against all 13 IFN-α subtypes plus other type I interferons (IFN-β, IFN-ε, IFN-κ, IFN-ω) using ELISA and/or LIPS (Luciferase Immunoprecipitation Systems)

• Western blot validation: Comparing reactivity patterns against recombinant IFNA10 versus other IFN-α subtypes, with expected band size of approximately 22 kDa

• Knockout/knockdown controls: Using IFNA10-deficient cells or tissues to confirm antibody specificity

• Epitope mapping: Identifying the specific amino acid sequences recognized by the antibody to confirm specificity to IFNA10-unique regions

• Immunoabsorption studies: Pre-incubating the antibody with recombinant IFNA10 protein to neutralize specific binding before testing on samples

For maximum confidence in specificity, researchers should conduct validation across multiple techniques and include appropriate positive and negative controls in each experiment.

What are the optimal methods for using anti-IFNA10 antibodies in different experimental contexts?

Based on validated applications, researchers should consider these methodological approaches for different experimental contexts:

Western Blot (WB):

• Recommended dilution: 1/200 for tissue lysates

• Expected band size: 22 kDa

• Sample preparation: Denature proteins under reducing conditions

• Controls: Include recombinant IFNA10 protein as positive control

• Optimization: Titrate antibody concentration to minimize background

Immunohistochemistry (IHC-P):

• Recommended dilution: 1/100 for paraffin-embedded tissues

• Antigen retrieval: Heat-induced epitope retrieval in citrate buffer (pH 6.0)

• Detection system: HRP-polymer or biotin-streptavidin systems

• Counterstain: Hematoxylin for nuclear visualization

• Controls: Include IFNA10-positive tissues (kidney samples shown to express IFNA10)

Flow Cytometry:

• Similar methodological approaches to those used for IFNAR1 detection can be applied

• Cell permeabilization may be required for intracellular detection

• Use appropriate isotype controls and FMO (fluorescence minus one) controls

ELISA and Multiplex Assays:

• Sandwich ELISA format recommended for detection in biological fluids

• Cross-validation with multiple detection antibodies to ensure specificity

• Consider multiplex formats for simultaneous detection of multiple IFN-α subtypes

How can researchers accurately measure IFNA10 expression in different cell types and tissues?

To accurately measure IFNA10 expression across different biological contexts:

• RNA-level detection:

  • qPCR with IFNA10-specific primers (ensuring specificity given high homology with other IFN-α genes)

  • RNA-seq with careful bioinformatic discrimination of closely related interferon transcripts

  • Nanostring technologies for direct quantification without amplification

• Protein-level detection:

  • Immunoassays (ELISA, multiplex bead-based assays) using validated antibodies

  • Mass spectrometry for unbiased detection and quantification

  • Western blotting for semi-quantitative assessment

• Cellular source identification:

  • Flow cytometry for cell-specific detection in mixed populations

  • Single-cell RNA-seq to identify specific cellular sources

  • Immunohistochemistry to visualize tissue distribution

• Functional assessment:

  • Reporter cell assays measuring IFNAR pathway activation

  • Cytopathic effect assays measuring antiviral activity

Research has shown that IFNA10 is produced by plasmacytoid dendritic cells, CD56ᵇʳⁱᵍʰᵗ NK cells, and correlates with Th1 CD4 T cell levels, while showing negative correlation with circulating plasmablasts .

How does IFNA10 function differ from other IFN-α subtypes in antiviral responses?

Research indicates that IFNA10 has distinct functionality compared to other IFN-α subtypes:

• Cellular association patterns:

  • IFNA10 shows strong association with Th1 CD4 T cells, CD56ᵇʳⁱᵍʰᵗ NK cells, and plasmacytoid dendritic cells

  • Unlike IFNB1, IFNA10 doesn't strongly associate with platelet degranulation markers

• Gene expression profiles:

  • IFNA10 correlates positively with IFN Alpha and Gamma Response gene signatures

  • Unlike IFNB1, IFNA6, IFNW1, and IFNA16, it doesn't correlate strongly with cell proliferation pathways (G2M checkpoint, E2F targets, MYC targets)

• Chemokine induction patterns:

  • IFNA10 has differential associations with chemokines compared to other subtypes:

    • Stronger association with CX3CL1 (fractalkine) than IFNA6

    • Different association pattern with CCL5 (RANTES) compared to IFNB1

• Receptor binding:

  • Like other type I interferons, IFNA10 binds to the heterodimeric IFNAR1/IFNAR2 receptor complex

  • Subtle differences in binding affinity may contribute to differential signaling outcomes

Methodologically, researchers should employ multiple approaches when comparing interferon subtypes, including transcriptomics, proteomics, and functional assays to comprehensively characterize these differences.

What roles does IFNA10 play in autoimmune diseases, and how can researchers study these mechanisms?

IFNA10 and other type I interferons play complex roles in autoimmune pathology, particularly in systemic lupus erythematosus (SLE):

• Pathogenic mechanisms in autoimmunity:

  • Persistent IFNA10 production contributes to chronic immune activation

  • In SLE, approximately 65% of adult patients and nearly all pediatric patients exhibit elevated type I IFN signatures

  • IFN-α pathway activation is associated with immune complex formation, tissue damage, and disease progression

• Methodological approaches to study IFNA10 in autoimmunity:

  • IFN signature measurement:

    • Quantitative PCR of IFN-stimulated genes

    • Microarray or RNA-seq analysis of IFN-regulated transcripts

    • Flow cytometry for IFN-induced proteins

  • Autoantibody detection:

    • ELISA, LIPS, and multiplex particle-based assays to detect anti-IFNA10 autoantibodies

    • Functional neutralization assays to determine if autoantibodies inhibit IFNA10 activity

  • Therapeutic response assessment:

    • Monitoring changes in IFNA10 levels and activity during anti-IFN therapy

    • Stratification of patients by interferon gene signature (IFNGS) status

• Clinical relevance:

  • Anti-type I interferon therapy (such as anifrolumab) targets the IFNAR receptor to block signaling from all type I interferons including IFNA10

  • Response to therapy can be stratified by interferon gene signature (IFNGS) status

  • Clinical trials support efficacy of IFN-targeting approaches in moderate-to-severe SLE patients

How can researchers differentiate between neutralizing and non-neutralizing antibodies against IFNA10?

Distinguishing neutralizing from non-neutralizing antibodies requires functional assays rather than simple binding detection:

• Functional neutralization assays:

  • Reporter cell assays: Measure inhibition of IFNAR signaling using cells expressing luciferase or other reporters under control of IFN-stimulated response elements (ISREs)

  • Gene induction measurement: Assess the ability of antibodies to block IFNA10-induced expression of ISGs like CXCL10 using qPCR

  • Cytopathic effect assays: Evaluate whether antibodies prevent the protective effect of IFNA10 against viral infection (e.g., vesicular stomatitis virus in MDBK cells)

  • Receptor binding inhibition: Determine if antibodies block interaction between IFNA10 and IFNAR1/IFNAR2 subunits

• Characterizing antibody features:

  • Epitope mapping: Neutralizing antibodies typically target regions involved in receptor binding

  • Binding kinetics: Measure association/dissociation rates using surface plasmon resonance

  • Avidity assessment: Higher avidity antibodies are more likely to be neutralizing

• Clinical implications:

  • Neutralizing anti-IFNA10 autoantibodies efficiently block interaction with both IFNAR1 and IFNAR2 receptor subunits

  • Non-neutralizing autoantibodies typically limit interaction with only one receptor subunit and display lower binding avidity

  • Neutralizing antibodies are associated with increased susceptibility to viral infections

What techniques can researchers use to study IFNA10 autoantibodies in patient samples?

For comprehensive characterization of anti-IFNA10 autoantibodies in patient samples:

• Detection methods:

  • Multiplex particle-based assays: Enable simultaneous detection of antibodies against multiple interferon subtypes

  • ELISA: Coat plates with recombinant IFNA10 and detect bound autoantibodies

  • LIPS (Luciferase Immunoprecipitation Systems): Use IFNA10-luciferase fusion proteins to detect autoantibodies with high sensitivity

• Functional characterization:

  • Cell-based neutralization assays: Measure inhibition of IFNA10-induced STAT phosphorylation or ISG expression

  • Viral protection assays: Assess whether patient antibodies block the antiviral activity of IFNA10

  • Receptor binding inhibition: Determine if autoantibodies prevent IFNA10-IFNAR interaction

• Clinical correlation approaches:

  • Longitudinal sampling: Monitor autoantibody levels over time relative to disease activity

  • Stratification by disease severity: Compare autoantibody prevalence in mild versus severe disease

  • Response to therapy: Assess changes in autoantibody levels following treatment

• Advanced characterization:

  • Footprint profiling: Delineate the specific epitopes recognized by neutralizing autoantibodies

  • Isotype and subclass determination: Identify whether autoantibodies are IgG, IgA, or IgM, and which IgG subclasses predominate

  • Affinity maturation analysis: Sequence antibody variable regions to assess somatic hypermutation

Research has demonstrated that neutralizing autoantibodies against type I interferons, including IFNA10, are present in approximately 10% of patients with severe COVID-19 but rare in those with mild disease or healthy controls . Similar autoantibodies were found in approximately 5% of patients with critical influenza under age 70 .

What are the methodological challenges in studying IFNA10-specific responses versus pan-IFN-α effects?

Researchers face several challenges when attempting to isolate IFNA10-specific effects:

• Homology challenges:

  • The 13 IFN-α subtypes share >95% amino acid sequence homology

  • Designing truly subtype-specific reagents requires careful epitope selection and validation

  • Cross-reactivity must be rigorously tested against all subtypes

• Detection specificity:

  • Antibody cross-reactivity: Many commercial antibodies cross-react with multiple IFN-α subtypes

  • qPCR primer design: Highly specific primers must target unique regions despite high sequence similarity

  • Protein detection: Mass spectrometry may be needed for definitive identification

• Functional redundancy:

  • All IFN-α subtypes signal through the same IFNAR receptor complex

  • Overlapping biological activities make isolating subtype-specific effects challenging

  • Knockout/knockdown of single subtypes may not show phenotypes due to compensation

• Methodological approaches to address challenges:

  • CRISPR-Cas9 gene editing: Generate IFNA10-specific knockouts while leaving other subtypes intact

  • Subtype-specific neutralizing antibodies: Develop and validate highly specific antibodies

  • Reporter systems: Create cell lines expressing reporters only in response to specific subtypes

  • Recombinant protein studies: Use purified recombinant IFNA10 to study direct effects

  • Single-cell approaches: Identify cells that specifically produce IFNA10 versus other subtypes

• Alternative strategies:

  • Study IFNA10 in species where fewer IFN-α subtypes exist (e.g., mouse models)

  • Focus on temporal dynamics, as different subtypes may be expressed at different times

  • Examine differential expression across tissues and cell types

What controls should researchers include when working with anti-IFNA10 antibodies?

Comprehensive control strategies for IFNA10 antibody research:

• Positive controls:

  • Recombinant human IFNA10 protein as a standard

  • Cell lines or tissues known to express IFNA10 (e.g., kidney tissue)

  • Cells stimulated with viral mimics (e.g., poly(I:C)) to induce IFNA10 expression

• Negative controls:

  • IFNA10 knockout/knockdown cells or tissues

  • Samples from unstimulated conditions where IFNA10 expression is minimal

  • Irrelevant primary antibody of the same isotype and concentration

• Specificity controls:

  • Pre-absorption with recombinant IFNA10 to confirm specific binding

  • Testing against other IFN-α subtypes to demonstrate specificity

  • Secondary antibody-only controls to rule out non-specific binding

• Application-specific controls:

  • Western blot: Molecular weight markers confirming the expected 22 kDa band

  • IHC/IF: Include isotype control antibody on serial sections

  • Flow cytometry: FMO (fluorescence minus one) controls and isotype controls

  • ELISA: Standard curve using recombinant IFNA10

• Validation across methods:

  • Confirm findings using multiple detection methods (e.g., qPCR, Western blot, immunostaining)

  • Use at least two different antibodies recognizing different epitopes when possible

How can researchers troubleshoot common problems with IFNA10 antibody-based experiments?

For research involving autoantibody detection in patient samples, additional troubleshooting approaches include using serial dilutions to address potential hook effects, running both neutralization and binding assays to confirm functionality, and including well-characterized positive and negative control samples in each assay .