PPP2R3A Antibody, FITC conjugated

What is PPP2R3A and why is it significant in cellular research?

PPP2R3A (Protein Phosphatase 2 Regulatory Subunit B''Alpha) belongs to the PP2A regulatory subunit B" family and plays crucial roles in regulating several important cancer-related signaling pathways. It has been found to regulate the Wnt-signaling cascade, epidermal growth factor (EGF)-EGF receptor (EGFR) signaling, and 5′ adenosine monophosphate-activated protein kinase (AMPK) activity . The protein is also known as PR72/B"α2 or PR130/B"α1 depending on the splice variant. In hepatocellular carcinoma (HCC), PPP2R3A expression is primarily localized in the cytoplasm of cancer cells and shows higher expression in tumor tissues compared to adjacent para-tumor tissues, suggesting its potential role in cancer progression .

When studying PPP2R3A, researchers should consider its context-specific functions across different cell types and diseases, as its regulatory effects can vary significantly based on the cellular environment.

What are the technical specifications of commercially available PPP2R3A antibodies with FITC conjugation?

The FITC-conjugated PPP2R3A antibodies available for research typically have the following specifications:

• Target specificity: Amino acids 256-508 of the Regulatory Subunit B of PPP2R3A

• Host species: Rabbit

• Antibody type: Polyclonal

• Purification method: Protein G purification (>95% purity)

• Immunogen: Recombinant Human Serine/threonine-protein phosphatase 2A regulatory subunit B'' subunit alpha protein (256-508AA)

• Isotype: IgG

• Species reactivity: Human

When selecting antibodies for experiments, researchers should verify the specific epitope recognition, as different antibodies targeting different regions of PPP2R3A may yield varying results in different applications.

How can I validate the specificity of PPP2R3A FITC-conjugated antibodies for my research?

Methodologically sound validation includes:

• Western blot analysis: Compare PPP2R3A expression between tissues known to have differential expression (e.g., HCC tumor tissues versus adjacent normal tissues)

• Immunohistochemistry controls: Include positive controls (e.g., HCC specimens that show high PPP2R3A expression) and negative controls (omitting primary antibody)

• Knockdown verification: Use shRNA to silence PPP2R3A expression and confirm reduced signal in immunofluorescence experiments

• Overexpression verification: Create cells overexpressing PPP2R3A and confirm increased signal intensity

• Cross-reactivity testing: Test antibody against related proteins in the PP2A family to ensure specificity

Evidence shows that in HCC samples, properly validated antibodies should detect higher PPP2R3A expression in tumor foci compared to adjacent para-tumor tissues, with primarily cytoplasmic localization .

What is the optimal experimental design for studying PPP2R3A's role in cancer cell proliferation?

Based on published research methodologies, an optimal experimental design should include:

• Cell models: Use multiple cancer cell lines (e.g., HepG2 and HuH7 for liver cancer studies)

• Genetic manipulation approaches:

  • Knockdown using multiple shRNA constructs targeting different regions of PPP2R3A

  • Overexpression using lentiviral vectors containing PPP2R3A cDNA

• Proliferation assessment: Employ multiple methods including:

  • CCK-8 assay with measurements at 24h, 48h, 72h, and 96h timepoints

  • Cell cycle analysis using flow cytometry with PI staining

• Molecular mechanism evaluation:

  • Western blotting to assess expression of cell cycle regulators (e.g., p53)

  • Analysis of downstream signaling pathways (Wnt, EGF-EGFR, AMPK)

Research has demonstrated that PPP2R3A knockdown significantly inhibits liver cancer cell proliferation (p < 0.05 at 48h, p < 0.01 at later timepoints), arrests cells in G1/S phase, and upregulates p53 expression, while overexpression promotes proliferation and alters cell cycle progression .

How should I design experiments to assess PPP2R3A's impact on cell migration and invasion?

A comprehensive experimental approach should include:

• Migration assays:

  • Transwell migration assay without Matrigel coating

  • Time-course measurements (24h, 48h, 72h)

  • Quantification of cells crossing the membrane under different conditions

• Invasion assays:

  • Transwell chambers with Matrigel coating

  • Standardized cell seeding density

  • Fixed incubation time (typically 24h)

• Genetic manipulation controls:

  • Control shRNA (non-targeting)

  • Multiple targeting shRNAs to rule out off-target effects

  • Rescue experiments with shRNA-resistant PPP2R3A constructs

• Quantification method:

  • Crystal violet staining

  • Counting multiple fields per chamber

  • Expression as percentage reduction compared to control

Published results show that PPP2R3A knockdown can reduce Huh-7 cell migration by 75-80% and HepG2 cell migration by 48-64%. Similarly, invasion potential decreased by 47-72% in Huh-7 cells and 59-63% in HepG2 cells following PPP2R3A silencing .

What controls are essential when analyzing PPP2R3A subcellular localization using FITC-conjugated antibodies?

Essential controls include:

• Antibody controls:

  • Isotype control antibody (rabbit IgG-FITC)

  • Secondary antibody-only control

  • Unstained control for autofluorescence assessment

• Biological controls:

  • PPP2R3A knockdown cells to confirm signal specificity

  • Fixed versus live-cell imaging comparisons

  • Co-staining with organelle markers (e.g., DAPI for nucleus, markers for cytoplasm, membrane)

• Technical controls:

  • Multiple fixation methods comparison (paraformaldehyde, methanol)

  • Permeabilization optimization

  • Signal-to-noise ratio assessment

Research has shown that PPP2R3A is predominantly located in the cytoplasm of HCC cells with some membrane expression, and this localization pattern can be confirmed through these careful controls .

How can I effectively use PPP2R3A antibody FITC conjugate in flow cytometry studies?

For optimal flow cytometry applications:

• Sample preparation protocol:

  • Harvest cells in logarithmic growth phase

  • Fix with 2-4% paraformaldehyde (10 minutes at room temperature)

  • Permeabilize with 0.1% Triton X-100 if studying intracellular PPP2R3A

  • Block with 3% BSA in PBS (30 minutes)

  • Incubate with FITC-conjugated PPP2R3A antibody (optimal dilution must be determined empirically)

  • Wash 3× with PBS

• Instrument settings optimization:

  • Use unstained and single-color controls for compensation

  • Set FITC detection in FL1 channel

  • Adjust voltage settings to place negative population in first decade of histogram

• Analysis considerations:

  • Gate on viable cells using forward/side scatter

  • Compare PPP2R3A expression between experimental conditions

  • Analyze both percentage of positive cells and mean fluorescence intensity

This approach enables quantitative assessment of PPP2R3A expression changes in response to treatments or genetic manipulations across large cell populations.

What are the methodological approaches for studying PPP2R3A's interactions with cancer signaling pathways?

To investigate PPP2R3A's role in cancer signaling networks:

• Co-immunoprecipitation studies:

  • Use anti-PPP2R3A antibodies to pull down protein complexes

  • Analyze interactions with components of Wnt, EGF-EGFR, and AMPK pathways

  • Confirm specificity with reverse co-IP experiments

• Phosphorylation state analysis:

  • Assess phosphorylation levels of downstream targets in PPP2R3A knockdown vs. control cells

  • Use phospho-specific antibodies for Western blotting

  • Quantify changes in phosphorylation ratios

• Pathway activation reporters:

  • Employ luciferase reporters for Wnt/β-catenin activity

  • Measure AMPK activity through substrate phosphorylation

  • Monitor EGFR degradation rates in the presence/absence of PPP2R3A

• Pharmacological pathway modulation:

  • Combine PPP2R3A manipulation with pathway-specific inhibitors

  • Assess whether PPP2R3A effects are dependent on specific pathways

  • Determine potential synergistic effects with established therapeutics

Research indicates that PPP2R3A may influence EGF-EGFR signaling by redistributing SHIP2 to the cell membrane, preventing EGF-induced EGFR degradation .

What methodological approaches can detect differences in PPP2R3A splice variants (PR130/B"α1 vs. PR72/B"α2)?

To differentiate between PPP2R3A splice variants:

• RT-PCR analysis:

  • Design primers specific to unique regions of each variant

  • Optimize PCR conditions for selective amplification

  • Quantify relative expression using real-time PCR

• Western blot differentiation:

  • Use antibodies targeting unique regions of each variant

  • Confirm band sizes (PR130 is larger than PR72)

  • Perform quantitative analysis of variant ratios

• Immunofluorescence with variant-specific antibodies:

  • Optimize staining protocols for each variant

  • Compare subcellular localization patterns

  • Quantify relative expression in different cellular compartments

• Functional studies with variant-specific knockdown:

  • Design siRNAs targeting unique exons of each variant

  • Confirm variant-specific knockdown by RT-PCR and Western blot

  • Compare functional outcomes in cellular assays

Research suggests that these variants may have distinct functions, with PR130 specifically involved in preventing EGF-induced EGFR degradation by redistributing SHIP2 to the cell membrane .

How can I troubleshoot non-specific background when using FITC-conjugated PPP2R3A antibodies in immunofluorescence?

Common causes and solutions for background issues:

• High background throughout sample:

  • Cause: Insufficient blocking or antibody concentration too high

  • Solution: Increase blocking time (2-3 hours), use 5% BSA or 10% normal serum, and optimize antibody dilution (try 1:100, 1:200, 1:500)

• Autofluorescence issues:

  • Cause: Fixative-induced autofluorescence or endogenous fluorescent compounds

  • Solution: Pre-treat samples with 0.1-1% sodium borohydride for 10 minutes or use Sudan Black B (0.1% in 70% ethanol) for 20 minutes

• Non-specific membrane staining:

  • Cause: Hydrophobic interactions with cell membranes

  • Solution: Add 0.1% Triton X-100 or 0.1% Tween-20 to antibody diluent

• Nuclear speckles/artifacts:

  • Cause: Nucleic acid binding by positively charged antibody regions

  • Solution: Add 50-100 μg/ml RNase-free DNase to staining buffer

• Validation methods:

  • Compare staining pattern with PPP2R3A knockdown cells

  • Perform peptide competition assay

  • Use alternative fixation methods (methanol vs. paraformaldehyde)

Successful immunofluorescence should show predominantly cytoplasmic staining pattern for PPP2R3A with some membrane localization, consistent with published observations in HCC cells .

What are the key technical considerations for quantifying PPP2R3A expression in tumor samples?

For accurate quantification of PPP2R3A in tumor samples:

• Tissue processing standardization:

  • Fix tissues in 10% neutral buffered formalin for consistent time (24h)

  • Process and embed samples using identical protocols

  • Cut sections at uniform thickness (4-5 μm)

• Staining protocol optimization:

  • Perform antigen retrieval optimization (citrate vs. EDTA buffers)

  • Include positive and negative controls with each batch

  • Process all samples in parallel when possible

• Quantification approaches:

  • H-score method: Intensity (0-3) × percentage of positive cells

  • Digital image analysis: Use software to quantify staining intensity and area

  • Comparative scoring: Compare tumor vs. adjacent normal tissue

• Statistical considerations:

  • Analysis of multiple fields per section (minimum 5)

  • Blinded scoring by multiple observers

  • Appropriate statistical tests for paired samples

Research has shown that PPP2R3A expression is higher in HCC tumor foci than in adjacent para-tumor tissues in approximately 75% of cases (6 out of 8 samples in published studies) .

How can I optimize the detection of low-abundance PPP2R3A protein in clinical samples?

To enhance detection sensitivity for low-abundance PPP2R3A:

• Signal amplification methods:

  • Tyramide signal amplification (TSA) for immunohistochemistry

  • Biotin-streptavidin systems for enhanced signal

  • Polymer-based detection systems

• Sample enrichment approaches:

  • Microdissection of areas with higher tumor cell content

  • Preliminary cell fractionation to isolate cytoplasmic components

  • Immunoprecipitation followed by Western blotting

• Protocol modifications:

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

  • Increased antibody concentration with reduced background (optimize blocking)

  • Modified permeabilization for better antibody access to target

• Alternative detection methods:

  • Proximity ligation assay (PLA) for protein-protein interactions

  • RNAscope for mRNA detection as a complementary approach

  • Mass spectrometry-based proteomics for absolute quantification

These optimizations can help detect PPP2R3A in difficult samples while maintaining specificity, which is critical since PPP2R3A expression patterns may have diagnostic or prognostic significance in cancer .

How should I design experiments to investigate PPP2R3A's role in tumor progression using xenograft models?

Based on published methodologies, optimal xenograft experimental design includes:

• Animal model selection:

  • Nude mice (BALB/c nu/nu, 6-8 weeks old, male)

  • Randomization into experimental groups (minimum n=5 per group)

  • Power analysis to determine appropriate sample size

• Cell preparation protocol:

  • Generate stable PPP2R3A knockdown or overexpression cell lines

  • Harvest cells in log phase growth (70-80% confluence)

  • Suspend 5×10^6 cells in 100 μl Matrigel/PBS mixture (1:1)

• Injection and monitoring procedures:

  • Subcutaneous injection in flank region

  • Measure tumor dimensions every 3-4 days using calipers

  • Calculate tumor volume using formula: V = (length × width^2)/2

• Analysis endpoints:

  • Tumor volume growth curves

  • Final tumor weight after sacrifice

  • Immunohistochemical analysis of excised tumors for:

    • PPP2R3A expression confirmation

    • Proliferation markers (Ki-67)

    • Apoptosis markers

    • Pathway activation markers

Research has demonstrated that PPP2R3A knockdown in liver cancer cells leads to significant reductions in tumor volume (p < 0.001) and decreased expression of Ki-67 in tumor tissues (p < 0.05) in xenograft models .

What are the methodological approaches for analyzing PPP2R3A's impact on cell cycle regulation?

To comprehensively investigate PPP2R3A's cell cycle effects:

• Flow cytometry-based cell cycle analysis:

  • Synchronize cells via serum starvation or chemical inhibitors

  • Perform propidium iodide staining for DNA content analysis

  • Quantify percentage of cells in G0/G1, S, and G2/M phases

  • Compare PPP2R3A knockdown/overexpression vs. control cells

• G1/S checkpoint protein analysis:

  • Western blotting for key regulators:

    • p53 and phospho-p53 (Ser15)

    • p21

    • Cyclins D and E

    • CDK2, CDK4, CDK6

    • Rb and phospho-Rb

• Real-time cell cycle progression monitoring:

  • FUCCI (fluorescent ubiquitination-based cell cycle indicator) system

  • Live-cell imaging with cell cycle phase markers

  • Time-lapse analysis of cell division rates

• Dual parameter flow cytometry:

  • BrdU incorporation and 7-AAD staining

  • Phospho-histone H3 and DNA content

  • EdU pulse-chase experiments

Research has established that PPP2R3A knockdown causes a significant delay in G1/S transition in liver cancer cell lines (p < 0.05) with increased p53 expression, while PPP2R3A overexpression has the opposite effect on cell cycle progression (p < 0.05) .

How can I effectively correlate PPP2R3A expression with clinical outcomes in cancer patient cohorts?

A methodologically sound approach includes:

• Patient cohort selection criteria:

  • Defined cancer type and stage (e.g., HCC with clear staging)

  • Adequate sample size based on power calculation

  • Inclusion of clinical follow-up data (minimum 5 years recommended)

  • Stratification based on treatment modalities

• PPP2R3A detection and quantification:

  • Standardized immunohistochemistry protocols

  • Tissue microarray construction for high-throughput analysis

  • Digital pathology quantification using validated algorithms

  • RNA expression analysis (qRT-PCR or RNA-seq) as complementary data

• Statistical analysis approach:

  • Kaplan-Meier survival analysis stratified by PPP2R3A expression levels

  • Cox proportional hazards regression for multivariate analysis

  • Correlation analysis with established prognostic markers

  • Receiver operating characteristic (ROC) curve analysis for cutoff determination

• Validation strategies:

  • Internal validation (training and validation sets)

  • External validation in independent cohorts

  • Cross-platform validation (protein vs. mRNA expression)

While preliminary studies suggest PPP2R3A is expressed at higher levels in HCC tumor tissues compared to adjacent normal tissues , comprehensive studies correlating expression with clinical outcomes are still needed to establish its prognostic significance.

What methodological approaches can determine if PPP2R3A is a viable therapeutic target in cancer?

To evaluate PPP2R3A as a potential therapeutic target:

• Target validation studies:

  • Genetic manipulation across multiple cancer and normal cell lines

  • Analysis of cancer-specific dependencies using CRISPR screens

  • Assessment of effects on non-transformed cells to predict toxicity

• Druggability assessment:

  • Structural analysis of PPP2R3A protein for potential binding pockets

  • In silico screening for small molecule inhibitors

  • Evaluation of protein-protein interaction interfaces as targets

• Combination therapy evaluation:

  • Testing PPP2R3A inhibition with standard-of-care treatments

  • Analysis of potential synergistic effects

  • Identification of synthetic lethal interactions

• Biomarker development:

  • Identification of patient subgroups likely to respond to PPP2R3A targeting

  • Development of companion diagnostics

  • Pharmacodynamic markers for target engagement

Research suggests PPP2R3A may be a promising target for liver cancer therapy due to its roles in regulating proliferation, cell cycle progression, and invasion, though additional studies are needed to fully validate its therapeutic potential .

How can I investigate the mechanisms through which PPP2R3A regulates EGFR signaling in cancer cells?

For mechanistic studies of PPP2R3A-EGFR interactions:

• Protein interaction analysis:

  • Co-immunoprecipitation of PPP2R3A with EGFR and SHIP2

  • Proximity ligation assay for in situ interaction detection

  • Domain mapping using truncated protein constructs

• EGFR trafficking studies:

  • Pulse-chase experiments with labeled EGF

  • Immunofluorescence tracking of EGFR internalization and degradation

  • Subcellular fractionation to quantify EGFR in membrane vs. endosomal compartments

• Phosphorylation dynamics assessment:

  • Phosphoproteomic analysis following PPP2R3A manipulation

  • Time-course studies of EGFR and downstream effector phosphorylation

  • Phosphatase activity assays with immunoprecipitated PPP2R3A complexes

• Functional rescue experiments:

  • Expression of SHIP2 phosphorylation site mutants

  • Chimeric PPP2R3A constructs to identify domains required for EGFR regulation

  • Small molecule interventions targeting specific steps in EGFR trafficking

Research indicates that the PPP2R3A subtype PR130 can redistribute SHIP2 to the cell membrane to prevent EGF-induced EGFR degradation, suggesting a mechanism for PPP2R3A's role in sustaining EGFR signaling in cancer cells .

What are the recommended approaches for studying PPP2R3A phosphatase activity in different subcellular compartments?

To analyze compartment-specific PPP2R3A activity:

• Subcellular fractionation protocols:

  • Differential centrifugation to isolate membrane, cytosolic, and nuclear fractions

  • Density gradient separation for further refinement

  • Verification of fraction purity using compartment-specific markers

• In situ phosphatase activity assays:

  • Fluorogenic phosphatase substrates with compartment-targeting signals

  • Live-cell imaging with genetically encoded phosphorylation sensors

  • Phosphatase activity overlay assays on fixed cells

• Compartment-targeted PPP2R3A constructs:

  • Generation of fusion proteins with compartment-specific targeting sequences

  • Inducible expression/translocation systems

  • Functional rescue experiments with compartment-restricted constructs

• Advanced microscopy approaches:

  • FRET-based phosphatase activity sensors

  • Super-resolution microscopy for precise localization

  • Correlative light and electron microscopy for ultrastructural context