PRAMEF10 Antibody

What is PRAMEF10 and why is it of interest to researchers?

PRAMEF10 (PRAME Family Member 10) belongs to the PRAME family of cancer testis antigens. It contains 3 LRR (leucine-rich) repeats and has a molecular weight of approximately 55 kDa . The PRAME family has gained significant research interest because while these proteins have restricted expression in normal somatic tissues, they are frequently re-expressed in various malignancies with poor prognosis . Although the specific function of PRAMEF10 remains largely unknown, its relationship to the broader PRAME family suggests potential implications in cancer biology and immunology . PRAMEF10 is encoded by a gene located on chromosome 1p36.21 and is predicted to be involved in proteasome-mediated ubiquitin-dependent protein catabolic processes .

What applications are suitable for PRAMEF10 antibodies in research settings?

PRAMEF10 antibodies have been validated for multiple research applications, with varying levels of efficiency depending on the specific antibody clone and format. The primary applications include:

• Western Blotting (WB): For detection of PRAMEF10 protein in cell or tissue lysates, with typical working dilutions of 1.0 μg/ml

• Flow Cytometry (FCM): For detection of PRAMEF10 in cells, with numerous antibody options available, including both conjugated and unconjugated formats

• Immunohistochemistry (IHC): For detection in fixed tissue samples, particularly useful in pathological examinations

• ELISA: For quantitative detection of PRAMEF10 in biological samples such as serum and plasma

• Immunocytochemistry (ICC) and Immunofluorescence (IF): For cellular localization studies

Researchers should select the appropriate application based on their specific experimental needs, available sample types, and desired outcomes .

How might PRAMEF10 expression impact immunotherapy effectiveness in cancer models?

Based on studies of the related PRAME protein, PRAMEF10 expression could significantly influence immunotherapy outcomes through several mechanisms. PRAME family expression has been shown to modulate immune checkpoint expression, with silencing of PRAME reducing expression of immune checkpoints and their ligands, including PD-1, LAG3, PD-L1, CD86, Gal-9, and VISTA . Interestingly, high PRAME expression in some tumors correlates with reduced PD-L1 expression, suggesting that PD-1/PD-L1 inhibitor therapies might be less effective in PRAME-overexpressing tumors due to reduced target availability .

When investigating PRAMEF10's specific role in immunotherapy response, researchers should consider:

• Assessing PRAMEF10 expression levels in correlation with immune checkpoint molecule expression

• Evaluating T cell activation and cytolytic activity in the presence of varying PRAMEF10 expression

• Measuring cytokine production profiles in PRAMEF10-high versus PRAMEF10-low tumor environments

• Testing combination therapies targeting both PRAMEF10 and immune checkpoint pathways

PRAME family proteins have been shown to suppress the expression and secretion of multiple pro-inflammatory cytokines and mediators of T cell activation, suggesting PRAMEF10 may play a similar immunomodulatory role that could be therapeutically targeted .

What are the optimal experimental design considerations when evaluating PRAMEF10 antibody specificity across different cancer models?

Designing experiments to evaluate PRAMEF10 antibody specificity across cancer models requires careful consideration of multiple factors:

Experimental Controls:

• Positive controls: Include cell lines or tissues with verified PRAMEF10 expression (e.g., certain melanoma lines or testicular tissue)

• Negative controls: Use tissues known to lack PRAMEF10 expression or PRAMEF10 knockout cell lines

• Peptide competition assays: Use the specific peptide (DLLRHTGGLS KLGLELYPAP LESLDYKGHV NWEILTPIRA ELMRTLREVR) that was used for raising the antibody to confirm specificity

Cross-Reactivity Assessment:

• Test across phylogenetically related proteins in the PRAME family

• Evaluate reactivity in multiple species if cross-species reactivity is claimed

• Verify specificity using orthogonal methods (e.g., mass spectrometry, RNA expression correlation)

Technical Validation Across Methods:

• Compare results across multiple detection methods (e.g., WB, IHC, FCM)

• Confirm subcellular localization is consistent with predicted protein function

• Evaluate antibody performance in denaturing versus non-denaturing conditions

Cancer Model Selection:
Include diverse cancer models representing:

• Different tissue origins (melanoma, breast cancer, hematological malignancies)

• Various stages of disease progression

• Different levels of predicted PRAMEF10 expression

A comprehensive experimental design should include both human samples and appropriate model systems, with careful documentation of antibody lot, dilution, incubation conditions, and detection methods to ensure reproducibility .

What are the optimal sample preparation protocols for PRAMEF10 antibody-based detection in different experimental systems?

Sample preparation is critical for successful PRAMEF10 detection. Here are optimized protocols for different experimental systems:

For Western Blotting:

• Cell/Tissue Lysis: Use RIPA buffer supplemented with protease inhibitors

• Protein Quantification: Bradford or BCA assay

• Sample Denaturation: Heat at 95°C for 5 minutes in Laemmli buffer containing DTT or β-mercaptoethanol

• Loading Amount: 20-50 μg total protein per lane

• Transfer Conditions: Wet transfer at 100V for 60-90 minutes or 30V overnight at 4°C

• Blocking: 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature

• Primary Antibody: Incubate with anti-PRAMEF10 at 1.0 μg/ml concentration overnight at 4°C

• Detection: HRP-conjugated secondary antibody with ECL substrate

For Immunohistochemistry:

• Fixation: 10% neutral buffered formalin, 24 hours

• Embedding: Paraffin embedding using standard protocols

• Sectioning: 4-5 μm sections

• Antigen Retrieval: Heat-induced epitope retrieval using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0)

• Blocking: 3% hydrogen peroxide followed by protein block

• Primary Antibody: Optimized dilution of anti-PRAMEF10 (typically 1:100-1:500) for 1 hour at room temperature

• Detection System: Polymer-based detection system

• Counterstain: Hematoxylin

For Flow Cytometry:

• Cell Preparation: Single-cell suspension (2-5 × 10^6 cells/mL)

• Fixation: 2-4% paraformaldehyde for 15 minutes

• Permeabilization: 0.1% Triton X-100 or commercial permeabilization buffer if intracellular detection is needed

• Blocking: Fc block and 2% BSA in PBS

• Antibody Staining: Incubate with fluorophore-conjugated anti-PRAMEF10 antibody at optimized concentration

• Washing: Multiple PBS washes before analysis

For ELISA:

• Sample Preparation: Serum or plasma should be collected using standard protocols and stored at -80°C

• Sample Dilution: Optimize based on expected PRAMEF10 concentration (typically 1:2 to 1:10 dilution)

• Follow standard sandwich ELISA protocol as recommended by the specific kit manufacturer

These protocols should be optimized for specific antibody clones, as optimal conditions may vary between suppliers .

How can researchers effectively validate PRAMEF10 antibody specificity and sensitivity in their experimental systems?

Validating PRAMEF10 antibody specificity and sensitivity is crucial for reliable research outcomes. A comprehensive validation approach should include:

Specificity Validation:

• Peptide Competition/Blocking Assays:

  • Pre-incubate the antibody with the immunizing peptide (DLLRHTGGLS KLGLELYPAP LESLDYKGHV NWEILTPIRA ELMRTLREVR)

  • Run parallel experiments with blocked and unblocked antibody

  • Specific signal should be significantly reduced or eliminated in the blocked sample

• Genetic Validation:

  • Test antibody on PRAMEF10 knockout or knockdown samples

  • Use CRISPR-Cas9 or siRNA technology to generate PRAMEF10-deficient controls

  • Compare signal between wild-type and PRAMEF10-depleted samples

• Orthogonal Detection Methods:

  • Correlate protein detection with mRNA expression data

  • Compare results using multiple antibody clones targeting different epitopes

  • Verify with mass spectrometry-based protein identification

Sensitivity Assessment:

• Titration Experiments:

  • Create a standard curve using recombinant PRAMEF10 protein

  • Determine limit of detection and quantification

  • Optimize antibody concentration for best signal-to-noise ratio

• Signal Linearity:

  • Test serial dilutions of positive control samples

  • Plot signal intensity versus sample concentration

  • Ensure linearity within the expected physiological range

• Reproducibility Testing:

  • Perform technical and biological replicates

  • Calculate coefficients of variation (CV)

  • Assess inter-assay and intra-assay precision

Application-Specific Validation:

• For Western Blotting:

  • Verify band size matches predicted molecular weight (55 kDa)

  • Include positive and negative control cell lines

  • Test different detection methods (chemiluminescence vs. fluorescent)

• For Immunohistochemistry:

  • Include known positive tissues (e.g., testis tissue)

  • Evaluate staining pattern (membranous and nuclear localization)

  • Perform isotype control staining

• For Flow Cytometry:

  • Use fluorescence-minus-one (FMO) controls

  • Calculate staining index

  • Verify with multiple fluorophore conjugates if available

By implementing these validation strategies, researchers can ensure their PRAMEF10 antibody provides specific and sensitive detection in their particular experimental system.

What are the recommended protocols for multiplex detection of PRAMEF10 alongside other cancer-related biomarkers?

Multiplex detection of PRAMEF10 with other cancer biomarkers provides valuable insights into the complex biology of cancer. Below are recommended protocols for different multiplex approaches:

Multiplex Immunohistochemistry/Immunofluorescence:

• Sequential Multiplex IHC Protocol:

  • Perform antigen retrieval appropriate for all target antigens

  • Block endogenous peroxidase and proteins

  • Apply first primary antibody (e.g., anti-PRAMEF10)

  • Detect with HRP-polymer and develop with a chromogen

  • Strip or bleach the section (using appropriate buffer)

  • Repeat process for subsequent markers

  • Counterstain and mount

• Tyramide Signal Amplification (TSA) Multiplex Immunofluorescence:

  • Perform heat-induced epitope retrieval

  • Block endogenous peroxidase and proteins

  • Incubate with first primary antibody (e.g., anti-PRAMEF10)

  • Apply HRP-conjugated secondary antibody

  • Develop with fluorophore-conjugated tyramide

  • Perform microwave treatment to strip antibodies but preserve fluorophores

  • Repeat for additional markers (PD-L1, immune cell markers, etc.)

  • Counterstain nuclei with DAPI and mount

  Note: For PRAME family proteins, which may have both membranous and nuclear localization, careful selection of fluorophores with distinct spectral properties is essential to avoid bleed-through

Multiplex Flow Cytometry:

• Panel Design Considerations:

  • Include PRAMEF10 alongside key immune checkpoint markers (PD-1, LAG-3, TIM-3, TIGIT)

  • Add lineage markers to identify cell populations of interest

  • Use fluorophores with minimal spectral overlap

• Sample Preparation Protocol:

  • Prepare single-cell suspensions from fresh samples

  • Fix cells with 2% paraformaldehyde for 15 minutes

  • Permeabilize with 0.1% Triton X-100 for intracellular markers

  • Block with Human Fc Block™

  • Stain with antibody cocktail including anti-PRAMEF10

  • Wash and analyze by flow cytometry

  Recommended multiplex panel example:

  • PRAMEF10 (PE or APC)

  • PD-1 (PE-Dazzle 594)

  • VISTA (BV421)

  • CTLA-4 (BV786)

  • TIM-3 (BV650)

  • LAG-3 (PE)

  • TIGIT (BUV395)

  • CD8 (BV510)

Digital Spatial Profiling:

For high-dimensional spatial analysis of PRAMEF10 in the tumor microenvironment:

• Prepare FFPE tissue sections (5 μm)

• Perform antigen retrieval

• Apply fluorescently labeled antibody cocktail including anti-PRAMEF10

• Image tissue to identify regions of interest

• Use digital spatial profiling platform to quantify protein expression

Considerations for Antibody Selection in Multiplex Assays:

• Choose antibodies from different host species when possible to avoid cross-reactivity

• Verify that antibody pairs don't compete for overlapping epitopes

• Ensure compatible fixation and antigen retrieval conditions for all targets

• Test each antibody individually before combining in multiplex panels

• Include appropriate controls for each marker in the panel

These protocols should be optimized based on specific research questions and sample types. When studying PRAMEF10 in cancer contexts, including markers of the tumor microenvironment is particularly valuable given the potential immunomodulatory functions of PRAME family proteins .

What are common challenges in PRAMEF10 antibody-based detection and how can they be addressed?

Researchers often encounter several challenges when working with PRAMEF10 antibodies. Here are common issues and their solutions:

Non-specific Binding:

• Problem: Multiple bands in Western blot or non-specific staining in IHC/IF
  Solutions:

  • Increase blocking time/concentration (5% BSA or milk for 2 hours)

  • Optimize primary antibody dilution (try 1:500-1:2000)

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

  • Use peptide competition assays to identify specific signals

  • Try alternative blocking agents (normal serum matching secondary antibody species)

Weak or No Signal:

• Problem: Insufficient detection of PRAMEF10
  Solutions:

  • Ensure proper sample preparation (fresh samples, appropriate lysis buffers)

  • Try different antigen retrieval methods (citrate vs. EDTA buffer)

  • Increase primary antibody concentration and incubation time (overnight at 4°C)

  • Use signal amplification systems (TSA for IHC/IF, enhanced ECL for WB)

  • Confirm PRAMEF10 expression in your sample type (reference expression databases)

  • For lyophilized antibodies, ensure proper reconstitution (add 100 μL distilled water to achieve 1 mg/mL)

Inconsistent Results:

• Problem: Variable staining between experiments
  Solutions:

  • Standardize protocols rigorously (fixation time, antibody lot, incubation conditions)

  • Prepare fresh working solutions for each experiment

  • Include positive and negative controls in every experiment

  • Avoid freeze/thaw cycles of antibody

  • Store antibody at -20°C long term

  • Use automated staining platforms if available

Cross-Reactivity with Other PRAME Family Members:

• Problem: Difficulty distinguishing PRAMEF10 from other PRAME family proteins
  Solutions:

  • Use antibodies raised against unique peptide sequences of PRAMEF10

  • Validate specificity using recombinant proteins of different PRAME family members

  • Confirm with orthogonal methods (RNA expression, mass spectrometry)

  • Consider using targeted approaches like RNA interference to validate signals

Tissue Autofluorescence (for IF/FCM):

• Problem: High background autofluorescence masking specific signal
  Solutions:

  • Use autofluorescence quenching reagents

  • Select fluorophores with emission spectra distinct from autofluorescence

  • Employ spectral unmixing during image/data acquisition

  • Consider longer wavelength fluorophores (far-red) that have less autofluorescence interference

Technical Considerations by Application:

• For WB: Optimize transfer conditions for high molecular weight proteins, use fresh transfer buffers

• For IHC: Test multiple fixation protocols, as over-fixation can mask epitopes

• For FCM: Ensure proper compensation when using multiple fluorophores, use viability dyes to exclude dead cells

• For ELISA: Optimize coating conditions and blocking to minimize background

Implementing these solutions should help address most common challenges encountered when working with PRAMEF10 antibodies.

How should researchers interpret discrepancies between PRAMEF10 protein detection and gene expression data?

Discrepancies between PRAMEF10 protein detection and gene expression data are not uncommon and can stem from various biological and technical factors. Understanding these discrepancies is crucial for accurate data interpretation:

Potential Causes of Discrepancies:

• Post-transcriptional Regulation:

  • mRNA may be transcribed but not efficiently translated

  • microRNAs might regulate PRAMEF10 mRNA translation

  • Analyze RNA-sequencing data for evidence of alternative splicing that might affect antibody epitope regions

• Post-translational Modifications:

  • Protein modifications may affect antibody epitope recognition

  • Check if the antibody epitope region contains potential modification sites

  • Consider using antibodies targeting different regions of PRAMEF10

• Protein Stability and Turnover:

  • PRAMEF10 may have a different half-life than its mRNA

  • The protein's predicted involvement in ubiquitin-dependent protein catabolic processes suggests potential regulation at the protein stability level

  • Measure protein turnover rates using cycloheximide chase experiments

• Technical Limitations:

  • Antibody sensitivity threshold may differ from mRNA detection methods

  • Epitope masking in certain tissue/fixation conditions

  • Sample preparation differences between protein and RNA analyses

Methodological Approaches to Resolve Discrepancies:

• Multi-antibody Validation:

  • Use multiple antibodies targeting different epitopes of PRAMEF10

  • Compare results across different antibody clones and detection methods

  • Create a correlation matrix between different detection methods

• Quantitative Analysis:

  • Perform quantitative Western blotting with recombinant protein standards

  • Use digital PCR for absolute mRNA quantification

  • Calculate protein-to-mRNA ratios across different samples

• Temporal Studies:

  • Assess both protein and mRNA levels over time

  • Investigate if discrepancies are consistent or change under different conditions

  • Consider pulse-chase experiments to track protein synthesis and degradation

• Subcellular Localization:

  • Perform subcellular fractionation to determine if PRAMEF10 localizes to specific compartments

  • Compare to predicted localization (cytoplasmic)

  • Assess if compartmentalization affects detection by certain methods

Data Interpretation Framework:

When faced with discrepancies between protein and mRNA data, consider the following interpretation approach:

• Biological Significance Assessment:

  Scenario	Potential Biological Significance	Recommended Further Investigation
  High mRNA, Low/No Protein	Post-transcriptional regulation or rapid protein degradation	Proteasome inhibition studies, RNA-binding protein analysis
  Low mRNA, High Protein	High protein stability or alternative transcript not detected	Protein half-life studies, alternative transcript analysis
  Spatial discrepancies (different locations)	Cell type-specific expression or protein trafficking	Single-cell RNA-seq, immunofluorescence co-localization
  Temporal discrepancies (different timing)	Time-lag between transcription and translation	Time-course experiments with shorter intervals

• Technical Validation:

  • Sequence verify the target gene in your experimental system

  • Check antibody lot-to-lot variation

  • Verify primers/probes for gene expression studies target the correct transcript variant

  • Consider absolute quantification methods for both protein and mRNA

• Contextual Integration:

  • Compare your findings with published literature on PRAMEF10 or other PRAME family members

  • Assess if discrepancies are consistent with known biology of cancer testis antigens

  • Consider the specific cellular context and disease state of your samples

Understanding these discrepancies can provide valuable insights into the biology of PRAMEF10 regulation and function, potentially revealing novel aspects of PRAMEF10 biology beyond what either protein or mRNA analysis alone would suggest.

What analytical frameworks should be used when correlating PRAMEF10 expression with clinical outcomes in cancer research?

Study Design Considerations:

• Sample Size Determination:

  • Perform power analysis to ensure adequate statistical power

  • Account for subgroup analyses in heterogeneous cancer populations

  • Consider effect sizes observed in studies of related PRAME family members

• Patient Cohort Selection:

  • Ensure well-characterized patient cohorts with complete clinical data

  • Include diverse cancer stages and subtypes

  • Consider matched normal tissue controls when available

  • Account for treatment history and potential confounding factors

• Expression Quantification Methods:

  • Define clear scoring criteria for PRAMEF10 positivity

  • For IHC: Use H-score, Allred score, or percentage of positive cells

  • For molecular methods: Normalize to appropriate housekeeping genes

  • Consider both intensity and distribution of expression

Statistical Analysis Framework:

• Categorical Analysis:

  • Define clinically relevant cutpoints for PRAMEF10 expression (e.g., negative vs. positive; or low, medium, high)

  • Use ROC curve analysis to determine optimal cutpoints

  • Apply consistent cutpoints across all analyses

• Survival Analysis Methodology:

  • Kaplan-Meier survival curves with log-rank tests for univariate analysis

  • Cox proportional hazards models for multivariate analysis

  • Include established prognostic factors (stage, grade, other biomarkers)

  • Test proportional hazards assumption and address violations appropriately

• Multivariate Models:

  • Control for known prognostic factors and potential confounders

  • Consider interaction terms between PRAMEF10 and treatment variables

  • Use stepwise selection procedures with caution and biological rationale

  • Report hazard ratios with 95% confidence intervals

• Advanced Statistical Approaches:

  • Consider time-dependent analysis if PRAMEF10 expression changes over time

  • Use competing risk models when appropriate

  • Apply machine learning algorithms for complex pattern recognition

  • Validate findings using bootstrap or cross-validation methods

Integration with Immune Parameters:

Given the potential immunomodulatory role of PRAME family proteins , consider:

Validation and Reporting:

• Internal Validation:

  • Split-sample validation (training/test sets)

  • Cross-validation techniques (k-fold, leave-one-out)

  • Bootstrap resampling to assess stability of findings

• External Validation:

  • Validate findings in independent patient cohorts

  • Compare with publicly available cancer datasets

  • Assess generalizability across different cancer types

• Transparent Reporting:

  • Follow REMARK guidelines for biomarker studies

  • Report all analyses performed (significant and non-significant)

  • Share detailed methods to ensure reproducibility

  • Include sample size calculations and power estimates

Interpretation Framework:

Interpret PRAMEF10 associations within biological context:

• Effect Size Evaluation:

  • Consider magnitude of effect in addition to statistical significance

  • Compare with effect sizes of established biomarkers

  • Assess clinical versus statistical significance

• Causal Inference:

  • Distinguish between prognostic (outcome irrespective of treatment) and predictive (treatment-specific outcome) value

  • Consider potential biological mechanisms linking PRAMEF10 to outcomes

  • Acknowledge limitations of correlative studies

• Biological Integration:

  • Connect findings to known biology of PRAME family proteins

  • Consider relationships with cancer hallmarks

  • Develop mechanistic hypotheses for further investigation