CPNE1 Antibody, FITC conjugated

What is CPNE1 and why is it relevant to cancer research?

CPNE1 is a highly conserved calcium-dependent phospholipid-binding protein that functions in regulating signal transduction and membrane trafficking . It contains a pair of C2 domains (C2A and C2B) at the N-terminus and a Von Willebrand factor A (VWA) domain at the C-terminus . CPNE1 has gained significant attention in cancer research due to its overexpression in multiple malignancies including colorectal cancer, osteosarcoma, and triple-negative breast cancer . Its relevance stems from its demonstrated role in promoting cancer cell proliferation, invasion, metastasis, and chemoresistance, particularly through activation of the AKT signaling pathway . When designing experiments to study CPNE1's role in cancer, researchers should consider its expression patterns across different cancer types and its involvement in various cellular processes including glycolysis and apoptosis regulation.

What experimental applications are most suitable for FITC-conjugated CPNE1 antibodies?

FITC-conjugated CPNE1 antibodies are particularly valuable for:

• Flow cytometry (FCM) at dilutions of 1:20-100

• Immunofluorescence on paraffin-embedded tissues (IF/IHC-P) at dilutions of 1:50-200

• Immunofluorescence on frozen sections (IF/IHC-F) at dilutions of 1:50-200

• Immunocytochemistry (IF/ICC) at dilutions of 1:50-200

• Live cell imaging for trafficking studies

When designing experiments, researchers should optimize antibody concentrations based on their specific application. For immunofluorescence studies examining CPNE1 subcellular localization, it's important to note that CPNE1 has been observed in both nuclear and cytoplasmic compartments of cancer cells . For quantitative analysis, flow cytometry can provide precise measurements of CPNE1 expression levels across different experimental conditions.

How can researchers validate the specificity of CPNE1 antibody staining?

When validating CPNE1 antibody specificity, researchers should implement multiple approaches:

• Positive and negative tissue controls: Use tissues known to express high levels of CPNE1 (e.g., osteosarcoma or colorectal cancer tissues) as positive controls and tissues with minimal expression (e.g., normal cartilage) as negative controls .

• siRNA/shRNA knockdown validation: Compare staining between wildtype cells and those with CPNE1 knockdown. The search results describe CPNE1 knockdown approaches using sequences such as 5′-CACACAACTGGTCTCATACTT-3′ for siRNA or shRNA constructs (shRNA#1: GCTACGCTTTGGAATCTAT; shRNA#2: GGTGCAATGCTCCGATTAT; shRNA#3: CCAACTTTGCACCCATCAT) .

• Western blot correlation: Confirm that immunofluorescence patterns correlate with protein expression levels determined by Western blot, using anti-CPNE1 antibody at 1:1,000 dilution .

• Peptide competition assay: Pre-incubate antibody with immunizing peptide before staining to confirm signal reduction.

• Multiple antibody comparison: Use antibodies targeting different epitopes of CPNE1 to confirm consistent localization patterns.

What are the optimal fixation and permeabilization protocols for CPNE1 immunofluorescence?

Based on published methodologies for CPNE1 detection:

For paraffin-embedded tissues:

• Cut sections to 4-μm thickness

• Dewax and rehydrate in xylol and graded alcohol

• Block endogenous peroxidase activity with 3% hydrogen peroxide in PBS for 15 min

• Block non-specific binding with 2% bovine serum for 20 min

• Incubate with CPNE1 primary antibody (1:100 dilution) for 18h at 4°C in 2% BSA/PBS

• For FITC-conjugated antibodies, proceed directly to nuclear counterstaining

• For unconjugated primary antibodies, incubate with appropriate fluorescent secondary antibody

• Counterstain nuclei with DAPI

• Mount with anti-fade mounting medium

For cultured cells:

• Fix cells with 4% paraformaldehyde for 15 minutes at room temperature

• Permeabilize with 0.1% Triton X-100 for 10 minutes

• Block with 5% normal serum in PBS for 1 hour

• Incubate with FITC-conjugated CPNE1 antibody (1:50-200 dilution) overnight at 4°C

• Counterstain nuclei with DAPI

• Mount with anti-fade mounting medium

These protocols should be optimized for specific cell types and experimental conditions.

What key controls should be included in CPNE1 antibody experiments?

A robust experimental design for CPNE1 antibody experiments should include:

These controls help distinguish genuine CPNE1 staining from artifacts and ensure experimental rigor.

How should researchers quantitatively analyze CPNE1 expression in immunofluorescence experiments?

For quantitative analysis of CPNE1 expression in immunofluorescence experiments:

• Image Acquisition:

  • Capture multiple fields (minimum 5-10) per sample

  • Use consistent exposure settings across all experimental groups

  • Include both nuclear and cytoplasmic regions as CPNE1 localizes to both compartments

• Analysis Methods:

  • Intensity measurement: Calculate mean fluorescence intensity (MFI) within regions of interest

  • Localization analysis: Quantify nuclear vs. cytoplasmic distribution using colocalization with nuclear markers

  • Single-cell analysis: Measure cell-to-cell variation in CPNE1 expression

  • Population analysis: Determine percentage of CPNE1-positive cells using appropriate thresholding

• Quantification Software:

  • ImageJ/FIJI with appropriate plugins

  • CellProfiler for automated analysis

  • Commercial confocal software packages

• Statistical Analysis:

  • Apply appropriate statistical tests based on data distribution

  • For clinical samples, correlate expression with clinicopathological parameters

  • Consider using scoring systems similar to those used in previous CPNE1 studies (e.g., percentage of positive cells combined with staining intensity)

How can FITC-conjugated CPNE1 antibodies be used to investigate CPNE1's role in cancer metabolism?

CPNE1 has been implicated in regulating cancer cell metabolism, particularly glycolysis. To investigate this role:

• Dual immunofluorescence approach:

  • Co-stain with FITC-conjugated CPNE1 antibody and antibodies against glycolytic enzymes such as GLUT1 and HK2, which are reported downstream targets of CPNE1-AKT signaling

  • Analyze colocalization patterns to determine spatial relationships

• Functional metabolic imaging:

  • Combine CPNE1 immunofluorescence with fluorescent glucose analogs (e.g., 2-NBDG)

  • Measure glucose uptake in cells with varying CPNE1 expression levels

• Live-cell metabolic imaging:

  • Use FITC-conjugated CPNE1 antibody in live cell imaging to track CPNE1 dynamics

  • Simultaneously monitor glycolytic activity using appropriate sensors

• Correlative analysis:

  • Quantify CPNE1 expression levels and correlate with extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) measurements

  • Analyze how CPNE1 expression correlates with expression of glycolysis-related genes

Research has shown that CPNE1 promotes glycolysis by activating AKT signaling and upregulating glucose transporter 1 (GLUT1) and hexokinase 2 (HK2) . This methodology allows researchers to visualize and quantify these relationships at the cellular level.

What approaches can be used to study CPNE1's role in chemoresistance mechanisms?

CPNE1 has been implicated in chemoresistance in multiple cancer types. To investigate this role using FITC-conjugated CPNE1 antibodies:

• Treatment response visualization:

  • Perform time-course immunofluorescence studies following chemotherapy treatment

  • Monitor changes in CPNE1 expression and localization in responsive versus resistant cells

  • Combine with apoptosis markers such as cleaved caspase-3 and cleaved PARP1

• Patient-derived xenograft (PDX) models:

  • Use FITC-conjugated CPNE1 antibodies to analyze CPNE1 expression in PDX tumors with varying chemotherapy responses

  • Correlate expression patterns with treatment outcomes

• Mechanistic studies:

  • Combine CPNE1 immunofluorescence with phospho-AKT staining to visualize activation of this pathway, which has been linked to CPNE1-mediated chemoresistance

  • Co-stain for other resistance-associated proteins such as cIAP2

• Knockdown/overexpression models:

  • Generate CPNE1 knockdown or overexpression cell lines

  • Treat with chemotherapeutics like oxaliplatin or adriamycin

  • Monitor drug response through viability assays and immunofluorescence

Research has demonstrated that CPNE1 knockdown increases sensitivity to chemotherapeutics in cancer models, potentially by downregulating anti-apoptotic proteins and disrupting key signaling pathways including AKT activation .

How can researchers investigate the relationship between CPNE1 and AKT signaling using immunofluorescence approaches?

The relationship between CPNE1 and AKT signaling is central to understanding CPNE1's role in cancer progression. To investigate this relationship:

• Dual immunofluorescence:

  • Use FITC-conjugated CPNE1 antibody together with phospho-AKT (p-AKT) antibodies

  • Analyze colocalization patterns and quantify correlation coefficients

  • Compare p-AKT levels in cells with varying CPNE1 expression

• Signaling dynamics:

  • Perform time-course experiments following growth factor stimulation

  • Monitor changes in CPNE1 localization and concurrent AKT phosphorylation

  • Quantify temporal relationships between CPNE1 redistribution and AKT activation

• Pathway inhibition studies:

  • Combine CPNE1 immunofluorescence with AKT inhibitor treatments

  • Analyze how AKT inhibition affects CPNE1 expression and localization

  • Monitor downstream effects on GLUT1 and HK2 expression

• Knockdown validation:

  • Compare p-AKT levels in CPNE1 knockdown versus control cells using both immunofluorescence and western blot approaches

  • Quantify the correlation between CPNE1 expression and p-AKT levels

Studies have shown that CPNE1 promotes cancer progression by activating the AKT-GLUT1/HK2 cascade . This relationship can be visualized at the cellular level using appropriate immunofluorescence techniques, providing spatial information not available through biochemical assays alone.

What are common challenges when using FITC-conjugated antibodies and how can they be addressed?

Researchers working with FITC-conjugated CPNE1 antibodies should be aware of these common challenges:

• Photobleaching:

  • FITC is relatively prone to photobleaching compared to other fluorophores

  • Solution: Use anti-fade mounting media containing anti-photobleaching agents

  • Solution: Minimize exposure to excitation light during imaging

  • Solution: Consider using more photostable fluorophores (e.g., Alexa Fluor 488) for extended imaging sessions

• Autofluorescence:

  • Tissues, particularly paraffin-embedded samples, may exhibit autofluorescence in the FITC channel

  • Solution: Include unstained controls to assess background autofluorescence

  • Solution: Use Sudan Black B (0.1% in 70% ethanol) to quench autofluorescence

  • Solution: Apply spectral unmixing during image acquisition/processing

• pH sensitivity:

  • FITC fluorescence is sensitive to pH changes

  • Solution: Maintain consistent pH in all buffers (optimally pH 8.0)

  • Solution: Use pH-buffered mounting media

• Signal intensity:

  • Suboptimal signal-to-noise ratio

  • Solution: Optimize antibody concentration using dilution series (1:20-1:200)

  • Solution: Extend incubation time to overnight at 4°C

  • Solution: Enhance detection using anti-FITC secondary antibodies (signal amplification)

• Specificity concerns:

  • Non-specific binding

  • Solution: Include appropriate blocking steps with 2-5% BSA or normal serum

  • Solution: Validate specificity using CPNE1 knockdown controls

  • Solution: Include isotype controls at matching concentrations

How can researchers optimize dual staining protocols involving FITC-conjugated CPNE1 antibodies?

For dual immunofluorescence studies involving FITC-conjugated CPNE1 antibodies and other markers:

• Fluorophore selection:

  • Pair FITC (excitation ~495nm, emission ~519nm) with spectrally distinct fluorophores

  • Recommended pairings: FITC with Cy3, Texas Red, or Cy5

  • Avoid fluorophores with spectral overlap (e.g., FITC with GFP)

• Sequential staining protocol:

  • Fix and permeabilize cells/tissues as described in section 2.1

  • Block with 5% normal serum in PBS for 1 hour

  • Incubate with FITC-conjugated CPNE1 antibody (1:50-200)

  • Wash thoroughly (3×5 minutes with PBS)

  • Block again with 5% normal serum for 30 minutes

  • Incubate with unconjugated primary antibody against target of interest

  • Wash thoroughly (3×5 minutes with PBS)

  • Incubate with appropriate fluorophore-conjugated secondary antibody

  • Counterstain nuclei with DAPI

  • Mount with anti-fade medium

• Controls for dual staining:

  • Single-stained controls for each antibody

  • Secondary antibody-only controls

  • No-antibody controls to assess autofluorescence

• Application-specific considerations:

  • For CPNE1 and p-AKT co-staining: Use FITC-CPNE1 with Cy3/Texas Red-conjugated p-AKT antibodies

  • For CPNE1 and glycolytic enzymes: Pair FITC-CPNE1 with antibodies against GLUT1 and HK2

  • For CPNE1 and apoptotic markers: Combine with cleaved caspase-3 or cleaved PARP1 antibodies

How should researchers interpret CPNE1 expression patterns in relation to clinical parameters?

When interpreting CPNE1 expression data in clinical samples, researchers should consider:

• Expression prevalence:

  • Studies have reported CPNE1 positivity in 84% of osteosarcoma samples

  • Compare expression rates across different cancer types and stages

• Localization patterns:

  • CPNE1 has been observed in both nuclear and cytoplasmic compartments

  • Evaluate whether subcellular localization correlates with clinical features

• Correlation with clinicopathological features:

  • Analyze associations with:

    • Tumor size

    • Metastatic status (CPNE1 expression has been linked to distant metastases)

    • Survival rates (CPNE1 overexpression correlates with poorer prognosis)

    • Treatment response (CPNE1 may contribute to chemoresistance)

• Multi-marker analysis:

  • Combine CPNE1 expression data with other biomarkers such as:

    • AKT pathway components (p-AKT)

    • Glycolytic enzymes (GLUT1, HK2)

    • Apoptosis markers (cleaved caspase-3, cleaved PARP1)

    • Other signaling pathway components (Ras, MEK-1/2, WNT1, β-catenin)

• Quantitative assessment:

  • Use standardized scoring systems combining:

    • Percentage of positive cells

    • Staining intensity

    • Subcellular distribution

What statistical approaches are appropriate for analyzing CPNE1 immunofluorescence data in experimental studies?

For rigorous analysis of CPNE1 immunofluorescence data:

• Descriptive statistics:

  • Mean fluorescence intensity (MFI) with standard deviation

  • Median fluorescence intensity with interquartile range (for non-normally distributed data)

  • Percentage of CPNE1-positive cells

• Comparative analyses:

  • Student's t-test for comparing two groups with normally distributed data

  • Mann-Whitney U test for non-parametric comparisons

  • ANOVA with post-hoc tests for multiple group comparisons

  • Chi-square test for categorical data (e.g., positive vs. negative staining)

• Correlation analyses:

  • Pearson correlation for normally distributed continuous variables

  • Spearman rank correlation for non-parametric data

  • Point-biserial correlation for continuous vs. binary variables

• Survival analyses:

  • Kaplan-Meier survival curves stratified by CPNE1 expression levels

  • Log-rank test to compare survival distributions

  • Cox proportional hazards regression for multivariate analysis

• Sample size considerations:

  • A priori power analysis to determine required sample size

  • Post-hoc power analysis to validate findings with small samples

• Advanced analyses for mechanistic studies:

  • Colocalization statistics (Pearson's or Mander's coefficients) for dual-labeling studies

  • Intensity correlation analysis for protein-protein interaction studies

  • Temporal correlation for time-course experiments

These approaches ensure robust statistical interpretation of CPNE1 expression data in both experimental and clinical contexts.