rergl Antibody

What is RERGL and what biological functions has it been associated with?

RERGL (RERG/RAS-like protein) is a protein-coding gene product with predicted G protein activity and GTP binding capabilities. It belongs to the RAS superfamily and is thought to possess intrinsic GTPase activity . According to UniProtKB data, RERGL binds GDP/GTP molecules and may function similarly to other small GTPases that serve as molecular switches in signal transduction pathways .

The protein has been identified in several tissue types, with notable expression in oocytes, granulosa cells, and smooth muscle. Interestingly, RERGL has been used as a marker for both smooth muscle and pericytes in certain studies . While its exact biological role remains under investigation, its structural similarity to RAS-family proteins suggests possible involvement in cellular growth regulation, differentiation, or vesicular trafficking.

What are the commonly available RERGL antibodies and their validated applications?

Several commercial RERGL antibodies are available for research applications:

The most extensively validated applications appear to be immunohistochemistry (IHC) and western blotting (WB), with standardized protocols available for these techniques. The Atlas Antibodies product (HPA041740) has been extensively characterized through the Human Protein Atlas project, with validation data available through their online portal .

What is the expression pattern of RERGL across human tissues?

Based on antibody-based proteomics studies, RERGL shows distinct expression patterns across different tissues:

• Reproductive tissues: Strong expression has been documented in oocytes, often accompanied by weaker staining in granulosa cells

• Smooth muscle: High expression levels, making it useful as a marker for smooth muscle and pericytes

• Endothelial cells: Some studies report RERGL expression in certain endothelial populations

In single-cell RNA sequencing studies of ovarian tissue, RERGL expression was confirmed at high levels in oocytes, consistent with the immunohistochemistry findings . This expression pattern appears consistent between individuals of reproductive age and postmenopausal women .

What are the recommended protocols for RERGL antibody validation in experimental settings?

Proper validation of RERGL antibodies should follow these methodological steps:

• Sequence specificity assessment: Verify that the antibody targets a unique sequence with minimal homology to other proteins. For instance, the immunogen sequence "CKRAVESAVFLVGNKRDLCHVREVGWEEGQKLALENRCQFCELSAAEQSLEVEMMFIRIIKDILINFKLKEKRRPSGSKSMA" used in some commercial antibodies shows highest ortholog sequence identity of 83% to mouse and 79% to rat RERGL .

• Expression correlation: Compare antibody staining patterns with transcriptomic data. In the Human Protein Atlas project, antibody staining patterns for RERGL were validated against RNAseq expression data (using cutoff of nTPM > 0.5) .

• Single-cell validation: Where possible, verify antibody specificity using single-cell techniques that combine protein and transcript analysis. In one study, researchers integrated immunohistochemistry results with single-cell RNA sequencing data to confirm RERGL expression in oocytes .

• Technical controls: Include appropriate positive and negative controls in each experiment. For RERGL, ovarian tissue sections containing follicles can serve as positive controls due to the consistent expression in oocytes .

• Orthogonal validation: Use multiple antibodies targeting different epitopes of RERGL to confirm findings.

What are the optimal conditions for using RERGL antibodies in IHC applications?

For optimal immunohistochemistry results with RERGL antibodies:

• Antibody dilution: For Atlas Antibodies HPA041740, a dilution range of 1:200-1:500 is recommended for IHC applications .

• Tissue preparation: Formalin-fixed, paraffin-embedded (FFPE) tissue sections have been successfully used in published studies. The Human Protein Atlas protocol includes antigen retrieval steps before antibody incubation .

• Detection systems: Both chromogenic (DAB-based) and fluorescent secondary antibody systems have been successfully employed. For chromogenic detection, slides can be scanned using systems such as the Aperio AT2 (LeicaAperio) with 40× objective magnification .

• Scoring system: When evaluating RERGL expression, a standardized scoring system (0-3 scale) can be used, where 0 = not detected, 1 = low, 2 = medium, and 3 = high staining intensity .

• Cell-type identification: Manual annotation of specific cell types (e.g., oocytes versus granulosa cells) is recommended for accurate assessment of expression patterns .

How can researchers troubleshoot non-specific staining or weak signals when using RERGL antibodies?

When encountering issues with RERGL antibody staining:

• Non-specific background: Increase blocking time/concentration and ensure adequate washing between steps. Consider using alternative blocking reagents if the standard protocol produces high background.

• Weak or absent signal:

  • Verify tissue fixation quality and consider extended antigen retrieval

  • Reduce antibody dilution (e.g., try 1:100 instead of 1:200)

  • Extend primary antibody incubation time or perform at 4°C overnight

  • Ensure the tissue sample contains cell types known to express RERGL (oocytes or smooth muscle can serve as internal positive controls)

  • Consider signal amplification systems for low-abundance targets

• Inconsistent results:

  • Use freshly prepared reagents

  • Standardize tissue processing and fixation times

  • Verify antibody storage conditions (most RERGL antibodies should be stored at -20°C)

  • Include known positive control tissues in each experiment

• Cross-reactivity assessment: RERGL antibodies with high sequence identity to orthologs (e.g., 83% for mouse) may exhibit cross-reactivity. When working with mouse or rat tissues, additional validation steps are recommended to confirm specificity .

How can single-cell approaches enhance RERGL protein characterization beyond conventional antibody techniques?

Single-cell methodologies offer powerful approaches for detailed RERGL characterization:

• Integrated single-cell analysis: Combining antibody-based detection with transcriptomics can resolve discrepancies between protein and mRNA expression. In ovarian tissue analysis, researchers integrated IHC findings with scRNA-seq data to validate RERGL expression in specific cell populations .

• Spatial transcriptomics: This approach preserves tissue architecture while providing transcript-level data. For proteins like RERGL with distinct spatial expression patterns (e.g., in follicular structures), spatial transcriptomics can complement antibody staining to create comprehensive expression maps .

• Cell population identification: In complex tissues, single-cell clustering can identify rare cell populations expressing RERGL. In one study, ovarian tissue was separated into six main cell types: oocytes (0.1%), granulosa cells (1.1%), immune cells (0.3%), endothelial cells (5.1%), smooth muscle cells (9.6%), and stromal cells (83.7%) .

• Cross-validation approach: When scRNA-seq and antibody staining show discrepancies (as was observed for some proteins in the referenced study), multiple validation approaches become essential. For RERGL, seven proteins showed concordant expression at both mRNA and protein levels, confirming antibody specificity .

What are the considerations for using RERGL antibodies in studies of reproductive biology and development?

RERGL has shown particular relevance in reproductive biology research:

How might RERGL antibodies be utilized in disease-association studies, particularly in cancer research?

Given RERGL's relationship to the RAS family, which has established roles in oncogenic signaling, several research approaches could be valuable:

• Tissue microarray analysis: RERGL antibodies can be used to screen tissue microarrays containing multiple cancer types. The Human Protein Atlas methodology includes testing antibodies on arrays containing 20 of the most common cancer types .

• Expression correlation studies: Researchers might investigate whether RERGL expression correlates with specific cancer phenotypes or patient outcomes. GeneCards indicates RERGL may be associated with prostate cancer , suggesting a potential research direction.

• Pathway analysis: Given RERGL's predicted GTPase activity, antibodies could be used in conjunction with other pathway components to elucidate its role in signaling networks potentially dysregulated in cancer.

• Specific research considerations:

  • Use multiple antibody dilutions for optimal staining across varied expression levels

  • Include normal adjacent tissue controls for comparison

  • Consider phospho-specific antibodies if post-translational modifications are relevant

  • Correlate protein expression with genomic alterations in the same samples

How do RERGL antibodies compare with other methodologies for studying small GTPases?

When investigating RERGL function within the broader context of small GTPases:

• Activity-specific detection: Unlike conventional antibodies that detect total protein levels, specialized approaches can distinguish active (GTP-bound) from inactive (GDP-bound) forms of small GTPases. Consider complementing standard RERGL antibodies with:

  • GTP-binding assays

  • Activity-specific pull-down methods

  • FRET-based biosensors for live-cell imaging

• Comparative analysis: When studying RERGL alongside other GTPases, standardize detection methods to enable accurate comparisons. The GTP-binding domain and effector interaction sites might differ from classical RAS proteins despite structural similarities.

• Functional correlation: Combine antibody detection with functional readouts such as:

  • Downstream signaling activation

  • Cellular phenotype analysis

  • Protein-protein interaction studies

What approaches can integrate RERGL antibody data with broader -omics studies?

Modern multi-omics approaches can contextualize RERGL antibody-derived data:

• Proteogenomic integration: Combine RERGL antibody-based detection with genomic analysis to identify potential regulatory mechanisms. In one study, researchers integrated transcriptomics with antibody-based proteomics to explore missing proteins in the human proteome .

• Network analysis: Place RERGL within protein interaction networks to predict functional associations. This might reveal unexpected relationships with other signaling pathways or cellular processes.

• Methodological framework:

  • Start with antibody-based detection to establish expression patterns

  • Correlate with transcriptomic data from matched samples

  • Expand to interaction proteomics to identify binding partners

  • Perform functional assays based on predictions from -omics data integration

• Data visualization: Utilize advanced visualization tools to represent multi-dimensional data incorporating RERGL expression, potential interactions, and functional outcomes.

What quality control metrics should be considered when evaluating RERGL antibodies for advanced research applications?

For researchers evaluating or generating RERGL antibodies for specialized applications:

• Epitope mapping: Determine the precise epitope recognized by the antibody. For example, some commercial antibodies target a C-terminal sequence of RERGL protein .

• Cross-reactivity profiling: Comprehensive assessment against related proteins, especially other small GTPases. Some commercial antibodies are tested against protein arrays containing 364 human recombinant protein fragments .

• Reproducibility assessment: Compare lot-to-lot variations through standardized assays. The Human Protein Atlas project uses a standardized process to ensure rigorous quality levels .

• Application-specific validation: Validate each antibody specifically for the intended application rather than assuming cross-application reliability. For example, an antibody validated for IHC might not perform equivalently in western blotting or immunoprecipitation.

• Signal-to-noise optimization: Determine optimal conditions for maximizing specific signal while minimizing background. For western blotting applications, the recommended concentration range for one RERGL antibody is 0.04-0.4 μg/mL .

How might RERGL antibodies be utilized in studying the relationship between protein expression and transcriptomic data?

The discrepancy between protein and mRNA levels represents a significant challenge in molecular biology research:

• Integrated analysis approach: As demonstrated in the Human Protein Atlas project, RERGL antibody staining can be correlated with RNAseq data to validate expression patterns. In ovarian tissue, seven proteins (including RERGL) showed concordant expression at both mRNA and protein levels .

• Discrepancy investigation: For some genes studied alongside RERGL, discrepancies between protein and mRNA levels were observed. These may result from:

  • Post-transcriptional regulation

  • Protein stability differences

  • Technical limitations in detection sensitivity

  • Mixed cell populations in bulk analysis

• Methodological considerations:

  • Use standardized cutoff values (e.g., nTPM > 0.5 used in Human Protein Atlas)

  • Consider cell-type specific expression patterns that may be diluted in bulk analysis

  • Implement appropriate statistical methods for correlation analysis

What considerations apply when using RERGL antibodies in B-cell repertoire and autoimmunity research?

While not directly investigated in the provided search results, researchers working at the intersection of antibody repertoire analysis and autoimmunity should consider:

• Cross-reactivity potential: Monitor whether RERGL might be recognized by autoantibodies in certain conditions. The B-cell receptor repertoire in autoimmune conditions shows skewed usage of certain variable gene families .

• Positive control selection: When validating RERGL antibodies in samples from autoimmune patients, select appropriate positive controls, as B-cell subpopulation frequencies may differ significantly from healthy controls .

• Methodological integration: Consider combining RERGL antibody detection with B-cell receptor sequencing approaches to investigate potential relationships between RERGL expression and immune dysregulation.

How can computational approaches enhance RERGL antibody specificity and application?

Advanced computational methods offer potential for improving antibody performance:

• Biophysics-informed modeling: As demonstrated in antibody design studies, computational models can identify distinct binding modes associated with specific ligands. Similar approaches might optimize RERGL antibody specificity .

• Epitope optimization: Computational analysis of RERGL protein structure could guide selection of highly specific epitopes for antibody generation, potentially reducing cross-reactivity with related GTPases.

• Implementation framework:

  • Start with structural analysis of RERGL protein

  • Identify regions with maximal divergence from related proteins

  • Use machine learning approaches to predict optimal antibody-epitope interactions

  • Validate computationally designed antibodies experimentally

These computational approaches could be particularly valuable for generating antibodies with customized specificity profiles for RERGL detection in complex experimental settings.