BTN3A1 Antibody

What is BTN3A1 and what is its primary function in the immune system?

BTN3A1 is a member of the butyrophilin family that plays a crucial role in T-cell activation and adaptive immune response. It regulates the proliferation of activated T-cells and controls the release of cytokines and interferon gamma by these cells . Methodologically, its function can be investigated through co-culture experiments with T-cells, where BTN3A1 expression is either enhanced through transfection or diminished through knockdown approaches, followed by measuring T-cell activation markers, proliferation rates, and cytokine production.

How does BTN3A1 interact with phosphoantigens to activate Vγ9Vδ2 T cells?

BTN3A1 mediates the response of T-cells toward infected and transformed cells characterized by high levels of phosphorylated metabolites like isopentenyl pyrophosphate . The cytoplasmic domain of BTN3A1 senses these intracellular phosphoantigens . For researchers investigating this interaction, pull-down assays combined with mass spectrometry can identify binding partners, while mutational analysis of the BTN3A1 cytoplasmic domain can determine which regions are critical for phosphoantigen sensing and subsequent T-cell activation.

What are the three isoforms of Butyrophilin 3A and how do they differ functionally?

The Butyrophilin 3A family consists of three isoforms: BTN3A1, BTN3A2, and BTN3A3, which are widely expressed across various tumor types . While all three share structural similarities, they have distinct functions. Researchers can differentiate between these isoforms using isoform-specific antibodies in immunohistochemistry. Specifically, BTN3A1-specific antibodies target the unique regions of this isoform, while pan-BTN3A antibodies recognize all three isoforms . Functional studies require selective silencing of individual isoforms followed by assessment of downstream immune responses.

What criteria should researchers consider when selecting a BTN3A1 antibody for their experiments?

When selecting a BTN3A1 antibody, researchers should consider: 1) specificity (whether the antibody targets just BTN3A1 or all BTN3A isoforms), 2) validated applications (WB, IHC, flow cytometry, ELISA), 3) reactivity with species of interest (human, mouse), 4) clonality (monoclonal vs. polyclonal), and 5) conjugation status (unconjugated or conjugated to tags like FITC, HRP) . For example, if studying human samples through flow cytometry, researchers might select a fluorescently-conjugated monoclonal antibody with validated human reactivity, such as the FITC-conjugated anti-human CD277/BTN3A1 antibody mentioned in the search results .

How can researchers validate the specificity of a BTN3A1 antibody?

Antibody specificity can be validated through multiple approaches. First, researchers should test the antibody on cell lines with known BTN3A1 expression levels, including transfected cells expressing individual BTN3A isoforms and triple knock-out cells lacking all three BTN3A isoforms . Western blotting should show bands at the predicted molecular weight (approximately 58 kDa for BTN3A1) . Additional validation methods include testing on tissue samples with established BTN3A1 expression patterns, such as human tonsil tissues which show differential expression of BTN3A isoforms in B cell subpopulations .

What are the common applications for BTN3A1 antibodies in research?

BTN3A1 antibodies are commonly used in Western blot (WB), immunohistochemistry on paraffin-embedded tissues (IHC-P), enzyme-linked immunosorbent assay (ELISA), and flow cytometry (FCM) . For instance, in Western blot applications, antibodies like ab236289 detect BTN3A1 in tissue lysates at a molecular weight of approximately 58 kDa . In IHC-P, these antibodies can visualize BTN3A1 expression in tissues such as human placenta . Flow cytometry applications are particularly valuable for analyzing BTN3A1 expression on immune cell surfaces, while ELISA can quantify soluble BTN3A1 levels in biological fluids.

What methods are most effective for detecting BTN3A1 expression in tissue samples?

For tissue analysis, immunohistochemistry (IHC) is the method of choice. Both fresh frozen (FF) tissues and formalin-fixed paraffin-embedded (FFPE) tissues can be used, though specific protocols may differ . For FFPE samples, antigen retrieval methods must be optimized to expose the BTN3A1 epitopes masked by fixation. Researchers should use tissue microarrays (TMAs) for high-throughput screening of multiple samples . For quantification, histo-score methods can be applied to assess expression levels across different samples . Additionally, multiplexed immunofluorescence can reveal co-expression patterns with other markers of interest.

How should researchers approach BTN3A1 mRNA expression analysis?

For mRNA analysis, researchers should extract total RNA from fresh frozen tissues using reagents like Trizol, followed by quality assessment using spectrophotometry . cDNA synthesis should be performed using random hexamer primers and reverse transcriptase . Quantitative RT-PCR using SYBR Green and primers specific for BTN3A1 is recommended, with β-Actin serving as an internal control . For comprehensive analysis, RNA sequencing followed by normalization (e.g., using DESeq2) and Log2 transformation allows for reliable differential gene expression analysis .

How do expression patterns of BTN3A1 differ between normal and cancerous tissues?

BTN3A1 expression varies significantly between normal and cancerous tissues. In glioblastoma (grade 4), elevated BTN3A1 expression has been documented compared to lower-grade gliomas, as evidenced by both TCGA database analysis and studies in Moroccan glioma patients . Within the TCGA cohort, BTN3A1 expression was particularly high in patients with wild-type IDH . To investigate these differences, researchers should employ IHC on paired normal and tumor tissues, or analyze public datasets like TCGA, stratifying samples by cancer type, grade, and molecular subtypes.

How does BTN3A1 expression correlate with immune infiltration in tumors?

BTN3A1 expression positively correlates with immune infiltration of various cell types including B cells, CD8+ T cells, naïve CD4+ T cells, and M2 macrophages in glioblastoma . This suggests BTN3A1 may influence the tumor immune microenvironment. To study this correlation, researchers should perform multiplexed immunohistochemistry or flow cytometry on dissociated tumor samples to simultaneously assess BTN3A1 expression and immune cell markers. Additionally, spatial transcriptomics or single-cell RNA sequencing can provide insights into the relationship between BTN3A1-expressing cells and immune cell populations within the tumor microenvironment.

What mechanisms explain the pro-tumorigenic effects of BTN3A1 in certain cancer types?

In glioblastoma, high BTN3A1 expression correlates with elevated levels of immunosuppressive cytokines (TGF-β, IL-10) and checkpoint molecules (TIM-3) , suggesting BTN3A1 may promote an immunosuppressive microenvironment favorable for tumor growth. To investigate these mechanisms, researchers should conduct co-culture experiments with BTN3A1-expressing tumor cells and immune cells, measuring changes in cytokine production, immune cell activation, and cytotoxic function. Knockdown or overexpression studies of BTN3A1 in tumor models can further elucidate its role in tumor progression and immune evasion.

How can BTN3A1 expression be utilized as a prognostic biomarker in oncology?

Patients with high BTN3A1 expression in glioblastoma show poorer prognosis compared to those with lower expression . To develop BTN3A1 as a prognostic biomarker, researchers should conduct large-scale retrospective studies correlating BTN3A1 expression (assessed by IHC or RNA-seq) with patient survival data, treatment response, and clinicopathological features. Establishing standardized cutoff values for "high" versus "low" expression using methods like median-based stratification is crucial . Multivariate analysis should be performed to determine if BTN3A1 expression provides independent prognostic value beyond established clinical factors.

How do therapeutic anti-BTN3A antibodies like ICT01 activate Vγ9Vδ2 T cells?

ICT01 is an anti-BTN3A monoclonal antibody that binds to all three BTN3A isoforms, triggering a cascade of events: 1) activation and trafficking of γ9δ2 T cells from circulation within 30 minutes, 2) production of IFNγ and TNFα leading to immune cell activation, 3) proliferation in the presence of cytokines, and 4) cytotoxic attack of malignant cells expressing a necessary second signal . Researchers studying this mechanism should employ time-course experiments tracking T cell activation markers, cytokine production, and cytotoxicity assays using live-cell imaging or flow cytometry to monitor the kinetics of these processes.

What factors determine patient response to BTN3A-targeting immunotherapies?

The level of BTN3A expression in tumor tissues may influence response to BTN3A-targeting therapies like ICT01, although the precise expression threshold required for clinical response remains undetermined . To identify predictive biomarkers of response, researchers should analyze pre- and post-treatment biopsies from clinical trial participants, assessing not only BTN3A expression but also baseline immune infiltration, tumor mutational burden, and expression of other immune regulatory molecules. Clinical trials like the EVICTION study (NCT04243499) provide valuable opportunities to correlate these factors with treatment outcomes .

How can researchers develop optimal combination strategies involving BTN3A1-targeting agents?

Combination strategies involving BTN3A1-targeting agents should consider the immunomodulatory effects of BTN3A1 and potential synergies with other immunotherapies. For instance, given that high BTN3A1 expression correlates with elevated TIM-3 levels in glioblastoma , combining BTN3A1-targeting therapies with TIM-3 blockade might be beneficial. Researchers should conduct in vitro and in vivo studies testing BTN3A1-targeting antibodies in combination with checkpoint inhibitors, cytokine therapies, or conventional treatments, measuring effects on immune activation, tumor growth, and survival outcomes.

What techniques can differentiate between BTN3A isoforms in research applications?

Differentiating between BTN3A isoforms requires isoform-specific antibodies and molecular approaches. Immunohistochemistry methods have been developed for specific detection: pan-BTN3A staining for fresh frozen tissues can detect all three isoforms, while BTN3A2 and BTN3A3 specific stainings have been developed for FFPE tissues . For molecular analysis, researchers should design isoform-specific primers for qRT-PCR and validate their specificity using cells transfected with individual isoforms. Western blotting with isoform-specific antibodies can also differentiate between the proteins based on subtle differences in molecular weight.

How can researchers optimize BTN3A1 detection in challenging tissue samples?

For challenging tissue samples, researchers should optimize sample preparation, antibody dilution, and detection systems. For FFPE samples with potential antigen masking, testing different antigen retrieval methods (heat-induced vs. enzymatic) and buffers (citrate vs. EDTA) is recommended. Signal amplification techniques such as tyramide signal amplification can enhance detection sensitivity. For tissues with high background, blocking steps should be optimized, and alternative detection systems (polymer-based vs. avidin-biotin) should be compared. Positive and negative controls, including transfected cell lines with known BTN3A1 expression , are essential for protocol validation.

What are the best approaches for quantifying BTN3A1 expression across different experimental platforms?

For consistent quantification across platforms, researchers should establish standardized protocols. In IHC, histo-score methods that account for both staining intensity and percentage of positive cells provide reliable quantification . For flow cytometry, mean fluorescence intensity (MFI) relative to isotype controls allows for quantitative comparison. In Western blot analysis, normalization to housekeeping proteins and use of standard curves with recombinant proteins can enable quantitative assessment. For mRNA quantification, absolute quantification using standard curves or relative quantification with validated reference genes like β-Actin should be employed .

How does BTN3A1 expression correlate with clinical outcomes in different cancer types?

In glioblastoma, patients with high BTN3A1 expression demonstrate poorer prognosis compared to those with lower expression . To comprehensively assess these correlations across cancer types, researchers should conduct meta-analyses of publicly available datasets (TCGA, GEO, etc.) stratifying by cancer type, stage, and molecular subtypes. Additionally, retrospective analyses of tumor biobanks with associated clinical data can provide insights into cancer-specific prognostic implications. Statistical approaches should include Kaplan-Meier survival analysis, Cox proportional hazards models, and multivariate analysis to account for confounding factors.

What is the current status of clinical trials targeting BTN3A1?

ICT01, an anti-BTN3A monoclonal antibody, is being evaluated in the EVICTION study (NCT04243499), an international, multi-center Phase 1/2a clinical trial . This study is investigating ICT01 as monotherapy and in combination with immune checkpoint inhibitors in patients with advanced, relapsed/refractory solid and hematologic tumors . Researchers interested in clinical applications should monitor trial updates, interim analyses, and subsequent publications. For those designing future trials, the heterogeneous BTN3A1 expression observed in patient samples suggests potential benefit from a precision medicine approach with patient selection based on BTN3A1 expression levels.

How does BTN3A1 expression influence the tumor immune microenvironment in clinical samples?

In clinical samples, BTN3A1 expression positively correlates with immune infiltration of B cells, CD8+ T cells, naïve CD4+ T cells, and M2 macrophages . Furthermore, high BTN3A1 expression is associated with elevated levels of immunosuppressive factors including TGF-β, IL-10, and TIM-3 . To investigate these relationships in depth, researchers should perform multiplex immunohistochemistry or cytometry by time-of-flight (CyTOF) on clinical specimens to simultaneously visualize BTN3A1 expression and immune cell populations. Spatial analysis techniques can further reveal the proximity and potential interactions between BTN3A1-expressing cells and specific immune cell subtypes.

How might BTN3A1-targeting approaches be combined with other immunotherapies?

Given BTN3A1's role in immune regulation and its correlation with immune checkpoint molecules like TIM-3 , combination strategies targeting multiple immune pathways may enhance therapeutic efficacy. Researchers should design preclinical studies testing BTN3A1-targeting antibodies in combination with checkpoint inhibitors (anti-PD-1, anti-CTLA-4), cytokine therapies, CAR-T approaches, or cancer vaccines. These studies should assess not only tumor control but also immune activation parameters, potential synergistic effects, and toxicity profiles. Mechanistic investigations should determine if these combinations address distinct aspects of immune evasion or converge on common pathways.

What role might BTN3A1 play in resistance to current immunotherapies?

High BTN3A1 expression correlates with immunosuppressive markers and poorer prognosis in glioblastoma , suggesting it might contribute to immunotherapy resistance. To investigate this hypothesis, researchers should analyze BTN3A1 expression in pre- and post-treatment samples from patients receiving immunotherapies, comparing responders versus non-responders. Functional studies in which BTN3A1 is manipulated in tumor models treated with immunotherapies can determine if BTN3A1 modulation affects treatment sensitivity. Single-cell analyses of resistant tumors may reveal if BTN3A1-expressing cells represent a specific subpopulation associated with treatment evasion.

How can BTN3A1 research inform the development of novel cancer biomarkers?

BTN3A1's differential expression across cancer types and its correlation with immune parameters position it as a potential component of multiparametric biomarker panels. Researchers should develop integrated biomarker approaches combining BTN3A1 with other immune-related markers to predict prognosis and treatment response. Machine learning algorithms applied to datasets with BTN3A1 expression, immune profile data, and clinical outcomes can identify optimal marker combinations. For clinical implementation, researchers should develop standardized assays suitable for routine pathology laboratories, potentially incorporating automated image analysis for consistent BTN3A1 quantification.