LILRA3 Antibody

What is LILRA3 and why is it significant in immunological research?

LILRA3 (Leukocyte Immunoglobulin-Like Receptor A3, also known as CD85e, ILT-6, or LIR-4) is a unique member of the LILR family that lacks a transmembrane domain and is actively secreted, unlike other family members. Its significance stems from its:

• Distinctive 6.7-kb deletion polymorphism that varies widely across populations

• Binding activity to both classical HLA-A0201 and non-classical HLA-G1 molecules, though with reduced affinity compared to LILRB1/HLA-A0201 and LILRB2/HLA-G1

• Association with multiple autoimmune diseases including Adult-onset Still's disease (AOSD) and Primary Sjögren's syndrome

• Potential role as an antagonist to inhibitory receptors like LIR-B1/LIR-B2 through shared ligands

LILRA3 has emerged as an important research target due to its immunomodulatory functions and genetic association with autoimmune conditions.

How can I differentiate between functional and non-functional LILRA3 in experimental samples?

Differentiating between functional and non-functional LILRA3 requires combined genetic and protein analysis:

Genetic Analysis Approach:

• PCR detection of the 6.7-kb deletion polymorphism to identify three genetic variations: homozygous non-deleted (functional, +/+), heterozygous (+/−), and homozygous deleted (−/−)

• Primers should be designed to flank the deletion region

Protein Analysis Considerations:

• Standard ELISA kits detect both functional and non-functional forms of LILRA3

• Functional assessment requires biological activity testing such as neutrophil activation assays

• In studies with AOSD patients, higher plasma levels of LIR-A3 were detected in deletion carriers (LILRA3 +/− and LILRA3 −/−) compared to non-deletion carriers (LILRA3 +/+), but the protein was likely non-functional

A comprehensive approach using both genetic typing and functional assays is recommended for accurate differentiation.

What are the typical expression patterns of LILRA3 in human immune cells?

LILRA3 shows specific expression patterns in human immune cells that can be detected through various techniques:

• Predominantly expressed in monocytes, as demonstrated by flow cytometry using anti-CD14 and anti-LILRA3 antibodies

• Requires intracellular staining with proper fixation and permeabilization protocols

• Expression can be upregulated in inflammatory conditions and certain autoimmune diseases

Flow cytometry data from commercial antibodies confirms detection in human peripheral blood monocytes using specific staining protocols with fixation and permeabilization steps .

What protocols yield optimal results for LILRA3 detection by flow cytometry?

For optimal LILRA3 detection by flow cytometry, the following methodology has been validated:

Validated Staining Protocol:

• Isolate peripheral blood monocytes using standard density gradient separation

• Co-stain with lineage markers (e.g., Mouse Anti-Human CD14 APC-conjugated Monoclonal Antibody)

• Apply anti-LILRA3 antibody (e.g., Goat Anti-Human LILRA3/CD85e Antibody)

• Follow with appropriate secondary antibody (e.g., Phycoerythrin-conjugated Anti-Goat IgG)

• For intracellular staining:

  • Fix cells with Flow Cytometry Fixation Buffer

  • Permeabilize with Flow Cytometry Permeabilization/Wash Buffer I

Control Requirements:

• Include appropriate isotype controls (e.g., Normal Goat IgG Control)

• Include both positive and negative cell populations to validate specificity

This validated approach ensures specific detection of LILRA3 in human monocytes while minimizing background staining.

How should researchers quantify LILRA3 protein levels in clinical samples?

Quantification of LILRA3 protein in clinical samples requires careful consideration of:

ELISA Protocol Considerations:

• Commercial ELISA kits have been validated for detection in serum/plasma samples

• These assays detect both functional and non-functional forms of LILRA3

• Typical LILRA3 levels in healthy controls: 0.38 ± 0.34 ng/mL

• Levels in disease states like SS with lymphoma: 1.27 ± 1.34 ng/mL

Sample Processing Guidelines:

• Ensure proper sample collection and storage (-80°C recommended)

• Include appropriate standards and controls

• Consider the impact of LILRA3 genotype on protein levels

• Factor in that LILRA3 levels may fluctuate with disease activity

When interpreting results, researchers should note that LILRA3 protein levels may not directly correlate with functional activity, especially in patients carrying deletion variants .

How can researchers investigate LILRA3's role in neutrophil activation and NET formation?

To investigate LILRA3's role in neutrophil activation and NET formation, the following experimental approach has been validated:

Experimental Design:

• Isolate neutrophils from patients and healthy controls

• Culture neutrophils in 96-well plates for 3.5 hours with:

  • Recombinant LILRA3 (optimal concentration: 500 ng/ml)

  • Positive control: 20 nM phorbol myristate acetate (PMA)

  • Negative control: unstimulated cells

• Detect NET formation using multiple complementary techniques:

  • Quant-iT PicoGreen dsDNA assay kit to quantify extracellular DNA

  • Myeloperoxidase (MPO) activity assessment

  • Fluorescence microscopy visualization

Mechanistic Investigation:

• Use inhibitors (e.g., 25 nM diphenyleneiodonium chloride) to assess ROS dependency

• Compare NET formation induced by sera from patients with different LILRA3 genotypes

• Correlate findings with disease activity measures and inflammatory markers

This multi-parametric approach allows comprehensive assessment of LILRA3's influence on neutrophil function in the context of inflammatory conditions.

What approaches help resolve contradictory data between LILRA3 genotype and protein levels?

Researchers often encounter apparent contradictions between LILRA3 genotype and detected protein levels. To resolve these:

Comprehensive Analysis Framework:

• Determine LILRA3 genotype status using PCR detection of the 6.7-kb deletion

• Measure LILRA3 protein levels using ELISA

• Assess protein functionality through bioactivity assays

• Consider ethnic background variations:

  • The 6.7-kb deletion frequency varies significantly across populations

  • In Han Chinese populations, deletion frequencies decrease from Northeast (80.6%) to South (47.4%)

Resolving Apparent Contradictions:

• Higher protein levels in deletion carriers may reflect detection of non-functional protein by ELISA

• In AOSD patients, despite higher detected LILRA3 levels in deletion carriers, inflammation markers were lower compared to non-deletion carriers

• Functional testing is essential to distinguish between active and inactive forms

This integrated approach helps reconcile discrepancies between genotype and protein detection data.

How do geographical variations in LILRA3 deletion frequency impact experimental design and data interpretation?

The striking geographical variation in LILRA3 deletion frequency has profound implications for research:

Geographic Distribution Data:

• Han Chinese populations show a gradient in deletion frequency from Northeast (80.6%) to South (47.4%)

• These differences are highly differentiated within what might be considered a single ethnic group

• Similar patterns may exist in other populations globally

Experimental Design Considerations:

• Stratify study cohorts by geographical origin

• Include appropriate ethnicity-matched controls

• Consider the potential confounding effect of population stratification

• When conducting association studies:

  • Analyze data both in the whole dataset and in geographic subsets

  • Apply statistical corrections for population stratification

  • Be cautious about pooling data from different regions without proper controls

Researchers should recognize that failure to account for these geographical variations may lead to spurious associations or mask true biological effects in disease association studies.

What methodological approaches are most effective for studying LILRA3's role in autoimmune diseases?

For investigating LILRA3's role in autoimmune conditions, researchers should employ a multifaceted approach:

Recommended Methodology:

• Genetic Association Studies:

  • Genotype the LILRA3 deletion polymorphism in case-control cohorts

  • Consider both allelic and genotypic models of inheritance

  • Stratify by clinical subphenotypes and disease severity

• Functional Correlation Studies:

  • Measure LILRA3 protein levels in different disease activity states

  • Correlate with clinical parameters and inflammatory markers

  • For example, in AOSD, LILRA3 levels correlated with disease activity markers including leukocyte counts, neutrophil counts, ESR, and CRP levels

• Mechanistic Investigations:

  • Assess LILRA3's effect on inflammatory pathways

  • Evaluate correlation with pro-inflammatory cytokines (IL-1β, IL-6, TNF, IL-18)

  • Study neutrophil activation and NET formation as potential disease mechanisms

This comprehensive approach has successfully identified LILRA3's role in conditions like AOSD and Primary Sjögren's syndrome with lymphoma development .

How can researchers distinguish between LILRA3 and other structurally similar LILR family members?

Distinguishing LILRA3 from other LILR family members requires careful experimental design:

Molecular Differentiation Strategies:

• Antibody Selection:

  • Choose antibodies with minimal cross-reactivity

  • Commercial antibodies show approximately 30% cross-reactivity with rhILT2, rhILT4, ILT5, and <5% with rhLIR5 and rhLIR7

  • Validate specificity through appropriate controls

• Expression Pattern Analysis:

  • LILRA3 lacks a transmembrane domain and is secreted, unlike other family members

  • Focus on detection in serum/plasma for LILRA3

  • Compare with membrane-bound LILR family members through cell surface staining

• Functional Discrimination:

  • LILRA3 binds HLA-A*0201 and HLA-G1 with reduced affinity compared to LILRB1/LILRB2

  • Design competitive binding assays to differentiate affinity profiles

  • Assess differential effects on downstream signaling pathways

These approaches help overcome the challenge of high sequence homology between LILR family members, particularly in the extracellular domains.

What are the key considerations when designing experiments to assess LILRA3's binding to HLA molecules?

When investigating LILRA3's interactions with HLA molecules, researchers should consider:

Experimental Design Framework:

• Binding Assay Selection:

  • Surface Plasmon Resonance (SPR) using BIAcore® has successfully demonstrated LILRA3 binding to HLA-A*0201 and HLA-G1

  • Consider that previous cytolysis or immuno-staining approaches failed to detect significant binding

• Comparative Analysis:

  • Include LILRB1 and LILRB2 as reference points

  • LILRA3 shows reduced binding affinity compared to LILRB1/HLA-A*0201 and LILRB2/HLA-G1

  • Assess binding to both classical and non-classical HLA class I molecules

• Structural Considerations:

  • The D1 domain of LILRA3 has critical importance for HLA binding

  • Consider the impact of the hydrophobic core composed of C and E strands

  • Evaluate the impact of genetic polymorphisms on binding properties

• Functional Consequences:

  • Assess whether LILRA3 acts as an antagonist to inhibitory receptors

  • Evaluate competition with LILRB1/LILRB2 for shared ligands

  • Determine downstream signaling effects following binding

This approach provides comprehensive characterization of LILRA3-HLA interactions and their functional significance.

How should researchers address inconsistent LILRA3 staining patterns in flow cytometry?

When encountering variable LILRA3 staining by flow cytometry, consider these troubleshooting approaches:

Problem-Solving Framework:

• Cell Preparation Factors:

  • Ensure optimal fixation: Use Flow Cytometry Fixation Buffer as validated in published protocols

  • Proper permeabilization: LILRA3 requires intracellular staining with Flow Cytometry Permeabilization/Wash Buffer I

  • Cell viability: Include a viability dye to exclude dead cells that may cause non-specific staining

• Antibody Optimization:

  • Titrate antibody concentration: Optimal concentration range reported is 5-20 μg/mL for immunostaining

  • Validate secondary antibody detection systems

  • Include fluorescence-minus-one (FMO) controls

• Population Considerations:

  • LILRA3 expression varies among cell types (predominantly in monocytes)

  • Co-stain with lineage markers such as CD14 for monocytes

  • Consider activation state of cells (expression may be upregulated in inflammatory conditions)

A systematic approach to these variables will help resolve inconsistent staining patterns and ensure reliable results.

What are the critical variables affecting ELISA-based quantification of LILRA3?

For optimal ELISA-based quantification of LILRA3, researchers should control for these critical variables:

Key Optimization Parameters:

• Sample Processing:

  • Consistent sample collection procedures (serum vs. plasma considerations)

  • Standardized freezing/thawing cycles (minimize multiple freeze-thaw)

  • Centrifugation protocol to remove cellular debris

• Assay Conditions:

  • Antibody coating concentration: 0.05-2 μg/mL reported as effective range

  • Blocking buffer optimization to reduce background

  • Incubation time and temperature standardization

• Interpretation Considerations:

  • ELISA detects both functional and non-functional forms of LILRA3

  • Reference ranges vary by population and disease state:

    • Healthy controls: 0.38 ± 0.34 ng/mL

    • Disease states (e.g., SS with lymphoma): 1.27 ± 1.34 ng/mL

  • Consider genetic background impact on baseline levels

Careful attention to these variables ensures reliable quantification and meaningful comparison between experimental groups.

Research Application Table