ITGA4 Antibody

What is ITGA4 and why is it important in immunological research?

ITGA4 (integrin subunit alpha 4) is a 1032-amino acid protein member of the Integrin alpha chain family. In humans, this membrane-associated protein has a molecular mass of approximately 114.9 kDa and is known to undergo post-translational modifications including phosphorylation and glycosylation . ITGA4 is critically important in immunological research because it plays a significant role in cell adhesion and B cell differentiation processes . Additionally, ITGA4 is expressed on immune cells and functions as a key molecule facilitating T cell migration into various organs, making it particularly relevant for researchers studying autoimmune conditions . The protein is also known by several synonyms including IA4, integrin alpha-4, CD49 antigen-like family member D, VLA-4 subunit alpha, and CD49D . Understanding ITGA4's functions provides insights into both normal immune cell trafficking and pathological inflammatory processes, making it a valuable research target.

What applications are anti-ITGA4 antibodies commonly used for in research settings?

Anti-ITGA4 antibodies are utilized across multiple experimental platforms in research settings. The most frequently employed applications include Western blotting (WB) for protein detection, immunohistochemistry (IHC) for tissue localization, flow cytometry (FCM) for cellular analysis, immunoprecipitation (IP) for protein isolation, and immunocytochemistry (ICC) or immunofluorescence (IF) for cellular visualization . Flow cytometry applications are particularly valuable as they allow researchers to identify and quantify ITGA4-expressing cells within heterogeneous populations . In tissue-based research, anti-ITGA4 antibodies can be used as markers to identify specific neuronal populations, including hippocampal gyrus CA1-3 and CA4 neurons . For experimental autoimmune disease models, these antibodies serve not only as detection tools but also as potential therapeutic agents that can be evaluated for their ability to modulate immune cell migration and function . The methodological approach should be tailored to the specific research question, with appropriate optimization of antibody concentration, incubation conditions, and detection systems.

How do researchers evaluate the specificity of anti-ITGA4 antibodies?

Evaluating antibody specificity is a critical step in ensuring experimental validity when working with anti-ITGA4 antibodies. A comprehensive validation approach should include multiple complementary techniques. Researchers typically begin with positive and negative control samples, using tissues or cell lines known to express or lack ITGA4 respectively . Western blot analysis should demonstrate a single band at the expected molecular weight of approximately 114.9 kDa, while multiple bands may indicate non-specific binding or detection of different isoforms . Competitive blocking experiments, where pre-incubation with the immunizing peptide prevents antibody binding, provide additional evidence of specificity. For advanced validation, genetic approaches such as comparing staining patterns in wildtype versus ITGA4 knockout models or ITGA4-silenced cells using siRNA/shRNA can definitively confirm antibody specificity. When performing immunohistochemistry, as demonstrated in gastric cancer research, appropriate controls include comparing staining intensity between tumor tissue and adjacent normal tissue while implementing standardized scoring systems (0-3 for intensity and 1-4 for percentage of positive cells) . Cross-reactivity testing against related integrins, particularly other alpha subunits, is also essential for confirming specificity.

What are the recommended sample preparation methods for ITGA4 detection in different tissues?

Sample preparation for ITGA4 detection varies depending on the tissue type and analytical method employed. For paraffin-embedded tissue sections, a standardized protocol includes deparaffinization, rehydration, and antigen retrieval under high pressure (150-200 kPa) for 10 minutes using appropriate antigen retrieval solution . This step is critical as formalin fixation can mask epitopes through protein cross-linking. Following antigen retrieval, blocking endogenous peroxidase activity with 3% hydrogen peroxide for 20 minutes and non-specific binding sites with 10% BSA for 60 minutes optimizes signal-to-noise ratio . For immunohistochemical detection, researchers should incubate sections with the primary anti-ITGA4 antibody (typically at 1:100 dilution) overnight at 4°C, followed by appropriate secondary antibody incubation (1:2000) for 1 hour . For cell-based assays, gentler fixation methods using 2-4% paraformaldehyde are preferred to preserve membrane protein integrity. When working with flow cytometry, live cell staining protocols that avoid permeabilization should be considered since ITGA4 is a membrane-associated protein . For all applications, careful optimization of antibody concentration, incubation times, and washing conditions is essential to maximize specific signal while minimizing background.

How do researchers select the appropriate anti-ITGA4 antibody for their specific experimental system?

Selecting the appropriate anti-ITGA4 antibody requires careful consideration of multiple factors to ensure optimal experimental outcomes. First, researchers should determine which species will be studied, as ITGA4 orthologs have been identified in multiple species including mouse, rat, bovine, frog, zebrafish, chimpanzee, and chicken . The antibody selected must have documented reactivity against the species of interest. Second, the specific application requirements must be considered - different antibody formats may perform optimally in different applications such as Western blotting, immunohistochemistry, flow cytometry, or immunoprecipitation . Third, researchers should evaluate whether a monoclonal or polyclonal antibody best suits their needs; monoclonals offer high specificity for a single epitope while polyclonals recognize multiple epitopes, potentially providing greater sensitivity . Fourth, consideration of the specific ITGA4 epitope is crucial, as antibodies targeting different domains may yield different results, especially if splice variants or post-translational modifications are present in the experimental system . Finally, researchers should review published validation data, including images showing expected staining patterns and appropriate controls. For quantitative applications, antibodies with demonstrated linear detection ranges should be selected. When available, isotype-matched control antibodies should be used in parallel experiments to identify any non-specific binding.

How are anti-ITGA4 antibodies utilized in autoimmune disease research?

In autoimmune disease research, anti-ITGA4 antibodies serve dual roles as both research tools and therapeutic targets. The integrin alpha 4 (ITGA4) protein is critically involved in immune cell migration across various tissues, making it particularly relevant for studying conditions characterized by pathological immune cell infiltration . Experimentally, researchers use anti-ITGA4 antibodies in flow cytometry to track and quantify specific T cell populations during disease progression. More significantly, monoclonal antibodies targeting ITGA4 have been developed as therapeutic agents for multiple sclerosis (MS), demonstrating the translation from basic research to clinical application . In experimental autoimmune encephalomyelitis (EAE), a mouse model of MS, studies have revealed that ITGA4 differentially affects the migration of various T cell subsets, including a selective inhibition of Th1 cells but not Th17 cells into the central nervous system . This discovery highlights the importance of detailed subset analysis when working with heterogeneous immune populations. For effective experimental design, researchers should consider testing anti-ITGA4 antibodies on different T cell populations (effector vs. regulatory T cells) isolated from both peripheral blood and target tissues to fully characterize migration patterns and potential therapeutic effects. Such experimental approaches provide insights into the mechanistic aspects of disease progression and identify potential therapeutic targets.

How do alternative splicing events affect ITGA4 antibody recognition and function?

Alternative splicing events significantly impact both ITGA4 function and antibody recognition, creating complex considerations for researchers. The ITGA4 gene undergoes alternative splicing that can produce multiple protein isoforms, with up to two different variants reported in humans . These splice variants may differ in domain structure, potentially affecting antibody epitope availability and protein functionality. Researchers have even developed antisense oligonucleotides specifically designed to induce exon skipping in the ITGA4 transcript as a therapeutic approach to reduce protein expression . When selecting antibodies for research applications, it is crucial to consider which exons or protein domains are targeted by the antibody and whether these regions may be absent in certain splice variants. For comprehensive detection of all ITGA4 isoforms, antibodies targeting conserved regions present in all splice variants should be selected. Conversely, isoform-specific antibodies that can distinguish between splice variants may be valuable for studies investigating the differential functions of specific ITGA4 isoforms. To validate antibody performance across splice variants, researchers should perform parallel assays using recombinant proteins representing each known isoform or cell lines engineered to express specific variants. Western blotting with high-resolution gels can help identify size differences between isoforms, while RT-PCR analysis of ITGA4 transcripts can complement protein-level studies by identifying which splice variants are present in the experimental system.

What techniques can be used to study ITGA4's role in the tumor microenvironment?

Studying ITGA4's role in the tumor microenvironment (TME) requires a multifaceted approach combining cellular, molecular, and computational techniques. Single-cell RNA sequencing represents a powerful method for analyzing ITGA4 expression patterns across diverse cell populations within the TME, as demonstrated in gastric cancer research using the GSE134520 and GSE167297 datasets analyzed through the Tumor Immune Single-cell Hub (TISCH) . This approach, implemented using "MAESTRO" and "Seurat" packages with t-SNE for cell clustering, allows researchers to map ITGA4 expression to specific cell types within the heterogeneous tumor ecosystem . Complementary to this, researchers can perform multiplex immunofluorescence staining to visualize spatial relationships between ITGA4-positive cells and other components of the TME, such as tumor cells, immune infiltrates, and vasculature. Correlation analyses between ITGA4 expression and various TME scores, along with immune cell infiltration patterns, provide insights into ITGA4's relationship with the immunological landscape of tumors . Functional studies using co-culture systems with ITGA4-expressing cells and other TME components can elucidate dynamic interactions. For gene expression analyses, researchers should identify differentially expressed genes (DEGs) between high and low ITGA4 expression groups using packages such as "DESeq2" and "edgeR," followed by pathway enrichment analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to understand the biological processes associated with ITGA4 expression in the TME . These approaches collectively provide a comprehensive understanding of how ITGA4 influences and is influenced by the complex tumor microenvironment.

How can researchers effectively use ITGA4 antibodies in single-cell analysis applications?

Integrating ITGA4 antibodies into single-cell analysis workflows requires specialized methodological considerations to maximize data quality and interpretability. For single-cell RNA sequencing applications, researchers can use computational tools like "MAESTRO" and "Seurat" packages with dimensionality reduction techniques such as t-SNE for effective cell clustering and visualization of ITGA4 expression patterns across cell populations . This approach allows for identification of cell-type-specific ITGA4 expression profiles and correlation with other genes of interest. For protein-level single-cell analysis, researchers should optimize antibody panels for multiparameter flow cytometry or mass cytometry (CyTOF), carefully selecting anti-ITGA4 antibodies with minimal spectral overlap with other fluorochromes in the panel. When working with tissue sections, multiplexed immunofluorescence or imaging mass cytometry can be employed to visualize ITGA4 distribution while maintaining spatial context. For single-cell functional assays, researchers can use FACS-sorted ITGA4-positive and ITGA4-negative populations for downstream applications such as single-cell RNA-seq or functional assays. In all applications, proper titration of antibodies is essential to achieve optimal signal-to-noise ratio without saturation. Researchers should include appropriate isotype controls and perform compensation when using multiple fluorochromes. For quantitative analysis of ITGA4 at the single-cell level, calibration beads can be used to convert fluorescence intensity into absolute numbers of molecules per cell. Integration of protein-level ITGA4 data with transcriptomic data from the same cells through CITE-seq or similar approaches provides a more comprehensive understanding of ITGA4 biology across heterogeneous cell populations.

What strategies can overcome technical challenges in detecting low-abundance ITGA4 in tissue samples?

Detecting low-abundance ITGA4 in tissue samples presents significant challenges that require specialized technical approaches. Signal amplification methods such as tyramide signal amplification (TSA) can substantially enhance detection sensitivity by generating additional reporter molecules at the site of antibody binding. For immunohistochemical applications, a methodical approach includes optimized antigen retrieval under high pressure (150-200 kPa) for 10 minutes to maximize epitope exposure . Researchers should implement a sequential blocking strategy with 3% hydrogen peroxide followed by 10% BSA to minimize background while optimizing signal detection . Extended primary antibody incubation periods (overnight at 4°C) with carefully optimized dilutions (typically 1:100 for anti-ITGA4 antibodies) maximize specific binding to low-abundance targets . Detection systems utilizing polymeric HRP-conjugated secondary antibodies provide superior sensitivity compared to traditional avidin-biotin methods. For fluorescence-based detection, high-sensitivity cameras and photomultiplier tubes coupled with deconvolution or super-resolution microscopy techniques can visualize low signal levels. Digital imaging systems allow for quantitative analysis of staining intensity across tissues, with standardized scoring systems accounting for both intensity (0-3 scale) and percentage of positive cells (1-4 scale) . Sample preparation should minimize autofluorescence through techniques such as sodium borohydride treatment or spectral unmixing. Additionally, researchers can employ pre-enrichment strategies such as laser capture microdissection to isolate regions with expected ITGA4 expression before analysis, thereby concentrating the target protein for more effective detection.

How do post-translational modifications of ITGA4 impact antibody selection and experimental design?

Post-translational modifications (PTMs) of ITGA4 significantly influence antibody selection and experimental design considerations. ITGA4 undergoes multiple forms of PTMs, including phosphorylation and glycosylation, which can alter protein conformation, function, and antibody recognition . These modifications create complexity in antibody selection, as some antibodies may preferentially recognize or be inhibited by specific PTM states. For glycosylation studies, researchers should consider using antibodies that target peptide sequences distant from known glycosylation sites to ensure consistent detection regardless of glycosylation state. Conversely, when studying specific glycoforms, lectins or glycoform-specific antibodies can be used in conjunction with general anti-ITGA4 antibodies. For phosphorylation analysis, phospho-specific antibodies targeting known phosphorylation sites provide insights into ITGA4 activation states. Experimental protocols should be designed to preserve PTMs of interest; for instance, phosphatase inhibitors should be included in lysis buffers when studying phosphorylation states. Appropriate controls for PTM studies include treatment with specific enzymes (such as phosphatases, glycosidases, or kinases) to modify PTM status, followed by antibody detection to confirm specificity. When quantifying ITGA4 levels across different experimental conditions, researchers should be aware that changes in PTMs might affect antibody binding affinity without reflecting actual changes in total protein abundance. Multiple antibodies recognizing different epitopes can help distinguish between changes in modification status versus total protein levels. Mass spectrometry-based approaches provide complementary information about specific PTM sites and their occupancy, which can be correlated with antibody-based detection methods.

What are the considerations for developing assays to evaluate anti-ITGA4 therapeutic antibodies in autoimmune models?

Developing assays to evaluate anti-ITGA4 therapeutic antibodies in autoimmune models requires careful consideration of multiple parameters to ensure meaningful translational outcomes. Researchers must first establish relevant disease models that recapitulate key aspects of human pathology, such as the experimental autoimmune encephalomyelitis (EAE) model for multiple sclerosis, where ITGA4 has been shown to differentially affect the migration of effector and regulatory T cells . Pharmacokinetic and pharmacodynamic assays should be implemented to monitor antibody concentration, half-life, and tissue distribution. Flow cytometry-based receptor occupancy assays can determine the percentage of ITGA4 molecules bound by therapeutic antibodies on target cells, providing insights into target engagement. Functional migration assays using transwell systems or in vivo imaging techniques can directly assess the antibody's ability to inhibit immune cell trafficking into target tissues. Since ITGA4 differentially affects distinct T cell subsets (e.g., Th1 versus Th17 cells), comprehensive immunophenotyping should be performed to determine subset-specific effects . Researchers should evaluate potential compensatory mechanisms, such as upregulation of alternative adhesion molecules or bypassing migration pathways. Safety monitoring should include surveillance for opportunistic infections, particularly in the central nervous system, given the association between anti-ITGA4 therapy and progressive multifocal leukoencephalopathy (PML) in clinical settings . Long-term studies should assess the durability of therapeutic effects and potential development of anti-drug antibodies. Integration of these assays provides a comprehensive assessment of therapeutic potential while identifying possible limitations or safety concerns that may arise in clinical translation.

How can splice-modulating antisense oligonucleotides targeting ITGA4 be evaluated for efficacy?

Evaluating the efficacy of splice-modulating antisense oligonucleotides (ASOs) targeting ITGA4 requires a comprehensive methodological framework spanning molecular, cellular, and in vivo analyses. At the molecular level, researchers should quantify exon skipping efficiency using RT-PCR and qPCR assays that can distinguish between normal and alternatively spliced ITGA4 transcripts . Sequencing of PCR products confirms the precise splicing events induced by the ASOs. Western blotting with antibodies recognizing epitopes both within and outside the targeted exon region can verify reduced full-length protein expression and potentially identify truncated protein products . At the cellular level, functional assays should assess whether ASO-induced exon skipping translates to reduced ITGA4-mediated cell adhesion, migration, or signaling activities. Cell surface expression of ITGA4 can be monitored by flow cytometry to quantify reduction in protein levels . For translational relevance, researchers should test ASOs in disease-relevant models, such as the experimental autoimmune encephalomyelitis (EAE) mouse model for multiple sclerosis, evaluating parameters such as disease progression, immune cell infiltration, and clinical scores . Peptide-conjugated phosphorodiamidate morpholino antisense oligomers have shown promise in ameliorating EAE disease progression, suggesting potential therapeutic applications . Dose-response studies determine optimal oligonucleotide concentrations for maximal exon skipping with minimal off-target effects. Comparison with direct protein inhibition approaches, such as monoclonal antibodies against ITGA4, provides context for the relative efficacy of splice modulation as a therapeutic strategy. Long-term studies should assess the durability of splice modulation effects and potential adaptive responses that might limit sustained efficacy.

What approaches can be used to investigate ITGA4's role in complex cellular interaction networks?

Investigating ITGA4's role in complex cellular interaction networks requires integrative approaches that span multiple scales of biological organization. At the molecular level, proximity labeling techniques such as BioID or APEX can identify proteins physically interacting with ITGA4 in living cells, revealing its immediate interaction partners. Co-immunoprecipitation coupled with mass spectrometry provides complementary information about stable protein complexes involving ITGA4. Functional genomics approaches using CRISPR-Cas9 screens can systematically identify genes that modulate ITGA4-dependent phenotypes. At the cellular level, high-content imaging of co-cultures labeled with anti-ITGA4 antibodies can visualize dynamic interactions between different cell types. For tissue-level analysis, spatial transcriptomics combined with immunohistochemistry maps the relationship between ITGA4 expression and tissue microarchitecture . Computational approaches include constructing protein-protein interaction networks centered on ITGA4, with tools like STRING or Ingenuity Pathway Analysis. Correlation analyses between ITGA4 expression and various tumor microenvironment scores provide insights into its relationship with different components of the cellular ecosystem . Differential gene expression analysis between high and low ITGA4-expressing samples, followed by pathway enrichment analysis using GO and KEGG databases, identifies biological processes associated with ITGA4 function . Gene Set Enrichment Analysis (GSEA) using the "clusterProfiler" package can further elucidate enriched pathways, with results visualized using "ggplot2" . Integration of multiple data types through systems biology approaches provides a comprehensive understanding of how ITGA4 functions within complex cellular networks, potentially revealing novel therapeutic targets or biomarkers associated with ITGA4-dependent processes.

How might single-cell technologies advance our understanding of ITGA4 biology in heterogeneous tissues?

Single-cell technologies offer unprecedented opportunities to decipher ITGA4 biology in heterogeneous tissues by revealing cell-type-specific expression patterns and functional roles. Single-cell RNA sequencing (scRNA-seq) can map ITGA4 expression across diverse cell populations within complex tissues, identifying specific cell types that preferentially express this integrin and potentially uncovering novel ITGA4-expressing populations . The integration of computational tools such as "MAESTRO" and "Seurat" packages with dimensionality reduction techniques like t-SNE enables effective visualization and analysis of cell clusters based on their transcriptional profiles, including ITGA4 expression patterns . Beyond simple expression mapping, single-cell multi-omics approaches combining transcriptomics with proteomics (CITE-seq) or chromatin accessibility (ATAC-seq) provide integrated views of ITGA4 regulation and function. Spatial transcriptomics technologies preserve tissue architecture while providing transcriptional information, allowing researchers to correlate ITGA4 expression with specific anatomical features or microenvironmental niches. For functional insights, single-cell secretome analysis can link ITGA4 expression to secretory phenotypes, while lineage tracing combined with ITGA4 detection can reveal developmental trajectories of ITGA4-expressing cells. In disease contexts, such as tumor microenvironments, single-cell approaches can identify shifts in ITGA4 expression across cell populations during disease progression or in response to therapy . The application of artificial intelligence and machine learning algorithms to single-cell datasets facilitates the identification of subtle patterns and relationships between ITGA4 expression and cellular phenotypes that might not be apparent through conventional analysis methods. These advanced technologies collectively promise to transform our understanding of ITGA4's heterogeneous expression and function across diverse cellular contexts.

What novel therapeutic applications of anti-ITGA4 antibodies are being explored beyond multiple sclerosis?

Novel therapeutic applications of anti-ITGA4 antibodies are expanding beyond multiple sclerosis into diverse disease contexts where pathological immune cell migration and adhesion play central roles. In cancer immunotherapy, researchers are exploring anti-ITGA4 approaches based on evidence that ITGA4 serves as a potential prognostic and immunotherapeutic biomarker . By disrupting tumor-stromal interactions mediated by ITGA4, these antibodies may enhance anti-tumor immune responses or block pro-tumorigenic inflammatory pathways. For inflammatory bowel diseases such as Crohn's disease and ulcerative colitis, anti-ITGA4 therapies aim to prevent lymphocyte trafficking to the gut mucosa, potentially reducing inflammation and tissue damage. In transplantation medicine, these antibodies are being investigated for their ability to prevent allograft rejection by inhibiting lymphocyte migration into transplanted tissues. Fibrotic disorders represent another frontier, as ITGA4-mediated interactions between immune cells and extracellular matrix components contribute to pathological fibrosis in multiple organs. For autoimmune arthritis, anti-ITGA4 approaches may limit immune cell infiltration into synovial tissues. Beyond conventional antibody formats, researchers are developing bispecific antibodies that simultaneously target ITGA4 and another relevant molecule, potentially achieving more selective inhibition of specific cellular interactions. Alternative modalities such as splice-modulating antisense oligonucleotides that reduce ITGA4 expression through exon skipping represent a distinct therapeutic strategy with promising results in experimental autoimmune encephalomyelitis . As these diverse applications advance, researchers must carefully monitor for potential adverse effects, particularly opportunistic infections like progressive multifocal leukoencephalopathy (PML) that have been associated with anti-ITGA4 therapy in multiple sclerosis .

How can computational approaches enhance ITGA4 antibody development and application in precision medicine?

Computational approaches are revolutionizing ITGA4 antibody development and applications in precision medicine through multiple innovative strategies. Structural bioinformatics using homology modeling and molecular dynamics simulations can predict optimal epitopes for antibody development, focusing on regions that are both accessible and functionally significant in the ITGA4 protein. Machine learning algorithms trained on antibody-antigen interaction data can predict binding affinities and cross-reactivity potential, accelerating the selection of lead candidates with optimal specificity profiles. For clinical applications, computational analysis of large-scale genomic and transcriptomic datasets enables the identification of patient subgroups likely to respond to anti-ITGA4 therapies based on molecular signatures . Sophisticated ROC curve analysis and time-dependent ROC curves generated through packages like "pROC" and "timeROC" can evaluate ITGA4's prognostic value across different timeframes, informing patient stratification strategies . Nomogram models constructed using the "rms" and "survival" packages provide personalized risk predictions based on ITGA4 expression combined with other clinical variables . Decision curve analysis (DCA) implemented through the "ggDCA" package assesses the clinical utility of these predictive models across different threshold probabilities . Network-based approaches mapping ITGA4's interactions within cellular signaling networks can identify potential combination therapy targets that might synergize with anti-ITGA4 treatments. Single-cell data analysis pipelines using tools like "MAESTRO" and "Seurat" enable precise identification of cell populations that should be targeted by anti-ITGA4 therapies in specific disease contexts . These computational methods collectively enhance both the development of next-generation anti-ITGA4 antibodies and their precise application in personalized treatment strategies, maximizing therapeutic efficacy while minimizing adverse effects.