ITGA7 Antibody

What is ITGA7 and why is it significant in cellular research?

ITGA7 (integrin alpha 7) is a 124 kDa transmembrane protein that functions as a critical mediator of cell adhesion and signaling pathways. It forms a heterodimer with integrin beta-1 (ITGB1) to create the primary laminin receptor on skeletal myoblasts and adult myofibers. This complex plays essential roles in myogenic differentiation by inducing changes in myoblast shape and mobility, facilitating their localization at laminin-rich sites during secondary fiber formation. ITGA7 is involved in maintaining myofiber cytoarchitecture and ensuring proper anchorage, viability, and functional integrity of muscle cells. Additionally, ITGA7 serves as a Schwann cell receptor for laminin-2 and mediates vascular smooth muscle cell maturation through its interaction with COMP. In airway smooth muscle cells, ITGA7 promotes contractile phenotype acquisition, highlighting its diverse physiological functions across multiple tissue types .

How do different ITGA7 isoforms impact experimental design considerations?

When designing experiments involving ITGA7, researchers must account for the functional differences between isoforms. The Alpha-7X2B and Alpha-7X1B isoforms exhibit distinct activities on different laminin substrates. While both promote myoblast migration on laminin 1 and laminin 2/4, Alpha-7X1B demonstrates reduced activity specifically on laminin 1 in vitro . This differential activity necessitates careful selection of the appropriate extracellular matrix components when studying isoform-specific functions. Researchers should consider:

• Explicitly identifying which isoform is being targeted in their experiments

• Selecting appropriate laminin substrates based on the isoform under investigation

• Using isoform-specific antibodies when available

• Interpreting migration and adhesion assays with awareness of these substrate preferences

The variable function of these isoforms on different laminin subtypes may explain contradictory results when comparing studies that do not specify which isoform or substrate was used.

What validation methods should be employed before using an ITGA7 antibody in research applications?

Thorough validation of ITGA7 antibodies is essential to ensure experimental reliability and reproducibility. A comprehensive validation protocol should include:

• Western blot analysis: Confirming the antibody detects a band of appropriate molecular weight (~124 kDa for full-length ITGA7) across relevant tissues .

• Immunohistochemistry crosschecking: Verifying that the staining pattern in tissues matches known ITGA7 expression profiles, such as enrichment in muscle tissues .

• RNA expression correlation: Assessing medium to high consistency between antibody staining patterns and RNA expression data from databases such as the Human Protein Atlas .

• Enhanced validation techniques: Employing siRNA knockdown to confirm specificity by demonstrating decreased staining upon ITGA7 downregulation, or using GFP-tagged ITGA7 to confirm signal overlap with antibody staining .

• Cross-reactivity testing: Ensuring the antibody does not detect unrelated proteins, especially other integrin family members .

• Multiple antibody comparison: Utilizing at least two independent antibodies targeting different epitopes to confirm consistent staining patterns .

Only antibodies passing these validation criteria should be employed in critical research applications.

What are the optimal protocols for using ITGA7 antibodies in immunohistochemistry?

Based on published protocols, the optimal methodology for ITGA7 immunohistochemistry involves:

• Tissue preparation: Formalin-fixed, paraffin-embedded tissue sections are widely used for ITGA7 detection, with deparaffinization and antigen retrieval steps being critical .

• Antibody dilution: Empirical data suggests a 1:200 dilution of primary antibody (such as ab203254) yields optimal signal-to-noise ratio .

• Incubation conditions: Primary antibody incubation should be conducted at 4°C overnight, followed by appropriate secondary antibody (such as goat anti-rabbit IgG, Cy3 conjugated) at 1:200 dilution for 40 minutes at 37°C .

• Detection systems: Both chromogenic detection using DAB and fluorescent detection using fluorophore-conjugated secondary antibodies are suitable, with the choice dependent on specific experimental requirements .

• Controls: Include negative controls (primary antibody omitted) and positive controls (tissues known to express ITGA7, such as skeletal muscle) in each experiment.

• Counterstaining: Hematoxylin for brightfield or DAPI for fluorescence microscopy provides optimal nuclear counterstaining.

This protocol has been successfully employed to detect ITGA7 in various tissues, including human lung carcinoma samples, yielding clear membrane and cytoplasmic staining patterns .

How should ITGA7 antibodies be optimized for flow cytometry applications?

For flow cytometric detection of ITGA7, researchers should implement the following optimization strategy:

• Titration experiments: Perform antibody titrations (typically ranging from 1:50 to 1:500) to determine the optimal concentration that provides maximum signal separation between positive and negative populations while minimizing background .

• Blocking protocol: Include a 30-minute blocking step with 5% normal serum from the same species as the secondary antibody to reduce non-specific binding.

• Permeabilization considerations: Since ITGA7 is primarily expressed on the cell surface, avoid harsh permeabilization protocols that may disrupt membrane integrity and epitope accessibility.

• Compensation controls: When multiplexing with other antibodies, prepare single-stained controls for each fluorophore to properly compensate for spectral overlap.

• Validation controls: Include isotype controls matched to the primary antibody's host species and immunoglobulin class to assess background staining levels.

• Cell preparation: For optimal detection, use freshly isolated cells rather than fixed samples when possible, as fixation can sometimes mask ITGA7 epitopes.

These methodological considerations are essential for generating reliable flow cytometry data when investigating ITGA7 expression in various cell populations .

What functional assays can be used to investigate ITGA7-mediated cellular behaviors?

Several functional assays can effectively examine ITGA7's role in cellular processes:

• Laminin adhesion assay: Plate cells on laminin-coated surfaces and quantify attachment in the presence or absence of anti-ITGA7 blocking antibodies to assess ITGA7's contribution to laminin binding. This has proven effective in demonstrating that anti-ITGA7 antibodies can block laminin binding function .

• Cell migration assays:

  • Wound healing: Create a scratch in a monolayer of cells expressing ITGA7 on laminin-coated plates and monitor closure rates with or without ITGA7 inhibition.

  • Transwell migration: Assess migration through laminin-coated membranes, comparing ITGA7-depleted cells with controls. This approach has demonstrated reduced invasiveness of ITGA7-depleted glioblastoma stem cells .

• Signaling cascade analysis: Examine phosphorylation of downstream targets including:

  • PI3K/AKT pathway components

  • Phospho-FOXO3a levels

  • p38 activation status

  • Src and focal adhesion kinase (FAK) phosphorylation

• Clonogenic survival assay: Perform limiting dilution assays with ITGA7-depleted cells compared to controls to determine the impact on stem cell self-renewal capacity, as demonstrated in glioblastoma stem cells .

• Spheroid formation: Assess the ability of cells with manipulated ITGA7 expression to form three-dimensional spheroids in non-adherent conditions, which can indicate stem-like properties.

These assays collectively provide a comprehensive profile of ITGA7's functional impact on cellular behaviors and signaling pathways.

How can ITGA7 antibodies be employed to investigate cancer stem cell properties?

ITGA7 antibodies have emerged as powerful tools for investigating cancer stem cell (CSC) properties, particularly in glioblastoma research. A methodological approach includes:

• Stem cell enrichment: Use anti-ITGA7 antibodies for fluorescence-activated cell sorting (FACS) to isolate ITGA7-high cell populations, which have demonstrated enhanced stem-like characteristics in glioblastoma samples .

• Stemness verification protocols:

  • Self-renewal: Compare sphere-forming capacity between ITGA7-high and ITGA7-low populations

  • Multi-lineage differentiation: Assess the ability of isolated cells to differentiate into multiple neural lineages

  • Stem cell marker co-expression: Analyze correlation between ITGA7 expression and established stem cell markers

• Functional blocking studies: Apply anti-ITGA7 blocking antibodies to tumor spheroids to assess disruption of stem cell maintenance signaling pathways, including:

  • PI3K/AKT pathway inhibition, measured through decreased phospho-FOXO3a levels

  • Cell cycle regulation via p27/Kip1 upregulation

  • Alterations in other cancer stem cell maintenance pathways

• In vivo limiting dilution xenograft assays: Inject decreasing numbers of ITGA7-high versus ITGA7-low cells into immunocompromised mice to determine tumor-initiating frequency, which measures stem cell functionality in vivo .

• Therapeutic response monitoring: Use ITGA7 antibodies to track changes in the cancer stem cell population following experimental therapies, providing insights into treatment resistance mechanisms.

Research has demonstrated that ITGA7 expression correlates with high-grade tumors and that ITGA7-depleted glioblastoma stem cells exhibit dramatically reduced tumor growth in orthotopic mouse models, supporting its value as both a biomarker and therapeutic target .

What approaches can be used to investigate ITGA7's role in tumor invasion mechanisms?

To comprehensively investigate ITGA7's role in tumor invasion, researchers should employ a multi-faceted approach:

• Laminin invasion assays: Use Transwell chambers coated with laminin to quantify the invasive capacity of cells with different ITGA7 expression levels. Published research has shown ITGA7-depleted glioblastoma stem cells demonstrate significantly reduced invasiveness through laminin matrices .

• Molecular signaling analysis: Examine the activation status of invasion-associated pathways when cells are cultured on laminin:

  • Phosphorylated Src levels (a well-known oncogene implicated in cell invasion)

  • Focal adhesion kinase (FAK) activation, which links to metastasis in multiple cancers

  • Matrix metalloproteinase expression and activity

• Live-cell imaging: Perform time-lapse microscopy of fluorescently labeled ITGA7-expressing cells to visualize invasion dynamics in 3D matrices, capturing:

  • Cell morphology changes during invasion

  • Speed and directionality of movement

  • Formation of invasive structures like invadopodia

• Co-immunoprecipitation studies: Identify ITGA7's binding partners during invasion by isolating protein complexes using anti-ITGA7 antibodies, followed by mass spectrometry analysis .

• In vivo invasion models: Utilize orthotopic xenograft models with GFP-expressing cells to visualize and quantify invasion patterns:

  • Compare control cells with ITGA7-depleted cells

  • Track cellular invasion into surrounding brain structures

  • Treat established tumors with anti-ITGA7 antibodies to assess therapeutic potential

Research using these approaches has revealed that anti-ITGA7 treatment significantly reduces invasion of glioblastoma cells in mouse brain tissue, with treated animals showing confined tumor growth compared to extensive invasion in control animals .

How can ITGA7 antibodies be used in the development of targeted cancer therapies?

The development of ITGA7-targeted cancer therapies represents an emerging research direction based on several promising methodological approaches:

• Therapeutic antibody screening:

  • Test panels of anti-ITGA7 antibodies for their ability to block laminin binding

  • Evaluate antibody-dependent cell-mediated cytotoxicity (ADCC) potential

  • Screen for internalization capability to deliver antibody-drug conjugates

  • Assess effects on downstream signaling pathways (PI3K/AKT, Src/FAK)

• In vivo therapeutic efficacy testing:

  • Subcutaneous tumor models have demonstrated significant reduction in tumor growth with anti-ITGA7 treatment

  • Intracranial xenograft models show extended survival in mice treated with anti-ITGA7 antibodies

  • GFP-expressing tumor models reveal reduced invasion following antibody treatment

• Combination therapy development:

  • Test anti-ITGA7 antibodies alongside conventional chemotherapies

  • Evaluate synergy with radiation treatment

  • Explore combinations with other targeted therapies, particularly those targeting other integrins

• Treatment resistance monitoring:

  • Track ITGA7 expression changes following treatment

  • Identify alternative signaling pathways activated after ITGA7 blockade

  • Develop strategies to overcome acquired resistance mechanisms

• Therapeutic antibody optimization:

  • Engineer antibodies with improved target binding and reduced immunogenicity

  • Develop antibody-drug conjugates specifically targeting ITGA7-expressing cells

  • Create bispecific antibodies targeting ITGA7 and other cancer-associated antigens

Research has shown that discontinuation of anti-ITGA7 treatment leads to resumed tumor growth, suggesting that sustained therapeutic intervention may be necessary or that combination approaches might be required for durable responses .

What are common pitfalls in ITGA7 antibody-based experiments and how can they be addressed?

Researchers frequently encounter several challenges when working with ITGA7 antibodies. Here are methodological solutions to common problems:

• Non-specific binding issues:

  • Problem: High background staining in immunohistochemistry or Western blots.

  • Solution: Implement comprehensive blocking protocols with 5% BSA or serum from the secondary antibody species. Pre-absorb antibodies with tissue homogenates from species lacking the target protein .

• Epitope masking during fixation:

  • Problem: Loss of antibody reactivity in fixed specimens.

  • Solution: Compare multiple fixation protocols (formalin, methanol, acetone) and optimize antigen retrieval methods (heat-induced vs. enzymatic). For ITGA7, heat-induced epitope retrieval in citrate buffer (pH 6.0) has shown superior results with formalin-fixed tissues .

• Isoform specificity challenges:

  • Problem: Antibody detects some but not all ITGA7 isoforms.

  • Solution: Verify which epitope region the antibody targets and confirm whether it spans isoform-specific regions. When possible, use multiple antibodies targeting different epitopes to ensure comprehensive detection .

• Inconsistent Western blot results:

  • Problem: Variable band patterns or unexpected molecular weights.

  • Solution: Carefully control protein denaturation conditions, as integrins are sensitive to reducing agents and heat. Use gradient gels (4-15%) to resolve high molecular weight proteins effectively. Include positive control lysates from tissues known to express ITGA7, such as skeletal muscle .

• Cross-reactivity with other integrins:

  • Problem: Antibody binds to related integrin family members.

  • Solution: Validate specificity using cell lines with ITGA7 knockdown or knockout. Perform competition assays with recombinant ITGA7 protein to confirm binding specificity .

By applying these methodological solutions, researchers can significantly improve the reliability and reproducibility of their ITGA7 antibody-based experiments.

How should researchers interpret conflicting data from different ITGA7 antibodies?

When faced with contradictory results from different ITGA7 antibodies, researchers should follow this systematic interpretive framework:

• Compare epitope locations:

  • Antibodies targeting different domains may yield different results due to:

    • Conformational changes in the protein structure

    • Domain-specific interactions with binding partners

    • Differential accessibility in various experimental conditions

  • Map each antibody's epitope (e.g., C-terminal region AA 1123-1139 vs. middle region AA 700-800) to interpret results in context

• Assess validation parameters:

  • Evaluate whether each antibody has undergone rigorous validation:

    • Standard validation (concordance with UniProtKB/Swiss-Prot data)

    • Enhanced validation (siRNA knockdown, GFP-tagged expression, or independent antibodies)

  • Prioritize data from antibodies with stronger validation profiles

• Review technical variables:

  • Compare experimental conditions including:

    • Sample preparation methods (fixation, permeabilization)

    • Antibody concentrations and incubation parameters

    • Detection systems (direct vs. indirect, chromogenic vs. fluorescent)

  • Standardize protocols when possible to eliminate technique-dependent variations

• Perform orthogonal validation:

  • Corroborate antibody data with non-antibody methods:

    • mRNA expression analysis

    • Functional assays (e.g., laminin binding)

    • Genetic manipulation (siRNA, CRISPR)

  • Triangulate findings to distinguish true biological signals from antibody artifacts

• Consider biological context:

  • Evaluate whether differences reflect biological variations:

    • Cell type-specific post-translational modifications

    • Context-dependent protein conformations

    • Variable isoform expression patterns

When properly applied, this interpretive framework allows researchers to extract meaningful biological insights despite apparent contradictions in antibody-based detection of ITGA7.

How can ITGA7 expression data be correlated with clinical outcomes in cancer research?

To effectively correlate ITGA7 expression with clinical outcomes, researchers should implement the following methodological approach:

• Quantitative expression analysis:

  • Use validated antibodies to conduct immunohistochemistry on tissue microarrays

  • Employ standardized scoring systems (H-score, Allred score) for consistent quantification

  • Supplement with quantitative protein methods (Western blot, ELISA) and mRNA analysis

  • Consider digital pathology tools for objective quantification

• Data stratification framework:

  • Establish clear expression thresholds (low/medium/high) based on:

    • Statistical distribution in the study population

    • Receiver operating characteristic (ROC) curve analysis

    • Established cutoffs from previous literature

  • Create patient subgroups based on these thresholds for comparative analysis

• Statistical correlation methods:

  • Perform Kaplan-Meier survival analysis comparing ITGA7-high vs. ITGA7-low groups

  • Calculate hazard ratios using Cox proportional hazards models

  • Adjust for relevant clinicopathological variables in multivariate analyses

  • Test for interactions between ITGA7 expression and treatment modalities

• Multi-parameter integration:

  • Combine ITGA7 data with other molecular markers

  • Develop predictive models incorporating multiple variables

  • Validate findings in independent patient cohorts

Published research has demonstrated significant correlations between high ITGA7 expression and high-grade tumors, particularly in glioblastoma, suggesting its potential value as a prognostic biomarker. Patients with ITGA7-high tumors showed poorer outcomes in analyzed datasets, strengthening the rationale for developing ITGA7-targeted therapies .

What bioinformatic approaches can be used to analyze ITGA7 expression across different cancer types?

A comprehensive bioinformatic analysis of ITGA7 expression across cancer types requires integration of multiple computational approaches:

• Multi-database expression analysis:

  • Extract ITGA7 expression data from:

    • The Cancer Genome Atlas (TCGA)

    • International Cancer Genome Consortium (ICGC)

    • Gene Expression Omnibus (GEO)

    • Human Protein Atlas (HPA)

  • Normalize data using appropriate methods (RPKM, TPM, z-scores)

  • Compare expression levels across cancer types and against matched normal tissues

• Correlation network construction:

  • Identify genes with expression patterns correlating with ITGA7

  • Build protein-protein interaction networks centered on ITGA7

  • Perform pathway enrichment analysis to identify biological processes associated with ITGA7 expression

• Survival analysis automation:

  • Develop scripts to perform systematic survival analyses across cancer types

  • Implement consistent thresholding methods for high/low expression groups

  • Generate forest plots to visualize hazard ratios across multiple cancer types

• Molecular subtype classification:

  • Evaluate ITGA7 expression across established molecular subtypes of each cancer

  • Determine whether ITGA7 expression defines novel patient subgroups

  • Integrate with mutation data to identify genetic alterations associated with ITGA7 expression

• Single-cell RNA sequencing analysis:

  • Examine cell type-specific expression patterns within tumors

  • Identify cell populations with highest ITGA7 expression

  • Correlate with stemness markers to validate association with cancer stem cell populations

Researchers have used these approaches to demonstrate that ITGA7 expression is significantly elevated in high-grade gliomas compared to low-grade gliomas, correlating with aggressive disease, and potentially identifying it as a therapeutic target across multiple cancer types .

How can researchers integrate ITGA7 antibody data with other -omics approaches for comprehensive pathway analysis?

An integrated multi-omics approach incorporating ITGA7 antibody data provides deeper insights into its biological context:

• Antibody-based proteomics integration:

  • Correlate ITGA7 protein levels (determined by antibody-based methods) with:

    • Phosphoproteomic data to map activation of downstream signaling

    • Total proteome profiles to identify co-regulated proteins

    • Protein-protein interaction networks from co-immunoprecipitation studies

• Transcriptomic coordination:

  • Compare antibody-detected ITGA7 protein expression with:

    • RNA-seq or microarray gene expression data

    • Alternative splicing patterns affecting isoform expression

    • Non-coding RNA regulators of ITGA7 expression

  • Identify discordances between protein and mRNA levels that may indicate post-transcriptional regulation

• Genomic context analysis:

  • Align antibody-based ITGA7 expression data with:

    • Copy number variations affecting the ITGA7 locus

    • Mutations within the gene or its regulatory regions

    • Epigenetic modifications (DNA methylation, histone marks)

• Functional network construction:

  • Build integrated networks connecting:

    • ITGA7 protein interactions (from antibody-based co-IP studies)

    • Transcriptional responses to ITGA7 manipulation

    • Signaling pathway alterations (PI3K/AKT, Src/FAK)

    • Phenotypic outcomes (invasion, proliferation, stemness)

• Multi-modal data visualization:

  • Develop comprehensive visualizations that simultaneously display:

    • Protein expression/localization (antibody data)

    • Activation state (phosphorylation status)

    • Transcript levels

    • Genetic/epigenetic alterations

    • Functional consequences

This integrated approach has successfully revealed that ITGA7 expression in glioblastoma activates both PI3K/AKT pathways affecting cell cycle progression and Src/FAK pathways driving invasion, demonstrating how multi-omics integration provides a systems-level understanding of ITGA7's role in cancer biology .