KLRD1 Antibody

What is KLRD1 and why is it significant in immunological research?

KLRD1 (Killer Cell Lectin-Like Receptor Subfamily D, Member 1), also known as CD94, is a 70 kDa type II transmembrane glycoprotein expressed predominantly on natural killer (NK) cells, NK-T cells, and subsets of CD8+ T cells and gamma/delta T cells . Its significance in immunological research stems from its critical role in antitumor immunity and immune regulation. The KLRD1-NKG2 complex plays a dual role in immune function - enhancing NK cell activation for tumor cell clearance while simultaneously preventing autoimmunity by suppressing T-cell immunity against self-tissues . This balanced regulation makes KLRD1 a valuable target for understanding immune responses in cancer and other diseases.

What cell types express KLRD1 and how can this expression be accurately measured?

KLRD1 is primarily expressed on NK cells, with significant expression also found in NK-T cells, subsets of CD8+ T cells, and gamma/delta T cells . Single-cell RNA sequencing has confirmed predominant expression in NK cells and exhausted CD8+ T cells (CD8Tex) across multiple cancer types including bladder cancer, head and neck squamous cell carcinoma (HNSC), and non-small cell lung cancer .

For accurate measurement of KLRD1 expression:

• Flow cytometry using specific monoclonal antibodies (such as clone HP-3D9) is the gold standard for cellular analysis

• RNA-seq and single-cell RNA sequencing provide transcriptomic-level expression data

• Immunohistochemistry can be used for tissue sections

• Western blotting can quantify total protein levels

When measuring KLRD1 expression, it's essential to use appropriate controls and consider the dynamic nature of expression that may change with cellular activation status.

What are the key considerations when selecting a KLRD1 antibody for research applications?

When selecting a KLRD1 antibody for research applications, consider:

• Antibody format: Different conjugates (e.g., PE-conjugated) are available for specific applications like flow cytometry

• Clone specificity: Clones like HP-3D9 recognize specific extracellular epitopes of CD94/KLRD1

• Host species: Mouse monoclonal antibodies are common, but consider compatibility with your experimental system

• Application validation: Ensure the antibody is validated for your specific application (FACS, WB, IHC, etc.)

• Epitope recognition: Some antibodies target specific amino acid regions (e.g., AA 32-179 or AA 42-168)

• Purification method: Consider how the antibody was purified (e.g., size-exclusion chromatography)

• Cross-reactivity: Verify species reactivity (human vs. mouse models)

The optimal antibody choice should align with your experimental design, target cells, and analytical methods.

How should flow cytometry panels be designed to effectively study KLRD1 in conjunction with other NK cell markers?

When designing flow cytometry panels for KLRD1/CD94 analysis:

• Core NK cell markers: Include CD56 and CD16 for NK cell identification

• KLRD1 dimerization partners: Include NKG2A/C/E markers as KLRD1 forms heterodimers with these molecules

• Activation markers: CD69, CD25, and NKp46 help assess NK cell activation status

• Additional receptors: KIRs, NKG2D, and NKp30 provide context for receptor balance

• Functional markers: Include markers for degranulation (CD107a) and cytokine production (IFN-γ, TNF-α)

• Viability dye: Critical to exclude dead cells that can bind antibodies non-specifically

• Fluorochrome selection: Choose bright fluorochromes (PE, APC) for KLRD1 if expression is expected to be low or variable

For optimal results, use PE-conjugated anti-KLRD1 antibodies such as clone HP-3D9, which recognizes an extracellular epitope of CD94 . Ensure appropriate compensation controls and FMO (fluorescence minus one) controls are included to accurately identify positive populations.

What are the recommended tissue preparation methods for analyzing KLRD1 expression in tumor microenvironments?

For optimal analysis of KLRD1 expression in tumor microenvironments:

• Fresh tissue handling:

  • Process tissues within 1-2 hours of collection

  • Use enzymatic digestion (collagenase IV, DNase I) at optimized concentrations to preserve epitopes

  • Filter cell suspensions to remove debris while retaining immune cells

• FFPE tissue preparation:

  • Use EDTA or citrate buffer for antigen retrieval

  • Optimize retrieval pH (typically 6.0-9.0) for KLRD1 epitopes

  • Block endogenous peroxidases and biotin before antibody application

• Single-cell preparation for RNA-seq:

  • Employ gentle dissociation methods to maintain cell viability

  • Enrich NK and T cell populations when necessary using negative selection

  • Consider fixation methods that preserve RNA quality while allowing protein detection

• Cryopreservation considerations:

  • Use controlled-rate freezing to preserve NK cell phenotypes

  • Include cryoprotectants that don't affect subsequent antibody binding

  • Validate marker expression stability after freeze-thaw cycles

Research has shown that KLRD1 expression has significant prognostic value across various cancers, particularly as an independent prognostic factor in head and neck squamous cell carcinoma . Therefore, proper tissue preparation is critical for accurately assessing its role in the tumor microenvironment.

How can researchers effectively validate KLRD1 antibody specificity for their experimental systems?

Validating KLRD1 antibody specificity requires a multi-faceted approach:

• Positive and negative control samples:

  • Use purified NK cells as positive controls

  • Use cell lines known to lack KLRD1 expression as negative controls

  • Include isotype controls matched to the primary antibody

• Genetic validation approaches:

  • siRNA or CRISPR knockout of KLRD1 should diminish or eliminate antibody binding

  • Overexpression systems can confirm specificity in otherwise negative cells

• Epitope blocking experiments:

  • Pre-incubation with unconjugated antibody should block binding of conjugated versions

  • Competitive binding assays with known KLRD1 ligands can confirm target validity

• Cross-technique validation:

  • Compare flow cytometry results with Western blot, immunohistochemistry, and RT-PCR

  • Results should be concordant across techniques, with consideration for post-translational modifications

• Epitope mapping considerations:

  • Different antibodies target various regions (AA 32-179, AA 42-168, etc.)

  • Consider whether your experiment requires specific epitope recognition

Remember that KLRD1 forms heterodimers with NKG2 family members, which might affect epitope accessibility in certain contexts.

How does KLRD1 expression correlate with immune infiltration in different cancer types?

KLRD1 expression shows significant correlation with immune infiltration across multiple cancer types, with particularly strong associations in head and neck squamous cell carcinoma (HNSC). Analysis reveals:

These findings highlight KLRD1's potential as a biomarker for characterizing the immune microenvironment in tumors and may inform patient stratification for immunotherapy approaches.

What methodologies are most effective for studying the KLRD1-NKG2 receptor complex formation and signaling?

Studying the KLRD1-NKG2 receptor complex requires specialized techniques:

• Co-immunoprecipitation (Co-IP) approaches:

  • Use anti-KLRD1 antibodies to pull down the complex, followed by Western blotting for NKG2 family members

  • Crosslinking prior to lysis can preserve transient interactions

  • Native PAGE conditions help maintain complex integrity

• Proximity ligation assays (PLA):

  • Allow visualization of protein-protein interactions in situ

  • Can detect KLRD1-NKG2 dimers within the native cellular environment

  • Provide spatial information about complex formation on the cell surface

• FRET/BRET techniques:

  • Förster/Bioluminescence Resonance Energy Transfer detects molecular proximity

  • Requires fluorescent/luminescent tagging of KLRD1 and NKG2 proteins

  • Can measure dynamic changes in complex formation upon stimulation

• Single-molecule imaging:

  • Super-resolution microscopy tracks individual receptor complexes

  • Reveals clustering behavior and membrane organization

  • Can be combined with functional readouts

• Signaling pathway analysis:

  • Phospho-flow cytometry detects downstream signaling events

  • Western blotting for phosphorylated DAP12/DAP10 adaptors

  • Calcium flux assays measure immediate signaling responses

• Functional readouts:

  • Cytotoxicity assays correlate complex formation with NK cell killing capacity

  • Cytokine production measurements assess functional outcomes

Research has shown that the KLRD1-NKG2 complex can enhance NK cell activation and tumor cell clearance while also preventing T-cell attack on healthy tissues . Understanding this dual functionality requires sophisticated methods to correlate complex formation with functional outcomes.

How can single-cell analysis techniques be optimized to study KLRD1 expression heterogeneity in the tumor microenvironment?

Optimizing single-cell analysis for KLRD1 expression heterogeneity requires:

• Single-cell RNA sequencing approaches:

  • Use protocols optimized for immune cells with high viability preservation

  • Consider targeted sequencing approaches to increase depth for KLRD1 and related genes

  • Integrate with protein-level detection through CITE-seq or similar approaches

  • Research has confirmed KLRD1 is predominantly expressed in NK cells and CD8Tex cells across multiple cancer types, with validation in datasets like LIHC_GSE140228, HNSC_GSE139324, and NSCLC_GSE127465

• Flow cytometry and mass cytometry optimizations:

  • Design panels that include KLRD1 along with lineage markers, exhaustion markers, and activation markers

  • Include markers for tissue residency and tumor reactivity

  • Use dimensionality reduction techniques (tSNE, UMAP) for visualizing heterogeneous populations

• Spatial transcriptomics and imaging approaches:

  • Integrate KLRD1 RNA detection with protein visualization

  • Map KLRD1+ cells in relation to tumor cells and other immune populations

  • Quantify spatial relationships between KLRD1+ cells and treatment response markers

• Analytical considerations:

  • Employ clustering algorithms that can detect rare populations

  • Use trajectory analysis to identify developmental relationships

  • Integrate multi-omics data to correlate KLRD1 expression with functional states

• Validation strategies:

  • Confirm key findings with orthogonal methods (flow cytometry, immunohistochemistry)

  • Use functional assays to correlate expression patterns with NK cell activity

  • Compare findings across multiple patient samples and cancer types

These approaches enable detailed characterization of KLRD1 expression patterns and their relationship to immune cell function in the complex tumor microenvironment.

What is the prognostic value of KLRD1 expression across different cancer types, and how should researchers interpret this data?

KLRD1 expression demonstrates significant prognostic value across multiple cancer types:

When interpreting KLRD1 prognostic data, researchers should consider both its direct biological effects and its role as a surrogate marker for broader immune activation states within the tumor microenvironment.

What methodological approaches are recommended for studying KLRD1's role in immunotherapy response prediction?

To effectively study KLRD1's role in predicting immunotherapy responses:

By combining these approaches, researchers can establish whether KLRD1 serves as a robust biomarker for immunotherapy response and develop strategies to target this pathway therapeutically.

How can KLRD1 antibodies be effectively utilized in therapeutic development and target validation studies?

KLRD1 antibodies play crucial roles in therapeutic development and target validation:

• Target engagement studies:

  • Use fluorescently labeled antibodies to quantify binding kinetics to KLRD1

  • Employ competition assays to evaluate binding specificity across epitopes

  • Assess receptor occupancy in preclinical models using flow cytometry

• Functional modulation approaches:

  • Develop and test blocking antibodies that inhibit KLRD1-NKG2 complex formation

  • Evaluate agonistic antibodies that enhance NK cell activation through KLRD1

  • Compare monoclonal antibodies with different epitope specificities for functional outcomes

• Antibody engineering strategies:

  • Develop bispecific antibodies linking KLRD1+ cells to tumor antigens

  • Create antibody-drug conjugates targeting KLRD1+ populations

  • Test Fc-engineered variants to modulate effector functions

• Preclinical model systems:

  • Establish immunocompetent models with human KLRD1 knockin

  • Validate target expression and antibody binding in patient-derived models

  • Monitor immune response dynamics using in vivo imaging with labeled antibodies

• Combination therapy assessment:

  • Evaluate KLRD1-targeting alongside established immunotherapy approaches

  • Research shows high KLRD1 expression is linked to increased sensitivity to cell division inhibitors

  • Test combination with drugs showing synergistic activity in drug sensitivity analyses

• Biomarker development:

  • Develop companion diagnostic approaches using standardized KLRD1 antibodies

  • Establish cutoffs for patient stratification based on quantitative measurements

  • Correlate KLRD1 expression with other immune markers to create multiparameter predictive panels

Research indicates that targeting the KLRD1-NKG2 complex can enhance NK cell activation against tumors while regulating T-cell immunity to prevent autoimmunity , making it a promising but complex target for therapeutic development.

What are common technical challenges when using KLRD1 antibodies in flow cytometry, and how can they be addressed?

Common technical challenges with KLRD1 antibodies in flow cytometry include:

• Low signal intensity issues:

  • Problem: Weak KLRD1 staining despite positive controls showing expression

  • Solutions:

    • Use bright fluorochromes like PE or APC for KLRD1 detection

    • Optimize antibody concentration through titration experiments

    • Ensure samples are properly fixed/permeabilized if detecting intracellular epitopes

    • Consider alternative clones if current antibody gives weak signal

• High background and non-specific binding:

  • Problem: High background making KLRD1+ populations difficult to distinguish

  • Solutions:

    • Implement more stringent blocking with 2% BSA or 10% serum

    • Include Fc receptor blocking step before antibody addition

    • Optimize washing steps (number and buffer composition)

    • Ensure proper compensation controls to account for spectral overlap

• Inconsistent KLRD1 detection across samples:

  • Problem: Variable KLRD1 staining between specimens or experiments

  • Solutions:

    • Standardize sample processing time to minimize ex vivo changes

    • Include consistent positive controls across experiments

    • Establish a normalized mean fluorescence intensity approach

    • Consider using stabilizing fixatives to preserve epitopes

• Co-expression analysis difficulties:

  • Problem: Challenges in accurately characterizing KLRD1+ subpopulations

  • Solutions:

    • Design panels with appropriate fluorochrome selection for co-expressed markers

    • Implement proper compensation and FMO controls

    • Use dimensionality reduction approaches (tSNE, UMAP) for complex populations

    • Consider sequential staining approaches for crowded panels

• Epitope masking in complex samples:

  • Problem: Reduced accessibility of KLRD1 epitopes in tissue-derived samples

  • Solutions:

    • Optimize enzymatic digestion protocols to preserve epitopes

    • Test alternative clone HP-3D9 which recognizes an extracellular epitope

    • Consider alternative sample preparation methods

    • Validate with orthogonal methods (IHC, Western blot)

Implementing these solutions can significantly improve KLRD1 detection reliability and consistency in flow cytometry applications.

How should researchers address data discrepancies between KLRD1 protein expression and gene expression in research studies?

When confronting discrepancies between KLRD1 protein and gene expression:

• Technical validation approaches:

  • Verify antibody specificity using multiple methods (Western blot, IP, flow cytometry)

  • Confirm primer specificity for qPCR and probe design for RNA-seq/microarray

  • Use multiple antibody clones targeting different epitopes to rule out detection artifacts

  • Include appropriate positive and negative controls for both protein and RNA detection

• Biological interpretation considerations:

  • Assess post-transcriptional regulation mechanisms:

    • microRNA-mediated repression of KLRD1 translation

    • RNA-binding protein effects on transcript stability

    • Alternative splicing creating variants not detected by certain antibodies

  • Evaluate protein stability and turnover rates:

    • Proteasomal degradation pathways affecting KLRD1 protein levels

    • Post-translational modifications altering epitope recognition

• Single-cell resolution analysis:

  • Utilize single-cell RNA-seq paired with protein measurements (CITE-seq)

  • Perform flow cytometry combined with cell sorting and subsequent qPCR

  • Consider spatial heterogeneity in tissue samples that may be lost in bulk analyses

• Temporal dynamics assessment:

  • Investigate time-course experiments to capture lags between transcription and translation

  • Consider stimulation conditions that may induce rapid protein changes without immediate transcriptional effects

  • Account for differential half-lives of mRNA versus protein

• Analytical frameworks:

  • Apply statistical methods specifically designed for multi-omics integration

  • Use correlation analyses that account for non-linear relationships

  • Implement machine learning approaches to identify patterns explaining discrepancies

Research has shown that KLRD1 expression patterns may vary across different cancer types and immune contexts , so these discrepancies could reflect genuine biological complexity rather than technical artifacts.

What are the key considerations for quantitative analysis of KLRD1 expression in immunohistochemistry studies?

Quantitative analysis of KLRD1 expression in immunohistochemistry requires careful attention to:

• Tissue preparation standardization:

  • Fixation time and conditions must be consistent across samples

  • Antigen retrieval protocols should be optimized for KLRD1 epitopes

  • Section thickness standardization ensures comparable staining intensity

  • Include positive and negative control tissues in each staining batch

• Antibody validation requirements:

  • Verify specific binding through knockout/knockdown controls

  • Test multiple antibody clones targeting different epitopes

  • Determine optimal antibody concentration through titration experiments

  • Confirm staining patterns align with known KLRD1 biology (NK cell and T cell subsets)

• Quantification methodology options:

  • Manual scoring systems:

    • H-score (combining intensity and percentage of positive cells)

    • Allred score (sum of proportion and intensity scores)

    • Quick score (multiplication of proportion and intensity values)

  • Digital pathology approaches:

    • Whole slide imaging with automated detection algorithms

    • Machine learning classifiers for cell phenotyping

    • Multiplex IHC for co-expression analysis

• Spatial context considerations:

  • Quantify KLRD1+ cells in different compartments (tumor core, invasive margin, stroma)

  • Measure distances between KLRD1+ cells and other immune or tumor cells

  • Analyze clustering patterns of KLRD1+ cells

• Data normalization strategies:

  • Normalize to tissue area or cell count

  • Use internal controls for batch effect correction

  • Apply color deconvolution algorithms for DAB quantification

  • Implement standardized intensity references

• Clinicopathological correlation approaches:

  • Correlate quantitative KLRD1 measurements with patient outcomes

  • Integrate with other prognostic markers

  • Apply cutpoints derived from statistical methods (ROC analysis, minimum p-value)

  • Validate findings in independent cohorts

Research shows KLRD1 has significant prognostic value across cancers, particularly in head and neck squamous cell carcinoma , making reliable quantification crucial for clinical correlation studies.

What emerging technologies show promise for studying KLRD1 function in the tumor microenvironment?

Several emerging technologies show exceptional promise for advancing KLRD1 research:

• Spatial multi-omics approaches:

  • Spatial transcriptomics combined with protein detection allows simultaneous visualization of KLRD1 expression and cellular interactions

  • Highly multiplexed imaging (CODEX, MIBI-TOF) can map dozens of proteins including KLRD1 and interaction partners

  • 3D tissue imaging techniques provide volumetric data on KLRD1+ cell distribution and contacts

• Advanced live cell imaging technologies:

  • Lattice light-sheet microscopy enables real-time visualization of KLRD1-mediated immune synapse formation

  • Optogenetic tools allow precise temporal control of KLRD1 signaling

  • Biosensors can measure downstream signaling events following KLRD1 engagement

• CRISPR-based functional genomics:

  • CRISPR activation/inhibition screens targeting KLRD1 pathway components

  • Base editing approaches for introducing specific KLRD1 variants

  • Prime editing for precise modification of regulatory elements controlling KLRD1 expression

• Engineered organoid and microfluidic systems:

  • Tumor-immune co-culture organoids incorporating KLRD1+ NK cells

  • Organ-on-chip platforms modeling KLRD1-dependent NK cell trafficking

  • Microfluidic devices capturing real-time killing dynamics of KLRD1+ cells

• Systems immunology computational approaches:

  • Multi-scale modeling of KLRD1 signaling networks

  • Machine learning integration of multi-omics data to predict KLRD1 function

  • Network analysis tools identifying KLRD1-centered regulatory hubs

• Advanced animal models:

  • Humanized mouse models with physiological KLRD1 expression patterns

  • CRISPR-engineered reporter mice for tracking KLRD1+ cells in vivo

  • Patient-derived xenografts with co-engrafted autologous immune components

These technologies can help address key questions about KLRD1's role in antitumor immunity, particularly its involvement in NK cell-mediated cytotoxicity and T cell receptor pathways , potentially advancing the development of KLRD1-targeted therapeutics.

How can researchers best integrate KLRD1 expression data with other immune parameters to develop comprehensive immune signatures?

Developing comprehensive immune signatures incorporating KLRD1 requires sophisticated integration approaches:

• Multi-parameter data integration strategies:

  • Employ canonical correlation analysis (CCA) to find relationships between KLRD1 expression and other immune parameters

  • Use MOFA (Multi-Omics Factor Analysis) for unsupervised integration of KLRD1 with other molecular data types

  • Apply network-based approaches (WGCNA) to identify co-expression modules containing KLRD1

  • Develop multivariate models incorporating KLRD1 with other immune checkpoints and HLA molecules

• Immune landscape characterization frameworks:

  • Integrate KLRD1 into existing immune classification schemes (Immunoscore, immunophenoscore)

  • Correlate KLRD1 with established immune subtypes (C1-C6) as research shows differential expression across these categories

  • Combine with interferon signatures, cytolytic activity scores, and T cell inflammation metrics

  • Incorporate stromal and myeloid components for a complete microenvironment assessment

• Functional readout correlations:

  • Link KLRD1 expression patterns to NK cell cytotoxicity metrics

  • Correlate with TCR diversity measurements and clonal expansion markers

  • Integrate with cytokine/chemokine profiles relevant to NK and T cell activity

  • Validate signatures using ex vivo functional assays

• Clinical outcome prediction models:

  • Develop predictive algorithms incorporating KLRD1-centered immune signatures

  • Use machine learning approaches (random forests, neural networks) trained on outcome data

  • Validate in independent cohorts with diverse clinical characteristics

  • Consider temporal dynamics through longitudinal sampling

• Visualization and communication strategies:

  • Create integrated visualization tools (radar plots, heatmaps) for complex signatures

  • Develop simplified scoring systems based on key parameters including KLRD1

  • Design interactive dashboards for exploring relationships between components

Research has shown that high KLRD1 expression correlates with increased immune pathway activity in HNSC , suggesting its value as a central component in comprehensive immune signatures with potential clinical applications.

What are the most promising approaches for targeting the KLRD1 pathway therapeutically based on current research?

Current research suggests several promising therapeutic approaches targeting KLRD1:

• Monoclonal antibody-based strategies:

  • Blocking antibodies preventing inhibitory KLRD1/NKG2A interactions

  • Agonistic antibodies enhancing stimulatory KLRD1/NKG2C signaling

  • Bispecific antibodies linking KLRD1+ NK cells to tumor targets

  • Research shows the KLRD1-NKG2 complex can enhance NK cell activation and tumor cell clearance

• Small molecule modulators:

  • Inhibitors disrupting inhibitory KLRD1 complexes

  • Stabilizers enhancing activating KLRD1 complexes

  • Targeted protein degraders (PROTACs) for specific complex removal

  • Drug screening has identified compounds like arachidonyltrifluoromethane with potential to counteract oncogenic effects

• Cell therapy approaches:

  • CAR-NK cells with modified KLRD1 signaling domains

  • Engineered T cells expressing KLRD1 to enhance tumor recognition

  • Ex vivo expanded NK cells with optimized KLRD1 expression profiles

  • Delivery systems targeting the tumor microenvironment

• Combination therapy strategies:

  • KLRD1 pathway targeting combined with checkpoint inhibitors

  • Integration with cell division inhibitors, as high KLRD1 expression correlates with increased sensitivity to these compounds

  • Synergistic approaches with cytokine therapy (IL-15, IL-2)

  • Rational combinations based on immune subtype classification

• Pathway-level interventions:

  • Targeting downstream signaling components of KLRD1

  • Modulating transcriptional regulators of KLRD1 expression

  • Altering tumor microenvironment to enhance KLRD1+ cell recruitment and function

  • Epigenetic modifiers affecting KLRD1 regulatory elements

These therapeutic strategies are supported by research showing KLRD1's critical role in antitumor immunity and its variable expression across cancer types . Targeting approaches should consider the dual role of KLRD1 in both enhancing NK cell activation and preventing autoimmunity through T cell suppression.