RXFP1 Antibody, HRP conjugated

What is RXFP1 and what are its primary biological functions?

RXFP1, also known as relaxin family peptide receptor 1 or LGR7, belongs to the leucine-rich repeat subfamily of G protein-coupled seven-transmembrane receptor superfamily. It functions as the primary receptor for the protein hormone relaxin. Upon binding to relaxin, RXFP1 activates G protein signaling pathways, particularly cAMP-mediated signaling .

RXFP1 plays crucial physiological roles in:

• Reproductive system regulation, including sperm motility, pregnancy, and labor

• Cardiovascular function, including reducing vascular resistance and pulmonary pressure

• Anti-fibrotic processes in multiple organ systems

• Enhancing cardiac output and improving perfusion in vital organs (kidneys, lungs, liver)

The receptor's distribution across multiple tissue types reflects its diverse biological functions, making it an important target for both basic research and therapeutic development.

What detection methods can be effectively employed with RXFP1 Antibody, HRP conjugated?

RXFP1 Antibody, HRP conjugated is specifically designed for enzyme-linked immunosorbent assay (ELISA) applications . The horseradish peroxidase conjugation enables direct detection without secondary antibodies, streamlining experimental workflows.

The primary detection methods include:

• ELISA: The most validated application, particularly suitable for quantitative detection of RXFP1 in various sample types including serum, plasma, cell culture supernatant, and tissue lysates .

• Immunohistochemistry: While the specific RXFP1 Antibody (CSB-PA875715LA01HU) has been validated for immunofluorescence applications, the HRP-conjugated version can be adapted for colorimetric detection in tissue sections .

The antibody demonstrates specific binding to human RXFP1 without obvious cross-reaction with other analogues, making it suitable for selective detection in complex biological samples .

What is the detection range and sensitivity of RXFP1 ELISA systems using HRP-conjugated antibodies?

RXFP1 ELISA systems utilizing HRP-conjugated antibodies offer the following performance characteristics:

• Detection range: 15.625-1000 pg/ml

• Sensitivity: 9.375 pg/ml

• Detection wavelength: OD450

• Reaction duration: 4 hours

This sensitivity range is appropriate for detecting physiologically relevant RXFP1 levels in most biological samples. The moderate reaction time balances throughput requirements with sensitivity considerations, making it suitable for both research and potential clinical applications.

What sample types can be analyzed using RXFP1 Antibody, HRP conjugated?

RXFP1 Antibody, HRP conjugated can be used to analyze multiple sample types, including:

• Serum

• Plasma

• Cell culture supernatant

• Cell or tissue lysates

• Other liquid biological samples

When working with tissue samples, proper sample preparation is essential. For example, in liver fibrosis research, investigators have successfully analyzed RXFP1 expression in liver biopsy specimens from patients with non-alcoholic steatohepatitis (NASH) and autoimmune hepatitis (AIH) . The detection of RXFP1 in such samples correlates with fibrotic scarring, demonstrating the utility of RXFP1 detection in pathological tissue specimens.

What is the recommended protocol for sandwich ELISA using RXFP1 Antibody, HRP conjugated?

The double antibody-sandwich ELISA method for RXFP1 detection follows this general protocol:

• Plate preparation: Use microplates pre-coated with anti-RXFP1 antibody.

• Sample addition: Add standards and properly diluted samples to appropriate wells.

• Incubation: Allow binding of RXFP1 to the pre-coated antibody.

• Washing: Remove unbound components.

• Detection antibody addition: Add biotinylated detection antibody, which binds to captured RXFP1.

• HRP-conjugate addition: Add HRP-Streptavidin Conjugate (SABC) or directly use RXFP1 Antibody, HRP conjugated.

• Washing: Remove unbound components.

• Substrate reaction: Add TMB substrate solution, which is catalyzed by HRP to produce a blue color product.

• Reaction termination: Add stop solution to change the color to yellow.

• Measurement: Read optical density at 450nm.

• Analysis: Calculate RXFP1 concentration using a standard curve .

The entire protocol requires approximately 4 hours to complete and provides quantitative data on RXFP1 concentrations in experimental samples.

How should researchers optimize sample preparation for RXFP1 detection in different tissue types?

Sample preparation strategies should be tailored to the specific tissue type and research question:

For liver tissue (where RXFP1 is implicated in fibrosis):

• Process biopsy samples using formalin fixation and paraffin embedding for in situ hybridization or immunohistochemistry

• For protein extraction, use buffer systems containing protease inhibitors to prevent degradation

• Consider both membrane and cytosolic fractions, as RXFP1 may redistribute during disease progression

For cell culture systems:

• When using cell lines like HEK293T-RXFP1 or LX-2 (hepatic stellate cells), consistent culture conditions are critical as RXFP1 expression can vary with cell density and passage number

• Cell lysis buffers should preserve membrane protein integrity

• Consider using BacMam systems for controlled RXFP1 expression in recombinant systems

For all sample types:

• Include appropriate controls to account for tissue-specific matrix effects

• Standardize protein quantification methods prior to analysis

• Consider enrichment strategies if RXFP1 concentrations fall near the lower detection limit

What controls are essential when establishing RXFP1 detection assays?

A robust control strategy is essential for reliable RXFP1 quantification:

• Analytical controls:

  • Complete standard curve (15.625-1000 pg/ml) using recombinant RXFP1

  • Blank controls to establish background signal

  • Internal quality control samples with known RXFP1 concentrations

• Biological controls:

  • Positive tissue controls known to express RXFP1 (e.g., fibrotic liver tissue shows upregulated RXFP1)

  • Negative controls with minimal RXFP1 expression

  • Competitive inhibition controls using recombinant RXFP1 protein

• Technical validation:

  • Replicate samples to assess precision

  • Dilution linearity tests to confirm assay dynamic range

  • Specificity evaluation against similar receptors (e.g., RXFP2)

For research involving liver fibrosis, controls should include both healthy liver tissue and samples with varying degrees of fibrosis to establish the relationship between disease progression and RXFP1 expression .

How can researchers correlate RXFP1 protein expression with functional outcomes?

To establish the functional significance of RXFP1 expression:

• cAMP signaling correlation: RXFP1 activation by relaxin (H2-RLX) increases intracellular cAMP. Researchers can correlate RXFP1 protein levels (detected by HRP-conjugated antibody) with cAMP responses measured using TR-FRET cAMP immunoassays .

• Gene expression analysis: Compare RXFP1 protein levels with changes in expression of fibrosis-related genes or other downstream targets.

• Intervention studies: Use RXFP1 agonists (like ML290) or siRNA knockdown approaches to modulate RXFP1 activity and correlate with disease phenotypes .

• Cell migration/proliferation assays: Correlate RXFP1 expression with cellular behaviors relevant to disease processes.

In prostate cancer research, for example, downregulation of RXFP1 expression using siRNA has been shown to reduce cancer growth and metastasis, establishing a functional link between receptor expression and disease progression .

How can RXFP1 Antibody, HRP conjugated be utilized to investigate liver fibrosis mechanisms?

RXFP1 has emerged as a potential therapeutic target for liver fibrosis treatment. Researchers can employ HRP-conjugated RXFP1 antibodies to:

• Quantify RXFP1 upregulation in fibrotic liver tissues: Studies have demonstrated increased RXFP1 expression in human fibrotic liver tissues, particularly in areas of fibrotic scarring identified by picrosirius red-stained collagen .

• Characterize cellular distribution: RXFP1 expression localizes to morphologically distinctive fibroblast-like cells within the fibrotic scar, which also express α-SMA (an activated hepatic stellate cell marker) .

• Correlate with disease progression: Research has shown a positive association between liver disease stage and RXFP1 transcript expression levels .

• Evaluate therapeutic interventions: Quantify changes in RXFP1 expression following treatment with potential anti-fibrotic agents.

• Compare RXFP1 across etiologies: Analyze RXFP1 expression in different liver disease etiologies, such as non-alcoholic steatohepatitis (NASH) and autoimmune hepatitis (AIH) .

These approaches can help establish RXFP1 as both a biomarker for liver fibrosis progression and a potential therapeutic target.

What methodological approaches can differentiate between membrane-bound and internalized RXFP1?

As a G protein-coupled receptor, RXFP1 undergoes internalization upon activation. To distinguish between membrane-bound and internalized receptor pools:

• Subcellular fractionation: Separate membrane and cytosolic fractions before ELISA quantification.

• Surface biotinylation: Selectively label and purify cell surface proteins before antibody detection.

• Immunofluorescence co-localization: Though not using HRP-conjugated antibodies directly, this complementary approach can visualize RXFP1 localization relative to membrane and endosomal markers.

• Flow cytometry: Distinguish between permeabilized and non-permeabilized cell conditions to differentiate total from surface RXFP1.

These techniques can help researchers investigate receptor trafficking dynamics and correlate them with signaling outcomes, providing deeper insights into RXFP1 regulation in normal and pathological conditions.

How can RXFP1 antibody detection be integrated with cAMP signaling assays?

The functional activity of RXFP1 is primarily assessed through cAMP accumulation. Integrating RXFP1 detection with functional readouts:

• Parallel assays: Divide cell or tissue samples for simultaneous RXFP1 quantification and cAMP measurement using specialized immunoassays (like the TR-FRET cAMP immunoassay described in the research) .

• Sequential analysis: First measure relaxin-induced cAMP responses in living cells, then fix and perform RXFP1 immunodetection.

• Dose-response correlation: Generate relaxin or ML290 (a small molecule RXFP1 agonist) dose-response curves for cAMP production and correlate with RXFP1 expression levels .

• Receptor desensitization studies: Monitor RXFP1 levels following prolonged agonist treatment and correlate with changes in cAMP responsiveness.

This integration helps establish the relationship between receptor expression and functional capacity, particularly important when evaluating RXFP1 as a therapeutic target.

What approaches can validate the specificity of RXFP1 Antibody, HRP conjugated in complex biological samples?

• Competitive inhibition: Pre-incubate antibody with recombinant RXFP1 protein before sample analysis to demonstrate specific binding.

• Genetic validation: Compare detection in wild-type versus RXFP1 knockdown/knockout samples. The siRXFP1 approach described in prostate cancer research provides a model for such validation .

• Correlation with mRNA: Compare protein detection with RXFP1 mRNA levels determined by RT-PCR or in situ hybridization .

• Cross-reactivity testing: Evaluate antibody performance with related receptors (e.g., RXFP2) to confirm specificity.

• Multiple antibody validation: Compare results using different antibodies targeting distinct RXFP1 epitopes.

• Western blot correlation: Confirm ELISA results with western blot analysis to verify detection of correctly-sized RXFP1 protein.

These validation steps ensure that experimental findings accurately reflect RXFP1 biology rather than artifacts of cross-reactivity.

How should researchers normalize RXFP1 expression data across diverse experimental conditions?

Appropriate normalization strategies are essential for meaningful comparisons:

• Sample-based normalization:

  • Total protein concentration for tissue/cell lysates

  • Cell number or viability metrics for cultured cells

  • Tissue weight or volume for solid tissue samples

• Reference protein normalization:

  • Housekeeping proteins for western blot validation

  • Membrane protein markers when analyzing membrane-enriched fractions

  • Multiple reference proteins for robust normalization

• Statistical normalization:

  • Z-score normalization for multi-sample comparisons

  • Quantile normalization for high-throughput datasets

  • Log transformation for data with non-normal distribution

• Experimental design considerations:

  • Include inter-plate calibrators for longitudinal studies

  • Process comparison groups simultaneously when possible

  • Document normalization approaches thoroughly

The choice of normalization strategy should align with experimental questions and anticipated sources of variation.

What statistical approaches are appropriate for analyzing RXFP1 expression in disease states?

Statistical analysis should be tailored to the specific research question:

• Binary comparisons (disease vs. healthy):

  • t-tests for normally distributed data

  • Mann-Whitney U tests for non-parametric comparisons

  • Consider paired analyses for matched samples

• Multiple group comparisons:

  • ANOVA with appropriate post-hoc tests

  • Kruskal-Wallis for non-parametric multi-group analysis

  • Include relevant covariates when appropriate

• Correlation analyses:

  • Pearson or Spearman correlation between RXFP1 levels and disease metrics

  • Multiple regression to account for confounding factors

  • Receiver Operating Characteristic (ROC) analysis to evaluate diagnostic potential

• Disease progression analysis:

  • Longitudinal modeling for time-course studies

  • Survival analysis when evaluating RXFP1 as a prognostic factor in cancer studies

For liver fibrosis research, statistical approaches should account for disease etiology, fibrosis stage, and potential confounding factors such as age, gender, and concurrent treatments .

How can researchers address variability in RXFP1 detection across different experimental systems?

Biological and technical variability must be carefully managed:

• Biological variability sources:

  • Cell line passage number effects on RXFP1 expression

  • Primary cell heterogeneity (as noted with hepatic stellate cells)

  • Patient-to-patient variation in clinical samples

• Technical variability management:

  • Standardized protocols for sample collection and processing

  • Consistent antibody concentrations and incubation conditions

  • Internal calibrators across experimental batches

• Experimental design considerations:

  • Adequate biological and technical replicates

  • Randomization and blinding where appropriate

  • Power analysis to determine appropriate sample sizes

• Analytical approaches:

  • Hierarchical statistical models accounting for nested variability

  • Batch effect correction algorithms for large datasets

  • Meta-analysis approaches for integrating multiple studies

The research on RXFP1 in liver fibrosis acknowledged the heterogeneity of primary hepatic stellate cells, demonstrating the importance of addressing variability in experimental design .

What considerations are important when comparing RXFP1 expression across different disease models?

Cross-model comparisons require careful methodological considerations:

• Species differences:

  • Human versus animal models of disease

  • Potential differences in antibody epitope conservation

  • Variations in receptor regulation mechanisms

• Disease model characteristics:

  • Acute versus chronic models

  • Chemical induction (e.g., CCl₄, bile duct ligation) versus genetic models

  • Different etiologies of the same pathology (e.g., NASH versus AIH in liver fibrosis)

• Temporal considerations:

  • Disease stage matching across models

  • Time-course alignment for progression studies

  • Consideration of intervention timing relative to disease evolution

• Analytical approach:

  • Standardized effect sizes for cross-model comparison

  • Multi-level modeling to account for model-specific variability

  • Careful interpretation acknowledging model-specific limitations

When comparing findings from different liver fibrosis models, researchers should consider both the etiology and the stage of fibrosis, as RXFP1 expression patterns may vary accordingly .

What are common pitfalls in RXFP1 detection and how can they be addressed?

Researchers may encounter several challenges when detecting RXFP1:

• Low signal intensity:

  • Optimize antibody concentration

  • Enhance sample preparation to maximize RXFP1 extraction

  • Consider using signal amplification systems compatible with HRP

  • Extend substrate development time (within linear range)

• High background signal:

  • Optimize blocking conditions

  • Increase washing stringency

  • Evaluate potential endogenous peroxidase activity in samples

  • Consider sample pre-clearing steps

• Poor reproducibility:

  • Standardize incubation times and temperatures

  • Ensure consistent sample handling

  • Implement automated washing when possible

  • Use calibration standards across experimental batches

• Non-linearity in standard curves:

  • Verify reagent quality and stability

  • Optimize standard dilution series

  • Consider alternative curve-fitting models

  • Evaluate potential matrix effects from samples

Addressing these issues systematically can significantly improve assay performance and data reliability.

How can researchers resolve discrepancies between RXFP1 protein detection and functional outcomes?

When RXFP1 protein detection does not correlate with expected functional responses:

• Receptor conformation considerations:

  • The antibody may detect both active and inactive receptor conformations

  • Consider complementary approaches to assess receptor activation state

• Signaling pathway evaluation:

  • Test integrity of cAMP signaling cascade components

  • Evaluate potential compensatory mechanisms

  • Consider alternative RXFP1 signaling pathways beyond cAMP

• Receptor localization analysis:

  • Surface versus internalized receptor pools

  • Membrane microdomain localization affecting signaling efficacy

  • Trafficking defects may separate expression from function

• Post-translational modifications:

  • Glycosylation status affecting ligand binding

  • Phosphorylation state influencing G-protein coupling

  • Potential receptor oligomerization affecting function

• Experimental validation approaches:

  • Positive control experiments with cells known to express functional RXFP1

  • Dose-response analysis with different RXFP1 agonists (H2-RLX versus ML290)

  • Genetic manipulation to confirm specificity of observed effects

Systematic investigation of these factors can help reconcile apparent discrepancies between receptor expression and function.

What methodological adaptations are needed when transitioning from cell lines to primary cells or tissues?

Moving from established cell lines to primary cells or tissues requires important adjustments:

• Detection sensitivity considerations:

  • Endogenous RXFP1 levels in primary cells may be significantly lower than in recombinant systems

  • Research has shown that primary hepatic stellate cells and LX-2 cells express approximately 400-fold lower RXFP1 mRNA levels than recombinant HEK293-RXFP1 cells

  • Signal amplification may be necessary for reliable detection

• Sample preparation adaptations:

  • More rigorous extraction procedures for tissues

  • Consideration of tissue-specific matrix effects

  • Optimization of dissociation protocols for primary cells

• Assay optimization:

  • Re-validation of antibody concentration and incubation conditions

  • Adjusted standard curves appropriate for expected concentration ranges

  • Modified blocking procedures to address tissue-specific background

• Interpretation frameworks:

  • Establishing new baseline expectations for signal windows

  • Accounting for greater biological variability in primary systems

  • Considering heterogeneity within primary cell populations

These adaptations are essential when transitioning from the controlled environment of cell lines to the greater complexity and variability of primary biological systems.

How can researchers optimize RXFP1 detection in high-throughput screening applications?

For high-throughput applications, such as screening for RXFP1 agonists or modulators:

• Assay miniaturization:

  • Adaptation to 384-well or 1536-well formats

  • Optimization of reagent volumes and concentrations

  • Automated liquid handling implementation

• Signal optimization:

  • Maximize signal-to-background ratio

  • Consider RXFP1 overexpression systems for enhanced detection

  • The BacMam system has been successfully used to optimize RXFP1 expression levels

• Readout technologies:

  • TR-FRET based detection systems offer advantages for HTS

  • Consider homogeneous assay formats to minimize wash steps

  • Evaluate alternative detection technologies compatible with automation

• Quality control measures:

  • Implement robust Z-prime calculations to monitor assay performance

  • Include appropriate positive controls (such as H2-RLX or ML290)

  • Develop counter-screening approaches to identify false positives

• Data analysis pipelines:

  • Automated data processing and normalization

  • Statistical methods for hit identification

  • Secondary confirmation strategies

These optimizations can facilitate effective high-throughput screening campaigns for novel RXFP1-targeted therapeutics, as demonstrated in previous successful screening efforts .