OVOL2 Antibody

What is the subcellular localization pattern of OVOL2 in cancer tissues?

OVOL2 expression is generally localized in both the nucleus and cytoplasm of cancer cells. Immunohistochemistry studies on hepatocellular carcinoma (HCC) tissues have shown this dual localization pattern, with nuclear staining indicated by red arrowheads in microscopy images . This distribution pattern may be related to the differentiation status of the tumor. Similar expression patterns have been documented for other EMT transcription factors such as ZEB2 in HCC, which also displays both cytoplasmic and nuclear staining . When designing experiments to detect OVOL2, researchers should be prepared to analyze both nuclear and cytoplasmic fractions to fully characterize its expression.

Which antibodies are recommended for OVOL2 detection in different applications?

Different antibodies are optimal for specific applications when studying OVOL2. For Western blotting, Santa Cruz Biotechnology's anti-OVOL2 antibody at 1:1,000 dilution has been successfully used in cancer research. For immunohistochemistry, Novus Biologicals' anti-OVOL2 antibody at 1:50 dilution has demonstrated good results . The choice of antibody should be guided by the specific application and tissue type being studied. Always validate antibodies in your specific experimental system before proceeding with larger studies.

How should OVOL2 expression data be quantified in immunohistochemistry experiments?

For immunohistochemistry, OVOL2 expression can be quantified using a percentage immunoreactivity scoring system on a four-point scale:

• 0: <10% positive cells

• 1: 10%–40% positive cells

• 2: 40%–70% positive cells

• 3: 70%–100% positive cells

This standardized scoring system allows for consistent evaluation across different tissue samples and studies. Statistical analysis methods such as Spearman correlation coefficient can then be applied to analyze relationships between OVOL2 expression and other factors like clinical parameters or expression of other proteins.

What are the key controls needed when studying OVOL2 expression in cancer tissues?

When studying OVOL2 expression, several controls are essential:

• Adjacent non-cancerous tissues should be included as comparative controls

• For compartment-specific analysis, samples from different regions (e.g., tumor invasion front vs. tumor center) should be examined

• Positive and negative controls for antibody specificity

• Internal controls for protein loading and transfer in Western blotting (e.g., GAPDH)

Including these controls helps ensure reliable and interpretable results when investigating OVOL2 expression patterns in cancer tissues.

How can researchers effectively isolate and compare OVOL2 expression in different tumor compartments?

To effectively analyze compartment-specific OVOL2 expression:

• Collect distinct samples from the tumor invasion front and tumor center during surgical resection

• Use laser capture microdissection to precisely isolate cells from different tumor regions if working with archival samples

• Process tissues separately for protein or RNA extraction

• Analyze expression using Western blotting, qPCR, or immunohistochemistry with appropriate normalization controls

• Compare expression patterns across compartments using statistical methods such as Student's t-test

Studies have shown significant differences in OVOL2 expression between tumor invasion fronts and tumor centers in HCC, with lower expression typically observed at the invasive edge . This compartment-specific analysis is crucial for understanding the role of OVOL2 in tumor progression and EMT.

What methodological approaches are recommended for investigating the relationship between OVOL2 and autophagy in cancer cells?

To investigate the relationship between OVOL2 and autophagy:

• Transmission electron microscopy (TEM) is the gold standard for visualizing autophagic structures

  • Cells should be fixed with glutaraldehyde and paraformaldehyde in PBS

  • Post-fix with osmium tetroxide and stain with uranyl acetate

  • Embed in appropriate medium and prepare ultrathin sections

  • Image using TEM at appropriate voltage (typically 80 kV)

• Immunoblotting for autophagy markers alongside OVOL2

  • Key markers include LC3-I/LC3-II conversion, p62/SQSTM1, Beclin-1

  • Use appropriate antibody dilutions and controls

  • Analyze band intensity using densitometry software

• Pharmacological or genetic manipulation of autophagy

  • Treat cells with autophagy inducers (rapamycin) or inhibitors (chloroquine)

  • Use siRNA/shRNA targeting autophagy-related genes

  • Monitor changes in OVOL2 expression and cellular phenotypes

Research has shown that autophagy is a key step in regulating OVOL2 expression and inducing EMT in lung adenocarcinoma, with OVOL2 regulating autophagy through the MAPK signaling pathway .

How should researchers design experiments to elucidate the role of OVOL2 in EMT regulation?

To investigate OVOL2's role in EMT regulation:

• Establish baseline expression profiles:

  • Analyze OVOL2 expression alongside EMT markers (E-cadherin, N-cadherin, vimentin, Snail, Slug, Twist, ZEB1) in:

    • Cancer cell lines with different EMT statuses

    • Patient-derived tissues with varying degrees of differentiation

• Perform genetic manipulation experiments:

  • Overexpress OVOL2 using lentiviral vectors (e.g., pFUGW-IRES-GFP constructs)

  • Knock down OVOL2 using shRNA or CRISPR-Cas9

  • Create domain-specific mutants to identify functional regions

• Conduct functional assays to assess EMT phenotypes:

  • Migration assays (wound healing, transwell)

  • Invasion assays (Matrigel-coated transwell)

  • Proliferation assays (CCK-8 reagent at 450nm)

  • Morphological analysis

• Perform pathway analysis:

  • Treat cells with specific pathway inhibitors (e.g., MAPK pathway inhibitors)

  • Analyze changes in OVOL2 expression and EMT markers

  • Conduct co-immunoprecipitation to identify interacting partners

Studies have demonstrated that OVOL2 expression is inversely correlated with mesenchymal markers and positively correlated with epithelial markers in HCC and lung adenocarcinoma , suggesting its role as an EMT suppressor.

How should discrepancies in OVOL2 expression across different cancer types be interpreted?

When encountering discrepancies in OVOL2 expression across cancer types:

• Consider tissue-specific contexts:

  • Different tissues have unique baseline expression levels of OVOL2

  • The regulatory network controlling OVOL2 may vary between tissues

  • Compare with matched normal tissues rather than across cancer types

• Analyze methodological differences:

  • Antibody sources and specificities may differ between studies

  • Detection methods (IHC vs. Western blot vs. qPCR) have varying sensitivities

  • Scoring and quantification systems may not be standardized

• Examine the tumor microenvironment:

  • Stromal interactions can influence OVOL2 expression

  • Inflammatory conditions may affect transcription factor activity

  • Hypoxic conditions in different tumors may alter expression patterns

• Consider statistical approaches:

  • Use meta-analysis techniques to integrate data across studies

  • Apply normalization methods to account for batch effects

  • Employ multivariate analysis to identify confounding factors

While studies show OVOL2 is generally downregulated in multiple cancer types including lung adenocarcinoma and hepatocellular carcinoma , the magnitude and prognostic significance may vary, necessitating careful interpretation of cross-cancer comparisons.

What statistical approaches are most appropriate for correlating OVOL2 expression with clinical outcomes?

For correlating OVOL2 expression with clinical outcomes:

What are the common challenges in detecting OVOL2 using immunohistochemistry and how can they be addressed?

Common challenges and solutions for OVOL2 immunohistochemistry:

• Weak or absent staining:

  • Optimize antigen retrieval methods (citrate buffer has been successful)

  • Extend primary antibody incubation (overnight at 4°C)

  • Use signal amplification systems

  • Try alternative antibody clones or sources (Novus Biologicals at 1:50 dilution has shown good results)

• High background:

  • Increase blocking time with serum

  • Use hydrogen peroxide treatment (3% for 10 minutes) to inhibit endogenous peroxidase

  • Optimize secondary antibody concentration

  • Include additional washing steps

• Inconsistent staining patterns:

  • Standardize tissue processing and fixation protocols

  • Use tissue microarrays for comparative analysis

  • Include positive and negative controls in each batch

  • Implement automated staining platforms if available

• Quantification challenges:

  • Use digital pathology software for objective assessment

  • Employ multiple independent scorers

  • Implement the 4-point scale (0-3) based on percentage of positive cells

  • Report both staining intensity and percentage of positive cells

Careful optimization of these parameters will improve the reliability and reproducibility of OVOL2 detection in tissue samples.

How can researchers troubleshoot inconsistent results when comparing OVOL2 protein and mRNA expression levels?

When troubleshooting discrepancies between OVOL2 protein and mRNA levels:

• Technical considerations:

  • Verify primers specificity for OVOL2 isoforms

  • Check antibody specificity using overexpression or knockdown controls

  • Ensure RNA and protein are extracted from the same samples or regions

  • Use multiple reference genes/proteins for normalization

• Biological explanations:

  • Post-transcriptional regulation (miRNAs, RNA binding proteins)

  • Post-translational modifications affecting protein stability

  • Different half-lives of mRNA versus protein

  • Protein translocation between cellular compartments

• Experimental approaches to resolve discrepancies:

  • Perform polysome profiling to assess translation efficiency

  • Use protein stability assays (cycloheximide chase)

  • Employ RNA-protein correlation analysis across larger sample sets

  • Investigate potential regulatory mechanisms through pathway inhibition

• Data integration strategies:

  • Use scatter plots to visualize protein-mRNA correlations

  • Calculate Spearman or Pearson correlation coefficients

  • Consider nonlinear relationships through appropriate statistical models

Studies have observed discrepancies between OVOL2 mRNA and protein levels in cancer tissues, highlighting the importance of examining both for comprehensive analysis .

How can researchers investigate the interaction between OVOL2 and the MAPK signaling pathway in the context of autophagy regulation?

To investigate OVOL2-MAPK interactions in autophagy regulation:

• Signaling pathway analysis:

  • Use Western blotting to detect phosphorylation status of MAPK pathway components (ERK1/2, JNK, p38) in conjunction with OVOL2 expression

  • Apply specific MAPK pathway inhibitors (U0126 for MEK/ERK, SP600125 for JNK, SB203580 for p38)

  • Monitor changes in autophagy markers (LC3, p62) and OVOL2 expression

• Genetic approaches:

  • Create phospho-mimetic or phospho-deficient OVOL2 mutants

  • Use CRISPR-Cas9 to knockout or knockin specific MAPK pathway components

  • Employ inducible expression systems to control timing of OVOL2 expression

• Protein-protein interaction studies:

  • Perform co-immunoprecipitation between OVOL2 and MAPK pathway components

  • Use proximity ligation assays to detect in situ interactions

  • Conduct FRET or BiFC assays for live-cell interaction analysis

• Functional readouts:

  • Monitor autophagosome formation using GFP-LC3 puncta assays

  • Quantify autophagic flux using tandem mRFP-GFP-LC3 constructs

  • Assess cell migration, invasion, and EMT marker expression

Research has established that OVOL2 regulates autophagy through the MAPK signaling pathway in lung adenocarcinoma, ultimately inhibiting malignant progression .

What methodological approaches would best elucidate the role of OVOL2 in different cancer stem cell populations?

To investigate OVOL2's role in cancer stem cell (CSC) populations:

• CSC isolation and characterization:

  • Use fluorescence-activated cell sorting (FACS) with established CSC markers (CD44, CD133, ALDH activity)

  • Employ sphere formation assays to enrich for CSCs

  • Compare OVOL2 expression between CSC and non-CSC populations

  • Analyze self-renewal capacity using limiting dilution assays

• Genetic manipulation in CSC context:

  • Overexpress or knock down OVOL2 in CSC-enriched populations

  • Assess changes in stemness markers (SOX2, OCT4, NANOG)

  • Examine effects on sphere formation efficiency and size

  • Evaluate tumor-initiating capacity in vivo through limiting dilution transplantation

• Lineage tracing experiments:

  • Develop OVOL2 reporter systems to track expression dynamically

  • Use inducible Cre-loxP systems for temporal control

  • Perform single-cell RNA sequencing to identify OVOL2-expressing subpopulations

  • Map differentiation trajectories in relation to OVOL2 expression

• Therapeutic implications:

  • Test CSC sensitivity to conventional therapies with OVOL2 modulation

  • Examine combination approaches targeting both OVOL2 and stemness pathways

  • Develop OVOL2-based biomarkers for CSC-rich tumor identification

Given OVOL2's role in inhibiting EMT , which is linked to stemness properties, these approaches would help elucidate its function in regulating cancer stem cell behavior.

What is the optimal methodology for a pan-cancer analysis of OVOL2 expression and function?

For comprehensive pan-cancer analysis of OVOL2:

• Multi-omics data integration:

  • Analyze RNA-seq, proteomics, and DNA methylation data across cancer types

  • Incorporate copy number variation and mutation data

  • Use standardized processing pipelines to minimize batch effects

  • Apply dimension reduction techniques (PCA, t-SNE) for visualization

• Tissue microarray approach:

  • Construct multi-tumor tissue microarrays representing diverse cancer types

  • Perform standardized immunohistochemistry with consistent protocols

  • Use automated image analysis for objective quantification

  • Include matched normal tissues for each cancer type

• Functional screening:

  • Conduct CRISPR-Cas9 screens across cancer cell line panels

  • Assess sensitivity to OVOL2 modulation in different cancer contexts

  • Identify synthetic lethal interactions with OVOL2 perturbation

  • Validate key findings in patient-derived xenograft models

• Bioinformatic analysis strategies:

  • Employ gene set enrichment analysis to identify conserved pathways

  • Use clustering approaches to group cancers by OVOL2-associated signatures

  • Develop prognostic models incorporating OVOL2 and related genes

  • Apply network analysis to identify cancer-specific OVOL2 interaction partners

This methodology would build upon existing research showing OVOL2 downregulation in lung adenocarcinoma and hepatocellular carcinoma , potentially revealing pan-cancer patterns and cancer-specific mechanisms.

How can researchers accurately compare antibody performance across different experimental systems when studying OVOL2?

To compare antibody performance across experimental systems:

• Systematic validation approach:

  • Test multiple antibodies against the same samples

  • Include positive controls (overexpression systems) and negative controls (knockdown/knockout)

  • Use peptide competition assays to confirm specificity

  • Compare detection of endogenous versus tagged recombinant OVOL2

• Cross-platform comparison:

  • Evaluate antibody performance across multiple applications (WB, IHC, IF, IP)

  • Use standardized protocols with limited variables

  • Document epitope information and species reactivity

  • Create validation data tables with quantitative metrics

• Reproducibility assessment:

  • Test inter-laboratory variability with identical samples

  • Evaluate lot-to-lot consistency from the same manufacturer

  • Assess stability under different storage conditions

  • Determine sensitivity limits using dilution series

• Documentation and reporting standards:

  • Maintain detailed antibody validation profiles

  • Report catalog numbers, lot numbers, and dilutions

  • Share images of full Western blots including molecular weight markers

  • Consider publishing validation data as supplementary material

Based on the literature, researchers have successfully used specific antibodies at established dilutions for different applications (Western blotting: Santa Cruz Biotechnology 1:1,000; IHC: Novus Biologicals 1:50) , providing a starting point for cross-study comparisons.

Data Table: OVOL2 Antibody Application Parameters

Note: This table compiles information from available research papers on OVOL2 antibody applications. Researchers should optimize conditions for their specific experimental systems.

What are the most promising future research directions for OVOL2 antibody applications in cancer research?

Future research directions for OVOL2 antibody applications should focus on several key areas:

• Development of therapeutic applications:

  • Generation of function-blocking antibodies targeting OVOL2 regulatory domains

  • Creation of antibody-drug conjugates for selective targeting

  • Investigation of OVOL2 as a biomarker for treatment response

  • Exploration of combinatorial approaches with EMT or autophagy modulators

• Advanced detection methods:

  • Single-cell protein analysis of OVOL2 in heterogeneous tumors

  • Multiplex immunofluorescence panels including OVOL2 and EMT markers

  • In vivo imaging using radiolabeled or fluorescently tagged antibodies

  • Proximity-based assays to identify novel interaction partners

• Clinical translation:

  • Development of standardized clinical assays for OVOL2 detection

  • Correlation of OVOL2 expression with response to specific therapies

  • Integration into predictive models for patient stratification

  • Prospective clinical trials incorporating OVOL2 as a biomarker

• Technical innovations:

  • Creation of recombinant antibody fragments with enhanced tissue penetration

  • Development of conformation-specific antibodies to detect active OVOL2

  • Application of proteomics approaches to identify post-translational modifications

  • Implementation of automated image analysis algorithms for consistent quantification