EVA1C Antibody

What is EVA1C and what is its biological significance?

EVA1C (Epithelial V-like Antigen 1C) is a mammalian homologue of the novel C. elegans Slit receptor Eva-1. It functions as a transmembrane protein involved in axon guidance and neural development. EVA1C is expressed by axons contributing to commissures, tracts, and nerve pathways of the developing spinal cord and forebrain . The protein plays critical roles in both Robo-dependent and -independent responses to Slit signaling pathways . In pathological conditions, EVA1C has been identified as a potential biomarker in gliomas, where its expression correlates with immune cell infiltration and patient prognosis . Recent research has demonstrated that EVA1C expression positively associates with immune infiltration levels of various immune cells, including B cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells in the tumor microenvironment .

What are the standard methods for detecting EVA1C expression in tissue samples?

Detection of EVA1C in tissue samples typically employs immunohistochemistry techniques using specific anti-EVA1C antibodies. As demonstrated in developmental studies, researchers can use cryostat sections incubated with primary antibodies such as rabbit anti-C21ORF63 (human EVA1C) at 1:200 dilution in 2% BSA in 0.1 M TBS with 0.3% Triton-X-100 . For quantitative analysis, western blotting can be performed using tissue extracts prepared in appropriate buffers containing protease inhibitors like PMSF, Aprotinin, and Pepstatin A . Additionally, flow cytometry can be employed for detecting EVA1C expression in cell suspensions, which requires careful sample preparation and appropriate controls to ensure specific binding of EVA1C antibodies .

How does EVA1C expression vary during neural development?

EVA1C demonstrates dynamic spatiotemporal expression patterns during neural development. In the embryonic spinal cord, EVA1C is strongly expressed at E10.5 when commissural axons pioneer the ventral commissural pathway, and at E17.5 when principal longitudinal axon tracts are established . In the developing brain, EVA1C expression shows region-specific patterns. For instance, in the suprachiasmatic nucleus, EVA1C exhibits weak mosaic staining at E17.5, widespread expression throughout the nucleus by P0, followed by decreased expression to pre-E17.5 levels by P10 . In the corpus callosum, EVA1C is weakly expressed by embryonic commissural axons before crossing the midline but becomes uniformly expressed in both pre-crossing and crossing portions by birth . These temporal expression patterns suggest EVA1C plays critical roles in axon guidance and neural circuit formation during specific developmental windows.

How can researchers optimize EVA1C antibody-based flow cytometry protocols?

Optimizing EVA1C antibody-based flow cytometry requires careful consideration of multiple factors. First, researchers should establish appropriate controls including unstained cells, isotype controls, and single-color compensation controls to account for spectral overlap when using multiple fluorophores . For intracellular detection, permeabilization protocols must be carefully selected to maintain cellular integrity while allowing antibody access.

The most critical parameters for optimization include:

For dual labeling with other neural markers, sequential staining may be necessary, and careful validation of antibody combinations should be performed to avoid interference .

What are the key methodological considerations when investigating EVA1C's role in immune infiltration in gliomas?

When investigating EVA1C's role in immune infiltration in gliomas, researchers should employ a multi-faceted approach. First, quantification of EVA1C expression levels should be performed using validated antibodies through immunohistochemistry or western blotting in patient-derived samples . This should be followed by correlation analysis with immune cell markers to establish associations.

For comprehensive immune infiltration analysis:

• Use multiparameter flow cytometry to simultaneously detect EVA1C and immune cell markers (CD20 for B cells, CD4 for T cells, CD11c for dendritic cells, etc.)

• Employ single-cell RNA sequencing to analyze cell-specific expression patterns

• Conduct spatial transcriptomics or multiplex immunofluorescence to visualize the physical relationship between EVA1C-expressing cells and immune infiltrates

• Validate findings using in vitro co-culture systems with EVA1C-overexpressing or knockdown glioma cells and immune cells

Statistical analysis should account for confounding factors such as tumor grade, patient age, and treatment history. The positive correlation between EVA1C expression and immune infiltration levels of B cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells suggests EVA1C may play a role in modulating the tumor immune microenvironment .

How can researchers evaluate the specificity and cross-reactivity of EVA1C antibodies?

Evaluating EVA1C antibody specificity requires rigorous validation through multiple complementary approaches:

• Genetic validation: Test antibody reactivity in EVA1C knockout/knockdown models versus wild-type controls

• Peptide competition assays: Pre-incubate antibody with excess purified EVA1C protein/peptide to demonstrate specific blocking of immunoreactivity

• Western blot analysis: Confirm detection of protein bands at the expected molecular weight (approximately 48-52 kDa for human EVA1C)

• Immunoprecipitation followed by mass spectrometry: Verify that the antibody pulls down EVA1C specifically

• Cross-species reactivity testing: If the antibody is claimed to recognize EVA1C from multiple species, validation should be performed in each species

Additionally, researchers should test for cross-reactivity with structurally similar proteins, particularly other members of the Epithelial V-like Antigen family. This can be accomplished by overexpressing related proteins and testing for antibody binding. Importantly, antibody validation should be performed in the specific experimental context (fixed versus fresh tissue, western blot versus immunohistochemistry) as epitope accessibility may vary depending on sample preparation methods .

What evidence supports EVA1C as a therapeutic target in glioblastoma?

Recent research has established EVA1C as a promising therapeutic target in glioblastoma (GBM) through several lines of evidence. Studies have identified epithelial-V-like antigen 1 (EVA1) as a novel functional factor specific to glioblastoma-initiating cells (GICs), which are known to be tumorigenic and resistant to conventional treatments . The B2E5 antibody, which demonstrates high affinity for human EVA1, effectively kills EVA1-expressing cell lines and GICs through antibody-dependent cell cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) mechanisms .

Most significantly, the B2E5-antibody drug conjugate (B2E5-ADC) has shown strong cytotoxicity against GICs in culture and prevented their tumorigenesis in the brain when administered intracranially to tumor-bearing brain in animal models . This indicates that EVA1C-targeted antibody-based therapies represent a novel and promising therapeutic strategy for GBM, particularly considering the challenges in developing effective treatments for this aggressive brain cancer.

How should researchers design experiments to evaluate EVA1C antibody-drug conjugates (ADCs) in preclinical models?

Designing experiments to evaluate EVA1C antibody-drug conjugates requires systematic consideration of multiple parameters. The following experimental design considerations are essential:

• Selection of appropriate ADC components:

  • Antibody: Use high-affinity antibodies (like B2E5) with demonstrated specificity for EVA1C

  • Linker: Test both cleavable and non-cleavable linkers to determine optimal drug release kinetics

  • Payload: Compare cytotoxic agents with different mechanisms of action (microtubule inhibitors, DNA-damaging agents, etc.)

• In vitro evaluation protocol:

  • Confirm target binding and internalization using fluorescently-labeled antibodies

  • Assess cytotoxicity in multiple EVA1C-positive and EVA1C-negative cell lines to determine specificity

  • Establish dose-response relationships and calculate IC50 values

• In vivo study design:

• Mechanistic studies:

  • Investigate tumor penetration using ex vivo imaging

  • Perform pharmacokinetic analysis to determine ADC stability and clearance

  • Assess immune cell recruitment to determine contribution of ADCC to efficacy

The experimental design should also include histopathological analysis of normal tissues to evaluate off-target toxicity and biomarker analysis to identify predictors of response.

What are the challenges in developing flow cytometry protocols for detecting EVA1C in primary patient samples?

Developing flow cytometry protocols for EVA1C detection in primary patient samples presents several distinct challenges that researchers must address:

• Sample heterogeneity and preservation:

  • Fresh tissue versus frozen samples: EVA1C epitopes may be differently preserved

  • Dissociation protocols must balance efficient cell separation with epitope preservation

  • Variability between patient samples requires standardization of gating strategies

• Technical limitations:

  • Autofluorescence from brain tissue can interfere with signal detection

  • Limited cell numbers from small biopsies reduce statistical power

  • Dead/dying cells may bind antibodies non-specifically

• Protocol optimization strategies:

  • Implement live/dead discrimination dyes to exclude non-viable cells

  • Use appropriate blocking steps (Fc block, serum proteins) to minimize non-specific binding

  • Include fluorescence-minus-one (FMO) controls to set accurate gates

  • Consider dual staining with lineage markers to identify specific cell populations expressing EVA1C

• Validation requirements:

  • Compare flow cytometry results with other methods (IHC, western blot) on split samples

  • Confirm antibody specificity using positive and negative control cells

  • Establish reproducibility across different patient samples and operators

Researchers should also optimize fixation and permeabilization protocols specifically for EVA1C detection, as different protocols can significantly affect antibody binding. When detecting EVA1C in heterogeneous brain tumor samples, multiparameter analysis with additional markers for tumor cells, immune cells, and neural cells is recommended to accurately identify EVA1C-expressing cell populations .

How can researchers reliably quantify EVA1C expression levels across different experimental platforms?

Reliable quantification of EVA1C expression requires platform-specific optimization and standardization. For western blot analysis, researchers should use validated housekeeping proteins (β-actin, GAPDH) as loading controls and employ densitometry software for quantification . In immunohistochemistry, standardized scoring systems such as H-score or Allred should be implemented, or digital image analysis can be used for objective quantification.

For RT-qPCR, reference genes must be carefully selected and validated for the specific tissue or cell type being studied. Absolute quantification using standard curves is preferable to relative quantification when comparing across different experimental conditions or studies.

For flow cytometry, quantification can be achieved through:

• Mean/median fluorescence intensity (MFI) normalization to isotype controls

• Molecules of equivalent soluble fluorochrome (MESF) beads for standardization

• Antibody binding capacity (ABC) determination to estimate receptor numbers per cell

Regardless of the platform, researchers should:

• Include appropriate positive and negative controls in each experiment

• Establish standard operating procedures for sample collection and processing

• Use the same antibody clone and lot when possible across experiments

• Implement statistical methods to account for technical and biological variability

Cross-platform validation (comparing protein and mRNA levels) can provide more comprehensive assessment of EVA1C expression and account for post-transcriptional regulation.

What controls are essential when investigating EVA1C's interactions with the Slit/Robo signaling pathway?

When investigating EVA1C's interactions with the Slit/Robo signaling pathway, several essential controls must be implemented:

• Genetic controls:

  • EVA1C knockout/knockdown models to confirm specificity of observed effects

  • Slit ligand and Robo receptor knockout/knockdown controls to distinguish Robo-dependent versus Robo-independent functions of EVA1C

  • Rescue experiments with wild-type and mutant EVA1C to identify functional domains

• Protein interaction controls:

  • Co-immunoprecipitation negative controls using non-specific IgG

  • Competition assays with recombinant Slit protein fragments

  • Proximity ligation assays with appropriate antibody controls

• Functional assays:

  • Comparison of axon guidance in the presence and absence of EVA1C

  • Dose-response curves for Slit proteins in EVA1C-expressing versus non-expressing cells

  • Time-course experiments to capture dynamics of signaling events

• Specificity controls:

  • Testing multiple Slit family members (Slit1, Slit2, Slit3) and Robo receptors (Robo1, Robo2, Robo3) to determine specificity

  • Using cells from different developmental stages to account for temporal regulation

Research has shown that EVA1C is expressed by axons that display both Robo-dependent and -independent functions of Slit, suggesting that EVA1C might act either in conjunction with or independently of Robos to regulate axon guidance . Therefore, experimental designs should carefully distinguish between these possibilities through appropriate genetic and biochemical controls.

How should researchers approach the validation of EVA1C as a prognostic biomarker in glioma?

Validating EVA1C as a prognostic biomarker in glioma requires a systematic approach that addresses both technical and clinical considerations:

What are common technical challenges when using EVA1C antibodies and how can they be addressed?

Researchers frequently encounter several technical challenges when working with EVA1C antibodies that require systematic troubleshooting approaches:

• High background signal in immunostaining:

  • Increase blocking time and concentration (5% BSA/normal serum for 1-2 hours)

  • Reduce primary antibody concentration through careful titration

  • Include additional washing steps (minimum 3×10 minutes each)

  • Use more selective secondary antibodies with minimal cross-reactivity

  • Include 0.1-0.3% Triton X-100 in buffers for better penetration and reduced non-specific binding

• Weak or absent signal in western blotting:

  • Optimize protein extraction methods (RIPA buffer with protease inhibitors)

  • Increase transfer time for high molecular weight proteins

  • Reduce washing stringency (lower salt concentration)

  • Try alternative epitope retrieval methods for fixed tissues

  • Consider using signal enhancement systems (biotin-streptavidin amplification)

• Batch-to-batch variability in antibody performance:

  • Maintain reference samples for comparative testing of new antibody lots

  • Use recombinant EVA1C protein as a positive control

  • Document detailed protocols and standardize critical parameters

  • Consider developing monoclonal antibodies for greater consistency

• Cross-reactivity with similar proteins:

  • Validate antibody specificity using knockout/knockdown models

  • Perform peptide competition assays to confirm specific binding

  • Use tissues known to be negative for EVA1C as controls

  • Consider purifying antibodies through affinity chromatography

For flow cytometry applications specifically, researchers should optimize fixation and permeabilization protocols, as excessive fixation may mask EVA1C epitopes while insufficient permeabilization may prevent antibody access to intracellular domains .

How can researchers design experiments to elucidate EVA1C's role in the tumor immune microenvironment?

Designing experiments to elucidate EVA1C's role in the tumor immune microenvironment requires an integrated approach combining in vitro, in vivo, and clinical analyses:

• Cell-based co-culture systems:

  • Establish co-cultures of EVA1C-expressing tumor cells with different immune cell populations

  • Compare wild-type versus EVA1C-knockout/knockdown tumor cells for effects on immune cell recruitment, activation, and function

  • Analyze secreted cytokines/chemokines using multiplex assays to identify EVA1C-dependent immune signaling pathways

• In vivo models:

  • Generate orthotopic EVA1C-modulated (overexpression/knockdown) tumor models in immunocompetent mice

  • Assess immune infiltration through flow cytometry and multiplex immunohistochemistry

  • Evaluate therapeutic responses to immune checkpoint inhibitors in relation to EVA1C expression

  • Perform adoptive transfer experiments with labeled immune cells to track recruitment dynamics

• Mechanistic investigations:

  • Identify potential EVA1C binding partners on immune cells through protein-protein interaction assays

  • Perform transcriptome analysis of tumor and immune cells with variable EVA1C expression

  • Use CRISPR-Cas9 screening to identify genes that modify EVA1C's effects on immune responses

• Clinical correlations:

  • Analyze patient samples for correlations between EVA1C expression patterns and:

    • Immune cell infiltration signatures

    • Expression of immune checkpoint molecules

    • Response to immunotherapies

    • Inflammatory markers and cytokine profiles

Research has shown that EVA1C expression positively correlates with immune infiltration levels of B cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells . This suggests EVA1C may play a role in regulating immune cell recruitment or retention within the tumor microenvironment, potentially through direct or indirect mechanisms that warrant further investigation.

What advanced imaging techniques can be combined with EVA1C antibodies for studying its subcellular localization and trafficking?

Advanced imaging techniques can provide unprecedented insights into EVA1C's subcellular localization, trafficking, and functional interactions. Researchers should consider these cutting-edge approaches:

• Super-resolution microscopy:

  • Stimulated emission depletion (STED) microscopy to visualize EVA1C distribution with 30-50 nm resolution

  • Single-molecule localization microscopy (PALM/STORM) to track individual EVA1C molecules

  • Structured illumination microscopy (SIM) for 3D visualization of EVA1C in relation to cellular structures

• Live-cell imaging approaches:

  • CRISPR knock-in of fluorescent tags (GFP, mCherry) to the endogenous EVA1C gene

  • Photoactivatable or photoconvertible fluorescent protein fusions to track protein movement

  • Fluorescence recovery after photobleaching (FRAP) to measure EVA1C mobility and membrane dynamics

• Correlative light and electron microscopy (CLEM):

  • Combine fluorescence imaging of EVA1C with ultrastructural analysis

  • Use immunogold labeling for transmission electron microscopy

  • Implement cryo-electron tomography for preserved cellular context

• Multiplexed imaging:

  • Mass cytometry imaging (imaging mass cytometry or multiplexed ion beam imaging) for simultaneous detection of >40 proteins alongside EVA1C

  • Iterative fluorescence imaging techniques (CycIF, CODEX) for high-parameter analysis

  • Spatial transcriptomics combined with protein detection to correlate EVA1C protein with mRNA expression

• Functional imaging applications:

  • Förster resonance energy transfer (FRET) to detect protein-protein interactions involving EVA1C

  • Bimolecular fluorescence complementation (BiFC) to visualize dimeric forms or complexes

  • Optogenetic approaches to manipulate EVA1C function with spatiotemporal precision

These advanced techniques can be particularly valuable for understanding EVA1C's dynamics during developmental processes, such as its expression in corpus callosal axons before and after crossing the midline , or for visualizing its interactions with Slit/Robo signaling components in real-time.