VSIR Antibody

What is VSIR and why is it important in immunological research?

VSIR (V-domain Ig containing suppressor of T-cell activation), also known as VISTA, B7-H5, or PD-1H, is a product of the VSIR gene and functions as a critical immunomodulatory receptor. While VSIR bears sequence homology to PD-L1, it displays a distinct expression pattern, predominantly on hematopoietic cells with highest densities on myeloid cells and lower levels on T cells, while typically absent on B cells . VSIR is important in immunological research because it directly suppresses proliferation and cytokine production of both CD4 and CD8 T cells when expressed by antigen-presenting cells . This inhibitory function makes VSIR a promising target for cancer immunotherapy approaches aiming to enhance anti-tumor immune responses. Understanding VSIR biology is crucial for developing new therapeutic strategies in cancer, autoimmunity, and transplantation research contexts.

What are the standard applications for VSIR antibodies in research?

VSIR antibodies are applied across multiple experimental techniques in immunological and cancer research. Standard applications include Enzyme-Linked Immunosorbent Assay (ELISA), Western Blotting (WB), Immunohistochemistry (IHC), Immunocytochemistry (ICC), and Immunofluorescence (IF) . For immunohistochemistry applications with PFA-fixed samples, standard protocols typically involve incubating the antibody for 30 minutes at room temperature . When working with formalin-fixed tissues, sample preparation requires heating tissue sections in 10mM Tris with 1mM EDTA (pH 9.0) for 45 minutes at 95°C, followed by cooling at room temperature for 20 minutes to achieve optimal antigen retrieval . These technical applications enable researchers to detect and quantify VSIR expression across different cell types and tissues, facilitating studies on its role in immune regulation and tumor microenvironments.

How should VSIR antibodies be stored and handled to maintain optimal activity?

Proper storage and handling of VSIR antibodies is crucial for maintaining their specificity and sensitivity in experimental applications. VSIR antibodies can typically be stored at 4°C for up to three months for ongoing experiments . For longer-term storage, maintaining the antibody at -20°C provides stability for up to one year . It is important to avoid repeated freeze-thaw cycles as these can progressively degrade antibody quality and performance . Additionally, antibodies should not be exposed to prolonged high temperatures, which can lead to denaturation and loss of binding capacity . Most commercial VSIR antibodies are supplied in PBS containing 0.02% sodium azide as a preservative . When working with these antibodies, standard laboratory safety protocols for handling biological materials containing sodium azide should be followed. Proper aliquoting upon first use can help minimize freeze-thaw cycles and extend the functional lifespan of the antibody preparation.

What are the known protein and gene aliases for VSIR in the scientific literature?

VSIR is referenced under multiple protein and gene aliases in scientific literature, which is important to recognize when conducting comprehensive literature searches or database queries. Protein aliases include Death Domain1alpha, PD-H1, PDCD1 homolog, Platelet receptor Gi24, Sisp-1, Stress-induced secreted protein-1, V-domain Ig suppressor of T cell activation, V-set domain-containing immunoregulatory receptor, V-set immunoregulatory receptor, and V-type immunoglobulin domain-containing suppressor of T-cell activation . Gene aliases include B7-H5, B7H5, C10orf54, DD1alpha, GI24, PD-1H, PP2135, SISP1, UNQ730/PRO1412, VISTA, and VSIR . In human studies, VSIR is identified by the UniProt ID Q9H7M9 and Entrez Gene ID 64115 . This multiplicity of names reflects the independent discovery of this molecule by different research groups and its characterization in various biological contexts. When designing experiments or interpreting literature, researchers should be aware of these alternative designations to ensure comprehensive coverage of relevant information.

How does VSIR expression vary across different cancer types, and what are the implications for antibody-based detection methods?

VSIR expression demonstrates significant variation across cancer types, creating important considerations for antibody-based detection strategies. Analysis of RNA-seq data from public databases reveals that VSIR is significantly upregulated in several cancer types compared to normal tissues, including glioblastoma multiforme (GBM), stomach adenocarcinoma (STAD), cholangiocarcinoma (CHOL), liver hepatocellular carcinoma (LIHC), pancreatic adenocarcinoma (PAAD), brain lower-grade glioma (LGG), kidney renal clear cell carcinoma (KIRC), and acute myeloid leukemia (LAML) . Conversely, upper tract urothelial cancer (UTUC) and penile squamous cell carcinoma (PSCC) show lower VSIR expression than normal tissues, while laryngeal squamous cell carcinoma (LSCC) exhibits higher expression .

Interestingly, prostate adenocarcinoma (PRAD) samples with higher Gleason scores (≥8) demonstrate increased VSIR expression compared to those with lower Gleason scores . These variable expression patterns across cancer types necessitate careful optimization of antibody-based detection protocols. Researchers should consider tissue-specific positive and negative controls, appropriate antigen retrieval methods, and validation across multiple detection platforms. For cancers with lower VSIR expression, more sensitive detection methods such as signal amplification techniques may be required, while those with higher expression might benefit from antibody titration to prevent signal saturation and enable accurate quantification. These expression differences also suggest that VSIR antibodies may have varying utility as diagnostic or prognostic tools depending on the cancer type being investigated.

What methodological considerations are important when using VSIR antibodies for multiplexed immunofluorescence in the tumor microenvironment?

Multiplexed immunofluorescence (mIF) with VSIR antibodies presents several methodological challenges that require careful optimization. When designing mIF panels for tumor microenvironment analysis, researchers should consider that VSIR is expressed on multiple cell types including cancer cells, fibroblasts, macrophages, and T cells, as demonstrated by single-cell sequencing analysis . VSIR has been found to co-express with M2 macrophage markers CD68 and CD163 in immunofluorescence staining , which necessitates careful antibody selection to avoid spectral overlap when designing panels.

For optimal results, sequential immunofluorescence protocols using tyramide signal amplification (TSA) systems with FITC-TSA, CY3-TSA, 594-TSA, and 647-TSA have been successfully implemented . This approach allows for multiplexed detection of VSIR alongside lineage markers for different cell populations. When performing mIF, critical steps include:

• Thorough antibody validation using appropriate positive and negative controls

• Careful titration of primary antibodies to determine optimal concentrations

• Sequential application of antibodies with complete stripping between rounds

• Implementation of spectral unmixing algorithms during image analysis

Analysis of multispectral images should be conducted at the single-cell level using specialized software such as Caseviewer (CV 2.3, CV 2.0) and Pannoramic viewer (PV 1.15.3) . This allows for quantitative assessment of VSIR expression in different cellular compartments of the tumor microenvironment, providing insights into its functional relevance in cancer immunity. Negative control procedures should always include omission of the primary antibody to detect non-specific binding .

How can researchers evaluate the functional impact of VSIR blockade using neutralizing antibodies in experimental models?

Evaluating the functional impact of VSIR blockade requires a multifaceted experimental approach that combines in vitro and in vivo methodologies. Previous studies have demonstrated that specific antibodies that neutralize VSIR can effectively suppress tumor growth in mouse models , providing a foundation for experimental design. When planning VSIR blockade experiments, researchers should consider:

• Antibody selection: Use well-characterized neutralizing antibodies with demonstrated specificity for VSIR. Validation should include binding assays, epitope mapping, and functional blockade confirmation.

• In vitro assays: Implement T cell proliferation assays using CFSE labeling or 3H-thymidine incorporation to measure the impact of VSIR blockade on T cell expansion. Cytokine production assays (ELISA, ELISpot, or flow cytometry) can assess functional restoration of T cell activity. Co-culture systems with antigen-presenting cells expressing VSIR and T cells can help elucidate the mechanism of action.

• In vivo models: Establish appropriate tumor models based on the cancer type of interest. Consider syngeneic models for immunocompetent settings or humanized mouse models for evaluating human-specific VSIR antibodies. Treatment protocols typically involve antibody administration at defined intervals with appropriate isotype controls.

• Evaluation endpoints: Monitor tumor growth kinetics, survival outcomes, and changes in the tumor microenvironment. Flow cytometric analysis of tumor-infiltrating lymphocytes can assess changes in T cell activation status (CD69, CD25), exhaustion markers (PD-1, TIM-3), and effector function (IFN-γ, TNF-α, Granzyme B).

• Combination approaches: Evaluate VSIR blockade in combination with other checkpoint inhibitors (anti-PD-1, anti-CTLA-4) to assess potential synergistic effects, as VSIR has a different expression pattern compared to PD-L1 .

It's important to note that VSIR plays both negative and positive roles in tumor immunity across different cancer contexts , so researchers should design experiments that can capture potential bidirectional effects of VSIR blockade.

What statistical approaches are recommended for analyzing VSIR expression data in relation to patient outcomes?

Analysis of VSIR expression in relation to patient outcomes requires robust statistical methodologies that account for the complexity of cancer datasets. Based on approaches used in published studies, the following statistical framework is recommended:

Previous research has demonstrated contradictory findings regarding VSIR's prognostic significance across different cancer types. Higher VSIR levels have been associated with better clinical prognosis in epithelioid mesothelioma, non-small-cell lung cancer, and esophageal adenocarcinoma, while increased VSIR was linked to unfavorable disease-specific survival in primary cutaneous melanoma . These contradictions highlight the importance of cancer-specific analyses and careful interpretation of statistical results.

What are the optimal antigen retrieval methods for VSIR detection in different tissue fixation protocols?

Antigen retrieval is a critical step for successful VSIR detection in fixed tissues, and protocols must be tailored to the specific fixation method used. For formalin-fixed tissues, a heat-induced epitope retrieval (HIER) method is required, specifically heating tissue sections in 10mM Tris with 1mM EDTA, pH 9.0, for 45 minutes at 95°C, followed by cooling at room temperature for 20 minutes . This alkaline buffer system effectively breaks protein cross-links formed during formalin fixation, exposing the VSIR epitopes for antibody binding.

When troubleshooting poor staining results, consider these strategies:

• Extend retrieval time for particularly challenging tissues

• Test alternative buffer systems (citrate buffer pH 6.0 versus Tris-EDTA pH 9.0)

• Optimize antibody concentration through titration experiments

• Include positive control tissues known to express VSIR (such as spleen or lung tissue)

• For dual immunofluorescence protocols, determine whether sequential or simultaneous antibody incubation yields better results

Each new tissue type or fixation protocol will likely require empirical optimization of antigen retrieval conditions to achieve optimal signal-to-noise ratio while preserving tissue morphology.

How can researchers validate VSIR antibody specificity for their experimental systems?

Validating VSIR antibody specificity is essential for generating reliable and reproducible results. A comprehensive validation strategy should include multiple complementary approaches:

• Genetic controls: Use VSIR knockout or knockdown systems where available. Compare antibody staining between wild-type and VSIR-deficient samples to confirm specificity. CRISPR-Cas9 engineered cell lines or siRNA-treated samples can serve as negative controls.

• Peptide competition assays: Pre-incubate the VSIR antibody with the immunizing peptide before application to samples. Specific binding should be blocked by the peptide, resulting in signal reduction or elimination.

• Multiple antibody verification: Test multiple antibodies targeting different VSIR epitopes. Concordant results across antibodies increase confidence in specificity.

• Expression correlation: Compare protein detection with mRNA expression data. While not perfect due to post-transcriptional regulation, general correlation between transcript and protein levels supports antibody specificity.

• Size verification: For Western blot applications, confirm that the detected band matches the expected molecular weight of VSIR. The presence of predicted post-translational modifications should be considered when evaluating band patterns.

• Expression pattern analysis: Verify that the cellular and subcellular localization pattern matches established VSIR biology. VSIR is predominantly expressed on hematopoietic cells with highest densities on myeloid cells, lower levels on T cells, and is typically absent on B cells .

• Cross-reactivity assessment: If working with non-human species, confirm specificity for the target species. While VSIR is conserved across species, epitope recognition may vary.

Documentation of validation experiments should be maintained according to research reproducibility guidelines, and periodic revalidation should be performed, particularly with new antibody lots or experimental systems.

What strategies can address non-specific binding when using VSIR antibodies in complex tissue samples?

Non-specific binding is a common challenge when using VSIR antibodies in complex tissue samples, particularly in tissues with high endogenous peroxidase activity or biotin content. Several strategies can be implemented to improve signal specificity:

• Optimized blocking protocols: Extend blocking steps using a combination of serum from the same species as the secondary antibody (typically 5-10%) and protein blockers (BSA, casein, or commercial blocker solutions). For tissues with high background, a 1-2 hour blocking period at room temperature may be beneficial.

• Antibody titration: Perform careful titration experiments to determine the optimal antibody concentration that maximizes specific signal while minimizing background. Working with dilution series ranging from 1:100 to 1:3000 can help identify the ideal concentration for each tissue type.

• Endogenous enzyme inactivation: For immunohistochemistry applications, thoroughly quench endogenous peroxidase or alkaline phosphatase activity. For peroxidase, treat sections with 0.3% H₂O₂ in methanol for 30 minutes before antibody application.

• Avidin-biotin blocking: If using biotin-based detection systems, block endogenous biotin using commercial avidin-biotin blocking kits, particularly important for tissues like liver, kidney, and brain.

• Secondary antibody selection: Use highly cross-adsorbed secondary antibodies specifically designed to minimize cross-reactivity with endogenous immunoglobulins in the tissue.

• Alternative detection systems: Consider polymer-based detection systems that eliminate biotin-related background or directly conjugated primary antibodies that bypass secondary antibody requirements altogether.

• Negative controls: Always include appropriate negative controls in which the primary antibody is omitted to identify non-specific binding of the detection system .

• Tissue-specific modifications: For tissues with high autofluorescence (like brain or liver), consider additional treatments such as Sudan Black B (0.1% in 70% ethanol) for immunofluorescence applications to reduce background.

Implementation of these strategies should be systematic, changing one variable at a time to identify the specific source of non-specific binding for each experimental system.

How should researchers approach data analysis when comparing VSIR expression across different cancer types?

Analyzing VSIR expression across different cancer types requires a structured analytical approach that accounts for tissue-specific variation and integrates multiple data types. Based on successful methodologies in published research, the following approach is recommended:

• Data normalization: Apply appropriate normalization methods to account for technical variation between samples and platforms. For RNA-seq data, TPM (Transcripts Per Million) or FPKM (Fragments Per Kilobase Million) values should be log-transformed to approximate normal distribution .

• Batch effect correction: Implement batch effect correction methods (ComBat, Surrogate Variable Analysis) when integrating data from multiple sources to minimize non-biological variation.

• Cancer-normal comparisons: For each cancer type, compare VSIR expression between tumor and matched normal tissues when available. Student's t-test or Kruskal-Wallis test should be applied depending on data distribution characteristics .

• Cross-cancer analysis: Develop a standardized scoring system to compare relative VSIR expression across cancer types, accounting for tissue-specific baseline expression. This can be achieved through z-score transformation relative to normal tissue expression.

• Integration with clinical data: Correlate VSIR expression with clinical parameters (stage, grade, patient demographics) using appropriate statistical tests. For survival analysis, determine optimal cut-points for each cancer type separately using statistical approaches like the R package "survminer" .

• Molecular subtype stratification: Analyze VSIR expression in the context of established molecular subtypes for each cancer to identify subtype-specific patterns.

• Multi-omics integration: Correlate VSIR expression with other molecular features including mutational status, copy number alterations, and methylation patterns to provide biological context.

• Visualization strategies: Implement appropriate visualization methods including heatmaps, box plots, and forest plots to effectively communicate complex patterns across cancer types.

This approach has revealed that VSIR expression patterns vary significantly across cancer types, with upregulation in GBM, STAD, CHOL, LIHC, PAAD, LGG, KIRC, and LAML compared to normal tissues, while showing different patterns in UTUC, PSCC, and LSCC . These differences highlight the importance of cancer-specific analysis rather than generalizing across all cancer types.

How might VSIR antibodies be developed as therapeutic agents for cancer immunotherapy?

The development of VSIR antibodies as therapeutic agents represents a promising frontier in cancer immunotherapy, building on the understanding that VSIR functions as a negative regulator of T cell immunity. Several research directions show potential for translating VSIR antibodies from research tools to therapeutic agents:

• Antibody engineering approaches: Humanization of existing murine anti-VSIR antibodies or development of fully human antibodies using phage display or transgenic mouse platforms can generate candidates with reduced immunogenicity. Fc engineering to enhance antibody-dependent cellular cytotoxicity (ADCC) or complement-dependent cytotoxicity (CDC) may provide additional anti-tumor mechanisms beyond immune checkpoint blockade.

• Mechanism of action optimization: Evidence indicates that VSIR expressed by antigen presenting cells directly suppresses proliferation and cytokine production of CD4 and CD8 T cells . Therapeutic antibodies should be designed to specifically block this interaction. Epitope mapping studies can identify the critical binding domains involved in VSIR's immunosuppressive function, enabling the development of antibodies that precisely target these regions.

• Cancer-specific considerations: VSIR expression varies significantly across cancer types, with upregulation observed in GBM, STAD, CHOL, LIHC, PAAD, LGG, KIRC, and LAML compared to normal tissues . This suggests that patient selection strategies should incorporate VSIR expression profiling to identify those most likely to benefit from anti-VSIR therapy.

• Combinatorial approaches: Given that VSIR has a different expression pattern compared to PD-L1 , exploring combinations of anti-VSIR antibodies with established checkpoint inhibitors (anti-PD-1, anti-CTLA-4) may yield synergistic effects. Preclinical models have demonstrated that specific antibodies neutralizing VSIR can effectively suppress tumor growth , providing a rationale for such combinations.

• Biomarker development: Concurrent development of companion diagnostics using validated VSIR antibodies for immunohistochemistry or multiplexed immunofluorescence can facilitate patient selection and treatment monitoring. The co-expression of VSIR with M2 macrophage markers CD68 and CD163 suggests that macrophage polarization status may serve as an additional biomarker.

The dual role of VSIR in tumor immunity, with both negative and positive effects reported , necessitates careful clinical development strategies that monitor for unexpected immune-related adverse events while maximizing anti-tumor efficacy.

What are the emerging applications of VSIR antibodies in single-cell analysis of the tumor microenvironment?

Single-cell analysis technologies are revolutionizing our understanding of the tumor microenvironment, with VSIR antibodies playing an increasingly important role in these applications. Several emerging approaches demonstrate particular promise:

• Mass cytometry (CyTOF) applications: Integration of metal-conjugated VSIR antibodies into CyTOF panels allows simultaneous detection of VSIR alongside dozens of other markers at single-cell resolution. This approach enables comprehensive immunophenotyping of VSIR-expressing cells and correlation with activation states, lineage markers, and functional parameters without the limitations of spectral overlap found in conventional flow cytometry.

• Spatial transcriptomics integration: Combining VSIR antibody-based immunofluorescence with spatial transcriptomics technologies (e.g., Visium, MERFISH) provides insights into the spatial context of VSIR expression relative to other cell types and soluble factors in the tumor microenvironment. This approach can reveal spatial relationships between VSIR+ cells and other immune populations that may influence therapeutic responses.

• Single-cell proteogenomics: Techniques like CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) utilize oligonucleotide-labeled VSIR antibodies to simultaneously capture surface protein expression and transcriptome data from individual cells. This allows researchers to correlate VSIR protein levels with gene expression programs at single-cell resolution, providing insights into the regulatory mechanisms governing VSIR expression.

• Imaging mass cytometry: This technique combines the specificity of immunocytochemistry with the multiplexing capabilities of mass spectrometry to visualize dozens of proteins simultaneously in tissue sections. Metal-tagged VSIR antibodies can be incorporated into imaging mass cytometry panels to map the spatial distribution of VSIR+ cells relative to other immune and stromal cell populations with subcellular resolution.

• Live-cell imaging: Development of non-blocking fluorescently labeled VSIR antibody fragments enables tracking of VSIR+ cells in live-cell imaging experiments, providing insights into the dynamics of VSIR-expressing cells within the tumor microenvironment and their interactions with other immune populations.

These approaches build upon findings that VSIR is expressed on multiple cell types including cancer cells, fibroblasts, macrophages, and T cells as demonstrated by single-cell sequencing analysis . The co-expression of VSIR with M2 macrophage markers CD68 and CD163 provides a particular focus for investigating macrophage-related immunosuppression in the tumor microenvironment through these advanced single-cell techniques.

How can VSIR antibodies be utilized to study the relationship between VSIR and other immune checkpoint molecules?

VSIR antibodies offer valuable tools for investigating the complex relationships between VSIR and other immune checkpoint molecules, which is crucial for designing effective combinatorial immunotherapy approaches. Several methodological strategies can be implemented:

• Co-expression analysis: Multiplexed immunofluorescence or flow cytometry panels incorporating VSIR antibodies alongside antibodies against other checkpoint molecules (PD-1, PD-L1, CTLA-4, LAG-3, TIM-3) can quantify co-expression patterns at the single-cell level. This approach can identify cell populations that simultaneously express multiple checkpoint receptors and may be particularly susceptible to combinatorial blockade.

• Sequential checkpoint blockade experiments: In vitro functional assays using purified T cells or co-culture systems can incorporate VSIR-blocking antibodies alone or in combination with other checkpoint-blocking antibodies to assess additive or synergistic effects on T cell proliferation, cytokine production, and cytotoxic activity. The sequence of antibody administration may influence outcomes and should be systematically investigated.

• Receptor-ligand interaction studies: Surface plasmon resonance (SPR) or biolayer interferometry (BLI) using purified proteins can characterize the binding kinetics between VSIR and its ligands (CD28H, VSIG-3, and VSIG-8) . Competition assays with other checkpoint receptors and ligands can determine whether shared binding partners exist or whether receptor clustering influences binding affinity.

• Signaling pathway integration: Phospho-flow cytometry or Western blotting with phospho-specific antibodies can examine how VSIR signaling intersects with pathways downstream of other checkpoint receptors. VSIR antibodies can be used to trigger or block receptor signaling while monitoring effects on shared pathway components.

• Transcriptional profiling: RNA-seq analysis of sorted VSIR+ immune populations following treatment with VSIR antibodies, other checkpoint antibodies, or combinations can identify shared or distinct transcriptional programs regulated by these receptors.

This research is particularly relevant given that VSIR bears sequence homology to PD-L1 but has a different expression pattern , suggesting both overlapping and distinct roles in immune regulation. While elevated VSIR expression correlates with infiltrated inflammatory cells, neoantigens expression, microsatellite instability, tumor mutational burden, and classical immune checkpoints in the tumor microenvironment , the mechanistic basis for these correlations remains to be fully elucidated.

What computational approaches can predict sensitivity to VSIR-targeting therapeutics based on antibody-derived data?

Computational methods leveraging antibody-derived data offer powerful approaches to predict patient responses to VSIR-targeting therapeutics. Several promising strategies have emerged in the field:

• Expression-based sensitivity prediction: Computational models that integrate VSIR expression data from immunohistochemistry or RNA-seq with clinical outcomes can identify expression thresholds predictive of therapeutic response. The optimal cutoff of VSIR expression can be calculated using specialized R packages like "survminer" , which identifies the expression level that best separates patient outcomes.

• Immune signature integration: Machine learning algorithms can combine VSIR antibody staining patterns with broader immune signatures to develop multiparametric predictive models. These models can incorporate spatial relationships between VSIR+ cells and other immune populations quantified through multiplexed immunofluorescence or imaging mass cytometry.

• Multi-omics prediction frameworks: Integration of antibody-derived VSIR protein expression data with genomic, transcriptomic, and epigenomic features can generate comprehensive predictive models. Approaches such as TIDE (Tumor Immune Dysfunction and Exclusion) and TISMO that evaluate immunotherapy value can be adapted or expanded to specifically address VSIR-targeted therapies.

• Drug sensitivity databases: Predictive algorithms developed from databases like GDSC (Genomics of Drug Sensitivity in Cancer) and CTRP (Cancer Therapeutics Response Portal) that contain data on over 750 small-molecule drugs across 10,000 genomic profiles can be applied to predict sensitivity to VSIR-targeting agents based on molecular features.

• Network-based approaches: Computational methods that map VSIR into protein-protein interaction networks and signaling pathways can identify patient-specific vulnerabilities to VSIR targeting. These approaches can leverage antibody-derived data on VSIR co-expression with other immune markers like the M2 macrophage markers CD68 and CD163 .

• Digital pathology algorithms: Deep learning approaches applied to VSIR antibody-stained tissue sections can extract features beyond simple expression levels, including subcellular localization patterns, expression heterogeneity, and spatial relationships with the tumor microenvironment. These complex features may provide additional predictive power for therapeutic response.

These computational approaches align with findings that VSIR levels strongly correlate with clinical outcomes and tumor immunity across multiple cancer types , suggesting that VSIR expression patterns derived from antibody-based detection methods could serve as valuable biomarkers for patient stratification in immunotherapy trials.

What are the most significant recent advances in VSIR antibody research and application?

Recent advances in VSIR antibody research have significantly expanded our understanding of this immune checkpoint molecule and its therapeutic potential. Key developments include the systematic exploration of VSIR's prognostic and immune profile across 33 cancer types, revealing that VSIR expression is significantly related to patient prognosis in multiple tumor types including glioblastoma multiforme, kidney renal clear cell carcinoma, skin cutaneous melanoma, rectum adenocarcinoma, and prostate adenocarcinoma . This comprehensive pan-cancer analysis has established VSIR as a promising predictor in these cancer types.

Another significant advance is the characterization of VSIR's correlation with the tumor immune microenvironment. Recent research has demonstrated that elevated VSIR expression is closely associated with infiltrated inflammatory cells, neoantigen expression, microsatellite instability, tumor mutational burden, and classical immune checkpoints . These findings have important implications for combination immunotherapy strategies targeting VSIR alongside other checkpoint molecules.

Single-cell sequencing analysis has revealed that VSIR is expressed on multiple cell types within the tumor microenvironment, including cancer cells, fibroblasts, macrophages, and T cells . Particularly notable is the co-expression of VSIR with M2 macrophage markers CD68 and CD163, as demonstrated by immunofluorescence staining . This discovery suggests that VSIR may play a role in macrophage-mediated immunosuppression beyond its direct effects on T cells.

The development of sensitive and specific VSIR antibodies for multiple applications including ELISA, Western blotting, immunohistochemistry, immunocytochemistry, and immunofluorescence has facilitated these discoveries and enabled more detailed investigation of VSIR biology . These technical advances continue to drive progress in understanding VSIR's role in cancer and immune regulation.

What are the key areas for future research in VSIR antibody development and application?

Future research in VSIR antibody development and application should focus on several key areas to advance both basic understanding and clinical translation:

• Development of highly specific monoclonal antibodies: There is a need for next-generation VSIR antibodies with enhanced specificity, sensitivity, and versatility across multiple applications. Particular emphasis should be placed on developing antibodies that can distinguish between different functional states or conformations of VSIR.

• Mechanistic studies of VSIR signaling: While VSIR is known to suppress T cell proliferation and cytokine production , the precise molecular mechanisms remain incompletely understood. Antibodies that can selectively block specific VSIR functions without affecting others would be valuable tools for dissecting these pathways.

• Therapeutic antibody optimization: Building on evidence that VSIR neutralization can suppress tumor growth in mouse models , development of therapeutic-grade antibodies with optimized properties for clinical translation represents a significant opportunity. This includes humanization, Fc engineering, and formulation development.

• Biomarker development: Validation of VSIR antibodies for use as companion diagnostics in immunotherapy trials will be essential. This includes standardization of immunohistochemistry protocols and development of scoring systems that can reliably predict response to VSIR-targeted therapies.

• Combination therapy strategies: Given VSIR's different expression pattern compared to PD-L1 and its correlation with other immune checkpoints , systematic investigation of combination approaches with other immunotherapies is warranted. Antibody-based studies will be crucial for identifying synergistic combinations.

• Resolution of contradictory findings: Current literature contains contradictory findings regarding VSIR's prognostic significance across different cancer types . Future research should address these contradictions through larger, well-controlled studies with standardized antibody-based detection methods.

• Exploration beyond cancer: While much VSIR research has focused on cancer, its role in autoimmunity, infection, and transplantation remains relatively unexplored. Development of antibody tools optimized for these applications could open new therapeutic avenues.