OPRK1 Antibody, FITC conjugated

What is OPRK1 and what are its biological functions?

OPRK1 (kappa-opioid receptor) is a G-protein coupled receptor encoded by the Oprk1 gene that mediates the effects of endogenous dynorphins and exogenous kappa-opioid agonists. The receptor plays crucial roles in multiple biological processes, including pain regulation, stress responses, and neuroendocrine function. Recent evidence indicates that OPRK1 influences the suppression of frequency and/or baseline of hormonal secretion patterns . In cancer biology, OPRK1 has been implicated in promoting cell migration and epithelial-mesenchymal transition (EMT), particularly in breast cancer cells where it is often overexpressed compared to normal mammary epithelial cells . Understanding OPRK1 function is essential for research into pain management, addiction, neurological disorders, and certain cancer mechanisms.

What detection methods can be used with OPRK1 Antibody, FITC conjugated?

OPRK1 Antibody with FITC conjugation can be utilized in multiple detection methods, with immunofluorescence (IF) being the primary application due to the fluorescent properties of FITC. Specific applications include:

• Immunocytochemistry/Immunofluorescence (ICC/IF): For detecting OPRK1 expression in fixed and permeabilized cultured cells

• Flow Cytometry (FC): For quantifying OPRK1 expression in cell populations

• Immunohistochemistry on frozen sections (IHC-Fr): For visualizing OPRK1 distribution in tissue sections

The FITC conjugation eliminates the need for secondary antibody incubation, reducing background and cross-reactivity issues while simplifying the experimental workflow. The application should be selected based on research objectives, whether examining subcellular localization, quantifying expression levels, or determining tissue distribution patterns of OPRK1.

How do expression levels of OPRK1 differ between normal and cancer cells?

Research has demonstrated significant differences in OPRK1 expression between normal and cancer cells. In breast cancer specifically, OPRK1 is overexpressed in cancer cell lines (MDA-MB-231, MDA-MB-435, and MCF-7) compared to normal human mammary epithelial cells (MCF-10A) . This differential expression has been confirmed at both protein and mRNA levels through Western blot and RT-qPCR analyses. Notably, the expression pattern correlates with migratory capacity, with highly migratory MDA-MB-231 cells exhibiting higher OPRK1 expression than the less migratory MCF-7 cells . This suggests that OPRK1 expression levels may serve as a potential marker for aggressive cancer phenotypes and metastatic potential, highlighting the importance of accurately quantifying OPRK1 expression using tools like FITC-conjugated antibodies.

What controls should be included when using OPRK1 Antibody, FITC conjugated?

When designing experiments with FITC-conjugated OPRK1 antibody, implementing appropriate controls is critical for result validation. Essential controls include:

• Positive Control: Cell lines with verified OPRK1 expression, such as MDA-MB-231 breast cancer cells, which have been documented to express high levels of OPRK1 . This confirms antibody functionality.

• Negative Control: MCF-10A normal human mammary epithelial cells, which exhibit significantly lower OPRK1 expression than cancer cells , or samples treated with OPRK1 siRNA to knockdown expression.

• Isotype Control: A FITC-conjugated antibody of the same isotype but with irrelevant specificity to assess non-specific binding.

• Autofluorescence Control: Unstained samples to determine natural autofluorescence levels of the cells/tissues.

• Secondary Antibody-Only Control: While not directly applicable to conjugated antibodies, this principle can be applied by using a fluorescent molecule with similar spectral properties to FITC but without antibody attached.

These controls enable researchers to distinguish specific OPRK1 staining from background, non-specific binding, and autofluorescence, thereby increasing data reliability and interpretability.

How should experiments be designed to study the relationship between OPRK1 expression and cancer cell migration?

Based on recent findings regarding OPRK1's role in cancer cell migration, a comprehensive experimental design should include:

• Expression Analysis: Quantify baseline OPRK1 levels in cell lines with varying migratory potential using the FITC-conjugated antibody for flow cytometry or immunofluorescence microscopy .

• Knockdown Studies: Implement OPRK1 siRNA transfection to reduce expression, followed by migration assays such as wound healing or Transwell assays .

• EMT Marker Assessment: Evaluate expression of epithelial-mesenchymal transition markers (E-cadherin, N-cadherin, Snail, Vimentin, MMP2) after OPRK1 knockdown through immunofluorescence co-staining or Western blotting .

• Pathway Analysis: Investigate the activation status of the PI3K/AKT pathway using phospho-specific antibodies in conjunction with OPRK1 detection .

• Rescue Experiments: Apply pathway activators (e.g., Recilisib for PI3K/AKT) following OPRK1 knockdown to determine if migration defects can be reversed .

• Combination Approaches: Test combined effects of OPRK1 knockdown with pathway inhibitors (e.g., Buparlisib for PI3K) on cell viability and migration .

This design enables systematic investigation of OPRK1's role in cancer cell migration and its molecular mechanisms, potentially identifying therapeutic targets.

What sample preparation techniques optimize OPRK1 detection with FITC-conjugated antibodies?

Optimal sample preparation is crucial for accurate OPRK1 detection. Based on methodologies used in recent OPRK1 research, the following protocol is recommended:

• Cell Culture Samples:

  • Fix cells with 4% paraformaldehyde (10-15 minutes at room temperature)

  • Permeabilize with 0.1-0.3% Triton X-100 (5-10 minutes)

  • Block with 3-5% BSA or normal serum for 1 hour at room temperature

  • Apply FITC-conjugated OPRK1 antibody at manufacturer-recommended dilution

• Tissue Sections:

  • For frozen sections: Fix briefly in cold acetone or 4% paraformaldehyde

  • For paraffin sections: Perform antigen retrieval (citrate buffer, pH 6.0)

  • Block endogenous peroxidase activity if applicable

  • Apply blocking solution (3-5% normal serum)

  • Incubate with FITC-conjugated OPRK1 antibody

• Considerations for Flow Cytometry:

  • Fix cells in 1-2% paraformaldehyde

  • For intracellular staining, permeabilize with 0.1% saponin or commercial permeabilization buffer

  • Include protein transport inhibitors if examining newly synthesized OPRK1

• Fluorescence Preservation:

  • Mount samples using anti-fade mounting medium with DAPI for nuclear counterstaining

  • Store slides in the dark at 4°C

  • Capture images promptly to minimize photobleaching of FITC signal

Following these preparation techniques will help maximize signal-to-noise ratio and preserve OPRK1 antigenic sites for optimal detection with FITC-conjugated antibodies.

How can background fluorescence be minimized when using FITC-conjugated OPRK1 antibodies?

Background fluorescence presents a significant challenge when working with FITC-conjugated antibodies. To minimize this issue and improve signal-to-noise ratio:

• Optimize Antibody Concentration: Perform titration experiments to determine the minimum concentration providing sufficient specific signal .

• Improve Blocking: Use 5-10% normal serum from the same species as the experimental sample, or 3-5% BSA with 0.1-0.3% Triton X-100 for 1-2 hours at room temperature.

• Reduce Autofluorescence:

  • For fixed cells: Include 0.1-0.3% Sudan Black B in 70% ethanol for 10-20 minutes after antibody incubation

  • For tissues: Pretreat with 0.1-1% sodium borohydride for 10 minutes or 0.1-1 M glycine for 30 minutes

  • Commercial autofluorescence reducers may also be effective

• Washing Optimization: Extend washing steps with PBS containing 0.05-0.1% Tween-20, using at least 3-5 washes of 5-10 minutes each.

• Confocal Microscopy Settings: Adjust pinhole size, detector gain, and laser power to minimize background while preserving specific signal.

• Sample Storage: Prepare samples immediately before imaging when possible, as FITC signal can deteriorate and background can increase over time.

Implementing these strategies will help distinguish specific OPRK1 staining from background fluorescence, particularly important in tissues with high intrinsic autofluorescence like brain sections where OPRK1 is often studied .

What are common technical challenges in detecting OPRK1 in brain tissue sections?

Detecting OPRK1 in brain tissue presents unique challenges compared to cultured cells, particularly in regions like the arcuate nucleus where OPRK1-expressing cells have been studied :

• Antigen Accessibility: OPRK1 is a membrane-bound G-protein coupled receptor that may require specific permeabilization techniques to expose antigenic sites. Traditional methods may be insufficient for complete receptor detection.

• Specificity Verification: Given the complexity of brain tissue, antibody cross-reactivity is a concern. Validating specificity through OPRK1 knockout tissues or siRNA-treated sections is advisable .

• Low Expression Levels: In certain brain regions, OPRK1 expression may be sparse, requiring signal amplification techniques such as tyramide signal amplification (similar to that used for Kiss1 detection in published protocols) .

• Co-detection Challenges: When performing dual-labeling to correlate OPRK1 with other markers (e.g., Kiss1), spectral overlap between fluorophores must be considered .

• Tissue Autofluorescence: Brain tissue, particularly when fixed, exhibits significant autofluorescence in the FITC emission spectrum, necessitating additional blocking steps or alternative detection methods.

• Quantification Complexity: Accurate cell counting in three-dimensional brain structures requires systematic sampling approaches, such as analyzing every fourth section through the region of interest .

Alternative approaches like in situ hybridization (ISH) for OPRK1 mRNA, as described for arcuate nucleus studies, may complement or replace antibody-based detection in particularly challenging samples .

How can researchers determine if OPRK1 antibody cross-reactivity is affecting experimental results?

Cross-reactivity remains a persistent concern with antibody-based detection methods. To determine if OPRK1 antibody cross-reactivity is compromising experimental validity:

• Validation in Knockout/Knockdown Systems:

  • Test antibody in OPRK1 siRNA-treated samples, as demonstrated in breast cancer cell studies

  • If available, utilize OPRK1 knockout tissues/cells as definitive negative controls

• Antibody Validation Experiments:

  • Perform peptide competition assays using the immunizing peptide

  • Compare staining patterns from multiple antibodies targeting different OPRK1 epitopes

  • Correlate protein detection with mRNA expression via parallel in situ hybridization

• Western Blot Analysis:

  • Verify a single band of appropriate molecular weight (approximately 43 kDa for OPRK1)

  • Confirm band disappearance following OPRK1 knockdown

• Multi-method Confirmation:

  • Compare results across different detection techniques (e.g., ICC/IF, Western blot, flow cytometry)

  • Validate findings with functional assays sensitive to OPRK1 knockdown

• Known Expression Pattern Comparison:

  • Confirm that detected patterns match established OPRK1 distribution in tissues

  • For arcuate nucleus localization, compare with published in situ hybridization patterns

Thorough validation using these approaches increases confidence that observed signals represent authentic OPRK1 rather than cross-reactive artifacts, enhancing research reproducibility and reliability.

How can OPRK1 antibodies be used to investigate the role of OPRK1 in cancer progression mechanisms?

FITC-conjugated OPRK1 antibodies offer powerful tools for investigating OPRK1's role in cancer progression through several advanced applications:

• Epithelial-Mesenchymal Transition (EMT) Visualization:

  • Co-stain OPRK1 with EMT markers (E-cadherin, N-cadherin, Vimentin, Snail, MMP2)

  • Correlate OPRK1 expression patterns with EMT marker localization at single-cell resolution

  • Quantify how OPRK1 knockdown alters EMT marker expression and localization

• PI3K/AKT Pathway Interaction Analysis:

  • Combine OPRK1 immunofluorescence with phospho-AKT and phospho-PI3K detection

  • Perform proximity ligation assays to detect potential direct interactions

  • Visualize pathway components following modulation with activators (e.g., Recilisib) or inhibitors (e.g., Buparlisib)

• Live-Cell Migration Imaging:

  • Implement stable expression of fluorescently-tagged OPRK1 for real-time visualization

  • Perform time-lapse microscopy to track migrating cells with varying OPRK1 expression

  • Correlate migration velocity and directionality with OPRK1 expression levels

• Tumor Microenvironment Studies:

  • Analyze OPRK1 expression at invasion fronts in tissue samples

  • Examine interactions between OPRK1-expressing cancer cells and stromal components

  • Investigate whether OPRK1 expression correlates with tumor-infiltrating immune cells

• Clinicopathological Correlation:

  • Analyze OPRK1 expression in patient samples using tissue microarrays

  • Correlate expression patterns with metastatic potential and patient outcomes

These approaches can help elucidate the mechanistic role of OPRK1 in cancer progression, potentially identifying novel therapeutic targets for intervention, particularly in breast cancer where OPRK1 overexpression has been documented .

What methodologies enable simultaneous detection of OPRK1 protein and mRNA in the same sample?

Correlating OPRK1 protein and mRNA expression in identical samples provides valuable insights into regulatory mechanisms. Several sophisticated approaches enable this dual detection:

• Combined Immunofluorescence and RNA-FISH:

  • Perform standard immunofluorescence using FITC-conjugated OPRK1 antibody

  • Follow with fluorescence in situ hybridization (FISH) using differentially labeled probes targeting OPRK1 mRNA

  • Select fluorophores with minimal spectral overlap (e.g., FITC for protein, Alexa 568 for mRNA)

  • This approach resembles methods used for Kiss1 detection with FITC-labeled probes and tyramide-biotin amplification

• Sequential IF-ISH Protocol:

  • Begin with RNase-free immunofluorescence for OPRK1 protein detection

  • Fix to preserve antibody binding

  • Proceed with in situ hybridization using methods demonstrated for Oprk1 mRNA detection with DIG-labeled probes and alkaline phosphatase visualization

  • Document protein signal before ISH if signal interference is a concern

• Proximity Ligation Assay with Padlock Probes:

  • Detect OPRK1 protein using primary antibodies

  • Use padlock probes for OPRK1 mRNA detection

  • Amplify signals through rolling circle amplification

  • Visualize distinct signals for protein and mRNA within the same cell

• Advanced Digital Pathology:

  • Perform multiplex immunofluorescence for OPRK1 protein

  • Conduct in situ hybridization on sequential sections

  • Apply digital image registration to correlate protein and mRNA signals

  • Utilize machine learning algorithms for pattern recognition and colocalization analysis

These methods enable researchers to address critical questions about post-transcriptional regulation of OPRK1, potential discrepancies between mRNA and protein expression, and spatial relationships between transcription and translation sites within cells expressing this important receptor.

How can OPRK1 detection be integrated into studies of PI3K/AKT pathway activation in cancer?

The PI3K/AKT pathway has emerged as a critical mediator of OPRK1's effects on cancer cell migration and survival. Integration of OPRK1 detection with PI3K/AKT pathway analysis can be achieved through:

• Multiplex Phospho-Protein Detection:

  • Combine FITC-conjugated OPRK1 antibody with antibodies detecting phosphorylated AKT (p-AKT) and PI3K (p-PI3K)

  • Use spectral unmixing to distinguish fluorophores in multiplex imaging

  • Quantify correlation between OPRK1 expression levels and phosphorylation status of pathway components

• Sequential Inhibition Studies:

  • Visualize baseline OPRK1 and p-AKT/p-PI3K expression

  • Apply PI3K inhibitors (e.g., Buparlisib) or activators (e.g., Recilisib)

  • Monitor changes in protein localization and expression through time-course immunofluorescence

  • Correlate with functional assays of cell viability and migration

• OPRK1 Knockdown Combined with Pathway Modulation:

  • Transfect cells with OPRK1 siRNA (validated approach showing 70-80% knockdown efficiency)

  • Apply PI3K/AKT pathway modulators

  • Assess effects on cell viability and migration

  • Document changes in EMT marker expression through immunofluorescence

• Single-Cell Correlation Analysis:

  • Perform flow cytometry with FITC-conjugated OPRK1 antibody and antibodies against p-AKT/p-PI3K

  • Sort cells based on OPRK1 expression levels

  • Analyze pathway activation in OPRK1-high versus OPRK1-low populations

  • Confirm findings with immunofluorescence microscopy for spatial context

• Translational Relevance Assessment:

  • Apply these techniques to patient-derived xenografts or clinical samples

  • Correlate OPRK1/p-AKT/p-PI3K expression patterns with treatment response

  • Evaluate potential for combined targeting of OPRK1 and PI3K/AKT pathways

These integrated approaches can reveal whether OPRK1 directly influences PI3K/AKT pathway activation, as suggested by research showing decreased p-AKT and p-PI3K levels following OPRK1 knockdown in breast cancer cells .

What quantification methods should be used for OPRK1 expression analysis in immunofluorescence studies?

Accurate quantification of OPRK1 expression from immunofluorescence images requires rigorous methodological approaches:

• Cell Counting Strategies:

  • For tissue sections: Count OPRK1-positive cells in systematically sampled sections (e.g., every fourth section through the region of interest)

  • For arcuate nucleus studies, methods similar to those used for counting Oprk1 mRNA-expressing cells are applicable

  • Establish clear positivity thresholds based on control samples

• Intensity-Based Quantification:

  • Measure mean fluorescence intensity (MFI) within defined regions of interest

  • Use software like ImageJ for consistent analysis across samples

  • Apply background subtraction based on negative control samples

  • Consider z-stack acquisition for three-dimensional samples

• Subcellular Localization Analysis:

  • Quantify membrane versus cytoplasmic OPRK1 distribution

  • Perform colocalization analysis with membrane markers

  • Calculate Pearson's or Mander's coefficients for colocalization with other proteins of interest

• Population Distribution Analysis:

  • For heterogeneous samples, determine percentage of OPRK1-positive cells

  • Create intensity histograms to visualize expression distribution

  • Identify potential subpopulations based on expression levels

• Multidimensional Analysis:

  • For dual or triple staining, plot expression correlations between markers

  • Use clustering algorithms to identify distinct cellular phenotypes

  • Apply machine learning approaches for pattern recognition in complex datasets

• Standardization Approaches:

  • Include calibration standards in each experiment

  • Normalize to housekeeping proteins or structural markers

  • Use identical acquisition settings across comparable samples

These methodologies enhance reproducibility and enable meaningful comparisons between experimental conditions, critical for research into OPRK1's role in cancer progression and neurological functions.

How should researchers interpret discrepancies between OPRK1 mRNA and protein expression data?

Discrepancies between OPRK1 mRNA and protein expression are common and can provide insights into regulatory mechanisms. When interpreting such discrepancies:

• Consider Post-Transcriptional Regulation:

  • Evaluate potential microRNA-mediated regulation of OPRK1 mRNA

  • Assess mRNA stability through decay rate measurements

  • Investigate alternative splicing that might affect antibody recognition sites

• Examine Protein Stability Factors:

  • Analyze ubiquitination patterns and proteasome involvement in OPRK1 turnover

  • Consider receptor internalization and recycling dynamics

  • Evaluate effects of ligand exposure on receptor degradation rates

• Technical Considerations:

  • Verify antibody specificity against known controls

  • Confirm mRNA probe specificity through controls similar to those used for Oprk1 ISH

  • Consider sensitivity differences between protein and mRNA detection methods

• Biological Context Analysis:

  • Examine spatial patterns—mRNA may be concentrated in cell bodies while protein distributes to processes

  • Consider temporal dynamics—protein expression may lag behind mRNA upregulation

  • Investigate cell-type specific differences in translation efficiency

• Functional Validation Approaches:

  • Correlate functional assays (e.g., migration) with protein rather than mRNA levels

  • Use receptor activation assays to confirm functionality of detected protein

  • Implement OPRK1 knockdown to validate both mRNA and protein detection methods

Understanding these discrepancies can reveal important regulatory mechanisms controlling OPRK1 expression and function, potentially identifying novel points for therapeutic intervention in conditions where OPRK1 dysregulation contributes to pathology.

What statistical approaches are appropriate for analyzing OPRK1 expression changes in experimental studies?

• For Two-Group Comparisons:

  • Student's t-test for normally distributed data (e.g., comparing OPRK1 expression in normal vs. cancer cells)

  • Mann-Whitney U test for non-normally distributed data

  • Paired t-test for before-after comparisons in the same samples

• For Multi-Group Comparisons:

  • One-way ANOVA followed by post-hoc tests (e.g., Tukey or Bonferroni) for comparing OPRK1 expression across multiple cell lines

  • Kruskal-Wallis test with Dunn's post-hoc test for non-parametric data

  • Two-way ANOVA for examining effects of multiple factors (e.g., OPRK1 knockdown and PI3K inhibition)

• For Correlation Analyses:

  • Pearson correlation for normally distributed data (e.g., correlating OPRK1 expression with migration rates)

  • Spearman correlation for non-parametric data

  • Multiple regression to assess contributions of OPRK1 and other factors to functional outcomes

• For Time-Course Experiments:

  • Repeated measures ANOVA for tracking OPRK1 expression changes over time

  • Mixed-effects models for longitudinal data with missing timepoints

• For Image-Based Quantification:

  • Consider nested statistical approaches that account for multiple cells within fields and multiple fields within samples

  • Use bootstrapping approaches for more robust estimation of confidence intervals

• Sample Size Considerations:

  • Perform power analysis to determine appropriate sample sizes

  • Report effect sizes alongside p-values

  • Consider multiple testing corrections (e.g., Benjamini-Hochberg) when analyzing OPRK1 alongside other markers

These approaches ensure that reported changes in OPRK1 expression are statistically sound, enhancing reproducibility and facilitating meaningful interpretation of experimental results in both basic research and potential clinical applications.