RNF133 Antibody

What is RNF133 and what role does it play in cellular function?

RNF133 (Ring Finger Protein 133) is an E3 ubiquitin-protein ligase localized in the endoplasmic reticulum. In humans, the canonical protein has 376 amino acid residues with a molecular mass of approximately 42.3 kDa. RNF133 functions primarily in the ubiquitination pathway, targeting specific proteins for degradation via the proteasome system. This post-translational modification mechanism is crucial for protein quality control and cellular homeostasis .

The protein contains a RING finger domain characteristic of many E3 ubiquitin ligases, enabling it to facilitate the transfer of ubiquitin from an E2 ubiquitin-conjugating enzyme to specific substrate proteins. RNF133 is also known by synonyms including E3 ubiquitin-protein ligase RNF133 and RING-type E3 ubiquitin transferase RNF133 .

What are the common applications for RNF133 antibodies in research?

RNF133 antibodies are employed in various immunodetection techniques to study the expression, localization, and function of RNF133 protein. The most frequently used applications include:

• Western Blot (WB): For detecting RNF133 protein in cell or tissue lysates, providing information about protein expression levels and molecular weight.

• Enzyme-Linked Immunosorbent Assay (ELISA): For quantitative measurement of RNF133 protein in biological samples.

• Immunohistochemistry (IHC): For visualizing RNF133 distribution in tissue sections, providing spatial information about protein localization .

• Cytometric Bead Array: For multiplexed protein detection in solution-based assays .

When designing experiments, researchers should carefully select antibodies validated for their specific application and species of interest, as reactivity can vary significantly between products.

How is RNF133 involved in immune system regulation?

Recent research has uncovered significant roles for RNF133 in immune regulation, particularly in T cell function and tolerance. RNF133 shows high expression in tolerant T cells, suggesting it works alongside other molecules like GRAIL to establish and maintain T cell tolerance .

Experimental evidence from knockout models demonstrates that RNF133-deficient CD4+ T cells exhibit enhanced proliferation and cytokine production (particularly IL-17 and IL-21) when activated under tolerogenic conditions. This indicates RNF133's role in controlling T helper cell responses, specifically TH17 and T follicular helper (Tfh) cell responses .

RNF133 expression in regulatory T cells (Tregs) is essential for maintaining their suppressive function and stability, preventing them from acquiring pathogenic TH17 phenotypes. RNF133 knockout mice develop elevated levels of IgG and IgG1 in sera and increased percentages of TH17 and Tfh cells in peripheral lymphoid tissues, eventually leading to autoimmune symptoms by 5-6 months of age .

These findings position RNF133 as a potential checkpoint molecule in maintaining immunological tolerance and preventing inflammatory conditions, making it an important target for immunological research.

What factors should be considered when selecting the appropriate RNF133 antibody for specific applications?

When selecting an RNF133 antibody for your research, several critical factors must be evaluated:

• Application compatibility: Verify the antibody has been validated for your specific application (WB, IHC, ELISA, etc.). For example, antibody ABIN2034033 has been validated for both Western blot and Immunohistochemistry applications .

• Epitope recognition: Consider which region of RNF133 the antibody recognizes. Some antibodies target specific domains or regions that may be masked in certain experimental conditions. For instance, ABIN2034033 recognizes a region within amino acids 23-348 of RNF133 .

• Clonality:

  • Monoclonal antibodies (like clone 2C8B10 or 1F11D12 ) offer high specificity for a single epitope but may be sensitive to epitope modifications.

  • Polyclonal antibodies (like ABIN2034033 ) recognize multiple epitopes, providing robust detection but potentially lower specificity.

• Host species: Consider compatibility with your experimental system to avoid cross-reactivity. Document available antibodies are produced in mouse or rabbit hosts .

• Species reactivity: Ensure the antibody recognizes RNF133 from your species of interest. Available antibodies show reactivity to human RNF133, while some also cross-react with mouse, rabbit, rat, and horse proteins .

• Validation data: Review provided validation data (Western blots, IHC images) to confirm the antibody detects RNF133 with expected molecular weight and tissue distribution patterns.

• Storage and handling requirements: Note specific requirements such as storage at -80°C for certain products or avoiding sodium azide for antibodies used with HRP detection systems .

What is the recommended protocol for optimizing Western blot detection of RNF133?

Optimizing Western blot detection of RNF133 requires careful attention to several experimental parameters:

• Sample preparation:

  • Use appropriate lysis buffers containing protease inhibitors to prevent RNF133 degradation

  • For membrane-associated proteins like RNF133 (localized to ER), include detergents suitable for membrane protein extraction

  • Optimize protein loading (typically 20-50 μg total protein per lane)

• Gel selection and transfer:

  • Use 10-12% SDS-PAGE gels for optimal resolution of the 42.3 kDa RNF133 protein

  • Consider wet transfer for more efficient transfer of membrane proteins

  • Use PVDF membranes for better protein binding and signal-to-noise ratio

• Blocking and antibody incubation:

  • Test both BSA and non-fat dry milk blocking agents (3-5%)

  • Optimize primary antibody dilution (start with manufacturer recommendations, typically 1:500-1:2000)

  • For polyclonal antibodies like ABIN2034033, longer incubation at 4°C overnight may improve specific binding

• Detection optimization:

  • Use appropriate HRP-conjugated secondary antibodies matching the host species

  • Remember not to add sodium azide to antibody solutions when using HRP-based detection systems

  • Consider enhanced chemiluminescence (ECL) detection for sensitive visualization

• Controls:

  • Include positive control (tissue/cells known to express RNF133)

  • Use RNF133 knockout or knockdown samples as negative controls when available

  • Consider loading controls (GAPDH, β-actin) but be aware their expression may not directly correlate with membrane proteins

• Troubleshooting:

  • If multiple bands appear, optimize antibody concentration and washing steps

  • If signal is weak, consider longer exposure times or signal enhancement systems

  • For high background, increase washing duration and detergent concentration

How can RNF133 antibodies be used to investigate the role of RNF133 in T cell tolerance?

Investigating RNF133's role in T cell tolerance requires strategic experimental approaches combining antibody-based detection with functional assays:

• Expression profiling across T cell populations:

  • Use flow cytometry with RNF133 antibodies to quantify expression levels in different T cell subsets (naïve, effector, memory, regulatory)

  • Compare RNF133 expression in tolerant versus activated T cells using Western blot or immunofluorescence

  • Correlate expression with tolerance markers and activation status

• Subcellular localization studies:

  • Employ confocal microscopy with RNF133 antibodies alongside ER markers to confirm localization

  • Investigate whether localization changes during T cell activation or tolerance induction

  • Use cell fractionation followed by Western blot to biochemically confirm localization patterns

• Functional assays with genetic manipulation:

  • Generate RNF133 knockout or knockdown T cells and confirm protein absence using validated antibodies

  • Assess proliferation, cytokine production (especially IL-17 and IL-21), and tolerance induction in modified cells

  • Investigate the impact on regulatory T cell stability and function

• Protein interaction studies:

  • Use co-immunoprecipitation with RNF133 antibodies to identify binding partners

  • Confirm interactions with known tolerance mediators (e.g., GRAIL)

  • Identify ubiquitination targets using RNF133 antibodies to pull down complexes

• In vivo tolerance models:

  • Use RNF133 antibodies to monitor expression in various tissues during tolerance induction

  • Compare wild-type and RNF133 knockout mice for autoimmune phenotypes

  • Monitor markers including serum IgG/IgG1 levels and TH17/Tfh cell percentages in peripheral lymphoid tissues

When designing these experiments, it's crucial to include appropriate controls and validate antibody specificity in the context of each application.

How do post-translational modifications affect RNF133 antibody detection and what strategies can overcome these challenges?

Post-translational modifications (PTMs) of RNF133, particularly ubiquitination, present significant challenges for antibody detection. These modifications can alter epitope accessibility, protein conformation, and migration patterns in analytical techniques. Researchers should consider the following strategies:

• Epitope masking issues:

  • PTMs may physically block antibody binding sites

  • Use antibodies targeting different epitopes (N-terminal vs. C-terminal regions)

  • Compare results from multiple antibodies like the N-terminal region-specific antibody (ARP43397_P050) versus antibodies targeting other regions

• Migration pattern variations:

  • Ubiquitinated RNF133 will appear at higher molecular weights

  • Use gradient gels (4-15%) to resolve both modified and unmodified forms

  • Consider native gel electrophoresis to preserve protein complexes and modifications

• Enrichment techniques:

  • Use deubiquitinating enzyme inhibitors in lysates to preserve ubiquitinated forms

  • Apply ubiquitin-specific pull-down followed by RNF133 antibody detection

  • Consider phosphatase treatment to eliminate phosphorylation that might affect antibody binding

• Strategic antibody selection:

  • Choose antibodies raised against recombinant proteins with defined modification states

  • Consider using antibodies specifically designed to detect modified forms of RNF133

  • Validate antibody performance with both native and denatured protein samples

• Data interpretation considerations:

  • Document all bands observed, not just those at the expected molecular weight

  • Compare patterns across different experimental conditions known to alter PTM status

  • Use mass spectrometry as a complementary approach to confirm modifications

What experimental approaches can resolve contradictory findings when using different RNF133 antibodies?

When different RNF133 antibodies yield contradictory results, a systematic troubleshooting approach is essential:

• Epitope mapping comparison:

  • Determine the exact epitopes recognized by each antibody (e.g., AA 23-348 for ABIN2034033 )

  • Consider whether epitopes might be differentially accessible in various experimental conditions

  • Test accessibility using limited proteolysis followed by Western blotting with different antibodies

• Validation with knockout/knockdown controls:

  • Generate RNF133-deficient samples through CRISPR/Cas9 or siRNA approaches

  • Test all antibodies against these negative controls to establish specificity

  • Quantify signal-to-noise ratios for each antibody in identical samples

• Cross-platform validation:

  • Compare results across multiple techniques (WB, IHC, flow cytometry)

  • Use orthogonal methods not dependent on antibodies (mRNA expression, mass spectrometry)

  • Correlate findings with functional outcomes in knockout/overexpression systems

• Isoform and variant analysis:

  • Investigate whether contradictory results stem from detection of different RNF133 isoforms

  • Sequence the target region in your experimental system to confirm conservation

  • Design isoform-specific detection strategies if necessary

• Technical optimization matrix:

  • Systematically vary experimental conditions (fixation methods, blocking agents, antibody concentration)

  • Document conditions under which results converge or diverge

  • Establish optimal protocols for each antibody and application

• Independent laboratory validation:

  • Have experiments repeated by independent researchers or laboratories

  • Compare findings with published literature on RNF133 detection

  • Consider antibody validation services or collaborative studies for difficult cases

How can RNF133 antibodies be employed in multiplex assays to study its role in immune signaling networks?

Multiplexed approaches using RNF133 antibodies can provide comprehensive insights into immune signaling networks:

• Cytometric bead array applications:

  • Utilize validated RNF133 antibody pairs (e.g., MP50868-1) in cytometric bead arrays

  • Combine with detection of cytokines (IL-17, IL-21) and other signaling molecules

  • Quantify relationships between RNF133 expression and downstream immune mediators

  • Working range: 0.098-100 ng/mL for optimal detection sensitivity

• Multi-parameter flow cytometry:

  • Design panels including RNF133 alongside T cell subset markers (CD4, CD25, FOXP3)

  • Use different fluorochromes for each parameter to avoid spectral overlap

  • Analyze co-expression patterns in various activation and tolerance states

  • Consider intracellular staining protocols optimized for nuclear and ER-localized proteins

• Multiplexed imaging approaches:

  • Apply multiplexed immunofluorescence with RNF133 antibodies and other immune markers

  • Use sequential staining protocols to overcome host species limitations

  • Employ cyclic immunofluorescence or mass cytometry imaging for higher parameter counts

  • Analyze spatial relationships between RNF133-expressing cells and other immune populations

• Protein-protein interaction networks:

  • Combine proximity ligation assays with RNF133 antibodies to visualize interactions

  • Apply FRET/BRET approaches for living cell interaction studies

  • Use multiplexed co-immunoprecipitation followed by mass spectrometry to identify interaction networks

  • Map the dynamic changes in these networks during T cell activation versus tolerance induction

• Single-cell analysis integration:

  • Correlate RNF133 protein expression (by antibody detection) with single-cell transcriptomics

  • Develop computational approaches to integrate protein and mRNA data

  • Identify cell populations with coordinated expression of tolerance-associated molecules

What are the common sources of non-specific binding with RNF133 antibodies and how can they be minimized?

Non-specific binding represents a significant challenge when working with RNF133 antibodies. Researchers can address this issue through several strategies:

• Blocking optimization:

  • Test different blocking agents (BSA, normal serum, commercial blockers)

  • Increase blocking time (1-2 hours at room temperature)

  • Add 0.1-0.3% Triton X-100 to blocking buffer for improved penetration

  • Consider using the host species serum matching the secondary antibody

• Antibody dilution optimization:

  • Perform titration experiments to identify optimal antibody concentration

  • Start with manufacturer's recommended dilution and test 2-fold dilution series

  • Balance signal strength against background for optimal signal-to-noise ratio

  • For polyclonal antibodies like ABIN2034033, higher dilutions may reduce non-specific binding

• Cross-reactivity minimization:

  • Pre-absorb antibodies with lysates from species prone to cross-reactivity

  • Include additional blocking steps with irrelevant proteins

  • Use monoclonal antibodies for higher specificity when possible

  • Consider fragment antibodies (Fab, F(ab')2) to reduce Fc-mediated binding

• Washing protocol optimization:

  • Increase number and duration of washing steps

  • Use higher detergent concentrations in wash buffers (0.1-0.5% Tween-20)

  • Include salt (up to 500 mM NaCl) in wash buffers to reduce ionic interactions

  • Use agitation during washing to improve efficiency

• Secondary antibody considerations:

  • Use highly cross-adsorbed secondary antibodies

  • Include secondary-only controls in all experiments

  • Consider direct conjugation of primary antibodies to eliminate secondary antibody issues

  • Match secondary antibody class-specificity to primary antibody isotype (e.g., anti-IgG1 for mouse IgG1 primaries )

How should RNF133 antibodies be stored and handled to maintain optimal activity over time?

Proper storage and handling are crucial for maintaining antibody performance:

• Storage temperature requirements:

  • Store RNF133 antibodies as concentrated solutions at -20°C or -80°C as specified by manufacturer

  • MP50868-1 requires storage at -80°C , while ABIN2034033 can be stored at -20°C or below

  • Avoid repeated freeze-thaw cycles by preparing single-use aliquots upon receipt

• Aliquoting recommendations:

  • Prepare small volume aliquots (10-50 μL) based on typical experimental needs

  • Use sterile tubes with secure seals to prevent contamination and evaporation

  • Document preparation date, concentration, and number of freeze-thaw cycles on each tube

  • Consider adding carrier protein (BSA) to dilute antibodies for improved stability

• Working solution handling:

  • Prepare fresh working dilutions for each experiment

  • Return stock solutions to proper storage temperature immediately after use

  • Keep working solutions on ice during experiment preparation

  • Do NOT add sodium azide to antibodies used with HRP detection systems

• Contamination prevention:

  • Use sterile technique when handling antibody stocks

  • Include antimicrobial agents in working solutions if they will be stored (except sodium azide with HRP)

  • Filter solutions if visible precipitation occurs

  • Centrifuge antibody stocks briefly before opening to collect liquid at the bottom

• Stability monitoring:

  • Include positive controls in each experiment to monitor antibody performance over time

  • Document lot numbers and performance characteristics for each antibody

  • Consider validation experiments for aged antibodies before using in critical experiments

  • Maintain storage equipment with backup power and temperature monitoring

What controls are essential when validating a new RNF133 antibody for specific research applications?

Thorough validation of RNF133 antibodies requires a comprehensive set of controls:

• Positive expression controls:

  • Tissues/cells with confirmed RNF133 expression (based on literature or preliminary data)

  • Recombinant RNF133 protein as a standard for size verification

  • Overexpression systems with tagged RNF133 that can be detected by alternative methods

  • Known expression patterns across tissues for immunohistochemistry validation

• Negative controls:

  • RNF133 knockout or knockdown samples

  • Tissues known not to express RNF133

  • Pre-absorption of antibody with immunizing peptide/protein

  • Secondary antibody only controls to assess non-specific binding

• Specificity controls:

  • Western blot demonstration of single band at expected molecular weight (42.3 kDa)

  • Comparison with orthogonal detection methods (mRNA expression, mass spectrometry)

  • Cross-validation with multiple antibodies recognizing different epitopes

  • Peptide competition assays to confirm epitope specificity

• Application-specific controls:

  • For Western blot: Molecular weight markers and loading controls

  • For IHC/ICC: Isotype controls and autofluorescence controls

  • For IP/Co-IP: Non-immune IgG precipitations

  • For ELISA/cytometric bead arrays: Standard curves with recombinant protein (0.098-100 ng/mL range)

• Experimental validation:

  • Demonstration that antibody detects expected biological changes (e.g., upregulation in specific conditions)

  • Correlation of protein detection with functional outcomes

  • Reproducibility across multiple experimental replicates

  • Cross-laboratory validation when possible

How are RNF133 antibodies contributing to our understanding of autoimmune disease mechanisms?

Recent research utilizing RNF133 antibodies has revealed important connections between this E3 ubiquitin ligase and autoimmune regulation:

• T cell tolerance breakdown mechanisms:

  • RNF133 knockout models show development of autoimmune symptoms by 5-6 months of age

  • RNF133 antibodies have helped document elevated levels of IgG and IgG1 in sera and increased TH17 and Tfh cells in peripheral lymphoid tissues of knockout mice

  • These findings position RNF133 as a potential checkpoint molecule in maintaining immunological tolerance

• Regulatory T cell stability:

  • Antibody-based detection has demonstrated that RNF133 expression in regulatory T cells is essential for their suppressive function and stability

  • Loss of RNF133 leads to Tregs acquiring pathogenic TH17 phenotypes, contributing to autoimmunity

  • This finding identifies a previously unknown molecular mechanism for maintaining Treg identity

• Comparative analysis with other tolerance mediators:

  • Despite GRAIL's established role in T cell tolerance, GRAIL knockout mice don't develop early spontaneous autoimmunity

  • RNF133 shows the highest expression among GRAIL homologs in tolerant T cells

  • Combined analysis suggests cooperative functions between multiple E3 ligases in maintaining immune homeostasis

• Therapeutic target identification:

  • Characterization of RNF133's role provides new significant insights that may lead to therapeutic developments for inflammatory diseases

  • Antibody-based screening could identify compounds that modulate RNF133 activity in autoimmune conditions

  • RNF133 detection in patient samples might serve as a biomarker for autoimmune predisposition or disease progression

What emerging technologies are enhancing the specificity and applications of RNF133 antibodies?

Several technological advances are improving RNF133 antibody development and applications:

• Recombinant antibody engineering:

  • Development of recombinant mouse anti-human RNF133 antibodies with improved specificity

  • CRISPR-based epitope tagging of endogenous RNF133 enabling detection with highly specific tag antibodies

  • Single-chain variable fragment (scFv) development for improved tissue penetration in imaging applications

• Advanced validation technologies:

  • CRISPR knockout cell line panels for comprehensive antibody validation

  • Orthogonal protein detection methods (targeted mass spectrometry) for verification

  • Automated high-throughput screening of antibody performance across multiple applications

• Multiplexed detection systems:

  • Cytometric bead arrays validated for RNF133 detection with sensitivity range of 0.098-100 ng/mL

  • Multi-parameter flow cytometry panels incorporating RNF133 detection

  • Mass cytometry (CyTOF) approaches for simultaneous detection of RNF133 and dozens of other proteins

• Advanced imaging applications:

  • Super-resolution microscopy with RNF133 antibodies for detailed subcellular localization

  • Multi-spectral imaging systems for simultaneous detection of RNF133 and interaction partners

  • Live-cell imaging with minimally disruptive antibody-based detection systems

• Application-specific developments:

  • Matched antibody pairs optimized for specific applications (MP50868-1 for cytometric bead arrays)

  • PBS-only formulations (60611-1-PBS, 60611-2-PBS) ready for custom conjugation

  • Epitope-specific antibodies targeting functional domains (like ARP43397_P050 for N-terminal region)

How might single-cell analysis techniques incorporating RNF133 antibody detection enhance our understanding of its heterogeneous expression?

Single-cell analytical approaches offer unprecedented insights into RNF133 expression patterns:

• Single-cell protein-RNA correlation:

  • Combining RNF133 antibody detection with single-cell RNA sequencing

  • Revealing discrepancies between transcript and protein levels indicating post-transcriptional regulation

  • Identifying rare cell populations with distinctive RNF133 expression patterns

  • Characterizing regulatory networks associated with different RNF133 expression levels

• Spatial single-cell analysis:

  • Implementing multiplex immunofluorescence with RNF133 antibodies in tissue sections

  • Mapping spatial relationships between RNF133-expressing cells and other immune populations

  • Correlating expression patterns with tissue microenvironments in health and disease

  • Identifying niches supporting tolerogenic versus autoimmune phenotypes

• Temporal dynamics at single-cell resolution:

  • Time-course analyses of RNF133 expression during T cell activation and tolerance induction

  • Live-cell imaging with antibody-based sensors to track RNF133 dynamics

  • Correlation of expression changes with functional outcomes in individual cells

  • Identification of expression thresholds associated with tolerance versus activation

• Heterogeneity analysis in immune populations:

  • Characterizing RNF133 expression variance across T cell subsets (naïve, memory, effector, regulatory)

  • Identifying potential subpopulations with distinctive RNF133 expression levels

  • Correlating expression patterns with functional capacities (suppression, cytokine production)

  • Exploring how RNF133 expression heterogeneity contributes to population-level immune responses

• Integrated multi-omic approaches:

  • Combining antibody-based RNF133 detection with epigenetic profiling at single-cell level

  • Integrating protein, RNA, and epigenetic data to build comprehensive regulatory networks

  • Developing predictive models of RNF133 function based on multi-parametric single-cell data

  • Identifying novel therapeutic targets within the RNF133-associated regulatory network

What are the relative advantages and limitations of monoclonal versus polyclonal RNF133 antibodies?

Selecting between monoclonal and polyclonal RNF133 antibodies involves weighing several considerations:

When designing critical experiments, consider using both types complementarily to leverage their respective strengths while minimizing limitations.

How do different immunoassay platforms compare for RNF133 detection in terms of sensitivity, specificity, and throughput?

Different platforms offer varying performance characteristics for RNF133 detection:

When selecting a platform, researchers should consider their specific research questions, available sample types, and required data outputs.

What bioinformatic resources are available to help researchers interpret RNF133 antibody data in the context of broader signaling networks?

Several bioinformatic resources can enhance interpretation of RNF133 antibody-generated data:

• Protein interaction databases:

  • STRING-db: For visualizing known and predicted RNF133 protein interactions

  • BioGRID: For curated protein-protein interaction data

  • IntAct: For experimentally verified molecular interactions

  • These resources help place RNF133 in broader signaling networks and suggest potential functions

• Pathway analysis tools:

  • KEGG Pathway: For mapping RNF133 to known biological pathways

  • Reactome: For detailed biochemical pathway information

  • Gene Ontology: For functional classification of RNF133 and related proteins

  • These tools help interpret the functional significance of RNF133 expression patterns

• Expression databases:

  • Human Protein Atlas: For tissue-specific expression patterns of RNF133

  • ImmGen: For immune cell-specific expression profiles

  • Single Cell Portal: For single-cell RNA-seq data including RNF133 expression

  • These resources provide context for antibody-detected expression patterns

• Structure prediction and analysis:

  • AlphaFold: For RNF133 protein structure prediction

  • PyMOL: For visualizing antibody epitopes on predicted structures

  • SWISS-MODEL: For homology modeling of RNF133 domains

  • Structural information helps interpret antibody binding sites and functional domains

• Data integration platforms:

  • Cytoscape: For network visualization of RNF133 in protein interaction networks

  • R Bioconductor packages: For statistical analysis of antibody-generated data

  • Galaxy: For workflow creation and data integration

  • These platforms help integrate antibody data with other -omics datasets

• Specialized immune system databases:

  • ImmPort: For immunological data sharing and analysis

  • ImmuneSpace: For data analysis and visualization

  • These resources are particularly relevant given RNF133's roles in T cell function and tolerance

By leveraging these resources, researchers can place their antibody-generated data in broader biological contexts and generate testable hypotheses about RNF133 function.

What are the emerging trends in RNF133 research and how might antibody technologies evolve to address these needs?

RNF133 research is rapidly evolving, with several emerging trends that will shape future antibody technology development:

• Expanding functional understanding:

  • Recent discovery of RNF133's role in T cell tolerance and autoimmunity prevention opens new research directions

  • Future antibodies will need to target functional domains with higher specificity

  • Development of conformation-specific antibodies may help distinguish active versus inactive RNF133

• Therapeutic targeting:

  • As RNF133's role as an immunological checkpoint molecule becomes established, therapeutic modulation will gain interest

  • Antibodies serving as both research tools and potential therapeutics may emerge

  • Screening platforms using RNF133 antibodies will help identify small molecule modulators

• Single-cell and spatial biology integration:

  • Understanding RNF133's heterogeneous expression across immune cell populations requires advanced detection methods

  • Antibodies compatible with multiplexed imaging and single-cell protein analysis will become essential

  • Development of antibodies suitable for non-permeabilized cell detection to maintain spatial relationships

• Systems biology approaches:

  • Placing RNF133 within broader ubiquitination networks requires comprehensive detection tools

  • Antibodies targeting multiple E3 ligases simultaneously in standardized formats

  • Development of antibody panels for entire ubiquitination pathways

• Technological advancements:

  • Nanobodies and alternative binding proteins may offer improved tissue penetration and reduced immunogenicity

  • CRISPR-based epitope tagging systems paired with standardized tag antibodies

  • Machine learning approaches to predict optimal epitopes for antibody development

These trends will drive the evolution of more specific, sensitive, and versatile RNF133 antibody technologies to meet the growing needs of basic and translational research.

What key questions about RNF133 function remain unanswered and how might advanced antibody applications help address them?

Several critical questions about RNF133 biology remain unanswered:

• Substrate identification:

  • What are the specific protein targets ubiquitinated by RNF133?

  • Advanced co-immunoprecipitation with RNF133 antibodies followed by mass spectrometry

  • Proximity labeling approaches using RNF133 antibodies to identify nearby proteins

  • Development of antibodies recognizing RNF133-substrate complexes

• Regulatory mechanisms:

  • How is RNF133 itself regulated at transcriptional and post-translational levels?

  • Antibodies recognizing specific post-translational modifications of RNF133

  • ChIP-seq approaches with antibodies against transcription factors regulating RNF133

  • Antibody-based detection of RNF133 half-life and turnover rates

• Tissue-specific functions:

  • Does RNF133 play different roles in various tissues and cell types?

  • Immunohistochemistry with highly specific antibodies across tissue arrays

  • Single-cell resolution mapping of RNF133 expression in complex tissues

  • Correlation of tissue-specific expression with local functional outcomes

• Disease associations:

  • Beyond autoimmunity, does RNF133 dysfunction contribute to other pathologies?

  • Development of clinical-grade antibodies for patient sample analysis

  • Tissue microarray analysis of RNF133 in various disease states

  • Correlation of expression patterns with clinical outcomes and disease progression

• Therapeutic targeting:

  • Can modulation of RNF133 activity be therapeutically beneficial in autoimmune diseases?

  • Antibodies capable of modulating RNF133 activity (agonists/antagonists)

  • Screening platforms using RNF133 antibodies to identify small molecule modulators

  • Monitoring tools to assess therapeutic efficacy in preclinical and clinical settings

Advanced antibody applications will be instrumental in addressing these questions, particularly when integrated with complementary technologies like CRISPR-based genetic manipulation, mass spectrometry, and high-resolution imaging.