FCGR1A Monoclonal Antibody,FITC Conjugated

What is FCGR1A and why is it an important research target?

FCGR1A (Fc Fragment of IgG, High Affinity Ia, Receptor) also known as CD64, is a 72 kDa single chain type I glycoprotein expressed on monocytes/macrophages, dendritic cells, and activated granulocytes. It functions as a high-affinity Fc-gamma receptor playing critical roles in both innate and adaptive immune responses .

CD64 mediates IgG effector functions on monocytes, triggering antibody-dependent cellular cytotoxicity (ADCC) of virus-infected cells . As a key component in Fc-FcγR interactions, FCGR1A is essential for various therapeutic monoclonal antibody mechanisms, making it an important target for immunological research, particularly in cancer immunotherapy and infectious disease studies .

What are the key characteristics of FCGR1A monoclonal antibody, FITC conjugated?

The FCGR1A monoclonal antibody (clone 10.1) conjugated with FITC features:

The antibody recognizes an extracellular epitope on CD64 that is expressed on monocytes/macrophages, dendritic cells, and activated granulocytes. This epitope is sensitive to formalin fixation .

How is the FCGR1A-FITC antibody purified and what is its typical storage protocol?

The purification process involves conjugating the purified antibody with fluorescein isothiocyanate (FITC) under optimum conditions, followed by removing unconjugated antibody and free fluorochrome through size-exclusion chromatography .

For storage:

• The antibody should be stored at 4°C and should not be frozen

• It is typically supplied in PBS (pH 7.3-7.4) containing 0.2% BSA and 0.09% sodium azide as a stabilizer

• The antibody remains stable for approximately 12 months from the date of receipt when stored properly

• Repeated freeze-thaw cycles should be avoided as they may compromise antibody functionality

What are the optimal conditions for using FCGR1A-FITC antibody in flow cytometry experiments?

For optimal flow cytometry results with FCGR1A-FITC antibody:

• Sample preparation:

  • Use freshly isolated cells when possible

  • For whole blood: 4 μL reagent per 100 μL of whole blood

  • For isolated cells: 4 μL reagent per 10^6 cells

• Staining protocol:

  • Incubate samples for 15-20 minutes at room temperature in the dark

  • Wash cells twice with PBS containing 2% FBS

  • For whole blood samples, lyse red blood cells using an appropriate lysing solution before analysis

  • Fix cells in 0.5-1% paraformaldehyde if not analyzing immediately

• Instrument settings:

  • Use appropriate FITC channel (typically 530/30 nm bandpass filter)

  • Perform compensation if using multiple fluorochromes

  • Include proper isotype controls (Mouse IgG1-FITC) to determine background staining

How can I design multicolor flow cytometry panels that include FCGR1A-FITC while avoiding spectral overlap issues?

When designing multicolor panels including FCGR1A-FITC:

• Spectral considerations:

  • FITC emits at ~525 nm and has potential overlap with PE (~575 nm) and other green fluorochromes

  • Pair FITC with fluorochromes that have minimal spectral overlap such as APC (660 nm), APC-Cy7 (785 nm), and Pacific Blue (455 nm)

• Panel design strategy:

  • Place FITC on highly expressed antigens if using dim fluorochromes for rare markers

  • Reserve brighter fluorochromes (PE, APC) for lower density antigens

  • Utilize dump channels to exclude unwanted populations

• Compensation protocol:

  • Prepare single-stained controls for each fluorochrome

  • Use automated compensation algorithms but verify with manual adjustments

  • For spectral analyzers, include an unstained control for autofluorescence estimation

• Validation approach:

  • Perform FMO (Fluorescence Minus One) controls to establish proper gating boundaries

  • Compare FCGR1A expression patterns against established literature values for your cell types of interest

What protocols are recommended for FCGR1A detection in fixed tissue samples versus flow cytometry?

Flow cytometry and tissue staining require different approaches for optimal FCGR1A detection:

Flow cytometry protocol:

• Fresh or minimally fixed samples work best

• Brief fixation (up to 15 minutes) with 1-2% paraformaldehyde is acceptable

• The epitope recognized by clone 10.1 is sensitive to extensive formalin fixation

• Recommended concentration: 4 μL reagent per 10^6 cells or 100 μL whole blood

Immunohistochemistry considerations:

• The 10.1 clone epitope is sensitive to formalin fixation

• For formalin-fixed tissues:

  • Use heat-induced epitope retrieval methods

  • Consider alternative clones with better tolerance to fixation

  • Optimize by testing various dilutions (starting around 1:100 dilution)

• For frozen sections:

  • Fix briefly in cold acetone for 5-10 minutes

  • Unconjugated primary antibody with secondary detection systems often provides better sensitivity than direct FITC conjugates

It's important to note that while the clone 10.1 FCGR1A antibody is primarily optimized for flow cytometry, certain applications may need modified protocols or alternative clones for tissue-based studies .

How should researchers interpret different levels of FCGR1A expression across immune cell populations?

FCGR1A expression varies significantly across immune cell populations and activation states:

Normal expression patterns:

• Monocytes/macrophages: Constitutively high expression (10,000-100,000 receptors/cell)

• Dendritic cells: Moderate to high expression

• Neutrophils: Low or undetectable in resting state, upregulated upon activation

• Lymphocytes: Typically negative

Interpretation framework:

• Expression level categories:

  • High: MFI >10-fold above isotype control

  • Moderate: MFI 5-10-fold above isotype

  • Low: MFI 2-5-fold above isotype

  • Negative: MFI <2-fold above isotype

• Biological significance:

  • Increased neutrophil expression indicates activation (commonly seen in infections)

  • Upregulation on monocytes correlates with enhanced phagocytic capacity

  • Changes in expression may reflect disease states or therapeutic responses

• Expression variability factors:

  • CD64 expression can be induced by IFN-γ and other inflammatory cytokines

  • Expression levels may be affected by FCGR1A genetic variants (e.g., rs1848781, rs587598788, rs1050204)

  • CD64 upregulation serves as a useful biomarker for infection and inflammation

What are common troubleshooting issues with FCGR1A-FITC antibodies and their solutions?

For optimal results:

• Store antibody at 4°C, protected from light

• Avoid repeated freeze-thaw cycles

• Include proper isotype controls

• Standardize sample preparation protocols

How do genetic variants of FCGR1A affect antibody binding and experimental results?

Genetic variants of FCGR1A can significantly impact antibody binding and experimental outcomes:

Key FCGR1A variants and their effects:

• rs1848781 (c.-131) - Promoter region variant:

  • Affects CD64 expression levels

  • G allele shows significantly higher promoter activity than C allele

  • May result in variable detection levels between individuals

• rs587598788 - Intronic variant:

  • Associated with differential CD64 expression levels

  • Located within intron 5 of FCGR1A

• rs1050204 (c.970G>A or FcγRIA-p.D324N) - Coding region variant:

  • Affects CD64-mediated functions including:

    • Phagocytosis efficiency

    • Degranulation

    • Pro-inflammatory cytokine production

  • May alter antibody binding characteristics

Experimental implications:

• Subject-to-subject variability in flow cytometry results may reflect genetic differences

• Population studies require awareness of genetic distribution of these variants

• Functional assays (phagocytosis, ADCC) may show differential results based on donor genotypes

• When possible, genotyping for these variants provides valuable context for interpreting experimental data

For rigorous experimental design:

• Use multiple donors to account for genetic variability

• Consider including genotyping for key variants in critical studies

• Standardize analysis protocols to minimize technical variability that could obscure genetic effects

How does FCGR1A polymorphism affect monoclonal antibody therapy efficacy in cancer treatment?

FCGR1A polymorphisms can significantly influence therapeutic monoclonal antibody efficacy in cancer treatment through several mechanisms:

Implications for precision medicine:

• FcγR genotyping may help predict responders vs. non-responders

• Could inform therapeutic antibody selection and dosing strategies

• May guide development of next-generation antibodies with optimized Fc regions for specific genotypes

What roles do FCGR1A expressions play in characterizing immune checkpoint inhibitor responses?

FCGR1A expression patterns have emerged as important factors in immune checkpoint inhibitor (ICI) efficacy:

FCGR1A in checkpoint inhibitor mechanism:

• Fc-FcγR interactions in anti-PD-1/PD-L1 therapy:

  • Recent findings indicate Fc-FcγR interactions are crucial for some ICI antibody activity

  • Anti-PD-1 and anti-PD-L1 mAbs demonstrate different requirements for FcγR engagement:

    • Anti-PD-1 mAbs: Some Fc-FcγR interactions (IgG2a and IgG1) can be detrimental by facilitating macrophage-mediated depletion of PD-1+ effector T cells

    • Anti-PD-L1 mAbs: Enhanced anti-tumor activity when activating FcγR engagement is optimized

• FCGR1A expression as biomarker:

  • CD64 expression on tumor-associated macrophages correlates with immunosuppressive phenotypes

  • Expression patterns on circulating monocytes may predict response to checkpoint inhibitors

  • Dynamic changes during treatment could serve as pharmacodynamic markers

• Mechanistic considerations:

  • Anti-PD-L1 efficacy correlates with elimination of monocytes and modulation of myeloid cells within the tumor microenvironment

  • This pathway appears to synergize with FcγR-independent blocking activity, augmenting effector T cell anti-tumor activity

  • IgG subclass selection for therapeutic antibodies significantly impacts engagement with different FcγR types

Research implications:

• Monitoring FCGR1A expression may provide insights into therapeutic resistance mechanisms

• Antibody engineering to modulate Fc-FcγR interactions could enhance checkpoint inhibitor efficacy

• Combination therapies targeting both checkpoint pathways and Fc receptor functions represent promising research directions

How can researchers accurately assess FCGR1A-mediated functional responses in bioassays?

Developing robust bioassays to assess FCGR1A-mediated functional responses requires careful consideration of multiple factors:

Key bioassay methodologies:

• ADCC assays:

  • Primary immune cells (PBMCs or isolated NK cells) assess antibody-induced killing of target cells

  • Fcγ polymorphism effects can be evaluated using NK cells from donors with different variants

  • Readouts include target cell death, immune cell activation markers, or cytokine release

• Phagocytosis assays:

  • Assess CD64-mediated uptake of antibody-opsonized particles or cells

  • Optimization requires careful antibody:target ratios and appropriate time points

  • Flow cytometry or microscopy-based quantification methods provide reliable data

• Reporter-based systems:

  • Engineered cell lines expressing FCGR1A linked to reporter genes

  • Provide standardized, reproducible readouts

  • Can be designed to isolate specific signaling pathways

Critical assay considerations:

• Donor genetic background:

  • FCGR1A polymorphisms significantly affect functional outcomes

  • Variants like rs1050204 (FcγRIA-p.D324N) influence phagocytosis, degranulation, and cytokine production

  • Genotyping donors aids in interpreting variable responses

• Antibody characteristics:

  • Glycosylation patterns affect Fc-FcγR interactions

  • Post-translational modifications impact binding and function:

    • Deamidation of asparagine 325 (N325) disrupts binding between IgG1 Fc and FcγRs

    • Oxidation of methionine residues alters receptor engagement

• Assay standardization:

  • Cell-based assays show inherent variability

  • Reference standards and consistent protocols enhance reproducibility

  • Multiple donors across experiments account for genetic diversity

  • Different cell isolation methods may affect receptor expression/function

For comprehensive functional evaluation, researchers should:

• Characterize antibody properties (glycosylation, post-translational modifications)

• Account for donor genetics when interpreting functional variability

• Include multiple readouts that reflect different aspects of FCGR1A biology

How does FCGR1A (CD64) expression and function compare with other Fc gamma receptors in immune responses?

FCGR1A (CD64) has distinct characteristics compared to other Fc gamma receptors that influence its role in immune responses:

Comparative properties of FcγR family members:

Functional distinctions:

• Binding capabilities:

  • FCGR1A uniquely binds monomeric IgG with high affinity

  • Other FcγRs primarily bind immune complexes or aggregated IgG

  • This allows CD64 to initiate responses at lower antibody concentrations

• Signaling mechanisms:

  • FCGR1A contains three extracellular Ig-like domains, while FCGR2 and FCGR3 contain two

  • Signal transduction pathways show both overlap and distinct features among FcγRs

  • Differential recruitment of adaptor proteins leads to varied cellular responses

• Clinical relevance of polymorphisms:

  • FCGR3A V158F polymorphism is strongly associated with rituximab efficacy

  • FCGR2A H131R influences therapeutic outcomes for cetuximab

  • FCGR1A polymorphisms affect expression levels and functional responses

Understanding these differences helps researchers:

• Design therapeutic antibodies with optimized FcγR binding profiles

• Select appropriate FcγR targets for specific disease mechanisms

• Interpret experimental data in the context of the complete FcγR family

What are the advantages and limitations of FITC conjugation compared to other fluorochromes for FCGR1A detection?

FITC conjugation for FCGR1A detection offers distinct advantages and limitations compared to alternative fluorochromes:

Advantages of FITC conjugation:

• Well-established properties:

  • Extensive characterization in flow cytometry applications

  • Compatible with most flow cytometer configurations

  • Well-documented spectral characteristics (excitation ~495 nm, emission ~525 nm)

• Technical benefits:

  • Relatively stable conjugation chemistry

  • Cost-effective compared to newer fluorochromes

  • High quantum yield providing good brightness when properly excited

• Research applications:

  • Ideal for basic phenotyping applications

  • Works well for abundant targets like CD64 on monocytes

  • Compatible with common fixation and permeabilization protocols

Limitations of FITC versus alternatives:

Practical considerations:

• Cell autofluorescence:

  • FITC emission overlaps with cellular autofluorescence

  • Particularly problematic in:

    • Fixed samples

    • Macrophages and dendritic cells (which express CD64)

    • Aged or apoptotic cells

• Photobleaching concerns:

  • FITC is more susceptible to photobleaching than newer dyes

  • Critical for:

    • Imaging applications

    • Samples requiring prolonged analysis

    • Experiments needing repeat analysis

• Panel design implications:

  • FITC occupies a valuable fluorescence channel

  • Limits use of other green fluorochromes (e.g., CFSE for proliferation)

  • Can create compensation challenges with PE

For optimal selection:

• Use FITC for high-abundance targets like CD64 on monocytes

• Consider PE or APC for experiments requiring maximum sensitivity

• APC or Alexa Fluor 647 conjugates provide advantages for multicolor panels with minimal spectral overlap

How can FCGR1A antibodies be utilized in drug development and antibody engineering research?

FCGR1A antibodies serve multiple critical functions in therapeutic antibody development and engineering:

Applications in drug development:

• Target validation and screening:

  • FCGR1A antibodies help characterize Fc receptor expression in disease models

  • Screens can identify compounds modulating CD64 expression or function

  • Anti-CD64 antibodies can validate potential therapeutic targets

• Bioassay development:

  • Critical tools for developing functional bioassays that assess:

    • Fc effector functions of therapeutic antibodies

    • Effects of manufacturing changes on product quality

    • Batch-to-batch consistency of antibody therapeutics

  • Aid in developing reporter cell lines expressing defined FCGR1A variants

• Antibody engineering applications:

  • Guide Fc engineering to enhance or reduce FcγR interactions

  • Evaluate engineered antibodies for altered binding profiles

  • Assess effects of glycoengineering on FCGR1A binding properties

  • Characterize novel antibody formats and bispecific constructs

Methodological approaches:

• Binding characterization techniques:

  • Surface plasmon resonance (SPR) or bio-layer interferometry (BLI) with captured FCGR1A antibodies

  • Flow cytometry-based binding assays on cells expressing defined FCGR1A variants

  • Competition assays between therapeutic candidates and FCGR1A antibodies

• Functional assessment protocols:

  • ADCC assays with FCGR1A-expressing effector cells

  • Phagocytosis assays measuring CD64-mediated uptake

  • Cytokine release assays to assess inflammatory responses

  • Reporter gene assays measuring FCGR1A signaling activation

• Technology integration:

  • High-throughput screening platforms incorporating FCGR1A antibodies

  • In silico modeling validated with binding data

  • Correlation of in vitro binding data with in vivo efficacy

The strategic use of FCGR1A antibodies throughout the drug development process ensures therapeutic antibodies with optimal effector functions and predictable clinical performance .

What is the significance of FCGR1A in diagnostic and prognostic applications for inflammatory conditions?

FCGR1A (CD64) has emerged as an important diagnostic and prognostic marker across various inflammatory conditions:

Diagnostic applications:

• Infection and sepsis biomarker:

  • CD64 expression on neutrophils increases significantly during bacterial infections

  • Provides higher sensitivity and specificity than traditional markers:

    • More specific than C-reactive protein (CRP)

    • Earlier response than procalcitonin

    • Better discrimination between bacterial and viral infections

  • Quantitative flow cytometry using FITC-conjugated anti-CD64 allows standardized assessment

• Autoimmune disease monitoring:

  • CD64 expression correlates with disease activity in:

    • Rheumatoid arthritis

    • Systemic lupus erythematosus

    • Inflammatory bowel disease

  • Changes in expression can precede clinical flares

  • Helps differentiate between inflammatory and non-inflammatory conditions

• Sarcoidosis associations:

  • Significant associations between FCGR1A genotypes and sarcoidosis susceptibility

  • Genetic variants influence disease severity

  • FCGR1A variants (rs1848781, rs587598788, rs1050204) affect CD64 expression and function

  • Expression patterns correlate with granuloma formation and disease progression

Prognostic value:

• Therapeutic response prediction:

  • Baseline CD64 expression may predict response to:

    • Biologic therapies in autoimmune diseases

    • Antibiotics in bacterial infections

    • Immunomodulatory treatments in inflammatory conditions

  • Sequential monitoring can assess treatment efficacy

• Disease course prediction:

  • Persistently elevated CD64 associated with:

    • Chronic disease course

    • Higher risk of complications

    • Treatment resistance

  • Normalization of expression correlates with favorable outcomes

Implementation considerations:

• Standardization requirements:

  • Quantitative flow cytometry using calibrated beads

  • Consistent antibody clones and fluorochromes

  • Established reference ranges for different populations

  • Quality control measures to ensure reproducibility

• Clinical workflow integration:

  • Rapid sample processing protocols for acute care settings

  • Paired analysis with conventional inflammatory markers

  • Interpretation guidelines for different clinical contexts

  • Considerations for genetic variants affecting expression