IL32 Antibody

What is IL-32 and what makes it significant for research?

IL-32, previously known as Natural Killer Transcript 4 (NK4), is a pro-inflammatory cytokine that notably does not share sequence homology with known cytokine families. It is produced by mitogen-activated lymphocytes, IFNγ-activated epithelial cells, and IL-12/IL-18-activated NK cells. Its expression increases following T-cell activation by mitogens or NK cell activation by IL-2 .

IL-32 is significant for research because it activates NF-κB and p38 MAPK cytokine signal pathways, potentially playing crucial roles in autoimmune and inflammatory diseases such as rheumatoid arthritis. It's highly expressed in immune tissues and has been implicated in various cancer types, where it may serve as a prognostic marker .

What are the different isoforms of IL-32 and how do they differ?

IL-32 exists in at least four differentially spliced isoforms (α, β, γ, and δ) with a predicted molecular weight of approximately 26 kD . IL-32α is the shortest and most abundant splice variant. The isoforms have distinct biological characteristics:

• IL-32α: Tends to be more cell-associated compared to IL-32β, suggesting a potential intracellular function

• IL-32γ: Often considered the most biologically active isoform

• IL-32β and δ: Have intermediate biological activities

These isoforms can undergo post-translational modifications including myristoylation and N-glycosylation, which may affect their biological functions .

How do I select the appropriate anti-IL-32 antibody clone for my experiment?

The selection of an appropriate anti-IL-32 antibody clone depends on the specific isoform(s) you aim to detect and the experimental application. Based on epitope and reactivity analyses, consider the following characteristics of common antibody clones:

For sandwich ELISA, a combination of KU32-07 (coating) and biotinylated KU32-52 (detection) is highly specific for IL-32α with a detection limit of 80 pg/ml . Alternatively, KU32-56 (coating) with biotinylated KU32-52 (detection) can detect both IL-32α and IL-32β but not γ or δ isoforms .

What is the optimal protocol for IL-32 detection using sandwich ELISA?

For optimal IL-32 detection using sandwich ELISA, the following protocol is recommended based on validated research methods:

• Coating: Pre-coat microtiter plate wells with mAb KU32-07 (for IL-32α specificity) or mAb KU32-56 (for detecting IL-32α and β)

• Standard Curve Preparation: Prepare serial dilutions of recombinant human IL-32 ranging from 2000 to 15 pg/ml

• Sample Addition: Add samples or standards to the coated wells

• Detection: Add biotinylated mAb KU32-52 (0.2-1 μg/ml concentration range)

• Visualization: Probe with streptavidin conjugated to HRP

• Development and Measurement: Add substrate and measure optical density

This optimized sandwich ELISA has a minimal detection limit of 80 pg/ml (mean+3 SD of zero calibrator) and a measuring range up to 3000 pg/ml. Performance metrics show intra-assay coefficients of variation between 6-11% (n=16) and inter-assay coefficients between 5-10% (n=9) .

No cross-reactivity has been observed with other cytokines including IL-1α, IL-1β, IL-2, IL-6, IL-8, IL-10, IL-18, and TNF-α, confirming the specificity of this assay .

How can I optimize IL-32 antibody use for flow cytometry and intracellular staining?

For successful flow cytometry and intracellular staining of IL-32, follow these optimization steps:

• Cell Preparation: For peripheral blood mononuclear cells (PBMCs), stimulation with PMA and calcium ionomycin is effective to induce IL-32 expression

• Fixation: Use appropriate fixation buffer (e.g., Flow Cytometry Fixation Buffer) to preserve cellular morphology

• Permeabilization: Apply permeabilization/wash buffer to allow antibody access to intracellular targets

• Primary Antibody: Use rat anti-human IL-32 monoclonal antibody (such as clone 373821)

• Secondary Detection: Apply APC-conjugated anti-rat IgG secondary antibody

• Controls: Include appropriate isotype controls (e.g., rat IgG2A for clone 373821)

This protocol has been successfully validated for detection of IL-32 in human PBMCs. When visualized by flow cytometry, IL-32-positive cells show clear separation from the isotype control population .

For optimal results, concentration of the primary antibody should be titrated, and stimulation conditions may need to be adjusted depending on the cell type being investigated.

What are the critical considerations for Western blotting detection of IL-32 isoforms?

When performing Western blotting for IL-32 isoforms, consider these critical factors:

• Antibody Selection: Choose isoform-specific antibodies based on your research question. KU32-52 antibody can detect all isoforms, while others like KU32-56 have differential reactivity to specific isoforms

• Sample Preparation: Different IL-32 isoforms have distinct cellular localizations - IL-32α tends to be more cell-associated, requiring appropriate lysis conditions

• Expected Molecular Weight: The predicted molecular weight of IL-32 isoforms is approximately 26 kD, but post-translational modifications may alter migration patterns

• Antibody Compatibility: Note that some clones (e.g., KU32-09) do not work effectively for Western blotting despite being useful for other applications

• Detection Method: For optimal sensitivity, chemiluminescent detection systems are recommended

It's important to validate antibody specificity using recombinant protein controls for each isoform (α, β, γ, and δ) to ensure accurate interpretation of results .

How does IL-32 expression correlate with immune cell infiltration in cancer?

IL-32 expression shows significant correlation with immune cell infiltration in various cancer types, particularly with natural killer (NK) cells in skin cutaneous melanoma (SKCM). Comprehensive analysis reveals:

• NK Cell Correlation: In SKCM, IL-32 expression positively correlates with NK cell markers including:

  • KIR2DL3 (correlation coefficient = 0.621, p = 1.79 × 10^-51)

  • KIR3DL2 (correlation coefficient = 0.699, p = 2.63 × 10^-70)

  • KIR2DL4 (correlation coefficient = 0.737, p = 7.41 × 10^-82)

  • NCR1 (correlation coefficient = 0.599, p = 3.39 × 10^-47)

  • NCR3 (correlation coefficient = 0.827, p = 2.09 × 10^-119)

• Cancer Type Specificity: This correlation is highly cancer-type specific. For example, in liver hepatocellular carcinoma (LIHC), IL-32 expression shows no significant correlation with the same NK cell markers that are strongly correlated in SKCM .

• Activated vs. Resting NK Cells: The relationship between IL-32 expression and different NK cell activation states varies across cancer types, suggesting context-dependent functions of IL-32 in the tumor microenvironment .

These findings suggest IL-32 may have cancer-specific roles in modulating immune responses, particularly NK cell-mediated immunity, making it a potential immunotherapeutic target in certain cancer types.

What is the prognostic value of IL-32 expression in different cancer types?

IL-32 expression has demonstrated significant prognostic value across different cancer types, though with varying implications:

These findings suggest IL-32 may serve as a potential biomarker for cancer prognosis and could potentially be targeted in novel therapeutic approaches.

How can I address cross-reactivity issues when detecting specific IL-32 isoforms?

Addressing cross-reactivity when detecting specific IL-32 isoforms requires careful experimental design:

• Antibody Selection Strategy:

  • For IL-32α-specific detection, use mAb KU32-07 which specifically detects only IL-32α

  • For pan-IL-32 detection (all isoforms), use KU32-52

  • For detection excluding IL-32γ, use KU32-56 which detects α, β, and δ isoforms but not γ

• Validation Testing:

  • Perform direct ELISA using recombinant proteins of each isoform to quantify cross-reactivity

  • For example, when using monoclonal antibody 373821, expect approximately 30% cross-reactivity with IL-32β and 38% cross-reactivity with IL-32γ in direct ELISAs

• Sandwich ELISA Optimization:

  • A sandwich ELISA using KU32-07 (coating) and biotinylated KU32-52 (detection) exhibits high specificity for IL-32α with no cross-reactivity to other IL-32 isoforms or other cytokines

  • Alternatively, KU32-56 (coating) with biotinylated KU32-52 (detection) detects both IL-32α and IL-32β but not γ or δ isoforms

• Western Blot Confirmation:

  • When possible, confirm ELISA results with Western blotting using a combination of antibodies to verify isoform identity based on molecular weight and reactivity patterns

  • Note that different isoforms may have distinct cellular localizations, requiring appropriate extraction methods

By carefully selecting antibody combinations and implementing appropriate validation controls, researchers can achieve high specificity for individual IL-32 isoforms.

How does IL-32 contribute to autoimmune and inflammatory diseases?

IL-32 plays significant roles in autoimmune and inflammatory diseases through several mechanisms:

• Inflammatory Signaling: IL-32 activates NF-κB and p38 MAPK cytokine signal pathways, which are central to inflammatory responses. These pathways induce the production of additional inflammatory mediators, creating a pro-inflammatory feedback loop .

• Rheumatoid Arthritis: IL-32 has been particularly implicated in rheumatoid arthritis pathogenesis. It promotes inflammatory responses in synovial tissues, contributing to joint destruction and disease progression .

• Respiratory Inflammation: Studies have shown IL-32 involvement in respiratory inflammatory conditions, as evidenced by citations to research examining IL-32 in respiratory critical care medicine .

• Cytokine Network Interaction: IL-32 is part of the IL-1 family cytokine network, sharing functional similarities with IL-1 and IL-18. These cytokines collectively contribute to the development and pathogenesis of autoimmune and inflammatory diseases .

• TNF-α Induction: IL-32 was initially identified as a TNF-α-inducible factor (TAIFa,b,c,d), indicating its role in amplifying TNF-α-mediated inflammatory responses, which are central to many autoimmune conditions .

Research approaches targeting IL-32 in inflammatory diseases should consider the specific isoforms involved and their differential effects on disease pathogenesis.

What methodological approaches are recommended for studying IL-32 in cancer research?

For studying IL-32 in cancer research, the following methodological approaches are recommended:

• Expression Analysis:

  • Use immunohistochemistry with KU32-09 antibody, which has shown strong reactivity for this application

  • Employ quantitative PCR to measure IL-32 transcript levels across different cancer types and stages

  • Consider analyzing specific isoform expression patterns, as they may have differential effects on cancer progression

• Correlation with Clinical Parameters:

  • Analyze IL-32 expression in relation to TNM staging, as significant correlations have been observed with T stages in multiple cancers (p = 1.4e-4 for HNSC, p = 2.6e-3 for KIRC, p = 1.2e-3 for PAAD)

  • Evaluate relationship with metastasis status, particularly in KIRC where significant correlation with M stages was observed (p = 1.5e-4)

• Immune Infiltration Analysis:

  • Use computational methods like CIBERSORT to assess tumor immune cell infiltration

  • Apply ESTIMATE method to analyze correlation between IL-32 expression and stromal/immune components

  • Focus on NK cell markers (KIR2DL3, KIR3DL2, KIR2DL4, NCR1, NCR3) when studying IL-32 in skin cutaneous melanoma

• Survival Analysis:

  • Perform Kaplan-Meier analysis stratifying patients by IL-32 expression levels

  • Use multivariate Cox regression to adjust for confounding factors

• Stemness Characterization:

  • Incorporate stemness indices analysis (DMPss, DNAss, ENHss, EREG-METHss) when studying IL-32's role in cancer stemness

  • Transform expression values into log2(x+1) and calculate Spearman correlations with stemness indices

These approaches provide comprehensive characterization of IL-32's role in cancer pathogenesis and its potential as a prognostic biomarker.

How can I resolve inconsistent results when detecting IL-32 in experimental samples?

Inconsistent results when detecting IL-32 can stem from several factors. Here's a systematic approach to troubleshooting:

• Isoform-Specific Detection Issues:

  • Verify which IL-32 isoforms are present in your sample type

  • Ensure your antibody has appropriate specificity for the isoforms present (refer to the specificity table in section 1.3)

  • Consider that KU32-07 detects only IL-32α, KU32-52 binds all isoforms, and KU32-56 detects α, β, and δ but not γ

• Sample Processing Considerations:

  • IL-32α tends to be more cell-associated compared to IL-32β, suggesting differential cellular localization

  • Ensure your extraction method is appropriate for the cellular compartment where your target isoform resides

  • For membrane-associated or intracellular proteins, verify complete cell lysis and protein solubilization

• ELISA Optimization:

  • For sandwich ELISA, verify the combination of capture and detection antibodies is appropriate for your target isoform(s)

  • The minimal detection limit for optimized IL-32 ELISA is approximately 80 pg/ml

  • Expected intra-assay variation is 6-11% and inter-assay variation is 5-10%

  • If results fall outside these parameters, recalibrate your assay

• Flow Cytometry Considerations:

  • Ensure proper stimulation of cells (e.g., PMA and calcium ionomycin for PBMCs)

  • Verify fixation and permeabilization efficiency

  • Include appropriate isotype controls to establish background levels

• Biological Variability:

  • IL-32 expression varies significantly across different tissue types and disease states

  • Expression is increased following activation of T-cells by mitogens or NK cells by IL-2

  • Consider the activation status of your samples when interpreting results

By systematically addressing these potential issues, researchers can improve the consistency and reliability of IL-32 detection in experimental samples.