rgn Antibody

Basic Research Questions

• What is RGN and what is its significance in biomedical research?

Regucalcin (RGN) is a calcium-binding protein that functions as a potent inhibitory protein in calcium signaling pathways and is expressed in various tissues throughout the body. Research indicates that RGN plays important roles in cellular functions related to calcium homeostasis, signal transduction, and cell proliferation .

The significance of RGN in biomedical research has grown substantially as studies have demonstrated its potential role as a prognostic biomarker in diseases such as lung squamous cell carcinoma (LUSC). RGN expression has been found to be significantly downregulated in tumor tissues compared to normal tissues, and this altered expression is closely related to clinical factors and patient prognosis .

When conducting research with RGN antibodies, ensure you select antibodies validated for your specific application (Western blot, IHC, ICC-IF) and tissue of interest, as expression patterns vary significantly across different cellular contexts.

• What types of RGN antibodies are available for research applications?

Several types of RGN antibodies are available for research purposes:

Polyclonal antibodies against human RGN, such as those offered by commercial suppliers, are commonly used and validated for applications like immunohistochemistry, immunocytochemistry-immunofluorescence, and Western blotting .

When selecting an RGN antibody, consider its validation profile—antibodies validated using genetic approaches (testing on knockout models) generally demonstrate more reliable performance than those validated using only orthogonal approaches, particularly for immunofluorescence applications .

• What are the recommended methods for validating RGN antibody specificity?

Validation of RGN antibody specificity is crucial for ensuring experimental reproducibility and reliable results. Based on current best practices in antibody validation, the following methodological approaches are recommended:

Genetic validation approaches (gold standard):

• Testing on RGN knockout/knockdown cells or tissues

• Comparing antibody signals between parental and RGN-deficient samples

• Validation in multiple cell lines to ensure consistency

Orthogonal validation approaches:

• Correlation with RGN mRNA expression levels

• Comparison with other validated RGN antibodies

• Protein mass spectrometry confirmation

Research has shown that while orthogonal strategies may be somewhat suitable for Western blot applications, genetic strategies generate far more robust characterization data for immunofluorescence. In a systematic study, 80% of antibodies validated by manufacturers using genetic strategies for immunofluorescence were confirmed when tested against knockout cells, compared to only 38% of antibodies validated using orthogonal approaches .

• How should researchers optimize RGN antibody usage in common laboratory procedures?

Optimizing RGN antibody usage requires methodical testing of conditions for each application:

For Western Blotting:

• Recommended dilution range: Begin testing at 1:500-1:2000

• Sample preparation: Include phosphatase inhibitors as RGN function is linked to calcium signaling

• Blocking: 5% non-fat milk or BSA (test both as results may vary)

• Incubation time: Overnight at 4°C often yields best results for primary antibody

• Controls: Include positive (tissues known to express RGN) and negative controls

For Immunohistochemistry:

• Antigen retrieval: Test both heat-induced epitope retrieval methods (citrate buffer pH 6.0 and Tris-EDTA pH 9.0)

• Dilution: Start at manufacturer's recommended range, typically 1:100-1:500

• Incubation: 1-2 hours at room temperature or overnight at 4°C

• Detection system: Select based on host species of primary antibody

• Counterstaining: Hematoxylin for nuclear context

For Immunofluorescence:

• Fixation: Test both 4% paraformaldehyde and methanol fixation

• Permeabilization: 0.1-0.3% Triton X-100 in PBS

• Blocking: 5-10% normal serum from species of secondary antibody

• Primary antibody incubation: Overnight at 4°C

• Secondary antibody: Species-specific fluorophore-conjugated antibodies

Always validate the protocol for your specific experimental conditions by including appropriate positive and negative controls .

• What are the technical considerations for storage and handling of RGN antibodies?

Proper storage and handling of RGN antibodies is critical for maintaining their functionality:

Storage Conditions:

• Store antibodies according to manufacturer recommendations, typically at -20°C or -80°C for long-term storage

• Avoid repeated freeze-thaw cycles (aliquot upon receipt)

• For working solutions, store at 4°C with preservatives (e.g., 0.02% sodium azide)

Handling Best Practices:

• Centrifuge briefly before opening vials to collect liquid at the bottom

• Use sterile techniques when handling and preparing dilutions

• Prepare working solutions in clean vessels with appropriate diluents

• Document lot numbers and maintain validation data for reproducibility

Stability Considerations:

• Monitor performance over time with consistent positive controls

• Consider preparing new working solutions if signal quality deteriorates

• For critical experiments, validate antibody performance before use

Following standardized processes ensures the most rigorous levels of quality and reproducibility in RGN antibody-based experiments .

Advanced Research Questions

• How is RGN expression associated with the tumor immune microenvironment in cancer research?

RGN expression has been found to have significant associations with the tumor immune microenvironment (TIME), particularly in lung squamous cell carcinoma (LUSC). Research utilizing ESTIMATE and CIBERSORT algorithms has revealed complex relationships between RGN expression and immune cell infiltration:

Key findings on RGN and immune infiltration:

• Differential immune infiltration based on RGN expression levels:

  • Significant differences in infiltration of plasma cells, T cells CD4 memory resting, macrophages M0, macrophages M1, mast cells (both resting and activated), and neutrophils between high and low RGN expression groups

• Correlation analysis using TIMER database demonstrated:

  • Positive correlation between RGN expression and immune infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells

  • The infiltration of dendritic cells was associated with copy number alterations for RGN in LUSC

• Methodological approach to study RGN-immune cell relationships:

  • Construction of heatmaps containing ratios of 22 types of tumor immune infiltrating cells

  • Development of correlation analyses among immune cell types

  • Utilization of vioplots to display expression levels of different immune cells in RGN-high versus RGN-low profiles

• Implications for immunotherapy:

  • High RGN expression group showed higher TIDE score and dysfunction score

  • Lower MSI score in high RGN expression group

  • These findings suggest potentially lower efficacy of immunotherapy in patients with high RGN expression

This research demonstrates that RGN could serve as a promising biomarker for assessing immunotherapy efficacy and prognosis, providing a foundation for future investigations into therapeutic strategies targeting RGN-mediated immune responses .

• What mechanisms underlie RGN's role in immune cell regulation and tumor biology?

Research into RGN's role in immune cell regulation and tumor biology has revealed several potential mechanisms:

Anti-inflammatory Effects:

• Studies have demonstrated that RGN can exert anti-inflammatory effects on adipocytes cocultured with macrophages

• High RGN expression groups often showed lower levels of macrophage infiltrations, including macrophages M0 and M1

Proposed Mechanistic Pathway:

• Under stimulation by IFN-γ or other factors, macrophage M0 polarizes into macrophage M1

• M1 macrophages produce pro-inflammatory factors (IL-1, IL-6, TNF-α)

• These inflammatory mediators contribute to tumorigenesis and cancer progression

• RGN may regulate the tumor immune microenvironment by influencing and/or interacting with macrophages

Molecular Interactions:

• RGN expression correlates with markers of various immune cells:

  • B cell markers (CD19, CD79A)

  • CD8+ T cell markers (CD8A, CD8B)

  • CD4+ T cell markers (CD4)

  • M1 macrophage markers (NOS2, PTGS2, though not IRF5)

  • M2 macrophage markers (VSIG4, MS4A4A)

  • Neutrophil markers (CEACAM8, ITGAM, CCR7)

  • Dendritic cell markers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C, NRP1, ITGAX)

Growth Suppressive Effects:

• Previous studies have indicated that RGN expression plays a role in regulating various oncogenes and tumor suppressors

• RGN exerts growth suppressive effects, potentially explaining its downregulation in tumor tissues

Understanding these mechanisms provides opportunities for developing novel therapeutic approaches targeting RGN-mediated pathways in cancer and inflammatory diseases.

• How can computational approaches enhance RGN antibody design and optimization?

Computational approaches have revolutionized antibody design and optimization, including those targeting proteins like RGN. Several sophisticated methods can be employed:

Bio-inspired Antibody Language Models:

• Models like Bio-inspired Antibody Language Model (BALM) can be trained on large datasets of antibody sequences

• These models capture both unique and conserved properties specific to antibodies

• They demonstrate exceptional performance in antigen-binding prediction tasks and can evaluate binding affinity

Structure Prediction and Modeling:

• Methods like BALMFold, derived from language models, can predict full atomic antibody structures from individual sequences

• These outperform established methods like AlphaFold2, IgFold, and ESMFold in antibody benchmarks

• Accurate structure prediction helps in understanding the RGN-antibody interaction interface

Computational Approaches for RGN Antibody Design:

• Homology Modeling with Knowledge-Based Methods:

  • RosettaAntibody combines homology and ab initio modeling to build preliminary models

  • Selects different templates for frameworks and non-H3 CDRs

  • Models the H3 loop and optimizes heavy chain/light chain interface using ab initio methods

• Diffusion-Based Generative Models:

  • Advanced diffusion probabilistic models combined with equivariant neural networks

  • Capable of sequence-structure co-design and sequence design for given backbone structures

  • Can optimize existing antibodies against targets like RGN

• Affinity Maturation Simulation:

  • When antibody-antigen complex structures are available, in silico mutations can enhance binding affinities

  • Initial rigid backbone approach with discrete side-chain rotamer search

  • Re-evaluation using more accurate but computationally expensive models (Poisson-Boltzmann or Generalized Born continuum electrostatics)

These computational approaches significantly reduce the need for extensive experimental screening, accelerate development timelines, and enhance the specificity and affinity of antibodies targeting RGN .

• What strategies exist for engineering RGN antibodies with enhanced specificity and functionality?

Engineering RGN antibodies with enhanced specificity and functionality involves several advanced strategies:

Antibody Fragment Engineering:

• Creating smaller antibody fragments (Fab, scFv, or nanobodies) that maintain RGN binding but with improved tissue penetration

• Single-domain antibodies (nanobodies) derived from variable regions of heavy chain-only antibodies offer unique advantages for RGN detection in compact cellular compartments

Framework Optimization through Humanization:

• CDR Grafting with Framework Selection:

  • Selection of human germline templates based on multiple criteria:

    • Sequence similarity to original antibody

    • Identical canonical structures of CDRs

    • High sequence identity with closely related CDR canonical structure

• Combined Sequence and Structural Criteria:

  • Use of well-characterized templates like bevacizumab or human myeloma antibodies (NEW for VH, REI for VL)

  • Testing multiple VH-VL pairs to identify optimal combinations maintaining RGN binding affinity

Fc Engineering for Optimal Function:

• Modifying the Fc region to enhance stability, half-life, or effector functions

• Engineering Fc-Fc interactions through mutations (e.g., T437R and K248E) that facilitate antibody hexamerization when bound to target

• Isotype selection (IgG2 vs IgG1) can significantly impact functionality

Recombinant Expression System Optimization:

• Using dual promoter plasmids for efficient co-expression of heavy and light chains

• Optimizing expression vectors and host cell backgrounds for robust production

• Replacing endogenous heavy chain constant regions to confer detection with alternate secondary antibodies

These engineering approaches can yield RGN antibodies with improved affinity, specificity, stability, and functional properties for research and potential therapeutic applications .

• How can researchers address challenges in detecting low-abundance RGN in complex biological samples?

Detecting low-abundance RGN in complex biological samples presents significant challenges that can be addressed through several methodological approaches:

Sample Enrichment Techniques:

• Immunoprecipitation using validated anti-RGN antibodies prior to analysis

• Subcellular fractionation to concentrate RGN from relevant cellular compartments

• Proximity ligation assays (PLA) to amplify detection of low-abundance RGN protein interactions

Signal Amplification Methods:

• Tyramide signal amplification (TSA) for immunohistochemistry and immunofluorescence

• Polymeric detection systems with multiple secondary antibody binding sites

• Quantum dot-conjugated secondary antibodies for enhanced signal and stability

Optimized Protocol Parameters:

• Western Blot Enhancement:

  • Extended primary antibody incubation (overnight at 4°C)

  • Higher protein loading (50-100 μg per lane)

  • More sensitive detection substrates (femto-level chemiluminescence)

  • PVDF membranes with higher protein binding capacity

• Immunohistochemistry Refinement:

  • Multi-step antigen retrieval combining heat and enzymatic methods

  • Signal enhancement using avidin-biotin amplification systems

  • Extended development time with chromogenic substrates

  • Automated staining systems for consistent results

Validation Strategy for Low-Abundance Detection:

• Parallel analysis with orthogonal techniques (mass spectrometry, RT-qPCR)

• Use of positive control samples with known RGN expression levels

• Comparison of multiple anti-RGN antibodies targeting different epitopes

• Genetic validation using RGN-overexpressing systems as reference points

Novel Detection Platforms:

• Single-molecule detection methods

• Microfluidic-based immunoassays with lower detection limits

• Digital ELISA platforms with single-molecule resolution

By combining these approaches, researchers can overcome the challenges associated with detecting low-abundance RGN in complex biological samples and generate more reliable and reproducible results .