KIR2DL3 Antibody

What is KIR2DL3 and what specific HLA-C alleles does it interact with?

KIR2DL3 is an inhibitory killer cell immunoglobulin-like receptor expressed on natural killer (NK) cells and a subset of T cells. It specifically interacts with HLA-C alleles belonging to the C1 group, including HLA-Cw1, HLA-Cw3, and HLA-Cw7. Upon receptor-ligand interaction, KIR2DL3 inhibits NK cell activity, thereby preventing target cell lysis . This interaction is crucial for NK cell education and self-tolerance mechanisms. For effective experimental design, researchers should select target cells expressing appropriate HLA-C alleles when studying KIR2DL3 function.

How can I distinguish between KIR2DL3, KIR2DL2, and KIR2DS2 in flow cytometry experiments?

Distinguishing between these highly homologous receptors presents a significant challenge due to their sequence similarity. Consider these methodological approaches:

• Antibody selection: The D8L3D Rabbit mAb recognizes endogenous levels of KIR2DL3 but weakly cross-reacts with KIR2DL2 . The Mouse Anti-Human KIR2DL3/CD158b2 Monoclonal Antibody (clone 180701) stains cells transfected with human KIR2DL3 and does not stain cells transfected with KIR2DL1 .

• Control selection: Include BaF3 cells transfected with KIR2DL3 as positive controls and BaF3 cells transfected with KIR2DL1 as negative controls .

• Novel antibody combinations: Recent research has identified antibody combinations that can discriminate KIR2DS2 from both KIR2DL2 and KIR2DL3, allowing identification of NK cells with relatively high KIR2DS2 expression .

• Boolean gating strategy: Implement a gating strategy that identifies NK cell populations expressing only one of the KIR2DL1, KIR2DL2/S2, KIR2DL3, 3DL1, or NKG2A receptors for more precise functional analysis .

What are optimal storage conditions for KIR2DL3 antibodies to maintain functionality?

For optimal antibody performance, follow these storage protocols:

• Long-term storage: Store at -20°C to -70°C under sterile conditions for up to 12 months from the date of receipt .

• Medium-term storage: After reconstitution, antibodies can be stored at 2-8°C under sterile conditions for up to 1 month .

• Extended storage after reconstitution: For 6 months, store at -20°C to -70°C under sterile conditions .

• Avoid repeated freeze-thaw cycles: Use a manual defrost freezer for storage .

These conditions ensure antibody stability and consistent performance across experiments, which is crucial for reproducible results in KIR2DL3 research.

How does the amino acid at position 35 in KIR2DL3 affect its functionality, and how should this inform experimental design?

The amino acid at position 35 in KIR2DL3 significantly impacts receptor functionality:

• Functional difference: KIR2DL2/L3 variants with glutamic acid at position 35 (E35) are functionally stronger than those with glutamine (Q35) .

• Molecular basis: Glutamic acid (negatively charged) interacts with positively charged histidine at position 55, stabilizing the KIR2DL2/L3 dimer structure and reducing entropy loss when binding to HLA-C ligands .

• Functional consequence: NK cells from HLA-C1 positive donors with KIR2DL2/L3-E35 demonstrate enhanced killing of target cells lacking their ligands compared to NK cells with Q35 alleles, indicating better licensing (education) of NK cells with E35 alleles .

When designing experiments involving KIR2DL3, researchers should:

• Genotype their donors for the amino acid at position 35

• Consider this variation when interpreting functional differences between donor samples

• Include this parameter when analyzing clinical associations or transplantation outcomes

• Control for this variable in comparative studies of NK cell education and function

What methodological approaches should be used to evaluate NK cell education through KIR2DL3?

To effectively assess NK cell education (licensing) through KIR2DL3, implement these methodological approaches:

• Donor selection: Include donors with educating (KIR2DL3+/HLA-C1+) and non-educating (KIR2DL3+/HLA-C1-) combinations .

• Cell isolation techniques:

  • Isolate single-positive (SP) KIR2DL3+ NK cells

  • Use Boolean gating strategies to identify NK cells expressing only KIR2DL3 and no other educating receptors

• Functional assays:

  • ADCC-GranToxiLux (ADCC-GTL) assays to measure cytotoxic potential

  • Antibody-dependent NK cell activation (ADNKA) assays to assess responses by measuring:

    • Cytokine production (IFN-γ, CCL4)

    • Degranulation (CD107a expression)

• Data interpretation: Research indicates that education through KIR2DL3 may differ from education through other KIRs. While education through KIR3DL1 and KIR2DL1 conferred superior responsiveness in ADNKA assays, education through KIR2DL3 did not show the same enhancement .

How should researchers address cross-reactivity issues when using KIR2DL3 antibodies?

Cross-reactivity presents a significant challenge in KIR2DL3 research. Implement these methodological solutions:

• Comprehensive validation protocol:

  • Test antibodies on cells transfected with individual KIR proteins

  • Verify specificity using KIR knockout or silenced cell lines

  • Validate with multiple detection methods (flow cytometry, Western blot)

• Control selection:

  • Include BaF3 cells transfected with KIR2DL3 as positive controls

  • Use BaF3 cells transfected with related KIRs (KIR2DL1, KIR2DL2) as specificity controls

  • Include isotype control antibodies to assess non-specific binding

• Antibody combinations approach:

  • Use multiple antibodies with different epitope specificities

  • Develop discriminatory panels based on differential staining patterns

  • Implement the novel antibody combination approach described for distinguishing KIR2DS2 from KIR2DL2 and KIR2DL3

• Data analysis recommendations:

  • Apply compensation controls for spectral overlap

  • Use appropriate gating strategies

  • Consider known cross-reactivity (e.g., D8L3D Rabbit mAb weakly cross-reacts with KIR2DL2)

  • Validate findings with genetic analysis when possible

How does KIR2DL3 polymorphism influence disease susceptibility and transplantation outcomes?

KIR2DL3 polymorphism has significant implications for disease susceptibility and transplantation:

• Disease associations:

  • KIR2DL3 gene presence: Found in 87.8% of controls but only in 75.5% of multiple myeloma (MM) patients and 68.3% of MM patients with ISS > I

  • Complete absence of conventional iKIR2D/HLA-C licensing interactions, including single-KIR2DL3+/C1+ interactions, was over-represented in myeloma patients

• Survival impact:

  • KIR2DL1-L2+L3- genotype had dramatic negative impact on 3-year progression-free survival in multiple myeloma patients (20% vs. 83%, p<0.00001), particularly in those with low tumor burden

• Transplantation considerations:

  • KIR2DL3 polymorphism is an important factor for donor selection in allogeneic hematopoietic stem cell transplantation

  • Allelic variations (E35 vs. Q35) affect NK cell education and potentially transplant outcomes

Researchers investigating disease associations or transplantation outcomes should:

• Perform comprehensive KIR genotyping including allelic variants

• Consider HLA-C genotype interactions

• Analyze KIR2DL3 in the context of the entire KIR haplotype

• Assess the functional implications of specific allelic variants

What methodology should be used to analyze KIR2DL3+ NK cell responses against tumor cells with altered HLA expression?

To analyze KIR2DL3+ NK cell responses against tumor cells with altered HLA expression:

• Target cell preparation:

  • Characterize HLA-C expression on tumor cells (flow cytometry, qPCR)

  • Compare tumor cells to matched normal cells

  • Create cell lines with specific HLA-C expression patterns using CRISPR-Cas9 gene editing

• Experimental setup:

  • Isolate KIR2DL3+ NK cells from donors with known KIR/HLA genotypes

  • Use purified NK cell populations expressing only KIR2DL3 (single-positive)

  • Compare responses against HLA-C1+, HLA-C1-, and HLA-altered target cells

• Readout parameters:

  • Cytotoxicity assays (51Cr-release, flow cytometry-based killing assays)

  • ADCC-GranToxiLux to measure granzyme B delivery

  • Cytokine production (IFN-γ, TNF-α)

  • CD107a degranulation assay

• Data interpretation considerations:

  • Myeloma-PCs show substantial over-expression of HLA-I ("increasing-self" instead of missing-self), including HLA-C

  • Tumor cells may have mild expression of ligands for NK cell activating receptors (CD112, CD155, ULBP-1, MICA/B)

  • Patients with no conventional iKIR2D/HLA-C licensing interactions (KIR2DL1-L2+L3-/C2C2) showed reduced ability to lyse myeloma-PCs despite maintaining ability to lyse K562 cells

What structural features distinguish KIR2DL3 from related KIR receptors, and how can this knowledge guide antibody selection?

Understanding the structural distinctions between KIR2DL3 and related receptors is crucial for antibody selection:

• Extracellular domains:

  • KIR2DL3 belongs to the KIR2D family with two extracellular Ig-like domains (D1 and D2)

  • KIR2DL2 appears to be an evolutionary fusion gene formed by unequal crossing between the extracellular domains of KIR2DL3 and the intracellular tail of KIR2DL1

  • Specific epitopes in these domains can be targeted for differential antibody binding

• Key distinguishing residues:

  • Position 35: Glutamic acid (E35) vs. glutamine (Q35) affects receptor strength

  • Other polymorphic residues in the extracellular domains contribute to structural differences

• Antibody selection guidance:

  • Target epitopes unique to KIR2DL3

  • Consider clones validated for specificity in transfected cell systems

  • Verify non-cross-reactivity with KIR2DL2 and KIR2DS2

  • Mouse Anti-Human KIR2DL3/CD158b2 Monoclonal Antibody (clone 180701) stains cells transfected with KIR2DL3 but not KIR2DL1

  • D8L3D Rabbit mAb exhibits some cross-reactivity with KIR2DL2

• Application-specific considerations:

  • Flow cytometry may require different antibody characteristics than Western blot

  • Consider whether native conformation recognition is important

  • Determine if fixation affects epitope recognition

How do I optimize KIR2DL3 antibody concentrations for different experimental applications?

Methodical optimization of KIR2DL3 antibody concentrations is essential for experimental success:

• Flow cytometry optimization:

  • Starting dilution: 1:100 for flow cytometry of live cells

  • Titration range: Test 2-fold serial dilutions (1:50 to 1:400)

  • Positive controls: Use cell lines with known KIR2DL3 expression

  • Negative controls: Include isotype controls and KIR2DL3-negative cells

  • Evaluation metrics: Signal-to-noise ratio, stain index, separation between positive and negative populations

• Protocol optimization considerations:

  • Incubation time and temperature

  • Buffer composition

  • Secondary antibody concentration (if applicable)

  • Sample preparation (fresh vs. frozen cells)

  • Cell concentration

• Application-specific recommendations:

  • Western blotting may require higher antibody concentrations than flow cytometry

  • Immunohistochemistry typically requires optimization for fixation methods

  • Immunoprecipitation protocols need validation for specific lysis conditions

• Validation approaches:

  • Compare results with different antibody clones

  • Verify specificity with genotyped samples

  • Assess reproducibility across different donors or cell preparations

Why might KIR2DL3 staining show inconsistent results between donors, and how can I address this?

Inconsistent KIR2DL3 staining between donors can stem from multiple factors:

• Genetic and expression variability:

  • KIR gene content varies between individuals (only ~5% of individuals have all 15 KIR genes)

  • KIR2DL3 gene presence varies (87.8% in controls vs. 75.5% in MM patients)

  • Allelic variations (E35 vs. Q35) may affect antibody binding

  • Expression levels differ based on education status and activation state

• Methodological solutions:

  • Genotype donors for KIR2DL3 presence and allelic variants

  • Use multiple antibody clones targeting different epitopes

  • Include internal controls (other stable markers) for staining quality

  • Standardize protocols including fixation methods, incubation times, and temperatures

  • Ensure consistent sample preparation (fresh vs. frozen cells)

• Data analysis approaches:

  • Normalize to internal controls

  • Use appropriate gating strategies accounting for autofluorescence

  • Consider education status in data interpretation

  • Analyze KIR2DL3 expression in context of HLA genotype

• Validation recommendations:

  • Confirm unusual results with alternate detection methods

  • Test antibody on transfected cell lines with controlled expression

  • Include positive controls from previously successful experiments

How can I validate the specificity of KIR2DL3 antibody-mediated functional effects in NK cell assays?

To validate that observed functional effects are specifically mediated through KIR2DL3:

• Control antibodies panel:

  • Isotype-matched control antibodies

  • Non-blocking KIR2DL3 antibodies (different epitope)

  • Blocking antibodies against other inhibitory receptors

  • F(ab')2 fragments to eliminate Fc receptor effects

• Target cell panel:

  • HLA-C1+ targets (KIR2DL3 ligand-positive)

  • HLA-C1- targets (KIR2DL3 ligand-negative)

  • HLA-C1+ targets with specific peptides that modulate KIR2DL3 binding

  • Genetically modified targets with controlled HLA-C expression

• NK cell source considerations:

  • Compare NK cells from KIR2DL3+ and KIR2DL3- donors

  • Use NK cells from donors with known education status (KIR2DL3+/HLA-C1+ vs. KIR2DL3+/HLA-C1-)

  • Isolate single-positive KIR2DL3+ NK cells using flow sorting

  • Consider using NK92 cells transfected with KIR2DL3

• Functional validation requirements:

  • Demonstrate HLA-C1 dependency of observed effects

  • Show antibody dose-dependency

  • Confirm results using genetic approaches (siRNA knockdown, CRISPR knockout)

  • Verify pathway-specific signaling events downstream of KIR2DL3

How can single-cell analysis technologies advance our understanding of KIR2DL3 function in NK cell subpopulations?

Single-cell technologies offer powerful approaches to advance KIR2DL3 research:

• Single-cell RNA sequencing applications:

  • Identify transcriptional signatures associated with KIR2DL3 expression

  • Discover novel markers co-expressed with KIR2DL3

  • Map developmental trajectories of KIR2DL3+ NK cells

  • Characterize education-associated gene expression patterns

• Mass cytometry (CyTOF) approaches:

  • Create comprehensive panels including multiple KIRs and functional markers

  • Identify rare NK cell subpopulations with unique KIR2DL3 expression patterns

  • Analyze over 40 parameters simultaneously to contextualize KIR2DL3 expression

  • Perform high-dimensional clustering to discover novel NK cell subsets

• Imaging mass cytometry applications:

  • Visualize KIR2DL3+ NK cells in tissue contexts

  • Study spatial relationships between KIR2DL3+ NK cells and other immune cells

  • Analyze KIR2DL3 distribution at immune synapses

• Spectral flow cytometry benefits:

  • Improved resolution of closely related markers

  • Better separation of KIR2DL3 from other KIRs

  • Enhanced ability to identify NK cells expressing only KIR2DL3

These technologies enable researchers to move beyond bulk analysis to understand heterogeneity within KIR2DL3+ NK cell populations, revealing functional subsets with distinct roles in immunity and disease.

What are the latest approaches for studying the impact of peptide presentation on KIR2DL3-HLA-C interactions?

Advanced methodologies for studying peptide-dependent KIR2DL3-HLA-C interactions include:

• Peptide library screening strategies:

  • Positional scanning libraries to identify key anchor residues

  • Disease-relevant peptide pools (viral, tumor-associated)

  • Alanine scanning mutagenesis to map critical positions

  • Fluorescently labeled peptide-MHC complexes for direct binding assays

• Structural biology approaches:

  • X-ray crystallography of KIR2DL3-peptide-HLA-C complexes

  • Cryo-electron microscopy for larger molecular assemblies

  • Hydrogen-deuterium exchange mass spectrometry to map interaction sites

  • NMR spectroscopy to study binding dynamics

• Cellular assay innovations:

  • Reporter cell lines expressing KIR2DL3 and appropriate signaling components

  • FRET-based assays for real-time interaction monitoring

  • Single-molecule imaging of receptor-ligand interactions

  • Micropatterning approaches to control spatial distribution of receptors

• Computational methodologies:

  • Molecular dynamics simulations of peptide-dependent interactions

  • Machine learning algorithms to predict peptide impact on binding

  • Modeling of allosteric effects of peptide binding on HLA-C structure