CPK14 Antibody

What is CPK14 and what are its primary cellular functions?

CPK14 (Cyclin-dependent protein kinase 14) plays a crucial role in cell proliferation and cell cycle progression. Research indicates that CDK14 is involved in critical cellular processes including cell division, differentiation, and potentially in chemoresistance mechanisms in cancer cells. Studies have shown that CDK14 overexpression correlates with poor prognosis in ovarian cancer patients and is associated with chemoresistance . At the molecular level, CDK14 interacts with pathways involving β-catenin and is regulated by the TGF-β signaling pathway through direct binding of Smad2 to the region -437 to -446 upstream of the CDK14 transcription start site .

What are the recommended applications for CPK14 antibodies?

CPK14 antibodies are versatile tools that can be utilized in multiple research applications:

Each application requires protocol optimization with appropriate positive and negative controls to ensure specificity and reproducibility .

How can I validate the specificity of my CPK14 antibody?

Validating antibody specificity is critical for ensuring reliable experimental results. A comprehensive validation approach includes:

• Western blot analysis showing a single band at the expected molecular weight (approximately 77 kDa for CDK14)

• Reduced or absent signal in samples with CPK14 knockdown (siRNA/shRNA) or knockout (CRISPR-Cas9)

• Peptide competition assays showing signal reduction when the antibody is pre-incubated with immunizing peptide

• Cross-reactivity testing against closely related proteins (other CDKs)

• Immunofluorescence showing expected subcellular localization patterns

For flow cytometry applications, additional validation should include isotype controls and fluorescence-minus-one controls to assess non-specific binding and properly set gating parameters .

How do post-translational modifications affect CPK14 antibody binding?

Post-translational modifications (PTMs) can significantly impact antibody recognition of CPK14. Cyclin-dependent kinases often undergo regulatory phosphorylation, which can alter protein conformation and potentially mask or expose antibody epitopes. When studying CPK14:

• Determine whether your antibody targets a region susceptible to PTMs

• Use phospho-specific antibodies when studying activation states of CPK14

• Employ phosphatase inhibitors in lysis buffers when studying phosphorylated forms

• Consider using multiple antibodies recognizing different epitopes for comprehensive analysis

Mass spectrometry analysis can help identify specific PTMs on CPK14, informing antibody selection for particular research questions. When quantifying CPK14 expression levels in samples with varied PTM status, researchers should be cautious about potential variations in antibody affinity .

What is the optimal protocol for CPK14 co-immunoprecipitation studies?

For successful co-immunoprecipitation (co-IP) of CPK14 and its binding partners:

Include appropriate controls: isotype antibody controls, input controls, and when possible, samples where one interaction partner is depleted. For identifying novel CPK14 binding partners, consider crosslinking before lysis to capture transient interactions .

How do I optimize dual-luciferase reporter assays to study CPK14 promoter regulation?

Based on the experimental approach described in the literature for CDK14, the following protocol can be adapted for studying CPK14 promoter regulation:

• Amplify different lengths of the CPK14 promoter region, including and excluding potential transcription factor binding sites

• Ligate these fragments into a luciferase reporter vector (e.g., pGL4-Basic)

• Co-transfect cells with the reporter constructs and a control Renilla luciferase vector (e.g., pRL-SV40) for normalization

• After 24 hours, apply experimental treatments (e.g., TGF-β1 at 20 ng/mL for 48h)

• Measure firefly and Renilla luciferase activities using a dual-luciferase assay

• Calculate normalized luciferase activity (firefly/Renilla ratio) to quantify promoter activity

For identifying specific transcription factor binding sites, include constructs with site-directed mutations in predicted binding motifs .

What controls should be included in Western blot experiments with CPK14 antibodies?

A robust Western blot experiment with CPK14 antibodies should include:

• Positive control: Cell line with confirmed CPK14 expression (e.g., SK-OV-3 or OVCAR-3 cells based on CDK14 studies)

• Negative control: CPK14 knockdown/knockout samples

• Loading control: Detection of housekeeping proteins (β-actin, GAPDH, tubulin)

• Molecular weight marker: To confirm the expected molecular weight

• Secondary antibody only control: To identify non-specific binding of secondary antibodies

• Isotype control: Primary antibody of the same isotype but irrelevant specificity

• Peptide competition control: Primary antibody pre-incubated with immunizing peptide

For CDK14/CPK14 detection, optimized lysis buffers should contain phosphatase inhibitors to preserve phosphorylated forms that may be relevant to protein function .

How can I troubleshoot weak or absent CPK14 signal in immunofluorescence?

When facing challenges with CPK14 detection in immunofluorescence experiments:

Perform systematic optimization by changing one parameter at a time and documenting results carefully .

What are the critical parameters for flow cytometric analysis of CPK14?

For reliable flow cytometric detection of CPK14:

• Cell preparation:

  • Ensure single-cell suspensions (filter through 40-70 μm cell strainer)

  • Maintain cell viability (include viability dye to exclude dead cells)

  • Fix cells with 2-4% paraformaldehyde (10 min, room temperature)

• Permeabilization optimization:

  • Test multiple agents (0.1% saponin, 0.1-0.5% Triton X-100, 90% methanol)

  • Optimize duration (10-30 min) and temperature (4°C vs. room temperature)

• Antibody staining:

  • Titrate antibody to determine optimal concentration

  • Include isotype controls matched to primary antibody

  • Use fluorescence-minus-one (FMO) controls for proper gating

• Data analysis:

  • Establish consistent gating strategy based on controls

  • Use median fluorescence intensity for quantification

  • Apply appropriate statistical tests for comparisons

For cell cycle correlation studies, consider dual staining with DNA content markers (propidium iodide or DAPI) to relate CPK14 expression to specific cell cycle phases .

How should I interpret CPK14 expression differences across tissue samples?

When analyzing CPK14 expression across different tissue samples:

• Establish baseline expression in normal tissues:

  • Quantify expression in multiple normal tissue types

  • Document variation within normal tissues to establish reference ranges

• Compare disease-associated changes:

  • Use matched case-control designs when possible

  • Account for demographic factors (age, sex) that may influence expression

  • Consider tissue-specific differences in baseline expression

• Statistical analysis considerations:

  • Normalize expression data appropriately (e.g., to housekeeping genes)

  • Apply appropriate statistical tests based on data distribution

  • Include multiple testing correction for large-scale comparisons

  • Report effect sizes along with p-values

• Interpretation guidelines:

  • Consider biological significance beyond statistical significance

  • Validate findings across multiple experimental approaches

  • Correlate expression with functional outcomes or clinical parameters

Studies of CDK14 in ovarian cancer have demonstrated that increased expression correlates with chemoresistance and poor prognosis, providing a model for similar analyses with CPK14 .

What statistical approaches are appropriate for analyzing CPK14 knockdown effects?

When analyzing the effects of CPK14 knockdown experiments:

• For comparing expression between control and knockdown groups:

  • Student's t-test (parametric data with equal variances)

  • Welch's t-test (parametric data with unequal variances)

  • Mann-Whitney U test (non-parametric alternative)

• For time-course experiments:

  • Repeated measures ANOVA (parametric)

  • Mixed-effects models for handling missing data points

  • Area under the curve (AUC) analysis followed by appropriate comparison test

• For multiple experimental conditions:

  • One-way ANOVA with appropriate post-hoc tests (Tukey, Bonferroni)

  • Two-way ANOVA for examining effects of multiple factors simultaneously

  • Kruskal-Wallis with Dunn's post-hoc test (non-parametric alternative)

• For correlation analyses with other markers:

  • Pearson correlation (linear relationship, parametric data)

  • Spearman correlation (monotonic relationship, non-parametric data)

Researchers should report complete statistical information including test type, test statistic value, degrees of freedom, p-value, and effect size measures. Based on studies with CDK14, knockdown effects can be assessed by examining changes in MDR1 and β-catenin expression, as well as functional outcomes like cell proliferation and apoptosis .

How can I accurately quantify CPK14 localization changes in response to treatments?

For quantitative analysis of CPK14 subcellular localization changes:

• Image acquisition considerations:

  • Use consistent microscope settings across all samples

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

  • Include z-stacks to capture complete cellular volume

  • Use appropriate resolution for subcellular compartment analysis

• Quantification approaches:

  • Nuclear/cytoplasmic intensity ratio measurement

  • Colocalization analysis with organelle markers (Pearson's correlation coefficient)

  • Object-based colocalization using binary masks

  • Distance-based measurements from reference structures

• Software tools and analysis:

  • ImageJ/Fiji with Nuclear-Cytoplasmic Ratio plugin

  • CellProfiler for automated image segmentation and quantification

  • Specialized colocalization software (JACoP, Coloc2)

  • Machine learning approaches for complex pattern recognition

• Statistical analysis:

  • Compare distribution of measurements across cells (not just means)

  • Account for cell-to-cell variability within treatments

  • Consider hierarchical statistical approaches (cells nested within fields within samples)

When studying translocation events, time-lapse imaging can provide valuable insights into the kinetics of localization changes in response to treatments .

How can I use CPK14 antibodies in multiplexed imaging systems?

For multiplexed detection of CPK14 alongside other markers:

• Antibody panel design considerations:

  • Verify antibody compatibility (species, isotypes, detection systems)

  • Select antibodies with non-overlapping epitopes when targeting the same protein

  • Choose fluorophores with minimal spectral overlap

  • Include appropriate controls for each marker

• Sequential staining approach:

  • Apply antibodies in order of increasing sensitivity

  • Include stripping or blocking steps between rounds if necessary

  • Validate that earlier rounds don't affect subsequent staining

• Multiplexed immunofluorescence methods:

  • Conventional multiplexing (3-5 markers with spectrally distinct fluorophores)

  • Tyramide signal amplification (TSA) multiplexing (5-8 markers)

  • Cyclic immunofluorescence (CycIF) for higher multiplexing (20+ markers)

  • Mass cytometry (CyTOF) or imaging mass cytometry for highest multiplexing (40+ markers)

• Analysis considerations:

  • Use spectral unmixing algorithms for overlapping fluorophores

  • Employ cell segmentation for single-cell analysis

  • Consider dimensionality reduction techniques for data visualization

Multiplex approaches enable correlative analysis of CPK14 with pathway components, cell cycle markers, or other proteins of interest in the same sample .

What is the best approach for studying CPK14 interactions with TGF-β signaling components?

Based on the established relationship between CDK14 and TGF-β signaling:

• Co-immunoprecipitation studies:

  • Pull down CPK14 and probe for TGF-β pathway components (Smad2/3)

  • Perform reciprocal IP with Smad proteins and detect CPK14

  • Include controls for specificity (isotype controls, knockdown samples)

• Proximity ligation assay (PLA):

  • Visualize direct protein-protein interactions in situ

  • Quantify interaction frequency in different cellular compartments

  • Assess changes in interaction following TGF-β stimulation

• Chromatin immunoprecipitation (ChIP):

  • Investigate Smad2 binding to the CPK14 promoter region

  • Perform sequential ChIP to identify co-occupancy with other factors

  • Correlate binding with transcriptional changes

• Functional validation:

  • Assess CPK14 expression changes following TGF-β treatment (20 ng/mL)

  • Use TGF-β receptor inhibitors (e.g., SB-431542) to block signaling

  • Create Smad binding site mutants in the CPK14 promoter to validate direct regulation

This integrative approach can reveal mechanistic insights into how TGF-β signaling regulates CPK14 expression and function, similar to what has been observed with CDK14 in ovarian cancer models .