CD9 Antibody, FITC conjugated

What is CD9 and what cellular functions make it a significant research target?

CD9 is a 24 kDa transmembrane protein belonging to the tetraspanin family that orchestrates cholesterol-associated tetraspanin-enriched signaling microdomains within the plasma membrane. It forms complexes with integrins, membrane-anchored growth factors, and other proteins . CD9 serves multiple critical biological functions, including:

• Regulation of cell motility and adhesion

• Participation in osteoclastogenesis processes

• Facilitation of neurite outgrowth and myotube formation

• Critical role in sperm-egg fusion during fertilization

• Association of heterologous MHC II molecules on dendritic cell plasma membranes, which enhances T cell stimulation

• Suppression of metastasis in solid tumors, functioning as a metastasis suppressor

These diverse functions make CD9 a valuable target for research in developmental biology, immunology, and cancer research applications.

Which cell types express CD9 and what are optimal detection methods?

CD9 is expressed on multiple human cell types, making it an informative marker for various cell populations:

• Platelets

• Monocytes

• Pre-B lymphocytes

• Granulocytes

• Activated T lymphocytes

• Eosinophils and basophils

• Neurons and glial cells in the peripheral nervous system

For optimal detection, flow cytometry using FITC-conjugated anti-CD9 antibodies represents the standard method. For human blood cells, use 20 μl reagent per 100 μl of whole blood or 10^6 cells in suspension . Protocols typically recommend:

• Fresh samples for optimal results

• Proper blocking steps to minimize non-specific binding

• Including appropriate isotype controls

• Analyzing with excitation at 488 nm (standard argon laser) and detection at emission wavelength of approximately 535 nm

What are the experimental advantages of FITC-conjugated CD9 antibodies compared to unconjugated versions?

FITC-conjugated CD9 antibodies offer several methodological advantages:

• Direct detection: No secondary antibody is required, simplifying protocols and reducing background

• Ready-to-use formulation: Most commercial preparations are optimized and adjusted for direct use without reconstitution

• Standardized signal: FITC provides consistent excitation/emission properties (488 nm excitation, 535 nm emission), compatible with standard flow cytometry lasers

• Multiplexing capability: FITC's spectral properties make it suitable for multicolor panels with minimal compensation requirements

• Quantitative analysis: Enables direct correlation between fluorescence intensity and CD9 expression levels

What specific quality control parameters should be verified when selecting a CD9-FITC antibody?

When evaluating CD9-FITC antibodies for research applications, verify these critical parameters:

Additional verification can include reviewing published citations and manufacturer validation data showing specific staining of known CD9-positive cell populations.

How can flow cytometry protocols be optimized for detecting CD9 in rare cell populations?

For detecting CD9 in rare populations where sensitivity is crucial:

• Sample enrichment techniques:

  • Consider magnetic bead pre-enrichment of target populations

  • Use density gradient centrifugation to remove irrelevant cell types

• Signal optimization:

  • Titrate antibody concentration precisely (start with recommended 10 μl per 10^6 cells)

  • Include blocking steps with appropriate serum or blocking buffer

  • Reduce cellular autofluorescence with quenching agents

• Instrument settings:

  • Increase event collection to 500,000-1,000,000 events

  • Adjust PMT voltages specifically for FITC channel optimization

  • Use narrow bandpass filters for more precise FITC detection

• Gating strategy:

  • Implement hierarchical gating based on forward/side scatter first

  • Use viability dye to exclude dead cells

  • Apply Boolean gating to identify rare CD9+ subpopulations

  • Consider incorporating additional markers to further define the population

• Analysis approach:

  • Apply probability contour plots for visualizing rare events

  • Use statistical approaches like cluster analysis or t-SNE for unbiased identification

  • Calculate signal-to-noise ratio as quality metric

This comprehensive approach enhances detection sensitivity and specificity for rare CD9-expressing cell subsets.

How do CD9 expression patterns correlate with metastatic potential in different tumor types?

CD9 functions as a metastasis suppressor in multiple solid tumors, with expression patterns that provide valuable prognostic information:

Research methodologies should include:

• Quantitative flow cytometry with calibration beads for standardization

• Co-expression analysis with other metastasis markers

• Comparison between primary tumors and metastatic sites

• Functional validation through migration/invasion assays following antibody-mediated clustering of CD9

This expression data supports the potential use of CD9 as both a prognostic biomarker and therapeutic target in oncology research.

What are the known interactions between CD9 and integrin proteins, and how can these be investigated using FITC-conjugated CD9 antibodies?

CD9 forms functional complexes with multiple integrin partners that regulate cellular functions:

• Associates with CD81 and PTGFRN in myoblasts, inhibiting myotube fusion

• Forms complexes with CD81 and β1/β2 integrins in macrophages, preventing multinucleated giant cell formation

• Interacts with integrin CD41/CD61 (GPIIb/GPIIIa) in platelets, regulating activation and aggregation

To investigate these interactions using FITC-conjugated CD9 antibodies:

• Co-immunoprecipitation following crosslinking:

  • Use FITC-CD9 antibody for initial labeling

  • Apply membrane-permeable crosslinkers

  • Immunoprecipitate complexes and analyze interacting partners

• Proximity ligation assays:

  • Combine FITC-CD9 with differentially labeled anti-integrin antibodies

  • Quantify interaction signals through flow cytometry or microscopy

• Multicolor flow cytometry:

  • Design panels including FITC-CD9 with integrin markers

  • Analyze co-expression patterns in different cell states

  • Examine correlation coefficients between CD9 and integrin expression

• Functional studies:

  • Monitor integrin activation state (using conformation-specific antibodies) following CD9 clustering

  • Assess adhesion, migration, or signaling changes using FITC-CD9 to track cellular dynamics

These methodological approaches provide complementary data on CD9-integrin functional interactions in diverse cellular contexts.

What optimized approaches exist for multiplexing CD9-FITC with other tetraspanin markers?

For comprehensive tetraspanin profiling, researchers can employ these multiplexing strategies:

• Spectral compatibility planning:

  • Pair CD9-FITC (excitation: 488 nm, emission: 535 nm) with fluorophores having minimal spectral overlap

  • Recommended pairings:

    • CD63-PE (excitation: 496/566 nm, emission: 578 nm)

    • CD81-APC (excitation: 650 nm, emission: 660 nm)

    • CD151-PE-Cy7 (excitation: 496/566 nm, emission: 785 nm)

• Panel design considerations:

  • Assign brightest fluorophores to lowest expressing antigens

  • Use tandem dyes for markers with similar expression levels

  • Apply the "backbone-and-branch" approach: use CD9-FITC as backbone marker with additional tetraspanins as branch markers

• Optimization protocol:

  • Perform single-color controls for each tetraspanin marker

  • Create fluorescence minus one (FMO) controls

  • Validate antibody concentration ratios to prevent competition for overlapping epitopes

  • Establish compensation matrix using single-stained controls

• Analysis strategies:

  • Apply correlation analysis between tetraspanin markers

  • Use dimensionality reduction techniques (t-SNE, UMAP) to identify tetraspanin-defined microdomains

  • Employ ratio metrics between different tetraspanins for functional phenotyping

This systematic approach enables comprehensive characterization of tetraspanin-enriched microdomains in various cell types.

What considerations are critical when using CD9-FITC antibodies for exosome and extracellular vesicle research?

CD9 is a canonical exosome marker that requires specific methodological considerations:

• Sample preparation optimizations:

  • Use differential ultracentrifugation for exosome isolation

  • Consider size-exclusion chromatography for higher purity

  • Pre-clear samples to remove cellular debris before antibody labeling

• Flow cytometry adaptations:

  • Use bead-based capture systems to increase exosome size for standard flow detection

  • Apply specialized nanoscale flow cytometry for direct vesicle detection

  • Set appropriate thresholds to distinguish vesicles from background noise

• Quantification approaches:

  • Establish standard curves using fluorescent beads of known size

  • Use MESF (Molecules of Equivalent Soluble Fluorochrome) beads for antibody binding quantification

  • Calculate vesicle concentration based on light scatter properties and CD9-FITC signal

• Validation controls:

  • Include detergent controls (Triton X-100) to confirm vesicular nature

  • Use CD9-negative vesicle populations as biological controls

  • Apply size-marker beads to confirm vesicle size distribution

• Multiplexing strategies:

  • Combine CD9-FITC with other exosome markers (CD63, CD81) for subpopulation analysis

  • Include cargo-specific markers to correlate CD9 expression with vesicle content

  • Consider tetraspanin web components for functional characterization

This methodological framework supports rigorous characterization of CD9-positive extracellular vesicles in diverse research contexts.

How can researchers mitigate photobleaching effects when working with FITC-conjugated CD9 antibodies?

FITC is particularly susceptible to photobleaching, which can compromise experimental results. Implement these techniques to minimize this effect:

• Preventive measures during sample preparation:

  • Work in reduced ambient lighting conditions

  • Shield samples from direct light using amber tubes or aluminum foil

  • Complete staining procedures efficiently to minimize exposure time

  • Add antifade agents compatible with live cells if appropriate

• Storage optimization:

  • Maintain stock antibody solutions at 2-8°C in the dark

  • Avoid repeated freeze-thaw cycles

  • Consider aliquoting to minimize exposure during regular use

  • Monitor expiration dates carefully

• Instrument settings adjustments:

  • Reduce excitation laser power when possible

  • Minimize exposure time during acquisition

  • Utilize neutral density filters to reduce illumination intensity

  • Adjust PMT voltages to optimize signal while minimizing exposure

• Analysis strategies for compensating photobleaching effects:

  • Run controls in the same sequence to normalize for time-dependent signal loss

  • Consider time-to-intensity curves to mathematically correct for photobleaching

  • Use reference beads to normalize signal across samples and time points

These practices ensure consistent and reliable detection of CD9 even in extended experimental protocols.

What are the optimal fixation and permeabilization methods for preserving CD9 epitope recognition by FITC-conjugated antibodies?

Different fixation methods can affect CD9 epitope accessibility, particularly for the MEM-61 clone which recognizes the second extracellular domain (EC2) :

For permeabilization (if intracellular epitopes are targeted):

• Gentle non-ionic detergents (0.1% Triton X-100, 0.1% saponin) are preferred

• Brief exposure times (5-10 minutes) minimize epitope degradation

• Include blocking proteins (BSA, serum) to reduce non-specific binding

Validation approach:

• Test multiple fixation/permeabilization conditions

• Compare MFI values to identify optimal protocol

• Consider epitope retrieval methods if signal is compromised

• Evaluate background fluorescence levels across methods

For the MEM-61 clone specifically, mild paraformaldehyde fixation without permeabilization typically provides optimal results for detecting surface CD9 with minimal epitope disruption .

What are common causes of poor CD9-FITC antibody staining and their solutions?

When CD9-FITC staining does not meet expectations, consider these common issues and solutions:

Validation approaches:

• Compare results with alternative CD9 antibody clones

• Include isotype controls at matching concentrations

• Run parallel samples with unconjugated CD9 antibody followed by FITC-secondary

• Perform blocking experiments with unconjugated antibody

This systematic troubleshooting approach helps identify and address specific issues affecting CD9-FITC antibody performance.

How can researchers validate the specificity of CD9-FITC antibody staining?

To ensure CD9-FITC antibody specificity, implement these validation strategies:

• Control-based validation:

  • Isotype controls: Use FITC-conjugated mouse IgG1 isotype at matching concentration

  • Blocking experiments: Pre-incubate cells with excess unconjugated CD9 antibody before FITC-conjugated staining

  • Knockdown/knockout validation: Compare staining in CD9-silenced versus wild-type cells

  • Peptide competition: Pre-incubate antibody with CD9 EC2 domain peptide before staining

• Cellular validation:

  • Test on known CD9-positive cells (platelets, monocytes)

  • Compare with CD9-negative cell types as biological controls

  • Verify expected expression patterns across differentiation stages

• Technical validation:

  • Compare multiple CD9 antibody clones (e.g., MEM-61, Clone 01)

  • Correlate FITC-conjugated results with alternative detection methods

  • Verify co-localization with other tetraspanin family members

• Quantitative validation metrics:

  • Calculate signal-to-noise ratio between positive and negative populations

  • Determine staining index = (MFI positive - MFI negative) / (2 × SD negative)

  • Assess coefficient of variation across repeated measurements

This multi-level validation approach confirms that observed signals truly represent CD9 expression rather than technical artifacts or non-specific binding.

How is CD9-FITC antibody being utilized in cancer immunotherapy research?

CD9-FITC antibodies enable several novel applications in cancer immunotherapy research:

• Exosome engineering and monitoring:

  • Track CD9-positive tumor-derived exosomes and their immunomodulatory effects

  • Monitor exosome-based drug delivery systems using CD9 as a vesicle marker

  • Analyze CD9 distribution in engineered exosomes for therapeutic applications

• Immunotherapy response prediction:

  • Profile CD9 expression on tumor cells before and during immunotherapy

  • Correlate CD9 levels with response to immune checkpoint inhibitors

  • Study CD9's role in tumor immune evasion mechanisms

• CAR-T cell development:

  • Investigate CD9's role in CAR-T cell manufacturing and function

  • Monitor tetraspanin enriched microdomains in engineered T cells

  • Study CD9-mediated regulation of CAR signaling complexes

• Tumor microenvironment analysis:

  • Characterize CD9 expression on tumor-infiltrating lymphocytes

  • Assess CD9's role in immune synapse formation within tumors

  • Study CD9-mediated intercellular communication in the tumor milieu

• Metastasis inhibition strategies:

  • Develop therapeutic approaches targeting CD9 to suppress metastasis

  • Monitor CD9 restoration in response to experimental therapies

  • Investigate CD9-based biomarkers for metastatic potential

These applications leverage CD9's roles in both tumor biology and immune cell function, positioning CD9-FITC antibodies as valuable tools in next-generation cancer immunotherapy research.

What methodological advances are improving CD9 detection in complex tissue microenvironments?

Recent methodological innovations have enhanced CD9 detection in complex tissues:

• Advanced microscopy techniques:

  • Super-resolution microscopy for nanoscale visualization of CD9 in tetraspanin-enriched microdomains

  • Multiphoton microscopy with FITC-CD9 for deeper tissue penetration

  • Light sheet microscopy for 3D reconstruction of CD9 distribution in intact tissues

• Multi-omics integration:

  • Correlation of CD9 protein expression (detected by FITC antibodies) with single-cell transcriptomics

  • Spatial proteomics combining CD9-FITC with mass spectrometry imaging

  • Integration of CD9 detection with glycomic and lipidomic analyses of tetraspanin webs

• Tissue clearing and 3D analysis:

  • CLARITY and iDISCO compatibility with FITC-CD9 immunostaining

  • Whole-organ CD9 mapping using light sheet microscopy after clearing

  • Quantitative 3D analysis of CD9 distribution within tissue architecture

• Artificial intelligence applications:

  • Machine learning algorithms for automated identification of CD9-positive cells in tissues

  • Deep learning approaches for pattern recognition in CD9 distribution

  • Computer vision techniques for quantifying CD9 expression across tissue compartments