RORC Antibody

What is RORC and why is it an important research target?

RORC (also known as RORγ, NR1F3) is a DNA-binding transcription factor belonging to the NR1 subfamily of nuclear hormone receptors. This 58.2 kDa protein plays critical roles in:

• T helper 17 (Th17) cell differentiation and function

• Immune system development

• Various pathological conditions including autoimmune diseases and cancer

Research significance stems from RORC's key regulatory functions in immunity and inflammation, making RORC antibodies essential tools for understanding disease mechanisms in autoimmunity, cancer biology, and immunotherapy development .

What are the key differences between RORC isoforms that antibodies might detect?

RORC has two main isoforms researchers should be aware of:

• RORC/RORγ: The full-length protein expressed in multiple tissues

• RORγt: A truncated form predominantly expressed in immune cells, particularly Th17 cells

When selecting antibodies, researchers should verify which isoform(s) the antibody recognizes. For example, search result data indicates that some antibodies specifically detect RORγ but not RORγt (verified through ELISA with recombinant proteins) . This specificity is critical when studying tissue-specific versus immune-specific functions of RORC .

What standard validation methods should be used to confirm RORC antibody specificity?

Comprehensive validation should include:

• Western blot analysis: A specific band should be detected at approximately 58-60 kDa for RORC. Search results show validation using human thymus tissue with a specific band detected at ~60 kDa .

• Direct ELISA: Test binding to recombinant RORC protein and cross-reactivity with related proteins (ROR1, ROR2, RORα). Quality antibodies show dose-dependent binding to target protein .

• Surface Plasmon Resonance (SPR): Determine binding kinetics and affinity constant. Research shows effective antibodies have KD values in the nanomolar range .

• Immunofluorescence/Flow cytometry: Validate binding to endogenous RORC in positive vs. negative cell lines .

• Knockout/knockdown controls: Essential for confirming specificity, especially in complex samples .

How should RORC antibodies be optimized for Western blot applications?

Based on the search results, optimal Western blot conditions for RORC detection include:

Additional recommendations include using human thymus tissue as a positive control and Immunoblot Buffer Group 1 for optimal results .

What are the best practices for using RORC antibodies in immunohistochemistry?

For optimal IHC results with RORC antibodies:

• Fixation: Formalin-fixed, paraffin-embedded (FFPE) tissues are commonly used, but overfixation can mask epitopes.

• Antigen retrieval: Critical step - heat-induced epitope retrieval (HIER) using citrate buffer (pH 6.0) is commonly effective.

• Antibody concentration: Start with manufacturer recommendations (typically 1:100-1:200 dilution) and optimize.

• Detection system: HRP/DAB systems provide good sensitivity and permanence.

• Controls: Include:

  • Positive tissue controls (thymus tissue works well)

  • Negative controls (omitting primary antibody)

  • Ideally, RORC-knockout tissue (if available)

• Counterstaining: Hematoxylin provides good nuclear contrast .

How can RORC antibodies be effectively used for flow cytometry in immune cell analysis?

For optimal flow cytometry using RORC antibodies:

• Cell preparation: Single cell suspensions from relevant tissues (thymus, spleen, lymph nodes, or cell cultures).

• Fixation/permeabilization: Since RORC is primarily nuclear, use dedicated nuclear transcription factor staining buffers (not standard intracellular cytokine staining protocols).

• Antibody titration: Critical step - determine optimal concentration to maximize signal-to-noise ratio.

• Multiparameter panels: RORC often combined with:

  • Surface markers: CD3, CD4, CD8, CD161

  • Other transcription factors: T-bet, FOXP3

  • Cytokines: IL-17A, IL-22, IFN-γ

• Gating strategy: Include FMO (fluorescence minus one) controls to set proper gates.

• Troubleshooting: If signal is weak, consider longer incubation times with primary antibody (30-60 minutes) .

How does RORC expression correlate with cancer prognosis and therapeutic response?

Research has revealed significant associations between RORC expression and cancer outcomes:

• Cancer-specific expression patterns:

  • Significantly higher RORC expression in breast cancer (BRCA), colorectal adenocarcinoma (COAD), lung adenocarcinoma (LUAD), ovarian cancer (OV), and uterine cancers compared to normal tissues .

  • Expression varies by tumor stage in some cancers.

• Prognostic value:

  • Most pronounced in kidney renal clear cell carcinoma (KIRC), low-grade glioma (LGG), and mesothelioma (MESO) .

  • LGG patients with upregulated RORC show poorer prognosis.

• Correlation with immune checkpoint therapy biomarkers:

  • Positive correlation with tumor mutational burden (TMB) in liver hepatocellular carcinoma (LIHC), low-grade glioma (LGG), and esophageal carcinoma (ESCA).

  • Negative correlation with TMB in thymic epithelial tumors (THYM), thyroid carcinoma (THCA), and multiple other cancers.

  • Variable correlation with PD-L1 expression across cancer types .

This information can guide researchers in prioritizing cancer types for RORC-targeted studies and considering RORC as a potential biomarker in immunotherapy research.

What approaches should be used when designing antibodies with customized RORC specificity profiles?

Advanced antibody engineering for customized RORC specificity requires:

• Epitope mapping: Identify critical binding regions that confer specificity.

• Computational modeling: Recent research demonstrates:

  • Identification of distinct binding modes associated with specific ligands

  • Energy function optimization to achieve desired specificity profiles

  • Machine learning approaches to predict binding characteristics

• Phage display optimization:

  • Selection against multiple ligands to identify cross-reactive or highly specific clones

  • High-throughput sequencing to analyze selection outcomes

  • Computational analysis to disentangle binding modes

• Validation strategy:

  • Surface Plasmon Resonance (SPR) to quantify binding kinetics

  • Cross-reactivity panel testing

  • Functional assays in relevant cellular contexts

This combined experimental-computational approach has successfully generated antibodies with either high specificity for particular RORC epitopes or controlled cross-reactivity profiles .

How do anti-Ro52 and anti-Ro60 antibody profiles relate to connective tissue disease phenotypes?

While not directly related to RORC antibodies, the search results contain valuable information about anti-Ro antibodies in connective tissue diseases that illustrates important principles of antibody profiling in autoimmune research:

Different anti-Ro antibody profiles show distinct clinical associations:

These distinct profiles demonstrate how antibody patterns can correlate with specific clinical phenotypes, a principle that applies to research with RORC antibodies in characterizing patient subsets .

What are the key technical considerations when developing monoclonal antibodies against RORC?

Based on the detailed methodologies described in search result , key considerations include:

• Immunogen design:

  • Recombinant human RORC protein fragments (e.g., Met1-Gln121) expressed in E. coli systems

  • Careful quality control to ensure proper folding and epitope exposure

• Screening methodology:

  • Multiple rounds of sub-clone affinity screening

  • ELISA with pre-coated recombinant RORC protein

  • Verification of binding to both recombinant protein and endogenous RORC

• Antibody engineering:

  • For chimeric antibodies: successful cloning of both heavy chain Fd (~800 bp) and light chain L (~750 bp)

  • Verification by DNA sequencing to confirm sequence integrity

  • Prokaryotic expression systems for consistent production

• Validation requirements:

  • Binding affinity calculations from Biacore X100 SPR analysis

  • Specificity testing against related proteins

  • Functional assays to demonstrate biological activity

  • Cell-based assays with appropriate positive and negative controls

• Applications testing:

  • Western blot optimization (reducing conditions, appropriate buffers)

  • Flow cytometry protocols specific to nuclear proteins

  • Immunofluorescence microscopy to confirm cellular localization

What storage and handling conditions ensure optimal RORC antibody performance?

Based on manufacturer recommendations in the search results:

Critical handling guidelines:

• Use a manual defrost freezer and avoid repeated freeze-thaw cycles

• Store in small aliquots to minimize freeze-thaw damage

• Maintain sterile conditions after reconstitution

• Some formulations contain preservatives (0.09% sodium azide) and stabilizers (2% sucrose)

• For liquid antibodies, avoid exposure to light and maintain recommended temperature

How can inconsistent results with RORC antibodies in flow cytometry be resolved?

Common issues and solutions:

• Poor signal strength:

  • Increase antibody concentration (carefully titrate)

  • Optimize fixation/permeabilization protocols specifically for nuclear factors

  • Extend incubation time (30-60 minutes)

  • Try different clones - some work better for flow than others

• High background:

  • Improve blocking (use 2-5% serum from secondary antibody species)

  • Reduce antibody concentration

  • Include proper FcR blocking

  • Filter buffers to remove precipitates

• No distinct positive population:

  • Verify expression in your cell type (use positive control cells)

  • Ensure cells are properly stimulated if needed

  • Test alternative clones

  • Verify antibody functionality by Western blot

• Weak separation between positive and negative cells:

  • Use indirect staining with secondary antibodies for signal amplification

  • Try alternative fluorochromes with higher brightness

  • Optimize voltage settings on flow cytometer

  • Ensure appropriate fixation/permeabilization buffer system

What are the most reliable positive controls for validating RORC antibody specificity?

Based on the search results, recommended positive controls include:

• Tissue samples:

  • Human thymus tissue (consistently used in validation studies)

  • Mouse thymus tissue (for cross-reactive antibodies)

• Cell lines:

  • A2780 cells (human ovarian cancer cell line, ROR1-positive)

  • Th17-polarized primary T cells (high RORC expression)

• Recombinant proteins:

  • E. coli-derived recombinant human RORC (Met1-Gln121)

  • Fully-length recombinant RORC protein for Western blot standards

• Negative controls:

  • Iose386 cells (ROR1-negative control)

  • Non-immune isotype-matched antibodies

  • ROR1-knockout cell lines (where available)

These controls should be run in parallel with experimental samples to confirm specificity and establish appropriate gating or detection thresholds .

How can RORC antibodies contribute to cancer immunotherapy research?

RORC antibodies enable several key research approaches in cancer immunotherapy:

• Biomarker development:

  • Patient stratification based on RORC expression profiles

  • Correlating RORC with established biomarkers (TMB, PD-L1, MSI)

  • Monitoring changes in RORC+ immune populations during treatment

• Therapeutic antibody development:

  • Template for developing therapeutic anti-RORC antibodies

  • Research shows RORC-targeting antibodies can inhibit tumor cell proliferation and migration and induce apoptosis in ROR1-positive cancer cells

• Mechanistic studies:

  • Elucidating the relationship between RORC expression and mismatch repair (MMR) gene expression (MLH1, MSH2, MSH6, PMS2, EPCAM)

  • Understanding correlation with checkpoint inhibitor response

  • Analyzing RORC+ immune cell infiltration in tumors

• Predictive medicine:

  • Research indicates RORC correlates differentially with TMB and PD-L1 across cancer types

  • Distinct expression patterns in responders vs. non-responders to checkpoint therapy

These applications demonstrate the diverse utility of RORC antibodies in advancing cancer immunotherapy research.

What are the emerging applications of RORC antibodies in autoimmune disease research?

RORC antibodies are increasingly valuable in autoimmune disease research:

• Disease subtyping:

  • Characterizing RORC expression across different autoimmune conditions

  • Determining Th17 cell involvement in specific disease subtypes

  • Correlating with clinical phenotypes similar to anti-Ro antibody profiles in connective tissue diseases

• Therapeutic target validation:

  • Assessing RORC as a potential therapeutic target in autoimmunity

  • Monitoring changes in RORC+ cell populations during treatment

  • Evaluating effects of experimental RORC inhibitors

• Pathogenesis studies:

  • Investigating the role of RORC in interstitial lung disease (ILD) development

  • Understanding pulmonary arterial hypertension mechanisms in autoimmunity

  • Exploring the relationship between RORC and other autoantibodies (e.g., anti-Jo1)

• Biomarker development:

  • Using RORC antibodies to identify high-risk patients

  • Monitoring disease progression through RORC expression changes

  • Predicting treatment response in autoimmune conditions

These applications highlight the versatility of RORC antibodies in advancing our understanding of autoimmune disease mechanisms and potential therapeutic approaches.