ifnlr1 Antibody, FITC conjugated

What is IFNLR1 and why is it important for immunological research?

IFNLR1 forms half of the heterodimeric receptor (with IL-10RB) that mediates responses to type III interferons (IFN-λs). This receptor is expressed in a tissue-specific manner, with particularly important roles at mucosal surfaces. Research has demonstrated that IFNLR1 expression on intestinal epithelial cells (IECs) is critical for enteric antiviral activity, determining the efficacy of IFN-λ in resolving persistent viral infections such as murine norovirus . Unlike what has been observed in mouse models, human studies reveal direct interaction between IFN-λ3 and the adaptive immune system, with expression on specific lymphocyte subsets, highlighting potential applications in both mucosal and blood-borne viral infections .

Which cell types express IFNLR1 and how does expression vary across tissues?

IFNLR1 shows a distinctive expression pattern that differs from type I interferon receptors. In the intestinal tract, IFNLR1 is predominantly expressed on epithelial cells, which has been confirmed through genetic approaches using conditional knockout mice . In human peripheral blood, B cells and CD8+ T cells express functional IFNLR1 and respond directly to IFN-λ3 stimulation, while monocytes, neutrophils, natural killer cells, and resting CD4+ T cells show minimal response . Notably, activation conditions can significantly alter IFNLR1 expression patterns—T cell receptor stimulation potently upregulates membrane-bound IFNLR1 in CD4+ T cells, enabling greater antiviral gene induction .

What are the advantages of using FITC-conjugated antibodies for IFNLR1 detection?

FITC-conjugated antibodies provide several methodological advantages for IFNLR1 detection. The fluorescein isothiocyanate (FITC) fluorophore enables direct visualization of receptor expression without requiring secondary detection reagents, streamlining experimental workflows. Based on similar protocols for cytokine detection, FITC conjugates are especially suitable for immunofluorescent staining and flow cytometric analysis to identify and enumerate cells expressing specific receptors within mixed populations . The bright fluorescence signal of FITC facilitates detection of even low-level receptor expression, though titration is essential (typically ≤0.5 μg mAb/million cells) to achieve optimal signal-to-noise ratios in flow cytometry applications .

What are the recommended protocols for sample preparation when studying IFNLR1?

For optimal detection of IFNLR1 using flow cytometry, samples typically require careful preparation similar to intracellular cytokine staining protocols. This generally involves:

• Initial surface marker staining to identify cell populations of interest

• Fixation with paraformaldehyde (if studying intracellular domains)

• Permeabilization with saponin if examining intracellular epitopes

• Blocking with appropriate isotype controls

• Staining with titrated FITC-conjugated anti-IFNLR1 antibody

For demonstrating staining specificity, pre-blocking fixed/permeabilized cells with unlabeled antibody prior to staining with the FITC-conjugated version can serve as an important control, similar to approaches used for cytokine detection . Researchers should assess baseline autofluorescence and use appropriate isotype controls at identical concentrations to the primary antibody .

How should researchers optimize staining conditions for IFNLR1 detection?

Optimization of staining conditions is critical for accurate IFNLR1 detection. Key considerations include:

• Antibody titration: Each investigator should titrate the reagent to obtain optimal results, typically testing concentrations ≤0.5 μg mAb/million cells for flow cytometry applications

• Appropriate controls: Include isotype controls at the same concentration as the test antibody to assess background staining levels

• Cell-specific considerations: Given the differential expression of IFNLR1 across cell types, optimization should be performed for each specific cell population of interest

• Fixation impact: Evaluate whether the chosen fixation method affects epitope recognition by the antibody

• Stimulation timing: Consider that stimulation conditions can significantly alter IFNLR1 expression levels, particularly in lymphocytes

How do different IFNLR1 isoforms affect experimental outcomes when using antibodies?

Multiple IFNLR1 isoforms significantly complicate antibody-based detection and functional studies. Research has identified at least three isoforms of IFNLR1, with isoform 1 being the canonical membrane-bound receptor, while isoforms 2 and 3 may act as negative regulators of IFNL responses in a concentration-dependent manner . When designing experiments, researchers must consider:

• Epitope recognition: Confirm whether the antibody recognizes all isoforms or is isoform-specific

• Soluble vs. membrane-bound detection: The presence of soluble IFNLR1 (sIFN-λR1) in biological samples can interfere with detection of membrane-bound receptors

• Functional implications: The ratio of different isoforms can dramatically alter cellular responses to IFN-λ, with soluble variants inhibiting ISG induction by 54-78% when added alongside IFN-λ3

These considerations are particularly important in comparative studies across different cell types or patient samples, as varying ratios of receptor isoforms could lead to misinterpretation of results.

What methodological approaches can help overcome challenges in detecting low-level IFNLR1 expression?

Detecting low-level IFNLR1 expression presents several challenges that can be addressed through advanced methodological approaches:

• Signal Amplification Systems: Consider using biotin-streptavidin systems or tyramide signal amplification to enhance detection sensitivity

• Multi-parameter Flow Cytometry: Combine IFNLR1 staining with lineage markers and activation markers to identify specific subpopulations with differential receptor expression

• RNA-Protein Correlation: Validate antibody staining results with transcript analysis, noting that IFNLR1 transcript levels don't always correlate with protein expression—B cells and lung epithelial cells can have similar IFNLR1 transcript levels but significantly different protein expression and IFN-λ3 binding capacity

• Receptor Upregulation: Consider stimulation conditions that upregulate receptor expression before detection (e.g., B cells upregulate IFNLR1 after stimulation with anti-BCR and anti-CD40 or TLR7/8 ligands like R848)

• Negative Regulators: Account for soluble IFNLR1 variants which may mask detection—in some experiments, neutralizing endogenous sIFN-λR1 may improve detection of membrane-bound receptors

How can researchers distinguish between membrane-bound and soluble IFNLR1 variants?

Distinguishing between membrane-bound and soluble IFNLR1 variants requires specialized approaches:

• PCR-Based Discrimination: Design PCR primers to specifically amplify full-length membrane (mLR1) or soluble (sLR1) IFNLR1 variant transcripts, as demonstrated in studies of CD4+ T cells before and after TCR stimulation

• Imaging Flow Cytometry: This technique can visualize binding patterns of receptors to distinguish surface expression from internalized or secreted forms

• Sequential Extraction Protocols: Use differential detergent extraction to separate membrane-bound from soluble forms

• Binding Competition Assays: The addition of recombinant sIFN-λR1 alongside IFN-λ3 can lead to 5-15 fold greater binding of IFN-λ3 to cell surfaces compared to IFN-λ3 alone, providing a method to assess the influence of soluble variants

• Functional Validation: Measure ISG induction with and without sIFN-λR1 addition—recombinant sIFN-λR1 dramatically inhibits IFN-λ3-mediated ISG induction in both PBMCs and hepatocyte cell lines

This multi-modal approach is crucial as the ratio of membrane to soluble forms varies significantly across cell types and can be dynamically regulated by cellular activation states.

What controls should be included when studying IFNLR1 expression changes upon cellular stimulation?

When studying changes in IFNLR1 expression following cellular stimulation, several critical controls should be included:

• Temporal Controls: Measure IFNLR1 expression at multiple time points (0h, 24h, 48h, 72h) to capture the kinetics of expression changes

• Isotype Controls: Use appropriate isotype control antibodies matched to the primary antibody's isotype (e.g., mouse IgG1 for B27 clone antibodies), applied at identical concentrations

• Pre-blocking Controls: Pre-block with unlabeled antibody prior to staining with FITC-conjugated antibody to demonstrate staining specificity

• Isoform-Specific Controls: Include PCR analysis of both membrane-bound (mLR1) and soluble (sLR1) IFNLR1 variant transcripts alongside protein detection

• Stimulation-Matched Controls: For studies involving T cell receptor stimulation, include anti-CD3/anti-CD28 stimulated controls, as these conditions potently upregulate membrane-bound IFNLR1 in CD4+ T cells

• Cross-Regulatory Controls: Include type I IFN (IFN-α2) treatment controls, as they can decrease IFNLR1 expression on B cells, contrary to their effect on hepatocytes

How do stimulation conditions affect IFNLR1 detection with FITC-conjugated antibodies?

The impact of stimulation conditions on IFNLR1 detection is substantial and varies by cell type:

B cells:

• Anti-BCR (IgM/IgG/IgA) and anti-CD40 stimulation increases IFN-λ3 binding to total B cells

• TLR7/8 ligand R848 significantly increases IFN-λ3 binding, particularly on naïve B cells

• IFN-α2 treatment decreases the percentage of total B cells binding IFN-λ3 by approximately 62%

• IFN-γ specifically decreases IFN-λ3 binding to memory B cells

T cells:

• TCR stimulation with anti-CD3/anti-CD28 dramatically upregulates membrane-bound IFNLR1 expression in CD4+ T cells, enabling greater IFN-λ3 binding and antiviral gene induction

• This upregulation specifically increases the full-length membrane-bound receptor while potentially altering the ratio of membrane to soluble forms

Neutrophils:

• Despite low basal IFNLR1 expression, stimulation can induce receptor expression in neutrophils

• Different stimuli may selectively upregulate specific receptor isoforms

The differential regulation across cell types highlights the importance of tailoring detection protocols to specific experimental questions and cell populations. Researchers should consider these stimulation-dependent changes when designing flow cytometry panels and interpreting results.

What are the key differences between studying IFNLR1 in epithelial cells versus immune cells?

Studying IFNLR1 in epithelial versus immune cells reveals important differences in expression levels, regulation, and function:

Expression Level Differences:

• Epithelial cells (particularly intestinal and lung) generally exhibit higher IFNLR1 expression than most immune cells

• Despite similar IFNLR1 transcript levels in B cells and lung epithelial cells, lung epithelial cells bind significantly more IFN-λ3, resulting in approximately 50-fold greater ISG induction

Methodological Implications:

• Epithelial cells may require lower antibody concentrations for optimal detection

• Immune cells often need signal amplification or pre-stimulation to enhance detection

• Different permeabilization protocols may be required as epithelial cells form tight junctions that can impede antibody access

Response Kinetics:

• Epithelial cells typically show more rapid and robust responses to IFN-λ

• Immune cell responses are often conditional on activation state and may require additional co-stimulation

Isoform Distribution:

• The reduced response of B cells compared to epithelial cells can be attributed to higher expression of soluble IFNLR1 variants

• This differential isoform expression necessitates careful selection of detection antibodies that can distinguish between variants

How can IFNLR1 antibodies be validated for specificity and sensitivity?

Rigorous validation of IFNLR1 antibodies is essential to ensure reliable experimental results. Key validation approaches include:

• Genetic Controls: Testing on IFNLR1-knockout cells or comparing conditional knockout models (e.g., intestinal epithelial cell-specific IFNLR1 deletion) with complete knockouts

• Competitive Binding Assays: Pre-incubation with unlabeled antibody should block binding of the FITC-conjugated version, demonstrating epitope-specific binding

• Isotype Control Comparison: Background staining should be assessed using an isotype-matched control antibody (e.g., FITC-MOPC-21 immunoglobulin for mouse IgG1 antibodies)

• Stimulus-Response Correlation: Verify that detected receptor levels correlate with functional responses (e.g., ISG induction after IFN-λ stimulation)

• Cross-Species Reactivity Assessment: Confirm species specificity, as IFNLR1 conservation varies across species, with notable distinctions in soluble variant expression between primates and lower mammals

• Multimodal Validation: Correlate antibody staining with transcript analysis using isoform-specific PCR, though keeping in mind that transcript and protein levels may not always directly correlate

What quantification methods provide the most reliable assessment of IFNLR1 expression levels?

Accurate quantification of IFNLR1 expression requires careful selection of appropriate methods:

• Flow Cytometry Standardization:

  • Use quantitative beads to convert fluorescence intensity to absolute antibody binding capacity

  • Report both percentage of positive cells and median fluorescence intensity

  • Include biological calibrators (cell lines with defined receptor expression) across experiments

• Transcript Quantification:

  • Employ isoform-specific RT-qPCR with appropriate reference genes

  • Consider digital PCR for absolute quantification of rare transcripts

  • Distinguish between total IFNLR1, membrane-bound (mLR1), and soluble (sLR1) variant transcripts

• Ligand Binding Assays:

  • Quantify IFN-λ3 binding to cell surfaces as a functional measure of receptor expression

  • Report both percentage of cells binding ligand and fold increase in median fluorescence

• Imaging-Based Quantification:

  • Use imaging flow cytometry to simultaneously quantify and visualize receptor localization

  • Apply automated image analysis to reduce subjective interpretation

• Protein-Level Validation:

  • Consider mass spectrometry for absolute quantification of receptor proteins

  • Use Western blotting with titrated standards for semi-quantitative assessment

The optimal approach often combines multiple methods, acknowledging that different techniques may capture different aspects of receptor biology.

How should researchers design experiments to study IFNLR1-mediated antiviral responses?

Designing experiments to study IFNLR1-mediated antiviral responses requires careful consideration of several factors:

• Cell Type Selection:

  • Choose relevant cell types based on research questions—intestinal epithelial cells for enteric viruses, hepatocytes for hepatotropic viruses, or specific immune cells for immunological questions

  • Consider using primary cells rather than cell lines when possible, as receptor expression patterns may differ

• Viral Challenge Models:

  • Select appropriate viral systems—murine norovirus and reovirus have been successfully used to evaluate IFNLR1-dependent responses in vivo

  • Titrate viral inoculum to achieve measurable responses without overwhelming the system

• Readout Selection:

  • Measure both viral parameters (tissue titers, fecal shedding) and host responses (ISG induction)

  • Focus on IFN-λ-responsive ISGs such as ISG15, IFIT1, and IFI44, which have shown consistent induction patterns

• Timing Considerations:

  • Include multiple time points to capture both immediate and sustained responses

  • For persistent infections like murine norovirus, evaluate both acute control and clearance of established infection

• Genetic Approaches:

  • Consider conditional knockout models to isolate cell-specific responses

  • Compare phenotypes between complete IFNLR1 knockouts and cell-specific deletions

• Combination with Adaptive Immunity:

  • Assess the relative contribution of innate vs. adaptive immunity using Rag1-deficient mice or isolated immune cell populations

  • Consider that human and mouse systems may differ—human studies show direct IFN-λ effects on adaptive immune cells not observed in mouse models

What are the best practices for using IFNLR1 antibodies in immunohistochemistry or immunofluorescence microscopy?

When using IFNLR1 antibodies for tissue microscopy applications, researchers should follow these best practices:

• Fixation Optimization:

  • Test multiple fixation protocols as some epitopes may be fixation-sensitive

  • Consider antigen retrieval methods to expose masked epitopes in formalin-fixed tissues

  • Be aware that the IFNLR1 antibody may not bind to denatured forms of the receptor, similar to issues reported with some anti-cytokine antibodies

• Control Selection:

  • Include tissues from IFNLR1-knockout animals as negative controls

  • Use tissues with known high expression (e.g., intestinal epithelium) as positive controls

  • Include isotype-matched control antibodies to assess non-specific binding

• Co-localization Studies:

  • Combine IFNLR1 staining with markers for specific cell types (e.g., EpCAM for epithelial cells)

  • Consider co-staining for the IL-10RB co-receptor to identify cells with complete receptor complexes

  • Include markers for tight junctions in epithelial tissues to assess receptor polarization

• Signal Amplification:

  • For low-abundance receptors, consider tyramide signal amplification or other amplification systems

  • Balance amplification with maintaining signal specificity

• Quantification Methods:

  • Develop consistent scoring systems for receptor positivity

  • Use digital image analysis with appropriate thresholding for objective quantification

  • Report both staining intensity and percentage of positive cells in defined tissue regions

• Technical Considerations:

  • For FITC-conjugated antibodies, be aware of potential photobleaching during extended imaging

  • Mount samples with anti-fade media containing appropriate preservatives

  • Consider spectral unmixing approaches if autofluorescence is problematic in tissues of interest

How do IFNLR1 isoform ratios influence experimental interpretation across different cellular models?

The ratio of IFNLR1 isoforms significantly impacts experimental interpretation and varies across cellular models:

• Functional Impact Assessment:

  • Soluble IFNLR1 variants can dramatically inhibit IFN-λ3-mediated ISG induction by 54-78% in both PBMCs and hepatocyte cell lines

  • The ratio between membrane-bound and soluble forms directly affects cellular responsiveness to IFN-λ

• Cell Type Comparisons:

  • B cells show reduced responses to IFN-λ3 compared to lung epithelial cells despite similar transcript levels, explained by higher soluble IFNLR1 expression

  • T cells upregulate membrane-bound IFNLR1 after TCR stimulation, altering their response profile

• Binding Dynamics:

  • Recombinant soluble IFNLR1 can increase IFN-λ3 binding to cell surfaces by 5-15 fold, complicating interpretation of binding studies

  • This effect varies by cell type, with monocytes showing the greatest sIFN-λR1 binding among peripheral immune cells

• Regulatory Implications:

  • Dynamic regulation of isoform expression serves as a mechanism to fine-tune IFN-λ responses

  • Different stimuli selectively regulate specific isoforms—for example, TCR stimulation primarily affects membrane-bound forms in CD4+ T cells

• Experimental Strategies:

  • Design PCR assays to specifically quantify membrane vs. soluble isoforms

  • Consider the impact of experimental manipulations on isoform ratios

  • Account for species differences—soluble IFNLR1 variants appear to be present in primates but may not be found in lower mammals

Understanding these dynamics is essential for accurate interpretation of results across different experimental systems and may help explain apparently contradictory findings between studies using different cell types or models.

What are common pitfalls when detecting IFNLR1 with FITC-conjugated antibodies and how can they be addressed?

Several common challenges arise when detecting IFNLR1 with FITC-conjugated antibodies:

• Low Signal Intensity:

  • Problem: FITC has relatively low fluorescence intensity compared to newer fluorophores

  • Solution: Consider signal amplification systems, optimize antibody concentration through careful titration (≤0.5 μg mAb/million cells), or switch to brighter fluorophores if available

• High Background:

  • Problem: Non-specific binding or autofluorescence in the FITC channel

  • Solution: Include proper blocking steps, use carefully matched isotype controls, and consider alternative fluorophores for highly autofluorescent tissues

• Epitope Masking:

  • Problem: Fixation or permeabilization may mask critical epitopes

  • Solution: Test multiple fixation/permeabilization protocols, consider fixing after antibody staining for surface epitopes

• Variable Expression:

  • Problem: IFNLR1 expression varies significantly between cell types and activation states

  • Solution: Include positive control cells with known expression, consider stimulation to upregulate receptor expression before detection

• Isoform Complexity:

  • Problem: Antibodies may recognize multiple isoforms or be blocked by soluble variants

  • Solution: Confirm antibody specificity for target isoforms, consider complementary detection methods like PCR to distinguish variants

• Photobleaching:

  • Problem: FITC is prone to photobleaching during extended imaging

  • Solution: Minimize exposure to excitation light, use anti-fade mounting media, acquire images from unexposed fields

• Inconsistent Results:

  • Problem: Day-to-day variability in staining intensity

  • Solution: Standardize protocols rigorously, include calibration beads, and use consistent positive controls across experiments

How can researchers optimize IFNLR1 antibody staining protocols for different applications?

Optimizing IFNLR1 antibody staining requires application-specific adjustments:

For Flow Cytometry:

• Titration: Determine optimal antibody concentration through systematic titration, typically starting at ≤0.5 μg mAb/million cells and testing serial dilutions

• Compensation: Carefully set compensation if using multiple fluorophores, particularly for FITC which has spectral overlap with other common fluorophores

• Viability Dyes: Include viability dyes to exclude dead cells which often show non-specific antibody binding

• Controls: Use fluorescence-minus-one (FMO) controls to set accurate gates, especially for markers with continuous expression patterns

For Microscopy:

• Antigen Retrieval: Test multiple antigen retrieval methods if using fixed tissues

• Signal Amplification: Consider tyramide signal amplification for low-abundance targets

• Counterstaining: Include appropriate nuclear and structural counterstains for context

• Z-stack Acquisition: Collect z-stacks for accurate localization of membrane-bound receptors

For Western Blotting:

• Sample Preparation: Optimize lysis conditions to preserve membrane proteins

• Controls: Include recombinant IFNLR1 as a positive control and lysates from knockout cells as negative controls

• Antibody Incubation: Test both short high-concentration and overnight low-concentration incubation protocols

• Detection Systems: Compare chemiluminescence vs. fluorescent detection systems for sensitivity and dynamic range

General Considerations:

• Blocking Optimization: Test different blocking reagents (BSA, normal serum, commercial blockers) to reduce background

• Incubation Temperature: Compare room temperature vs. 4°C incubation for optimal signal-to-noise ratio

• Detergent Concentration: Adjust detergent levels in wash buffers to balance specific binding with background reduction

• Stimulation Protocols: Consider pre-stimulation to upregulate receptor expression in certain cell types