PSME3 Antibody

What is PSME3 and what is its biological significance?

PSME3 (Proteasome activator subunit 3) is a critical component of the 11S REG-gamma (also called PA28-gamma) proteasome regulator. It forms a doughnut-shaped homoheptamer that associates with the proteasome and selectively activates the trypsin-like catalytic subunit while inhibiting the chymotrypsin-like and postglutamyl-preferring subunits . Located on chromosome 17q21.31, PSME3 has multiple biological roles including:

• Facilitating MDM2-p53/TP53 interaction that promotes ubiquitination and proteasomal degradation of p53/TP53

• Regulating the degradation of cell cycle inhibitor p21

• Promoting the degradation of SRC-3 proteins through ubiquitin- and ATP-independent mechanisms

• Participating in cell cycle regulation

• Mediating CCAR2 and CHEK2-dependent SIRT1 inhibition

Recent research has revealed PSME3's significant involvement in various cancers and immune regulation processes, making it a promising target for cancer research and potential therapeutic development .

What are the primary research applications for PSME3 antibodies?

PSME3 antibodies serve as essential tools for investigating this protein's expression, localization, and function across multiple experimental contexts:

• Western Blotting (WB): For quantitative assessment of PSME3 protein levels in cell and tissue lysates

• Immunoprecipitation (IP): To study protein-protein interactions involving PSME3

• Immunohistochemistry-Paraffin (IHC-P): For examining PSME3 distribution and expression in tissue sections

• Flow Cytometry: For analyzing PSME3 in relation to other cellular markers, particularly in immune cells

• Immunofluorescence: For subcellular localization studies

These applications have been instrumental in establishing PSME3's roles in cancer development, immune regulation, and its potential as a prognostic biomarker .

How should researchers select appropriate PSME3 antibodies for their experiments?

Selection of PSME3 antibodies should be based on several critical factors:

• Experimental application: Different applications (WB, IHC-P, IP, etc.) may require antibodies with specific properties

• Species reactivity: Confirm the antibody recognizes PSME3 in your experimental model (human, mouse, etc.)

• Epitope location: Consider whether the epitope is within a functional domain that might be masked in certain contexts

• Validation data: Review existing validation in literature and manufacturer data

• Clonality: Polyclonal antibodies (like ab157157) offer high sensitivity and recognize multiple epitopes, while monoclonal antibodies provide higher specificity for a single epitope

When studying complex interactions or specific domains of PSME3, consider the immunogen information. For example, ab157157 was raised against a synthetic peptide within the C-terminal region (aa 200 to C-terminus) of human PSME3 .

What are the optimal protocols for using PSME3 antibodies in Western blotting?

Western blotting with PSME3 antibodies requires careful optimization:

• Sample preparation:

  • Use RIPA or NP-40 buffer supplemented with protease inhibitors

  • Include phosphatase inhibitors if studying phosphorylation status

  • Maintain samples at 4°C during lysis to prevent degradation

• Gel electrophoresis:

  • 10-12% SDS-PAGE gels are typically suitable for resolving PSME3 (~30 kDa)

  • Load appropriate positive controls (e.g., cell lines known to express PSME3)

• Transfer and detection:

  • PVDF membranes often provide better results than nitrocellulose

  • Block with 5% non-fat milk or BSA in TBST for 1 hour at room temperature

  • Incubate with primary PSME3 antibody (typically 1:1000 dilution) overnight at 4°C

  • Use appropriate HRP-conjugated secondary antibody (typically 1:5000-1:10000)

  • Develop using ECL reagents optimized for the expected expression level

• Controls:

  • Include PSME3 knockdown/overexpression samples as specificity controls

  • Consider GAPDH, β-actin, or α-tubulin as loading controls

For cancer studies, A549 (lung adenocarcinoma), Hut7 (liver cancer), and T24 (bladder cancer) cell lines have been validated for PSME3 expression and can serve as positive controls .

How can researchers effectively use PSME3 antibodies for immunohistochemistry?

For optimal immunohistochemistry results with PSME3 antibodies:

• Tissue preparation:

  • Fix tissues in 10% neutral buffered formalin for 24-48 hours

  • Process and embed in paraffin following standard protocols

  • Cut sections at 4-5 μm thickness

• Antigen retrieval:

  • Heat-induced epitope retrieval in citrate buffer (pH 6.0) is typically effective

  • Pressure cooker methods often yield superior results

• Staining protocol:

  • Block endogenous peroxidase activity with 3% H₂O₂

  • Block non-specific binding with serum-free protein block

  • Incubate with PSME3 primary antibody (1:100-1:200 dilution) overnight at 4°C

  • Use appropriate detection system (HRP-polymer or ABC method)

  • Counterstain with hematoxylin, dehydrate, and mount

• Controls and scoring:

  • Include positive control tissues based on The Human Protein Atlas data

  • Implement negative controls (primary antibody omission)

  • Score based on intensity (0-3) and percentage of positive cells

  • Consider both nuclear and cytoplasmic staining as PSME3 functions in both compartments

This approach has been validated in studies examining PSME3 expression across multiple cancer types, confirming high consistency between protein levels detected by IHC and mRNA expression data .

What approaches should be used for PSME3 knockdown or overexpression validation studies?

When conducting PSME3 manipulation studies, proper validation is essential:

• For PSME3 overexpression:

  • Use pCDNA3.1-Flag-PSME3 plasmid for transfection-based approaches

  • For viral delivery, use an optimal ratio of pMD2G:psPAX2:plasmid:PEI = 1:2:4:12 (μg:μg:μg:μl) for packaging in HEK 293T cells

  • Confirm overexpression by Western blot, qRT-PCR, and immunofluorescence

• For PSME3 knockdown:

  • Design multiple siRNA/shRNA sequences targeting different regions

  • When using lentiviral vectors, infect cells at approximately 40% density

  • Change medium on day 2 post-infection

  • Begin selection with appropriate antibiotics on day 3

  • Validate knockdown efficiency using multiple methods (Western blot, qRT-PCR)

• Functional validation approaches:

  • Cell proliferation assays (CCK-8) at multiple time points (24h, 48h)

  • Migration/invasion assays (wound healing, transwell)

  • Cell cycle analysis by flow cytometry

  • Apoptosis assessment (Annexin V/PI staining)

These validation approaches have been successfully employed in studies demonstrating PSME3's role in promoting lung adenocarcinoma cell proliferation, migration, invasion, and inhibiting apoptosis .

How can PSME3 antibodies be used to investigate immune regulation mechanisms?

PSME3 plays significant roles in immune regulation that can be investigated using specialized antibody-based approaches:

• Immune checkpoint analysis:

  • Use flow cytometry with dual staining for PSME3 and immune checkpoint proteins

  • Prepare antibody combinations (1:100 dilution) including anti-PSME3 and anti-CD276 (B7-H3)

  • Stain resuspended cells on ice for 30 minutes

  • Wash twice and fix with 1% paraformaldehyde

  • Analyze using flow cytometry software (e.g., BD Diva, FlowJo)

• Tumor microenvironment studies:

  • Multiplex immunofluorescence combining PSME3 with markers for:

    • Neutrophils (CD15+)

    • B cells (CD19+, CD20+)

    • T cells (CD4+, CD8+)

    • M2 macrophages (CD68+, CD163+)

  • Analysis of spatial co-localization patterns

  • Correlation with immunoScore, stromalScore, and ESTIMATEScore

• Immune regulation mechanisms:

  • Co-immunoprecipitation to identify PSME3 interactions with immune regulators

  • ChIP assays to examine transcriptional regulation of immune-related genes

  • Proximity ligation assays to confirm direct protein interactions in situ

This approach has revealed PSME3's association with immune checkpoints and confirmed its role in positively regulating CD276 expression, suggesting PSME3 as a potential therapeutic target in immunotherapy .

What methodological considerations are important when studying PSME3 in different cancer models?

When investigating PSME3 across different cancer models, researchers should consider several methodological aspects:

This comprehensive approach reveals cancer-specific roles of PSME3, as demonstrated by studies showing its correlation with adverse clinical outcomes and cancer progression particularly in liver cancer (LIHC) .

How should researchers interpret conflicting PSME3 expression data across different experimental platforms?

Researchers often encounter discrepancies in PSME3 expression data between different experimental platforms. A systematic approach to resolving these conflicts includes:

• Platform-specific considerations:

  • RNA-seq vs. qRT-PCR: RNA-seq measures total transcript abundance while qRT-PCR may be isoform-specific

  • Protein detection methods: Western blot quantifies total protein, while IHC reveals spatial distribution

  • Flow cytometry: Measures per-cell expression but may be affected by fixation conditions

• Analytical approach for reconciling discrepancies:

  • Begin with biological replicates to establish consistency within each platform

  • Perform parallel validation using orthogonal methods

  • Consider splice variants and post-translational modifications

  • Evaluate antibody epitope accessibility in different experimental conditions

  • Assess cell-type specific expression in heterogeneous samples

• Methodology for comprehensive validation:

  • Employ multiple antibodies targeting different epitopes

  • Include positive controls with known PSME3 expression

  • Validate with genetic approaches (knockdown/overexpression)

  • Consider the impact of tumor heterogeneity in cancer studies

Research demonstrates that while PSME3 mRNA and protein levels generally show high consistency across cancer types, microenvironmental factors can influence expression patterns and protein function in ways not reflected at the transcript level .

How can PSME3 antibodies contribute to biomarker development in cancer immunotherapy?

PSME3 shows promise as a biomarker for cancer immunotherapy response, with antibody-based detection playing a central role:

• Biomarker potential assessment methodology:

  • Correlate PSME3 expression with established immunotherapy markers:

    • TMB (Tumor Mutational Burden)

    • MSI (Microsatellite Instability)

    • MMR gene expression

  • Analyze relationship with immune checkpoint expression

  • Evaluate association with immune cell infiltration patterns

• Implementation strategies:

  • Develop standardized IHC protocols for clinical sample analysis

  • Establish scoring systems that account for intensity and distribution

  • Create multiplex assays combining PSME3 with other immune markers

  • Validate cut-off values for stratifying patient responses

• Clinical validation approach:

  • Retrospective analysis correlating PSME3 expression with treatment outcomes

  • Prospective studies in patients receiving immune checkpoint inhibitors

  • Investigation of PSME3 as a companion diagnostic for novel immunotherapies

Research has shown that PSME3 positively regulates CD276 (B7-H3) expression, suggesting its potential relevance as a biomarker for immunotherapy response. The association between PSME3 and TMB/MSI status further supports its utility in predicting response to immune checkpoint inhibitors across multiple cancer types .

What are the cutting-edge approaches for studying PSME3 interactions with the proteasome system?

Advanced techniques for investigating PSME3-proteasome interactions include:

• Structural biology approaches:

  • Cryo-EM analysis of PSME3-proteasome complexes

  • Hydrogen-deuterium exchange mass spectrometry to map interaction surfaces

  • FRET-based assays to monitor dynamic associations in living cells

• Proteasomal activity assessment:

  • Fluorogenic substrate assays measuring trypsin-like, chymotrypsin-like, and PGPH activities

  • Cell-based proteasome sensors to monitor activity in real-time

  • In vitro reconstitution assays with purified components

• Protein interaction network mapping:

  • Proximity labeling approaches (BioID, APEX) to identify transient interactions

  • Quantitative interaction proteomics following immunoprecipitation

  • Yeast two-hybrid or mammalian two-hybrid screens for novel interactors

Research has identified key PSME3-interacting proteins including PSME1, PSME2, PSMA5, PSMD8, PSMD14, PSMEIP1, NCOA3, RXRA, TNF, and IFNG. Additionally, the interaction between PSME3 and PIP30 enhances its binding to the cellular 20S proteasome and affects its substrate specificity .

How can single-cell analysis techniques be integrated with PSME3 antibody applications?

Single-cell analysis offers unprecedented insights into PSME3 biology at the cellular level:

• Single-cell proteomics approaches:

  • Mass cytometry (CyTOF) with metal-conjugated PSME3 antibodies

  • CITE-seq combining PSME3 antibodies with transcriptome analysis

  • Imaging mass cytometry for spatial resolution of PSME3 expression

• Spatial transcriptomics integration:

  • Correlate PSME3 spatial expression with immune cell markers

  • Validate co-expression patterns observed in spatial transcriptome data

  • Map PSME3 expression to specific tissue microenvironments

• Single-cell functional assays:

  • Microfluidic approaches for correlating PSME3 levels with cellular phenotypes

  • Live-cell imaging with fluorescently tagged antibodies or nanobodies

  • Single-cell secretome analysis in relation to PSME3 expression

These approaches have revealed that PSME3 exhibits spatial co-expression with M2 macrophage biomarkers (CD68 and CD163) in certain tissues, with overlapping geographical distribution patterns that suggest functional relationships in the tumor microenvironment .

How can researchers address common challenges when working with PSME3 antibodies?

When working with PSME3 antibodies, researchers may encounter several challenges that require systematic troubleshooting:

• Western blot issues:

  • Non-specific bands: Optimize antibody dilution (typically 1:1000), increase blocking time, and use PVDF membranes

  • Weak signal: Increase protein loading, extend primary antibody incubation time, or use enhanced detection systems

  • High background: Increase washing steps, reduce secondary antibody concentration, and use freshly prepared buffers

• Immunohistochemistry challenges:

  • Variable staining: Standardize fixation time, optimize antigen retrieval (citrate buffer, pH 6.0), and titrate antibody

  • Background staining: Increase blocking time, use avidin/biotin blocking for biotin-based detection systems

  • Loss of antigenicity: Minimize storage time of cut sections and use freshly prepared buffers

• Flow cytometry considerations:

  • Fixation effects: Compare results with different fixation methods (1% paraformaldehyde is recommended)

  • Antibody penetration: Include permeabilization step when analyzing intracellular PSME3

  • Compensation: Properly compensate when combining PSME3 detection with other fluorochromes

• Validation approaches:

  • Always include positive controls (A549, Hut7, T24 cell lines)

  • Perform siRNA knockdown as negative control

  • Consider testing multiple antibody clones when results are inconsistent

These troubleshooting approaches are essential for generating reliable and reproducible data when studying PSME3 in various experimental contexts.

What are the best practices for quantitative analysis of PSME3 expression?

Accurate quantification of PSME3 expression requires rigorous methodology:

• Western blot quantification:

  • Use linear range of detection (avoid saturated signals)

  • Normalize to appropriate loading controls (GAPDH, β-actin, total protein)

  • Apply densitometry using software like ImageJ with background subtraction

  • Include calibration standards when absolute quantification is needed

• Immunohistochemistry scoring:

  • Implement standardized scoring systems (H-score = intensity × percentage)

  • Intensity scale: 0 (negative), 1+ (weak), 2+ (moderate), 3+ (strong)

  • Percentage of positive cells: 0-100%

  • Consider automated image analysis for reproducibility

  • Account for both nuclear and cytoplasmic staining independently

• qRT-PCR quantification:

  • Use validated reference genes (GAPDH, ACTB, 18S rRNA)

  • Apply delta-delta Ct (2^-ΔΔCt) method for relative quantification

  • Include standard curves for absolute quantification

  • Account for PCR efficiency in calculations

• Flow cytometry analysis:

  • Report mean fluorescence intensity (MFI) and percentage of positive cells

  • Use isotype controls to set negative thresholds

  • Apply consistent gating strategies across experiments

These quantitative approaches have been instrumental in establishing the prognostic significance of PSME3 expression across various cancer types, particularly in lung adenocarcinoma and liver cancer .