ENO2 Antibody

What is ENO2 and what cellular functions does it perform in normal and pathological conditions?

ENO2, also known as neuron-specific enolase (NSE), gamma-enolase, or 2-phospho-D-glycerate hydro-lyase, is a 47.3 kilodalton glycolytic isoenzyme primarily distributed in central and peripheral neurons and neuroendocrine cells . In normal physiology, ENO2 functions as a critical enzyme in glycolysis, catalyzing the conversion of 2-phosphoglycerate to phosphoenolpyruvate. Beyond its metabolic role, ENO2 serves as an important biomarker for neuronal damage when released into cerebrospinal fluid following neural tissue injury .

In pathological contexts, ENO2 has emerged as a significant cancer marker with multifaceted roles in tumor progression. Recent research demonstrates that ENO2 overexpression promotes proliferation, invasion, and migration in clear cell renal cell carcinoma (ccRCC) . Furthermore, ENO2 participates in the epithelial-mesenchymal transition (EMT) process, a critical step in cancer metastasis, and influences the tumor immune microenvironment, potentially affecting response to immunotherapy .

How should researchers distinguish between ENO2 and other enolase isoforms when designing experiments?

When designing experiments targeting ENO2 specifically, researchers must consider the high sequence homology between the three mammalian enolase isoforms:

For accurate experimental outcomes, researchers should:

• Select antibodies targeting unique epitopes specific to ENO2

• Validate antibody specificity using positive and negative control tissues

• Consider using tissue-specific expression patterns to differentiate between isoforms

• Employ orthogonal detection methods (e.g., mass spectrometry) for confirmation in ambiguous cases

What evidence supports ENO2 as a biomarker in cancer research?

Multiple lines of evidence support ENO2's utility as a cancer biomarker:

• Expression analysis from large-scale genomic databases (GEO, TCGA) demonstrates elevated ENO2 expression in ccRCC compared to normal kidney tissue

• Immunohistochemical studies show increased ENO2 staining intensity and frequency in ccRCC tissues (n=191) compared to adjacent normal tissues (n=177)

• Survival analyses demonstrate that high ENO2 expression correlates with poorer prognosis in ccRCC patients, with an area under the curve (AUC) of 0.788 for 5-year survival prediction

• Multivariate Cox regression analyses identify ENO2 as an independent prognostic factor in ccRCC

• Functional studies show that ENO2 knockdown inhibits ccRCC cell proliferation and migration

What critical validation steps should be performed before using a new ENO2 antibody in research?

Before employing any ENO2 antibody in research, comprehensive validation is essential to ensure reliability and reproducibility:

• Specificity verification:

  • Western blot analysis showing a single band at approximately 47.3 kDa

  • Comparison with recombinant ENO2 protein as positive control

  • Testing in ENO2-knockout or ENO2-knockdown systems

  • Cross-reactivity assessment with ENO1 and ENO3

• Application-specific validation:

  • For immunohistochemistry: Test on known ENO2-positive tissues (neuronal/neuroendocrine)

  • For Western blot: Verify optimal protein loading (30 μg/lane recommended)

  • For immunofluorescence: Confirm co-localization with established neuronal markers

  • For ELISA: Establish standard curves using recombinant ENO2

• Reproducibility assessment:

  • Antibody lot-to-lot variation testing

  • Inter-laboratory validation if possible

  • Performance testing under various sample preparation conditions

How do monoclonal and polyclonal ENO2 antibodies differ in research applications?

When selecting between monoclonal and polyclonal antibodies, researchers should consider their specific experimental goals. For studying conformational changes in ENO2 during cancer progression, polyclonal antibodies may provide advantages by recognizing multiple epitopes. Conversely, for quantitative assessment of ENO2 expression across patient samples, monoclonal antibodies may offer superior reproducibility and specificity.

What approach should researchers take when validating ENO2 antibodies for species cross-reactivity?

ENO2 is highly conserved across species, but subtle sequence variations may affect antibody binding. A systematic approach to cross-reactivity validation includes:

• Sequence alignment analysis:

  • Compare ENO2 sequences across target species

  • Identify epitope regions recognized by the antibody

  • Predict potential cross-reactivity based on sequence homology

• Experimental validation in multiple species:

  • Test antibody in positive control tissues from each species

  • Perform Western blot on tissue lysates from various species

  • Compare band patterns and molecular weights across species

• Cross-reactivity documentation:

  • Create a cross-reactivity table with empirical results

  • Document optimal dilutions for each species

  • Note any differences in performance between applications

Available ENO2 antibodies show varying cross-reactivity profiles, with some recognizing ENO2 across a wide range of species (human, mouse, rabbit, rat, bovine, dog, goat, guinea pig, horse, sheep, zebrafish) , while others have more limited reactivity (human, mouse, rat) .

What is the optimal protocol for ENO2 immunohistochemistry in tissue samples?

Based on published protocols, the following optimized IHC methodology is recommended for ENO2 detection:

Sample preparation:

• Fix tissue samples in 10% neutral buffered formalin for 24-48 hours

• Process and embed in paraffin

• Section at 4-5 μm thickness

• Mount on positively charged slides

Staining protocol:

• Deparaffinize and rehydrate sections through xylene and graded alcohols

• Perform heat-induced epitope retrieval using citrate buffer (pH 6.0) for 20 minutes

• Block endogenous peroxidase with 3% hydrogen peroxide for 10 minutes

• Apply protein blocking solution for 10 minutes

• Incubate with primary anti-ENO2 antibody (1:1,000 dilution of cat. no. ab79757 Abcam is recommended based on published literature)

• Incubate with secondary antibody (HRP-conjugated)

• Develop with DAB substrate

• Counterstain with hematoxylin

• Dehydrate, clear, and mount

Scoring system:
For semi-quantitative analysis, implement a dual scoring system based on:

• Staining intensity: Negative (0), weak (1), moderate (2), strong (3)

• Percentage of positive cells: <5% (0), 5-25% (1), 26-50% (2), 51-75% (3), >75% (4)

• Calculate H-score by multiplying intensity and frequency scores

What methodology should be followed for Western blot detection of ENO2?

The following Western blot protocol has been validated for ENO2 detection:

Sample preparation:

• Extract proteins from tissues or cells using RIPA buffer with protease inhibitors

• Quantify protein concentration using BCA or Bradford assay

• Prepare samples with loading buffer containing SDS and reducing agent

• Heat samples at 95°C for 5 minutes

Electrophoresis and transfer:

• Load 30 μg protein per lane on 10% SDS-PAGE gel

• Run gel at 100V until adequate separation

• Transfer proteins to nitrocellulose membrane at 100V for 90 minutes or 30V overnight

Immunodetection:

• Block membrane with Protein Free Rapid Blocking buffer for 10 minutes at room temperature

• Incubate with anti-ENO2 primary antibody (1:1,000 dilution, cat. no. ab79757, Abcam) overnight at 4°C

• Wash membrane with TBST buffer (3 × 5 minutes)

• Incubate with HRP-conjugated secondary antibody (1:5,000 dilution) for 30 minutes at room temperature

• Wash membrane with TBST (3 × 5 minutes)

• Develop using enhanced chemiluminescence reagent

• Image using digital imaging system

Controls and interpretation:

• Include β-actin (1:50,000) as loading control

• Expected ENO2 band size: 47.3 kDa

• Validate specificity using ENO2-knockdown samples as negative controls

How should researchers approach ENO2 knockdown studies to investigate its functional role?

Based on published methodologies, the following approach is recommended for ENO2 knockdown studies:

shRNA design and selection:

• Target sequences with high efficiency and specificity (validated sequence: 5′-GCCGGACATAACTTCCGTAAT-3′)

• Design appropriate controls (scrambled sequences)

• Clone into appropriate lentiviral vectors (e.g., PDS278_pL-U6-shRNA-GFP-ccdB-puro)

Lentiviral transduction protocol:

• Generate lentiviral particles using packaging cell lines

• Transduce target cells at MOI 5-10

• Select transduced cells using puromycin (5 μg/ml)

• Confirm knockdown efficiency by Western blot and qRT-PCR

Functional assays:

• Proliferation assessment using Cell Counting Kit-8

• Migration analysis using Transwell assays

• Invasion evaluation using Matrigel-coated Transwell chambers

• EMT marker analysis (N-cadherin, VIM) by Western blot

How should researchers interpret contradictory ENO2 expression data across different detection methods?

When faced with discrepancies in ENO2 expression data across different platforms or methodologies, researchers should implement this systematic troubleshooting approach:

• Method-specific considerations:

  • IHC: Evaluate antibody specificity, tissue fixation, antigen retrieval methods

  • Western blot: Assess protein extraction efficiency, loading controls, antibody specificity

  • qRT-PCR: Check primer specificity, reference gene stability, RNA quality

  • Proteomics: Consider post-translational modifications, protein-protein interactions

• Resolution strategies:

  • Employ orthogonal validation using independent detection methods

  • Test additional antibodies targeting different ENO2 epitopes

  • Consider subcellular localization differences in interpretation

  • Evaluate tissue heterogeneity and sampling effects

• Biological context analysis:

  • Assess ENO2 expression in relation to disease stage or cell differentiation state

  • Consider regulatory mechanisms affecting transcription vs. translation

  • Evaluate potential isoform-specific expression patterns

What statistical approaches are most appropriate for analyzing ENO2 expression in patient cohorts?

For robust analysis of ENO2 expression in clinical samples, the following statistical methods are recommended:

• Differential expression analysis:

  • For paired samples: Wilcoxon signed-rank test

  • For unpaired samples: Wilcoxon rank-sum test

  • For multi-group comparisons: Kruskal-Wallis test followed by Dunn's post-hoc test

• Survival analysis:

  • Kaplan-Meier curves with log-rank test for comparing high vs. low ENO2 expression groups

  • Determination of optimal cut-off values using ROC curve analysis (AUC calculation)

  • Univariate and multivariate Cox regression to identify independent prognostic factors

• Correlation analysis:

  • Pearson correlation for relationships between ENO2 and other continuous variables

  • Spearman correlation for non-parametric relationships

  • Correction for multiple testing (e.g., Benjamini-Hochberg method)

• Predictive modeling:

  • Time-dependent ROC analysis for prognostic value assessment

  • Calculation of area under the curve (AUC) at clinically relevant timepoints (3-, 5-, and 10-year)

How do researchers determine the optimal threshold for "high" versus "low" ENO2 expression in prognostic studies?

Determining appropriate expression thresholds is critical for translational ENO2 research:

• Data-driven approaches:

  • ROC curve analysis using survival status at specific timepoints (e.g., 5-year survival)

  • Calculation of optimal cut-off value maximizing sensitivity and specificity

  • Validation of cut-off value in independent cohorts

• Statistical methods:

  • Median-based dichotomization (simplest approach)

  • Quartile-based stratification (creating multiple expression groups)

  • Continuous analysis using Cox proportional hazards models with splines

• Biological considerations:

  • Correlation with established thresholds in the literature

  • Relationship to normal tissue expression levels

  • Assessment of differential expression across disease stages

In a published ccRCC study, researchers determined an optimal cutoff H-score of 6 based on ROC curve analysis with an AUC of 0.788, effectively stratifying patients into high (n=130) and low (n=61) ENO2 expression groups with significantly different survival outcomes .

How can ENO2 antibodies be utilized to investigate the tumor immune microenvironment?

ENO2 has emerging importance in tumor immunology research, with antibodies enabling several advanced applications:

• Immune cell infiltration analysis:

  • Multiplex immunofluorescence combining ENO2 with immune cell markers

  • Correlation of ENO2 expression with immune cell populations quantified by ssGSEA or other computational methods

  • Spatial analysis of ENO2-expressing cells relative to immune infiltrates

• Immunotherapy response prediction:

  • Assessment of ENO2 expression in relation to TIDE (Tumor Immune Dysfunction and Exclusion) scores

  • Correlation analysis between ENO2 levels and response to immune checkpoint blockade therapy

  • Longitudinal ENO2 monitoring during immunotherapy

• Mechanistic investigations:

  • ENO2 knockdown followed by immune cell co-culture experiments

  • Cytokine profiling in ENO2-high versus ENO2-low tumors

  • Assessment of immune checkpoint molecule expression in relation to ENO2 levels

Researchers have identified significant correlations between ENO2 expression and various immunosuppressive indicators, suggesting ENO2 may serve as a predictor of immunotherapy efficacy in certain cancers .

What methodologies can researchers employ to investigate ENO2's role in the epithelial-mesenchymal transition?

To elucidate ENO2's function in EMT, researchers can implement these advanced methodologies:

• Gene expression analysis:

  • Correlation of ENO2 with established EMT markers (N-cadherin, Vimentin, E-cadherin)

  • Gene Set Enrichment Analysis (GSEA) focusing on EMT pathways

  • Single-cell RNA sequencing to identify ENO2-expressing cells within EMT transition states

• Protein-protein interaction studies:

  • Co-immunoprecipitation to identify ENO2 binding partners in EMT

  • Proximity ligation assays to visualize protein interactions in situ

  • Mass spectrometry-based interactome analysis of ENO2 in epithelial versus mesenchymal states

• Functional assessments:

  • ENO2 overexpression and knockdown systems to assess EMT marker expression

  • Cell morphology and migration studies (Transwell, wound healing assays)

  • 3D organoid models to visualize EMT dynamics in relation to ENO2 expression

Research has demonstrated that ENO2 knockdown affects the EMT process by modulating N-cadherin and Vimentin expression, consequently inhibiting migration and invasion of cancer cells .

How can researchers integrate multi-omics data to comprehensively assess ENO2's role in cancer progression?

A comprehensive multi-omics approach to ENO2 research includes:

• Integrated data collection:

  • Transcriptomics: RNA-seq for ENO2 mRNA expression

  • Proteomics: Mass spectrometry for ENO2 protein levels and post-translational modifications

  • Epigenomics: ChIP-seq for regulatory elements affecting ENO2 expression

  • Metabolomics: Assessment of glycolytic intermediates affected by ENO2 activity

• Computational integration methods:

  • Protein-protein interaction network analysis using String PPI

  • Cytoscape with cytoHubba plugin for hub gene identification

  • Gene Ontology (GO) and pathway enrichment analysis

  • Multi-omics factor analysis to identify latent factors driving ENO2-related phenotypes

• Validation experiments:

  • CRISPR-Cas9 editing of ENO2 regulatory elements identified in multi-omics analysis

  • Metabolic flux analysis in ENO2-modulated systems

  • Patient-derived xenograft models to validate clinical predictions

This approach has successfully identified ENO2 as a hub gene in ccRCC through integrated analysis of multiple datasets (GSE40435, GSE46699, GSE53757, TCGA), revealing its involvement in cancer progression through both glycolytic and non-glycolytic mechanisms .