TPS12 Antibody

What is PTPN12 and what cellular functions does it regulate?

PTPN12, also known as Tyrosine-protein phosphatase non-receptor type 12, PTP-PEST, or Protein-tyrosine phosphatase G1, functions primarily as a protein that dephosphorylates a range of substrates, thereby regulating cellular signaling cascades. PTPN12 specifically dephosphorylates cellular tyrosine kinases such as ERBB2 and PTK2B/PYK2, regulating signaling via these pathways. It selectively targets ERBB2 phosphorylated at specific tyrosine residues including 'Tyr-1112', 'Tyr-1196', and 'Tyr-1248' . This phosphatase activity is critical for maintaining proper signal transduction and cellular homeostasis in various physiological contexts.

What applications are PTPN12 antibodies typically used for in research?

PTPN12 antibodies, such as the rabbit polyclonal ab76942, are validated for multiple research applications including:

• Western Blotting (WB): For detecting PTPN12 protein in cell lysates

• Immunoprecipitation (IP): For isolating PTPN12 protein complexes

• Immunohistochemistry on paraffin-embedded tissues (IHC-P): For examining PTPN12 expression in tissue sections

• Simple Western automated capillary electrophoresis (Wes): For quantitative protein analysis
  These antibodies have been tested with human and mouse samples, with applications in cancer research, cell signaling studies, and protein-protein interaction investigations.

How should I validate a PTPN12 antibody before using it in my experiments?

Proper antibody validation requires a multi-step approach:

• Literature cross-reference: Verify the antibody has been cited in peer-reviewed publications for your intended application

• Positive control testing: Use samples known to express PTPN12 (such as HeLa cell lysate for human applications)

• Negative control testing: Employ samples lacking PTPN12 expression or use knockout/knockdown models

• Application-specific optimization:

  • For Western blotting: Test multiple dilutions (starting with 1μg/ml) and blocking conditions

  • For IHC-P: Begin with 1/200 dilution (approximately 1μg/ml) as demonstrated in validated stomach and colon carcinoma tests

• Cross-reactivity assessment: Confirm specificity using closely related proteins to ensure no off-target binding
  Validation should be performed for each specific application and sample type rather than assuming cross-application reliability.

What are the optimal conditions for using PTPN12 antibody in Western blotting?

For optimal Western blotting results with PTPN12 antibody:

How can I optimize PTPN12 antibody for immunohistochemistry on tissue sections?

Optimizing PTPN12 antibody for IHC-P requires attention to several critical parameters:

• Antigen retrieval: Heat-induced epitope retrieval (HIER) using citrate buffer (pH 6.0) is typically most effective for PTPN12 detection

• Antibody concentration: Begin with 1/200 dilution (1μg/ml) as validated for stomach and colon carcinoma tissues

• Incubation parameters:

  • Primary antibody: Overnight at 4°C or 1 hour at room temperature

  • Secondary detection system: 30-60 minutes at room temperature

• Signal development: DAB (3,3'-diaminobenzidine) has been validated for PTPN12 visualization

• Counterstaining: Hematoxylin provides appropriate nuclear contrast without obscuring cytoplasmic PTPN12 signals
  The staining pattern should show primarily cytoplasmic localization with potential membrane association in certain cell types. Always include both positive control tissues (stomach, colon) and negative controls (primary antibody omission) to ensure result validity.

What approaches can I use to quantify PTPN12 antibody staining in tissue sections?

Quantitative analysis of PTPN12 immunostaining requires systematic approaches:

• Manual scoring methods:

  • H-score system: Calculate intensity (0-3) × percentage of positive cells

  • Allred score: Combines proportion and intensity scores

  • Quick score: Similar to Allred but with different scaling

• Digital image analysis:

  • Software-based quantification (ImageJ, QuPath, etc.)

  • Parameters to measure: staining intensity, percentage positive cells, subcellular localization

  • Thresholding based on negative controls

• Statistical analysis considerations:

  • Data normalization using housekeeping proteins

  • Non-parametric tests when distribution is not normal

  • Correction for multiple testing using methods like Benjamini-Yekutieli procedure
    When reporting results, include both visual representations (representative images) and quantitative data (tables with means, medians, and statistical significance).

How can I use computational approaches to predict and design PTPN12 antibody specificity?

Advanced computational methods can enhance PTPN12 antibody specificity:

• Biophysics-informed modeling:

  • Identify distinct binding modes associated with specific ligands

  • Integrate high-throughput sequencing data with machine learning approaches

  • Generate models that can predict cross-reactivity with related phosphatases

• Phage display optimization:

  • Design libraries with systematic variation in complementarity determining regions (CDRs)

  • Perform selections against specific PTPN12 domains or phosphorylation states

  • Use counter-selection strategies to eliminate off-target binding

• Custom specificity profile design:

  • Minimize energy functions associated with desired epitopes

  • Maximize energy functions associated with undesired epitopes

  • Generate novel sequences not present in original libraries
    These computational approaches can be particularly valuable when designing antibodies that need to discriminate between closely related phosphatase family members or specific phosphorylation states of PTPN12.

What are the main sources of variability in PTPN12 antibody experiments and how can they be controlled?

Several factors contribute to variability in PTPN12 antibody experiments:

How can I distinguish between specific and non-specific binding when using PTPN12 antibody?

Distinguishing specific from non-specific PTPN12 antibody binding requires multiple validation approaches:

• Knockout/knockdown controls:

  • CRISPR-Cas9 PTPN12 knockout cell lines

  • siRNA knockdown of PTPN12 with >80% reduction

  • Isogenic cell lines with varying PTPN12 expression levels

• Peptide competition assays:

  • Pre-incubate antibody with immunizing peptide

  • Titrate peptide concentration to determine specificity threshold

  • Include non-specific peptide controls

• Cross-validation with multiple antibodies:

  • Use antibodies recognizing different PTPN12 epitopes

  • Compare polyclonal and monoclonal antibody patterns

  • Verify with antibodies from different host species or manufacturers

• Statistical approaches to determining optimal cutoffs:

  • Apply Shapiro-Wilk test to assess data distribution normality

  • For non-normal distributions, implement finite mixture models

  • Use chi-squared statistic maximization for threshold determination
    Documentation of these validation steps should be included in methods sections of publications to enhance reproducibility and confidence in experimental findings.

How does PTPN12 antibody selection strategy impact the analysis of experimental outcomes?

The antibody selection strategy significantly influences experimental outcomes:

• Statistical selection approaches:

  • Chi-squared statistic maximization can identify optimal cut-offs that differentiate experimental groups

  • Normality testing using Shapiro-Wilk determines appropriate statistical methods

  • Finite mixture modeling can identify latent serological populations

• Impact on predictive power:

  • Super-Learner classifier approaches using multiple antibody parameters can improve prediction accuracy

  • AUC estimates of approximately 0.713 have been achieved using optimized antibody selection strategies

  • Correlation between antibodies (average Spearman's correlation coefficient = 0.312) must be considered when interpreting results

• Adjustment for multiple testing:

  • False discovery rate control is essential when evaluating multiple antibody parameters

  • Benjamini-Yekutieli procedure under general dependence assumptions provides appropriate correction

  • This approach substantially reduces the number of statistically significant results after controlling for FDR of 5%
    Selection strategies should be determined before experimental initiation and thoroughly documented to ensure reproducibility and proper interpretation of findings.

What approaches can be used to study PTPN12 phosphatase activity rather than just protein expression?

Beyond expression studies, investigating PTPN12 phosphatase activity requires specialized approaches:

• In vitro phosphatase assays:

  • Immunoprecipitate PTPN12 using validated antibodies

  • Measure dephosphorylation of synthetic substrates containing known PTPN12 target sequences

  • Quantify released phosphate using malachite green or similar colorimetric assays

• Cellular phosphorylation status monitoring:

  • Antibodies against phosphorylated forms of known PTPN12 substrates (ERBB2 at Tyr-1112, Tyr-1196, Tyr-1248)

  • Phospho-specific Western blotting following PTPN12 modulation

  • Multiplex assays to simultaneously measure multiple phosphorylation sites

• Functional readouts:

  • Cell migration assays (PTPN12 regulates focal adhesion dynamics)

  • Cell proliferation and signaling pathway activation

  • Protein-protein interaction studies using proximity ligation assays
    When designing these experiments, include both positive controls (phosphatase inhibitors) and negative controls (catalytically inactive PTPN12 mutants) to confirm assay specificity.

How can I design experiments to study PTPN12 antibody cross-reactivity with other phosphatase family members?

Systematic investigation of PTPN12 antibody cross-reactivity requires:

• Sequence and structural analysis:

  • Align PTPN12 with related phosphatases to identify regions of homology

  • Map epitope recognition sites to determine potential cross-reactivity

• Experimental validation approaches:

  • Recombinant protein panel testing with related phosphatases

  • Overexpression systems with tagged phosphatase family members

  • Immunodepletion studies to identify cross-reactive species

• Advanced specificity engineering:

  • Phage display selection against multiple phosphatases simultaneously

  • Counter-selection strategies to eliminate cross-reactive antibodies

  • Biophysics-informed modeling to identify and disentangle binding modes

• Quantitative cross-reactivity assessment:

  • ELISA-based measurements comparing binding affinities

  • Surface plasmon resonance (SPR) for binding kinetics determination

  • Competition assays between PTPN12 and related phosphatases
    These approaches help generate antibodies with customized specificity profiles that either specifically recognize PTPN12 or cross-react with defined phosphatase family members depending on research needs .

How are computational approaches transforming PTPN12 antibody development and application?

Computational methods are revolutionizing antibody research in several ways:

• Advanced specificity engineering:

  • Biophysics-informed models can now associate distinct binding modes with specific ligands

  • High-throughput sequencing combined with machine learning allows predictions beyond experimentally observed sequences

  • Computational approaches enable inference of multiple physical properties from selection experiments

• Custom antibody design:

  • Generation of novel sequences with predefined binding profiles

  • Energy function optimization to create either highly specific or cross-reactive antibodies

  • Computational counter-selection strategies that are more efficient than experimental approaches

• Future development trajectories:

  • Integration of structural biology with machine learning

  • Prediction of antibody-antigen complex structures

  • Automated optimization of antibody properties beyond binding specificity
    These computational approaches significantly expand the possibilities for creating antibodies with precise binding characteristics that may not be achievable through traditional experimental methods alone.

What are the emerging applications of PTPN12 antibodies in cancer research and diagnostics?

PTPN12 antibodies are finding increasingly sophisticated applications in cancer research:

• Tumor classification and stratification:

  • Immunohistochemical analysis of PTPN12 expression in stomach and colon carcinomas

  • Correlation of expression patterns with clinical outcomes and treatment responses

  • Integration into multi-marker diagnostic panels

• Functional studies of PTPN12 in cancer progression:

  • Investigation of PTPN12's role in regulating ERBB2 signaling in breast cancer

  • Analysis of phosphatase activity in relation to metastatic potential

  • Therapeutic targeting of PTPN12-dependent pathways

• Predictive applications:

  • Super-Learner classifier approaches integrating multiple antibody parameters

  • Development of predictive models with AUC values of approximately 0.713

  • Personalized medicine approaches based on PTPN12 status and activity As research continues, PTPN12 antibodies will likely become increasingly important for both mechanistic studies and clinical applications in oncology.