ATL10 Antibody

What is AT10 Antibody and what does it specifically recognize?

AT10 is a mouse monoclonal antibody clone that specifically binds to human FcγRII (CD32), including its isoforms FcγRII-a, FcγRII-b, and FcγRII-c. This receptor plays crucial roles in regulating immune responses, particularly in B-cell activation and antibody-dependent cellular functions. The antibody recognizes specific epitopes on FcγRII, allowing for selective detection and functional studies of this receptor across various experimental conditions .

Unlike antibodies targeting other Fc receptors or similar structures, AT10 demonstrates high specificity for FcγRII. This specificity makes it particularly valuable for distinguishing FcγRII-mediated functions from those of other Fc receptors in complex immunological research.

Which cell types express FcγRII that can be detected by AT10 antibody?

AT10 antibody has been validated to detect FcγRII on several cell types:

• Human peripheral blood lymphocytes (PBLs) in flow cytometry applications

• Human K562 erythroleukemic cells in immunoprecipitation studies

• Human Burkitt's lymphoma Ramos cells transfected with FcγRIIB

• Human peripheral blood mononuclear cells (PBMCs)

Quantitative analysis has shown that K562 cells express approximately 1.5 x 10^5 FcγRII binding sites per cell, as determined through affinity binding studies with the Fab' fragment of AT10 . This information helps researchers estimate receptor density when designing experiments involving these cell types.

What applications has AT10 antibody been validated for?

Based on published research, AT10 antibody has been successfully employed in multiple applications:

• Flow Cytometry Analysis: AT10 has been used to detect FcγRII-positive peripheral blood lymphocytes and transfected cell lines expressing FcγRIIB. In typical protocols, 1.0 μg of antibody can effectively detect FcγRII in 1x10^6 human PBMCs .

• Immunoprecipitation Analysis: AT10 successfully immunoprecipitates FcγRII from human K562 erythroleukemic cells, allowing for protein isolation and further analysis .

• Affinity Binding Assays: The Fab' fragment of AT10 has been used in binding studies, revealing an equilibrium binding constant (Ka) of 5.3 x 10^8/M .

• Neutralization Analysis: Both Fab' and F(ab')2 fragments of AT10 effectively block various FcγRII-dependent functions, including cell rosetting and antibody-dependent cell-mediated cytolysis .

What controls should be included when using AT10 antibody in flow cytometry?

When designing flow cytometry experiments with AT10 antibody, incorporate the following controls to ensure reliable data interpretation:

• Unstained cells: Essential for establishing baseline autofluorescence and setting appropriate gates. This control helps identify false positive signals that may arise from endogenous fluorophores .

• Negative cell populations: Use cells known not to express FcγRII as negative controls to confirm the specificity of AT10 antibody binding and establish thresholds for positive staining .

• Isotype control: Include an antibody of the same class as AT10 (mouse IgG) but with no specificity for FcγRII. This control helps assess non-specific background staining due to Fc receptor binding. Non-specific Control IgG (such as Clone X63) can serve this purpose .

• Secondary antibody control: If using indirect staining methods, include samples treated with only the fluorophore-conjugated secondary antibody to assess its non-specific binding characteristics .

Implementing these controls systematically ensures proper validation of AT10 antibody performance and facilitates accurate interpretation of experimental results.

How should AT10 antibody fragments be prepared for blocking FcγRII-dependent functions?

AT10 antibody fragments have proven effective in blocking FcγRII-dependent functions, with several methodological considerations:

• Fragment selection: Both Fab' and F(ab')2 fragments of AT10 have been successfully used for blocking experiments. The F(ab')2 fragment is particularly effective for blocking FcγRII-dependent B-cell activation and preventing antibody-dependent cell-mediated cytolysis .

• Concentration optimization: Determine optimal concentrations through titration experiments. Published studies have shown efficacy at concentrations sufficient to saturate available FcγRII receptors.

• Control fragments: Always include control fragments (isotype-matched control IgG1 or control F(ab')2) alongside AT10 fragments to confirm specificity of blocking effects .

• Experimental validation: Verify blocking efficacy through functional assays, such as inhibition of cell rosetting with rabbit IgG-coated chick red blood cells or prevention of redirected cellular cytotoxicity .

• Pre-incubation timing: Allow sufficient pre-incubation time for AT10 fragments to bind FcγRII before introducing stimulatory agents or target cells.

What are the optimal conditions for using AT10 antibody in flow cytometry?

For optimal flow cytometry results with AT10 antibody, consider the following methodological parameters:

• Cell preparation: Prepare single-cell suspensions of target cells (e.g., PBMCs, K562 cells) using gentle dissociation techniques to preserve surface receptor integrity.

• Cell quantity: Use approximately 1x10^6 cells per sample for standard flow cytometry analysis. This cell number has been validated for detecting FcγRII in human PBMCs using 1.0 μg of AT10 antibody .

• Antibody dilution: If using conjugated AT10, follow manufacturer recommendations for dilution. For phycoerythrin (PE)-conjugated AT10, empirical optimization may be necessary to determine optimal staining conditions for specific cell types .

• Incubation conditions: Standard incubation involves 20-30 minutes at 4°C in appropriate buffer (typically PBS with 1-2% BSA or FBS) to minimize receptor internalization while allowing sufficient binding.

• Washing steps: Include adequate washing steps (usually 2-3 washes with excess buffer) to remove unbound antibody and reduce background.

• Data analysis: Set appropriate gates based on control samples and analyze FcγRII expression patterns using both frequency (percentage of positive cells) and intensity (mean or median fluorescence intensity) metrics.

How can AT10 antibody be used to study FcγRII-dependent B-cell activation?

AT10 antibody provides valuable insights into FcγRII-dependent B-cell activation through several experimental approaches:

• Blocking studies: The F(ab')2 fragment of AT10 effectively blocks FcγRII-dependent B-cell activation induced by chimeric antibodies. For example, AT10 F(ab')2 has been shown to block activation by ChiLob 7/4 h1 (a chimeric anti-CD40 mAb with human IgG1 Fc) when FcγRII-overexpressing 293F cells are present as crosslinking cells .

• Mechanistic investigations: By selectively blocking FcγRII while leaving other activation pathways intact, researchers can isolate the specific contribution of FcγRII to B-cell activation in complex experimental systems.

• Comparative analysis: Comparing B-cell responses in the presence and absence of AT10 blocking fragments allows for quantification of FcγRII dependency across different stimulation conditions.

• Receptor subtype studies: When combined with other antibodies or genetic approaches targeting specific FcγRII isoforms, AT10 can help delineate the relative contributions of FcγRII-a, FcγRII-b, and FcγRII-c to B-cell activation processes.

What binding characteristics distinguish AT10 antibody in research applications?

AT10 antibody exhibits several distinctive binding characteristics that influence its research applications:

• High affinity binding: Affinity binding studies with the Fab' fragment of AT10 reveal an equilibrium binding constant (Ka) of 5.3 x 10^8/M, indicating strong and specific interaction with FcγRII .

• Quantifiable binding sites: Studies on K562 cells demonstrate approximately 1.5 x 10^5 binding sites per cell, providing a quantitative basis for experimental design and interpretation .

• Specificity for human FcγRII: AT10 specifically recognizes human FcγRII without cross-reactivity to other Fc receptors, enabling precise investigation of FcγRII-specific functions.

• Fragment functionality: Both Fab' and F(ab')2 fragments of AT10 retain binding capacity and can be used for blocking studies without introducing potential complications from the Fc portion of the antibody .

These characteristics make AT10 particularly suitable for applications requiring high specificity and sensitivity in detecting or manipulating FcγRII-dependent processes.

How does AT10 antibody performance compare with bispecific antibodies in targeting immune receptors?

While AT10 is a conventional monospecific antibody targeting FcγRII, comparing its performance with bispecific antibodies reveals important methodological considerations:

• Targeting specificity: Unlike bispecific antibodies that engage two different antigens simultaneously, AT10 provides highly specific targeting of FcγRII alone. This makes AT10 valuable for isolating FcγRII-specific effects, while bispecific antibodies can bridge FcγRII with other receptors for novel therapeutic applications .

• Blocking versus activation: AT10 fragments primarily serve blocking functions to neutralize FcγRII activity, whereas many bispecific antibodies are designed to activate effector functions by bringing together different cell types or receptors .

• Research versus therapeutic applications: AT10 is primarily a research tool for investigating FcγRII biology, while bispecific antibodies are increasingly developed as therapeutic agents for conditions like multiple myeloma. The methodological requirements differ accordingly, with bispecific antibodies facing additional challenges related to structural stability and pharmacokinetics .

• Combinations in research: In some advanced experimental designs, AT10 can be used alongside bispecific antibodies to determine whether novel therapeutics depend on FcγRII engagement for their efficacy .

How can researchers address non-specific binding when using AT10 antibody?

Non-specific binding can complicate data interpretation when using AT10 antibody. Researchers should implement these methodological approaches to minimize this issue:

• Optimal antibody concentration: Titrate AT10 antibody to determine the concentration that maximizes specific binding while minimizing background. This is particularly important for flow cytometry applications where signal-to-noise ratio directly impacts data quality .

• Buffer optimization: Include appropriate proteins (1-2% BSA or FBS) in staining buffers to reduce non-specific interactions. For particularly challenging samples, consider testing different buffer compositions.

• Control implementation: Always run isotype controls in parallel to assess and quantify the level of non-specific binding. The difference between AT10 and isotype control staining represents specific signal .

• Fc receptor considerations: Since AT10 targets an Fc receptor (FcγRII), traditional Fc blocking strategies may interfere with desired binding. Instead, carefully design experiments to distinguish specific from non-specific binding through appropriate controls.

• Sample quality: Ensure high cell viability, as dead or dying cells often exhibit increased non-specific antibody binding. Include viability dyes in flow cytometry panels to exclude these populations during analysis .

What factors might affect AT10 antibody detection sensitivity in experimental settings?

Several variables can influence the sensitivity of FcγRII detection using AT10 antibody:

• Receptor expression levels: Detection sensitivity correlates with FcγRII expression density. K562 cells with approximately 1.5 x 10^5 binding sites per cell show robust detection, while cells with lower expression may require optimized protocols .

• Antibody quality and storage: Proper handling is essential, including aliquoting the antibody upon receipt and storing at -20°C to avoid repeated freeze/thaw cycles that can degrade IgG and affect performance .

• Detection method selection: Direct conjugation with bright fluorophores enhances sensitivity for flow cytometry. For immunoprecipitation or Western blotting, sensitivity can be improved through optimized lysis conditions and detection reagents.

• Signal amplification strategies: For cells with low FcγRII expression, consider signal amplification approaches such as biotin-streptavidin systems or polymer-based detection methods.

• Instrument settings: In flow cytometry applications, optimize PMT voltages and compensation settings for maximum sensitivity while maintaining appropriate resolution between positive and negative populations.

How should researchers interpret data from AT10 antibody blocking experiments?

When interpreting results from blocking experiments using AT10 antibody fragments, consider these methodological principles:

• Dose-dependent effects: Establish dose-response relationships by testing multiple concentrations of AT10 fragments. Complete blocking may require saturating concentrations, while partial blocking at sub-saturating concentrations can reveal the sensitivity of different FcγRII-dependent processes.

• Appropriate controls: Always include isotype-matched control antibody fragments (e.g., control F(ab')2) to distinguish specific blocking effects from non-specific interference .

• Functional readouts: Select relevant functional assays that directly measure FcγRII-dependent processes. Published examples include cell rosetting assays with IgG-coated red blood cells and redirected cellular cytotoxicity assays .

• Timing considerations: Account for the temporal dynamics of blocking. Pre-incubation with AT10 fragments before adding stimulatory agents typically provides more complete inhibition than simultaneous addition.

• Quantitative analysis: When possible, quantify blocking effects using metrics like percent inhibition relative to control conditions. This facilitates statistical analysis and comparison across experimental conditions.

How might AI tools enhance antibody research involving AT10 and related antibodies?

Recent advances in AI tools present significant opportunities for enhancing research with AT10 and similar antibodies:

• Binding prediction: Deep learning models like AF2Complex can predict antibody-antigen interactions, potentially optimizing AT10 derivatives with enhanced specificity or affinity for FcγRII. These computational approaches could accelerate the development of improved research reagents by predicting structural complexes .

• Epitope mapping: AI tools can help identify specific epitopes recognized by AT10 on FcγRII, facilitating more precise understanding of its mechanism of action and enabling the design of complementary antibodies targeting different epitopes .

• Experimental design optimization: Machine learning algorithms can analyze experimental variables to identify optimal conditions for AT10 use across different applications, potentially reducing experimental variability .

• Data interpretation: AI-assisted analysis of complex flow cytometry or imaging data from AT10 experiments could reveal subtle patterns that might otherwise be missed by conventional analysis approaches .

• Therapeutic development: While AT10 is primarily a research tool, insights from its binding characteristics could inform AI-driven design of therapeutic antibodies targeting FcγRII for inflammatory or autoimmune conditions .

What methodological advances are improving antibody research beyond traditional applications?

Several methodological innovations are expanding the capabilities of antibody research beyond conventional applications:

• Bispecific antibody technologies: While AT10 targets a single epitope, newer bispecific platforms allow simultaneous targeting of FcγRII and another antigen, creating opportunities for novel functional studies and therapeutic applications .

• Fragment-based approaches: Building on the success of Fab' and F(ab')2 fragments of antibodies like AT10, researchers are developing smaller antibody fragments and alternative binding proteins with improved tissue penetration and reduced immunogenicity .

• Single-cell analysis: Combining AT10 staining with single-cell technologies like mass cytometry or single-cell RNA-seq enables comprehensive characterization of FcγRII-expressing cell populations with unprecedented resolution.

• Imaging technologies: Advanced microscopy techniques allow visualization of AT10-labeled FcγRII in spatial and temporal dimensions, providing insights into receptor clustering, trafficking, and interactions with other cellular components.

• Genome editing integration: CRISPR-Cas9 approaches combined with AT10-based detection systems facilitate precise investigation of FcγRII function through targeted modifications of receptor expression or structure.

How can researchers address contradictory findings when using AT10 antibody across different experimental systems?

When faced with inconsistent results using AT10 antibody across different experimental systems, researchers should implement a systematic troubleshooting approach:

• Antibody validation: Confirm AT10 specificity in each experimental system using appropriate positive and negative controls. Previously validated cell lines like K562 or FcγRII-transfected Ramos cells can serve as reference standards .

• Receptor isoform consideration: Investigate whether different systems express distinct FcγRII isoforms (FcγRII-a, FcγRII-b, FcγRII-c) that might interact differently with AT10 or exhibit distinct functional properties despite similar detection.

• Expression level quantification: Quantify FcγRII expression levels across systems using calibrated flow cytometry approaches. Differences in receptor density (ranging from 10^4 to 10^5 receptors per cell) can significantly impact functional outcomes and detection sensitivity .

• Experimental condition standardization: Systematically standardize experimental variables including buffer composition, temperature, incubation time, and detection methods to minimize technical variability.

• Complementary approaches: Validate key findings using alternative detection methods or different anti-FcγRII antibodies that recognize distinct epitopes to distinguish antibody-specific effects from true biological differences.

• Context-dependent factors: Consider cell type-specific factors such as receptor glycosylation, membrane organization, or association with signaling partners that might modulate AT10 binding or downstream functional consequences.