PCNA Recombinant Monoclonal Antibody

What is PCNA and why is it important in cellular research?

PCNA is a 36 kDa nuclear protein that acts as a processivity factor for DNA polymerase δ during DNA replication. It forms a homotrimeric ring that encircles DNA, serving as a sliding clamp to enhance the efficiency of DNA synthesis. Beyond replication, PCNA plays critical roles in DNA repair, chromatin remodeling, and cell cycle regulation .

The protein's expression is tightly correlated with cellular proliferation, making PCNA antibodies invaluable tools for researchers studying cell division, cancer biology, developmental processes, and tissue regeneration. PCNA's dysregulation has been implicated in tumorigenesis and diseases associated with genomic instability, which further explains its importance as a research target .

For successful experimental applications, researchers should understand that while the calculated molecular weight of PCNA is 29 kDa, post-translationally modified PCNA typically appears at 36-38 kDa in Western blots and other protein analyses .

What distinguishes recombinant monoclonal antibodies against PCNA from conventional monoclonals?

Recombinant monoclonal antibodies against PCNA are produced using recombinant DNA technology, ensuring batch-to-batch consistency and eliminating the variability inherent in hybridoma-derived antibodies. These antibodies are typically generated by cloning the antibody genes from a single B cell clone into expression vectors, followed by production in a controlled expression system.

Key advantages include:

• Reproducibility: Consistent performance across different lots and experiments

• Defined specificity: Precisely engineered to recognize specific PCNA epitopes

• Reduced background: Lower cross-reactivity compared to conventional antibodies

• Sustainability: Production doesn't rely on animals or hybridomas

Many commercial suppliers, including Enzo, Zeta Corporation, and Proteintech, offer recombinant rabbit monoclonal antibodies against PCNA that have been validated for multiple applications including Western blotting, immunohistochemistry, immunofluorescence, and ELISA .

What applications are PCNA recombinant monoclonal antibodies optimized for?

PCNA recombinant monoclonal antibodies have been validated for multiple research applications, each requiring specific optimization:

Different antibody clones may exhibit varying performance across applications. For example, the Clone 144 (rabbit recombinant monoclonal) has been optimized for IHC on paraffin sections, ELISA, and Western blotting . Meanwhile, Proteintech's mouse monoclonal (60097-1-PBS) has demonstrated reactivity with human, mouse, rat, and pig samples across multiple applications .

How should PCNA antibodies be stored and handled to maintain optimal performance?

Proper storage and handling of PCNA antibodies is crucial for maintaining their performance and extending their usable lifespan:

• Storage temperature: Most PCNA antibodies should be stored at -20°C for long-term storage, though some require -80°C . Always check manufacturer specifications.

• Aliquoting: Upon receipt, divide the antibody into single-use aliquots to minimize freeze-thaw cycles.

• Freeze-thaw cycles: Avoid repeated freeze-thaw cycles as they can lead to protein denaturation and loss of antibody activity .

• Working dilutions: Prepare fresh working dilutions on the day of the experiment using appropriate buffers.

• Buffer compatibility: Most PCNA antibodies are formulated in PBS, sometimes with preservatives. Check if your application requires preservative-free formulations .

• Shipping conditions: Many PCNA antibodies are shipped on blue ice and should be stored immediately upon receipt .

• Expiration: Follow manufacturer's recommendations for expiration dates, typically 12-24 months when properly stored.

Following these guidelines will help ensure consistent experimental results and maximize the utility of these valuable research reagents.

How do different epitope specificities of PCNA antibodies affect their staining patterns?

Epitope specificity critically influences the staining patterns and research applications of PCNA antibodies. Research has demonstrated that PCNA antibodies recognizing different epitopes can produce markedly different results:

A comprehensive analysis of six anti-PCNA monoclonal antibodies revealed that five (19A2, 19F4, TO17, TO30, PC10) recognized the same protein region (amino acids 111-125), while one antibody (TOB7) recognized a separate region (amino acids 181-195) . Despite targeting the same region, these antibodies exhibited different immunofluorescence patterns due to epitope microheterogeneity.

Specifically:

• Antibodies 19A2, 19F4, and PC10 showed nuclear fluorescence with similar but not identical peptide recognition patterns

• TO17 and TO30 produced cytoplasmic filamentous staining with distinct epitope recognition profiles

• PC10 targeted a simpler epitope compared to 19A2 and 19F4

This epitope microheterogeneity explains why some PCNA antibodies perform better in certain applications. For accurate interpretation of experimental results, researchers should select antibodies with validated performance for their specific application and verify staining patterns with appropriate controls.

What are the best practices for validating PCNA antibodies for cell proliferation assessment?

Rigorous validation of PCNA antibodies is essential before using them for cell proliferation assessment:

• Positive and negative controls: Include tissues/cells with known high (e.g., germinal centers, tumor samples) and low (e.g., terminally differentiated cells) proliferation rates.

• Correlation with other proliferation markers: Compare results with other established methods such as Ki-67 immunostaining or BrdU incorporation.

• Epitope recovery assessment: Test different antigen retrieval methods to determine optimal conditions for epitope accessibility.

• Antibody titration: Perform a dilution series to identify the optimal antibody concentration that maximizes specific staining while minimizing background.

• Western blot verification: Confirm antibody specificity by Western blot to ensure a single band of expected molecular weight (36-38 kDa).

• Quantitative validation: Compare semi-quantitative immunocytochemical analysis with flow cytometry data as described in published research .

• Reproducibility testing: Assess intra- and inter-assay variability across different batches of the same antibody.

How can researchers address staining heterogeneity in tumor samples?

Heterogeneous PCNA staining in tumor samples presents both challenges and opportunities for researchers. To effectively address this heterogeneity:

• Systematic sampling: Employ grid-based or systematic random sampling across the entire tumor section to capture representative regions.

• Hot-spot analysis: Identify and analyze areas with the highest PCNA positivity, which often correlate with aggressive tumor behavior.

• Digital image analysis: Utilize whole-slide imaging and automated quantification software to objectively measure staining intensity and positive cell percentages.

• Microdissection techniques: For molecular analyses, consider laser capture microdissection to isolate areas with different PCNA expression profiles.

• Multiplex staining: Combine PCNA with other markers (e.g., differentiation markers, tumor type-specific markers) to characterize heterogeneous cell populations.

• Statistical approaches: Apply appropriate statistical methods for heterogeneous distributions rather than simple averaging.

Research has demonstrated that PCNA heterogeneity can provide more information than flow cytometry in cases where significant fractions of positive cells correspond to non-tumor stromal or inflammatory cells . This heterogeneity may have prognostic significance, as shown in studies of non-small cell lung cancer where PCNA expression patterns correlated with survival rates and tumor characteristics .

What are the technical considerations for PCNA immunohistochemistry on fixed tissues?

Successful PCNA immunohistochemistry on fixed tissues requires careful attention to several technical factors:

• Fixation method: Alcohol-based fixatives (e.g., methacarn) generally preserve PCNA immunoreactivity better than cross-linking fixatives like formalin. Studies have shown that monoclonal antibody 19A2 can be effectively used on methacarn-fixed, paraffin-embedded sections .

• Antigen retrieval: For formalin-fixed tissues, heat-induced epitope retrieval (HIER) in citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) is typically necessary to unmask PCNA epitopes.

• Detection system selection: Highly sensitive detection systems like streptavidin-biotin or polymer-based methods improve detection of PCNA in tissues with low expression levels.

• Counterstaining optimization: Adjust hematoxylin intensity to clearly visualize negative nuclei without obscuring positive PCNA staining.

• Section thickness: 4-5 μm sections generally provide optimal results for PCNA immunohistochemistry.

• Processing time considerations: Minimize the time between tissue acquisition and fixation to preserve PCNA antigenicity.

• Quantification approach: Employ a semi-quantitative scoring system as described in literature to evaluate the fraction of PCNA-positive cells .

Research has demonstrated that semi-quantitative immunocytochemical analysis with anti-PCNA antibodies provides a simple, reproducible technique for analyzing cell kinetic data in properly fixed, paraffin-embedded tissue, making it accessible for routine use by surgical pathologists .

How does PCNA expression correlate with clinical outcomes in different cancer types?

The relationship between PCNA expression and clinical outcomes varies across cancer types, offering important prognostic information:

In non-small cell lung cancer (NSCLC), high PCNA protein levels have been associated with poorer prognosis. Studies have demonstrated that:

In hepatocellular carcinoma (HCC), PCNA immunohistochemical analysis has proven valuable for:

• Defining tumor proliferation based on correlation with mitotic count

• Establishing relationships with histological grade and metastatic potential

• Assessing tumor invasiveness

• Providing prognostic information for HCC patients

Researchers investigating PCNA as a prognostic marker should:

• Use standardized scoring systems

• Correlate with established clinicopathological parameters

• Perform multivariate analysis to establish independence from other factors

• Consider PCNA in conjunction with other proliferation and molecular markers

These correlations highlight the clinical relevance of PCNA beyond its basic research applications and underscore its value in translational cancer research.

What controls should be included when establishing PCNA immunostaining protocols?

Appropriate controls are essential for reliable PCNA immunostaining. Researchers should include:

Positive Controls:

• Tissues with known high proliferation rates (intestinal crypts, germinal centers, tonsil)

• Cell lines with established PCNA expression levels

• Actively dividing tissues during specific developmental stages

Negative Controls:

• Primary antibody omission controls

• Isotype-matched irrelevant antibody controls

• Tissues with minimal proliferation (terminally differentiated tissues)

Analytical Controls:

• Serial dilutions of primary antibody to establish optimal concentration

• Different antigen retrieval methods to determine optimal epitope accessibility

• Adjacent sections stained with other proliferation markers (Ki-67, MCM proteins)

Validation Controls:

• Western blot confirmation of antibody specificity

• Peptide competition assays to verify epitope specificity

• Correlation with S-phase fraction determined by flow cytometry

For quantitative studies, researchers should establish inter-observer and intra-observer reproducibility by having multiple readers score the same slides and having the same reader score slides multiple times with appropriate time intervals.

How can PCNA antibodies be effectively used in multiplex immunofluorescence applications?

Multiplex immunofluorescence with PCNA antibodies enables simultaneous analysis of proliferation and other cellular markers:

• Antibody selection considerations:

  • Choose PCNA antibodies raised in different host species than other target antibodies

  • Verify that secondary antibodies do not cross-react

  • Ensure PCNA antibody compatibility with fixation and antigen retrieval conditions needed for other markers

• Technical optimization:

  • Establish single-color staining protocols before attempting multiplexing

  • Determine optimal antibody dilutions for fluorescence applications (typically 1:100-1:500)

  • Test different staining sequences to minimize epitope masking

  • Incorporate nuclear counterstains compatible with PCNA's nuclear localization

• Recommended combinations:

  • PCNA + cell cycle markers (cyclin D1, cyclin E, p21)

  • PCNA + DNA damage markers (γH2AX, 53BP1)

  • PCNA + cell type-specific markers (cytokeratins, CD markers)

  • PCNA + apoptosis markers (cleaved caspase-3)

• Analysis approaches:

  • Confocal microscopy for precise localization

  • High-content imaging for quantitative analysis

  • Co-localization analysis to identify cells in specific proliferative states

The ability of different PCNA antibody clones to recognize distinct epitopes can be leveraged in multiplex applications, enabling researchers to study not only proliferation but also specific PCNA functions in DNA repair and other cellular processes .

What are the differences between PCNA and other proliferation markers in research applications?

Understanding the strengths and limitations of PCNA compared to other proliferation markers is essential for selecting the appropriate marker for specific research questions:

For the most comprehensive analysis, researchers often employ multiple proliferation markers in parallel to overcome the limitations of individual markers.

How to address common issues with PCNA immunostaining?

Researchers frequently encounter challenges with PCNA immunostaining. Here are methodological solutions to common problems:

Problem: Weak or absent nuclear staining

• Solution: Optimize antigen retrieval (try different buffers and heating times)

• Solution: Verify antibody reactivity with your species of interest

• Solution: Increase antibody concentration or incubation time

• Solution: Ensure tissues were properly fixed (over-fixation can mask epitopes)

• Solution: Test a different antibody clone that may recognize a more accessible epitope

Problem: High background staining

• Solution: Increase blocking time with serum or BSA

• Solution: Dilute primary antibody further

• Solution: Include 0.1-0.3% Triton X-100 in wash buffers

• Solution: Use more stringent washing (increased duration and number of washes)

• Solution: Switch to a different detection system

Problem: Cytoplasmic instead of nuclear staining

• Solution: Check if your antibody clone is known to give cytoplasmic staining (e.g., TO17 and TO30 antibodies)

• Solution: Verify fixation protocol (improper fixation can lead to protein leakage)

• Solution: Ensure permeabilization is adequate for nuclear antigen access

• Solution: Test with a different antibody clone that specifically recognizes nuclear epitopes

Problem: Heterogeneous staining

• Solution: This may be biologically relevant; analyze multiple fields

• Solution: Compare with other proliferation markers

• Solution: Consider the heterogeneity as data rather than a technical problem

• Solution: Implement digital image analysis for objective quantification

Problem: Non-reproducible results

• Solution: Standardize all protocol steps (times, temperatures, reagent lots)

• Solution: Use automated staining platforms if available

• Solution: Implement detailed protocol documentation

• Solution: Consider switching to recombinant antibodies for better lot-to-lot consistency

What are the critical factors for quantifying PCNA expression in research samples?

Accurate quantification of PCNA expression requires attention to several critical methodological factors:

• Standardized scoring approach:

  • Establish clear criteria for positive vs. negative staining

  • Define intensity thresholds (e.g., weak, moderate, strong)

  • Use a consistent cell counting methodology (random fields vs. hot spots)

  • Consider implementing PCNA labeling index (percentage of positive cells)

• Sampling strategy:

  • Analyze multiple fields (minimum 5-10) at high magnification

  • Ensure representative sampling across tissue/tumor heterogeneity

  • Count sufficient cell numbers (typically 500-1000 cells)

  • Document specific areas analyzed for future reference

• Digital image analysis optimization:

  • Calibrate software to accurately detect nuclear staining

  • Validate automated counts against manual counts

  • Standardize image acquisition parameters (exposure, white balance)

  • Consider both intensity and percentage metrics in analysis

• Contextual interpretation:

  • Compare with normal tissue controls

  • Correlate with other proliferation markers

  • Account for non-tumor cells in the analysis

  • Consider cell cycle dynamics in rapidly vs. slowly proliferating tissues

• Reporting standards:

  • Clearly document scoring methodology

  • Report both positive percentage and intensity metrics

  • Include representative images of scoring categories

  • Address inter-observer variability if multiple scorers are involved

A semiquantitative scoring system for evaluating PCNA-positive cell fractions has been successfully used in research settings and has shown general agreement with flow cytometric S-phase analysis, though with important nuances in heterogeneous samples .

How might advances in PCNA antibody technology impact cancer research and diagnostics?

Emerging developments in PCNA antibody technology promise to transform cancer research and diagnostics:

• Next-generation recombinant antibodies:

  • Site-specific conjugation for precise reporter attachment

  • Engineered fragments (Fab, scFv) for improved tissue penetration

  • Humanized variants for potential therapeutic applications

  • Structurally optimized antibodies for detecting specific PCNA conformations

• Clinical application potential:

  • Standardized immunohistochemical protocols for prognostic assessment

  • Automated digital pathology platforms for objective PCNA quantification

  • Inclusion in multiplex diagnostic panels for comprehensive tumor profiling

  • Development of companion diagnostics for cell cycle-targeting therapeutics

• Single-cell analysis integration:

  • Combining PCNA antibodies with single-cell sequencing technologies

  • Spatial transcriptomics correlation with PCNA expression patterns

  • Mass cytometry applications for high-dimensional cellular profiling

  • Live-cell imaging with non-interfering PCNA antibody derivatives

• Therapeutic relevance:

  • Identification of patient populations likely to respond to cell cycle inhibitors

  • Monitoring treatment response through changes in PCNA expression

  • Development of PCNA-targeting strategies for cancer therapy

  • Correlation of PCNA patterns with immune infiltration and immunotherapy response

Research has already demonstrated PCNA's prognostic value in non-small cell lung cancer and hepatocellular carcinoma , suggesting that more sophisticated antibody-based approaches could further enhance its clinical utility in personalized oncology.

What research opportunities exist for studying post-translational modifications of PCNA?

Post-translational modifications (PTMs) of PCNA represent an exciting frontier for research, with several methodological approaches available:

• PTM-specific antibodies:

  • Development of antibodies recognizing ubiquitinated PCNA (DNA damage response)

  • Antibodies against SUMOylated PCNA (replication regulation)

  • Phospho-specific PCNA antibodies (cell cycle control)

  • Acetylated PCNA detection (regulatory mechanism)

• Analytical approaches:

  • Mass spectrometry characterization of PCNA modifications

  • Proximity ligation assays to detect specific PCNA interactions based on modification state

  • FRET-based sensors for real-time monitoring of PCNA modification

  • ChIP-seq applications to map modified PCNA at specific genomic loci

• Functional implications:

  • Correlation between PCNA modifications and DNA repair efficiency

  • Role of modified PCNA in therapy resistance mechanisms

  • Cell type-specific patterns of PCNA modifications

  • Impact of oncogenic signaling on PCNA modification state

• Technical considerations:

  • Preservation of labile modifications during sample preparation

  • Validation of modification-specific antibodies

  • Quantitative analysis of modification stoichiometry

  • Spatial organization of differently modified PCNA pools

The observation that the calculated molecular weight of PCNA (29 kDa) differs from its observed molecular weight (36-38 kDa) due to post-translational modifications highlights the biological significance of these modifications and their potential as research targets.