CD4 Human, Active

What is the basic structure and function of CD4 protein in human immune cells?

CD4 (cluster of differentiation 4) is a glycoprotein that functions as a co-receptor for the T-cell receptor (TCR). Structurally, CD4 belongs to the immunoglobulin superfamily and features four immunoglobulin domains (D1 to D4) exposed on the cell's extracellular surface. The D1 and D3 domains resemble immunoglobulin variable (IgV) domains, while D2 and D4 resemble immunoglobulin constant (IgC) domains. The D1 domain specifically interacts with the β2-domain of MHC class II molecules, enabling CD4+ T cells to recognize antigens presented by MHC class II (making them MHC class II-restricted) . In humans, the CD4 protein is encoded by the CD4 gene and was discovered in the late 1970s, initially known as leu-3 and T4 before receiving its current designation in 1984 .

How do CD4+ T cells contribute to the immune response pathway?

CD4+ T cells, often called helper T cells, play a crucial orchestrating role in immune responses rather than directly neutralizing infections. These cells are among the first to recognize harmful substances, initiating the immune cascade by secreting specific cytokines that activate different immune effector cells appropriate for the particular threat . Their primary function is to send signals to other immune cells, including CD8+ killer cells, which then destroy infectious particles . Additionally, CD4+ T cells help suppress the immune reaction after the threat has been eliminated, preventing prolonged inflammatory responses . This regulatory role makes them essential for balanced immune function. When CD4+ cells become depleted, as in untreated HIV infection or during immune suppression for transplants, the body becomes vulnerable to infections it would otherwise effectively combat .

What methodological approaches can be used to isolate functionally active CD4+ T cells from human samples?

Isolating functionally active CD4+ T cells requires careful attention to both purity and viability. The most common methodological approaches include:

• Flow Cytometry-Based Sorting: Using fluorescently labeled antibodies against CD4 and additional markers (CD26, CCR4, CCR6, CXCR3) to isolate specific subpopulations with high purity (>90%) .

• Magnetic Bead-Based Isolation: Either positive selection (directly targeting CD4+ cells) or negative selection (removing non-CD4+ cells) can be employed, with negative selection often preferred when downstream functional assays are planned to avoid potential receptor activation.

• Density Gradient Centrifugation: As a preliminary step before more specific isolation techniques to separate peripheral blood mononuclear cells (PBMCs) from other blood components.

For optimal functional assays, researchers should minimize mechanical stress during isolation, maintain physiological temperature and pH conditions, and supplement media with appropriate cytokines to prevent activation-induced cell death .

How can researchers distinguish between different CD4+ T cell subpopulations?

Researchers can distinguish between CD4+ T cell subpopulations through multiparameter analysis of surface markers, functional characteristics, and molecular signatures. Key methodological approaches include:

• Surface Marker Profiling: Different helper T cell subsets express characteristic patterns of chemokine receptors and other surface molecules:

  • TH1 cells: CXCR3+CCR4-CCR6-

  • TH2 cells: CXCR3-CCR4+CCR6-

  • TH17 cells: CCR4+CCR6+CXCR3+/-

  • CD26high cells: High CXCR3, high CCR6, nominal CCR4

• Cytokine Secretion Analysis: Following stimulation, CD4+ subsets produce signature cytokine profiles:

  • TH1 cells: Primarily IFN-γ

  • TH2 cells: IL-4, IL-5, IL-13

  • TH17 cells: IL-17A, some IFN-γ

  • CD26high cells: Co-secrete elevated IL-17A, IFN-γ, and IL-22

• Epigenetic and Transcriptional Analysis: Different subsets show distinct methylation patterns at key loci and unique transcriptional signatures, which can be assessed through techniques like ATAC-seq, RNA-seq, and methylation analysis .

Flow cytometric validation remains the gold standard for confirming subset identity before experimental use, with attention to both surface phenotype and functional response to stimulation .

What is the significance of CD26 expression levels in classifying CD4+ T cell subsets?

CD26 expression levels serve as a critical marker for classifying functionally distinct CD4+ T cell subsets with significant implications for immune response capabilities. Research has established that CD26 distinguishes three human CD4+ T cell populations with varying immunological properties:

• CD26neg: These cells demonstrate predominantly regulatory properties, potentially suppressing immune responses.

• CD26int: This subset displays a naïve/central memory phenotype, representing cells with potential for differentiation.

• CD26high: This population exhibits a durable stem memory profile with superior antitumor capabilities .

While TH17 cells have traditionally been associated with high CD26 expression, recent research reveals that CD26high T cells represent a distinct population with a unique functional profile. Although both CD26high and TH17 cells produce IL-17A, CD26high cells secrete comparable amounts of IL-17A but significantly greater levels of IFN-γ and IL-22 than classical TH17 cells, demonstrating a more polyfunctional nature .

Methodologically, researchers should consider CD26 expression in conjunction with chemokine receptors (CXCR3, CCR4, CCR6) and functional assays to accurately identify and classify these subsets for experimental applications, particularly in contexts requiring potent effector responses like tumor immunotherapy .

How do different activation protocols affect the functional properties of CD4+ T cell subsets?

Different activation protocols significantly impact the functional properties of CD4+ T cell subsets, with implications for both basic research and therapeutic applications. Experimental evidence demonstrates:

• CD3/CD28 vs. CD3/ICOS Activation:

  • TH1, TH17, and CD26high cells secrete elevated levels of IFN-γ and/or IL-17A when expanded with CD3/ICOS beads compared to standard CD3/CD28 beads .

  • CD3/CD28 bead expansion augments the frequency of regulatory T cells (Tregs) in CD4+ cultures relative to CD3/ICOS bead expansion .

  • TH17 cells specifically demonstrate enhanced co-secretion of IL-17A and IFN-γ after expansion with CD3/ICOS versus CD3/CD28 activator beads .

• Cytokine Microenvironment During Activation:

  • The presence of specific cytokines during activation directs differentiation pathways and functional properties.

  • While exogenous IL-2 supports initial expansion, it may not be critical for long-term persistence of all CD4+ subsets, particularly for CD26high and TH17 cells .

• Activation Strength and Duration:

  • Signal strength affects both the proliferative response and cytokine production profile.

  • Extended activation periods may lead to functional exhaustion in some subsets while enhancing the polyfunctionality of others.

When designing experiments, researchers should select activation protocols based on the specific CD4+ subset under investigation and the functional readouts of interest. For therapeutic applications, especially CAR T cell engineering, CD3/ICOS activation may provide superior functional properties for certain CD4+ subsets targeted at solid tumors .

How do different CD4+ T cell subsets contribute to antitumor immunity?

Different CD4+ T cell subsets demonstrate varying capabilities in antitumor immunity, with recent research highlighting remarkable functional differences:

• Comparative Antitumor Efficacy:

  • CD26high T cells engineered with chimeric antigen receptors (CARs) demonstrate superior antitumor activity compared to TH1, TH2, TH17, or unselected CD4+ T cells .

  • TH17 CAR T cells can transiently regress tumors but typically fail to achieve complete elimination, while CD26high CAR T cells can mediate curative responses even with suboptimal CAR constructs .

  • Both CD26high and TH17 CAR T cells outperform TH1 and TH2 CAR T cells in tumor regression models .

• Functional Mechanisms:

  • CD26high T cells demonstrate unique polyfunctionality, co-secreting elevated IL-17A, IFN-γ, and IL-22 .

  • These cells produce cytotoxic molecules and persist long-term in tumor models, even without the aid of CD8+ CAR T cells .

  • The epigenetic and transcriptional profiles of CD26high T cells distinguish them from classical TH1 or TH17 cells, contributing to their enhanced performance .

• Persistence and Memory Formation:

  • CD26high T cells exhibit a durable stem memory profile that enables long-term persistence after adoptive transfer .

  • This persistence appears to be independent of exogenous IL-2 support beyond the initial expansion phase .

For researchers developing CD4+ T cell-based immunotherapies, these findings suggest that CD26high T cells may offer particular advantages for solid tumor targeting. Methodologically, isolation strategies should incorporate CD26 expression analysis alongside traditional helper subset markers when preparing cells for therapeutic applications .

What experimental approaches can be used to study CD4+ T cell interactions with MHC class II molecules?

Studying CD4+ T cell interactions with MHC class II molecules requires sophisticated experimental approaches that capture both molecular binding dynamics and functional consequences. Key methodological strategies include:

• Structural and Binding Analysis:

  • Surface Plasmon Resonance (SPR): Enables quantitative measurement of binding kinetics between recombinant CD4 proteins and MHC class II molecules, allowing determination of association and dissociation rates.

  • X-ray Crystallography and Cryo-EM: Provides atomic-level structural information about the interface between CD4's D1 domain and the β2-domain of MHC class II molecules .

  • FRET-based Interaction Assays: Permits investigation of these interactions in living cells under various conditions.

• Functional Analysis:

  • Antigen Presentation Assays: Using APCs expressing wild-type or mutated MHC class II molecules to assess how CD4-MHC interactions affect T cell activation.

  • Site-directed Mutagenesis: Introducing specific mutations in the CD4 D1 domain to identify critical residues for MHC class II binding and subsequent signal transduction.

  • Blocking Antibody Experiments: Employing antibodies that target specific regions of CD4 or MHC class II to dissect the functional consequences of disrupting these interactions.

• Imaging Approaches:

  • Live-cell Microscopy: Visualizing the dynamic formation of immunological synapses between CD4+ T cells and antigen-presenting cells.

  • Super-resolution Techniques: Resolving nanoscale organization of CD4 and TCR complexes during MHC engagement.

These experimental approaches should be integrated to build a comprehensive understanding of how CD4-MHC class II interactions influence T cell activation, differentiation, and effector functions in both physiological and pathological contexts.

How does CD4+ T cell depletion affect susceptibility to different types of infections?

CD4+ T cell depletion profoundly impacts susceptibility to infections, with effects varying by pathogen type, depletion extent, and timing. Understanding these relationships requires comprehensive experimental approaches:

• Pathogen-Specific Vulnerabilities:

  • Opportunistic Infections: CD4+ depletion dramatically increases susceptibility to Pneumocystis pneumonia, Candida infections, Toxoplasma gondii, and Cryptococcus neoformans, which rarely cause disease in immunocompetent hosts .

  • Viral Infections: Enhanced vulnerability to CMV reactivation, EBV-associated lymphoproliferative disorders, and progressive multifocal leukoencephalopathy (JC virus) .

  • Mycobacterial Infections: Increased risk of tuberculosis reactivation and dissemination, with severity correlating with CD4+ count.

• Threshold Effects and Kinetics:

  • Different pathogens demonstrate varying CD4+ count thresholds below which risk significantly increases.

  • The rate of CD4+ decline influences infection susceptibility, with rapid depletion often having more severe consequences than gradual decline.

  • Timing of immune reconstitution after depletion affects resolution versus progression of established infections.

• Experimental Models and Assessment Methods:

  • Humanized Mouse Models: Enable study of human CD4+ depletion effects on pathogen susceptibility in vivo.

  • Ex vivo Infection Models: Using CD4-depleted human PBMCs or tissues to assess pathogen control.

  • Systems Biology Approaches: Integrating transcriptomic, proteomic, and immunophenotyping data to map networks affected by CD4+ depletion.

Researchers should implement comprehensive immune monitoring protocols when studying CD4-depleted subjects, including not only quantitative assessment of remaining CD4+ cells but also functional evaluation of their cytokine production, proliferation capacity, and TCR repertoire diversity .

What are the optimal conditions for maintaining functional activity of recombinant CD4 protein in experimental settings?

Maintaining the functional activity of recombinant CD4 protein requires careful attention to storage, handling, and experimental conditions. Based on research protocols, optimal conditions include:

• Storage Requirements:

  • Store frozen at -20°C for longer periods to prevent degradation .

  • For long-term storage, adding a carrier protein (0.1% HSA or BSA) is recommended to maintain stability .

  • Avoid multiple freeze-thaw cycles, as these can progressively reduce functional activity .

  • Working aliquots should be prepared to minimize repeated thawing of stock solutions.

• Buffer Composition:

  • Optimal formulation includes 40% glycerol in Phosphate-Buffered Saline (pH 7.4) for stability .

  • Maintaining physiological pH is critical, as CD4 undergoes conformational changes under acidic conditions.

  • Consider additives like reducing agents to maintain critical disulfide bonds in their native state.

• Experimental Handling:

  • Keep samples on ice when working at the bench.

  • Pre-treat surfaces to prevent protein adsorption.

  • For functional assays, recombinant CD4 remains biologically active at concentrations ≤ 10 μg/ml, as demonstrated by its ability to support the adhesion of HeLa cells when immobilized .

  • Validate activity when using in novel experimental systems by employing established functional readouts.

• Quality Control Metrics:

  • Verify purity (>90%) via SDS-PAGE before experimental use .

  • Consider dynamic light scattering to assess aggregation state.

  • Validate glycosylation status when production system is changed, as this affects functional properties.

By maintaining these conditions, researchers can ensure the recombinant CD4 protein retains its native conformation and functional capabilities throughout experimental workflows.

How can researchers design experiments to differentiate between direct and indirect effects of CD4+ T cells in immune responses?

Designing experiments to differentiate between direct and indirect effects of CD4+ T cells requires sophisticated approaches that isolate specific cellular interactions and signaling pathways. Key methodological strategies include:

• In Vitro Co-culture Systems:

  • Transwell Assays: Physically separate CD4+ T cells from target cells while allowing soluble factor exchange, distinguishing contact-dependent from cytokine-mediated effects.

  • Conditional Media Experiments: Transfer supernatants from activated CD4+ T cells to target cells to isolate effects of secreted factors.

  • Cell-specific Receptor Blockade: Use blocking antibodies against specific receptors on either CD4+ T cells or target cells to dissect interaction requirements.

• Genetic Manipulation Approaches:

  • CRISPR/Cas9 Editing: Create CD4+ T cells with specific cytokine or receptor deletions to determine essential mediators.

  • Inducible Gene Expression Systems: Enable temporal control of specific pathway components to establish causality.

  • Reporter Systems: Engineer target cells with pathway-specific reporters to monitor activation of distinct signaling cascades.

• In Vivo Models with Selective Targeting:

  • Conditional Depletion Models: Use CD4-CreERT2 systems for temporal control of gene deletion specifically in CD4+ T cells.

  • Adoptive Transfer Experiments: Transfer wild-type or genetically modified CD4+ T cells into lymphopenic hosts to isolate their specific contributions.

  • Parabiosis Studies: Connect circulatory systems of mice with different genetic backgrounds to distinguish local from systemic CD4+ effects.

• Systems Biology Integration:

  • Single-cell RNA Sequencing: Profile transcriptional changes in both CD4+ T cells and potential target cells simultaneously.

  • Spatial Transcriptomics: Map cellular interactions within tissue microenvironments.

  • Computational Modeling: Develop predictive models of CD4+ T cell interaction networks to generate testable hypotheses about direct versus indirect effects.

These methodological approaches should be combined with appropriate controls and time-course analyses to establish causality and distinguish primary from secondary effects of CD4+ T cell activity in complex immune responses.

What technical challenges exist in engineering CD4+ T cells with chimeric antigen receptors (CARs) for research and therapeutic applications?

Engineering CD4+ T cells with chimeric antigen receptors (CARs) presents several technical challenges spanning from basic research to clinical applications:

• Subset-Specific Optimization Challenges:

  • Different CD4+ subsets (TH1, TH2, TH17, CD26high) respond distinctly to CAR engineering protocols .

  • Activation methodology significantly impacts functional outcomes—CD3/ICOS activation yields superior cytokine production in TH17 and CD26high cells compared to standard CD3/CD28 stimulation .

  • Maintaining subset identity throughout the manufacturing process requires specialized culture conditions and monitoring protocols.

• Vector Design and Transduction Efficiency:

  • CD4+ T cells typically show lower transduction efficiencies than CD8+ T cells with standard protocols.

  • CAR construct design must account for CD4+ T cell signaling requirements, which may differ from CD8+ T cells.

  • Balancing CAR expression levels to avoid tonic signaling while maintaining sensitivity presents a significant challenge.

• Functional Assessment Complexities:

  • Evaluating CD4+ CAR T cell efficacy requires measuring not only direct cytotoxicity but also helper functions and cytokine production.

  • Long-term persistence mechanisms differ between CD4+ subsets, with CD26high cells demonstrating superior durability independent of exogenous IL-2 support .

  • Standard in vitro cytotoxicity assays may underestimate the therapeutic potential of CD4+ CAR T cells, which often mediate effects through indirect mechanisms.

• Translational Challenges:

  • GMP-compliant protocols for isolating specific CD4+ subsets like CD26high cells require development and validation.

  • The heterogeneity of CD4+ T cells in patient samples introduces variability in manufacturing outcomes.

  • Defining release criteria that capture the unique functional properties of CD4+ CAR T cells beyond traditional CD8-focused metrics.

Researchers developing CD4+ CAR T cell platforms should implement comprehensive phenotypic and functional characterization throughout the manufacturing process, with particular attention to maintaining subset-specific advantages like the polyfunctionality of CD26high cells . This approach may lead to more effective next-generation cell therapies, especially for solid tumors where standard CAR T approaches have shown limited efficacy.

How might single-cell analysis technologies advance our understanding of CD4+ T cell heterogeneity beyond current classification schemes?

Single-cell analysis technologies offer unprecedented opportunities to redefine our understanding of CD4+ T cell heterogeneity beyond traditional classification schemes. These methodological innovations promise to:

These technological advances will likely shift CD4+ T cell classification from discrete categories to multidimensional continua, with practical implications for both basic immunology and therapeutic development. Researchers should implement integrated single-cell analysis workflows that capture both phenotypic and functional parameters to fully leverage these methodological innovations.

What are the emerging methods for tracking CD4+ T cell responses in complex in vivo environments?

Emerging methods for tracking CD4+ T cell responses in complex in vivo environments are revolutionizing our ability to monitor these cells with unprecedented spatiotemporal resolution. Key methodological innovations include:

• Advanced Intravital Imaging Approaches:

  • Multiphoton Microscopy with Optogenetic Reporters: Enables real-time visualization of CD4+ T cell activation states and interactions in living tissues.

  • Adaptive Optics: Improves imaging depth and resolution in dense tissues like tumors.

  • FRET-based Biosensors: Reports specific signaling events within CD4+ T cells during antigen recognition and effector function execution.

• High-Dimensional In Situ Analysis:

  • Multiplexed Ion Beam Imaging (MIBI): Allows simultaneous detection of >40 proteins at subcellular resolution within tissue sections.

  • Co-Detection by Indexing (CODEX): Enables visualization of dozens of markers in intact tissues to map CD4+ subsets in their native microenvironment.

  • Spatial Transcriptomics: Combines location information with gene expression data to create spatially resolved maps of CD4+ T cell states.

• Innovative Fate Mapping Technologies:

  • Genetic Barcoding: Permits tracking of individual CD4+ T cell clones and their progeny through complex immune responses.

  • Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq): Simultaneously captures surface protein expression and transcriptomes at single-cell resolution.

  • Timestamping Techniques: Incorporates recordable elements into cells that mark temporal events during immune responses.

• Systems-level Integration Approaches:

  • Artificial Intelligence-based Image Analysis: Extracts patterns from complex imaging data to identify novel CD4+ T cell behaviors.

  • Mathematical Modeling: Predicts CD4+ T cell migration, differentiation, and functional impact in specific tissue contexts.

  • Multi-scale Integration: Connects molecular, cellular, and tissue-level data for comprehensive understanding of CD4+ T cell dynamics.

These methodological advances will help address critical questions about how distinct CD4+ populations like CD26high T cells function in complex environments such as tumors, potentially explaining their superior therapeutic efficacy observed in experimental models . Researchers should consider implementing complementary tracking approaches to capture both population-level dynamics and single-cell behaviors when studying CD4+ T cells in vivo.

How can computational modeling help predict CD4+ T cell responses to novel antigens for vaccine development and immunotherapy optimization?

Computational modeling offers powerful approaches to predict CD4+ T cell responses to novel antigens, with significant implications for vaccine development and immunotherapy optimization. Advanced methodological strategies include:

• Epitope Prediction and TCR Engagement Modeling:

  • Machine Learning Algorithms: Trained on experimental binding data to predict MHC class II presentation of peptide epitopes.

  • Molecular Dynamics Simulations: Model the structural dynamics of CD4-TCR-peptide-MHC interactions to predict binding affinities and activation thresholds.

  • Network Analysis: Identify potential cross-reactive epitopes by mapping similarities in TCR recognition patterns.

• Population Response Modeling:

  • Agent-based Models: Simulate individual CD4+ T cell behaviors and interactions to predict population-level responses.

  • Differential Equation Models: Capture dynamics of CD4+ subset differentiation and proliferation following antigen exposure.

  • Bayesian Networks: Integrate prior knowledge with experimental data to predict how antigen properties influence CD4+ subset polarization.

• Integration with Experimental Validation:

  • In Silico Screening: Computationally rank candidate antigens before experimental testing.

  • Iterative Refinement: Use experimental results to improve predictive models through machine learning approaches.

  • Digital Twin Concepts: Create virtual representations of individual immune systems to predict personalized CD4+ responses.

• Therapeutic Optimization Applications:

  • CAR T Cell Design: Optimize CAR constructs to preferentially expand beneficial subsets like CD26high cells that demonstrate superior antitumor activity .

  • Vaccine Adjuvant Selection: Predict which adjuvants will elicit desired CD4+ subset responses based on receptor expression and signaling pathway modeling.

  • Combination Therapy Sequencing: Model temporal aspects of CD4+ responses to optimize timing of immunotherapy components.

These computational approaches can help researchers address complex questions about how CD4+ T cells, particularly specialized subsets like CD26high cells, will respond to novel antigens or engineered receptors. By implementing these predictive models early in development pipelines, researchers can potentially accelerate therapeutic development and increase success rates in translational applications.

Comparative Characteristics of CD4+ T Cell Subsets

This table synthesizes data from research findings to provide a comparative view of the major CD4+ T cell subsets, highlighting their distinguishing characteristics relevant to experimental design and therapeutic applications .

Recombinant CD4 Human Protein Specifications for Research Applications

This table provides comprehensive specifications for recombinant CD4 Human protein, highlighting parameters crucial for experimental planning and quality control in research applications .

Functional Impact of CD4+ T Cell Depletion on Pathogen Susceptibility

This table summarizes the relationship between CD4+ T cell depletion and susceptibility to various pathogens, including relevant experimental approaches for studying these relationships. Data compiled from immunological research on opportunistic infections associated with CD4+ depletion .