RETNLB Antibody

What is RETNLB and why is it significant in research?

RETNLB (resistin like beta) is a secreted protein encoded by the RETNLB gene in humans. It consists of 111 amino acid residues with a molecular weight of approximately 12 kDa and functions as an intestinal goblet cell-specific protein that becomes notably upregulated during intestinal inflammation . The significance of RETNLB in research spans multiple areas, including inflammatory diseases, cancer biology (particularly in gastrointestinal and oral cancers), and metabolic function studies . Recent research has revealed that RETNLB may play a crucial role in modulating hepatic insulin action, as infusions of RETNLB in experimental models have been shown to induce severe hepatic insulin resistance and stimulate hepatic glucose production through increased flux via glucose-6-phosphatase .

What are the primary applications for RETNLB antibodies in research?

RETNLB antibodies are valuable research tools employed in multiple experimental techniques:

• Immunohistochemistry (IHC): Used to detect and localize RETNLB expression in tissue samples, with particular utility in studying gastrointestinal tissues and cancer specimens .

• Western Blot: Applied to determine RETNLB protein expression levels in cell and tissue lysates .

• ELISA: Used for quantitative measurement of RETNLB in biological samples .

These applications enable researchers to investigate RETNLB expression patterns, regulation mechanisms, and potential roles in pathophysiological processes across different experimental systems.

What are the key technical specifications to consider when selecting a RETNLB antibody?

When selecting a RETNLB antibody for research, consider these critical specifications:

The optimal antibody selection should align with your specific experimental objectives, sample types, and detection methods.

What are the optimal protocols for using RETNLB antibodies in immunohistochemistry?

For optimal immunohistochemical detection of RETNLB in tissue specimens:

• Tissue Preparation:

  • Fix tissues in 10% neutral buffered formalin

  • Process and embed in paraffin

  • Section at 4-6μm thickness

• Antigen Retrieval:

  • Primary recommendation: TE buffer pH 9.0

  • Alternative: Citrate buffer pH 6.0

• Antibody Incubation:

  • Blocking: Use appropriate serum (5-10%) for 30-60 minutes

  • Primary antibody: Dilute RETNLB antibody between 1:50-1:500 (optimize for your specific antibody)

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

• Detection System:

  • Use appropriate secondary antibody and detection system compatible with rabbit IgG

  • Include positive control (human small intestine tissue or human liver cancer tissue)

  • Include negative control (omit primary antibody)

• Counterstaining and Mounting:

  • Hematoxylin counterstaining

  • Dehydration and mounting with appropriate medium

The protocol may require optimization based on tissue type, fixation conditions, and specific antibody characteristics. Titration experiments are recommended to determine optimal dilution for your specific system .

What controls should be included when using RETNLB antibodies in experimental setups?

A robust experimental design with RETNLB antibodies requires several controls:

Positive Controls:

• Human small intestine tissue (known to express RETNLB)

• Human liver cancer tissue (demonstrated positive reactivity)

• Cell lines with confirmed RETNLB expression (e.g., CAL27 and TCA-83 oral squamous cell carcinoma lines)

Negative Controls:

• Antibody diluent without primary antibody on target tissue

• Isotype control (non-specific rabbit IgG at same concentration)

• Tissues known to lack RETNLB expression

• Cells with RETNLB knockdown (e.g., si-RETNLB transfected cells)

Method Validation Controls:

• Serial dilution of primary antibody to establish optimal concentration

• Preabsorption with immunizing peptide to confirm specificity

• Western blot analysis to confirm antibody specificity by molecular weight

Expression Modulation Controls:

• In vitro RETNLB knockdown models using siRNAs (for functional studies)

• Recombinant RETNLB protein standards (for quantitative applications)

Including these controls helps validate antibody specificity, optimize experimental conditions, and support the reliability of experimental findings.

How should RETNLB antibodies be stored and handled to maintain optimal performance?

Proper storage and handling of RETNLB antibodies is critical for maintaining their performance:

Storage Conditions:

• Store at -20°C as received

• Avoid repeated freeze-thaw cycles

• Antibodies are typically supplied in PBS pH 7.3 with 50% glycerol and 0.02-0.05% sodium azide as preservative

Stability:

• Most RETNLB antibodies remain stable for one year from the date of receipt when properly stored

• Aliquoting is generally unnecessary for -20°C storage due to the glycerol content

Working Solution Preparation:

• Thaw antibody completely before use

• Mix gently by inversion or pipetting (avoid vortexing)

• Briefly centrifuge to collect solution at bottom of tube

• Prepare working dilutions fresh before use

• Return stock solution to -20°C immediately after use

Safety Considerations:

• Handle with appropriate PPE due to sodium azide content

• Properly dispose of antibody-containing waste according to local regulations

Quality Indicators:

• Solution should be clear without visible precipitates

• If precipitation occurs, incubate at room temperature and mix gently

• If performance decreases, replace with fresh aliquot

Following these guidelines will help maximize antibody performance and extend shelf life.

How does RETNLB expression correlate with disease progression in cancer research models?

Research using RETNLB antibodies has revealed significant correlations between RETNLB expression and cancer progression, particularly in oral squamous cell carcinoma (OSCC):

These findings indicate RETNLB may serve as a potential biomarker for cancer progression and as a therapeutic target, particularly in oral, gastric, and colorectal malignancies.

What are the methodological approaches for analyzing RETNLB's role in inflammation?

To investigate RETNLB's functions in inflammatory processes, researchers employ several methodological approaches:

• Expression Analysis in Inflammatory Conditions:

  • IHC staining with RETNLB antibodies to assess protein expression in inflamed vs. normal tissues

  • Western blot quantification of RETNLB levels in inflammatory disease models

  • qRT-PCR measurement of RETNLB mRNA expression changes during inflammation

• Functional Modulation Studies:

  • siRNA-mediated knockdown of RETNLB in relevant cell models

  • Recombinant RETNLB administration to in vitro and in vivo models

  • Monitoring inflammatory marker changes (cytokines, acute phase proteins) following RETNLB modulation

• Signaling Pathway Analysis:

  • Western blot assessment of TLR2/4 and downstream inflammatory signaling components

  • Phosphorylation status of ERK and other MAPKs following RETNLB perturbation

  • Inhibitor studies to confirm pathway specificity

• Gene Expression Profiling:

  • Gene Set Enrichment Analysis (GSEA) to identify inflammatory gene signatures associated with RETNLB expression

  • RNA-Seq to comprehensively assess transcriptome changes in response to RETNLB modulation

• Cell-Specific Expression Studies:

  • Co-localization IHC using RETNLB antibodies alongside markers for inflammatory cell types

  • Flow cytometry analysis of RETNLB expression in specific immune cell populations

These approaches provide complementary insights into how RETNLB contributes to inflammatory processes at molecular, cellular, and tissue levels.

How can RETNLB antibodies be used to investigate metabolic pathway interactions?

RETNLB antibodies provide valuable tools for investigating metabolic pathways, particularly relating to insulin resistance and glucose metabolism:

• Tissue Expression Profiling:

  • IHC analysis of RETNLB expression in metabolic tissues (liver, adipose, gastrointestinal tract)

  • Correlation of expression patterns with metabolic parameters in animal models and clinical samples

• Glucose Metabolism Studies:

  • Detection of RETNLB levels before and after glucose challenge tests

  • Investigation of RETNLB's effects on glucose-6-phosphatase activity and hepatic glucose production

  • Correlation analysis between RETNLB expression and insulin sensitivity metrics

• Signaling Pathway Interrogation:

  • Western blot analysis of insulin signaling components (IRS, PI3K, AKT) in response to RETNLB modulation

  • Phosphorylation status assessment in metabolic signaling cascades

• Functional Assessment in Metabolic Cell Models:

  • Detection of RETNLB in conditioned media from relevant cell types

  • Analysis of glucose uptake, glycogen synthesis, and lipid metabolism following RETNLB treatment

  • Investigation of cross-talk between RETNLB and other metabolic hormones

• In Vivo Metabolic Phenotyping:

  • Correlation of RETNLB protein levels with:

    • Glucose tolerance test parameters

    • Insulin resistance indices

    • Hepatic glucose output measurements

    • Lipid profiles

• Receptor Identification Studies:

  • Co-immunoprecipitation with RETNLB antibodies to identify binding partners

  • Immunofluorescence co-localization studies to visualize receptor interactions

These approaches help elucidate RETNLB's role in the regulation of glucose metabolism and insulin action, particularly in the context of metabolic disorders.

What are common sources of non-specific staining with RETNLB antibodies and how can they be addressed?

When working with RETNLB antibodies, researchers may encounter several sources of non-specific staining, which can be addressed through specific optimization strategies:

For RETNLB-specific optimization:

• Validate antibody specificity using known positive controls (human small intestine tissue)

• Include a negative control by omitting primary antibody

• Consider using recombinant RETNLB protein for pre-absorption controls

• For IHC applications, carefully follow recommended antigen retrieval protocols specific for RETNLB detection

Through systematic optimization of these parameters, researchers can significantly improve the signal-to-noise ratio when using RETNLB antibodies.

How can researchers troubleshoot inconsistent detection of RETNLB across different experimental systems?

Inconsistent RETNLB detection across experimental systems is a common challenge that can be addressed through systematic troubleshooting:

• Sample Preparation Variables:

  • Fixation conditions: Standardize fixation protocols (time, temperature, fixative)

  • Tissue processing: Ensure consistent processing methods across samples

  • Storage conditions: Monitor sample storage duration and temperature

• Antibody-Related Factors:

  • Antibody lot variation: Test new lots against previous reference samples

  • Concentration optimization: Perform titration for each experimental system

  • Incubation conditions: Standardize time, temperature, and humidity

• Technical Considerations:

  • Epitope accessibility: Compare antigen retrieval methods (TE buffer pH 9.0 versus citrate buffer pH 6.0)

  • Detection systems: Ensure consistent secondary antibody and visualization methods

  • Equipment variation: Calibrate instruments and standardize settings

• Biological Variability:

  • Expression heterogeneity: Use multiple fields/replicates to account for heterogeneous expression

  • Developmental/physiological status: Control for age, disease state, and treatment conditions

  • Cell type specificity: RETNLB is primarily expressed in intestinal goblet cells; confirm appropriate cell populations are present

• Verification Approaches:

  • Multiple antibody validation: Test with alternative antibodies targeting different RETNLB epitopes

  • Complementary techniques: Confirm findings with orthogonal methods (e.g., qRT-PCR, Western blot)

  • Positive controls: Include standardized positive controls (e.g., human small intestine tissue or human liver cancer tissue)

By systematically addressing these factors, researchers can identify sources of variability and establish reproducible protocols for RETNLB detection across experimental systems.

What strategies can improve detection sensitivity for low-abundance RETNLB expression?

Detecting low-abundance RETNLB expression requires optimized protocols to enhance sensitivity without compromising specificity:

• Signal Amplification Techniques:

  • Tyramide signal amplification (TSA): Can enhance detection sensitivity 10-100 fold

  • Polymer-based detection systems: Provide higher sensitivity than traditional ABC methods

  • Multiple-step detection: Sequential amplification steps for ultra-sensitive detection

• Antibody Optimization:

  • Concentration adjustment: Use optimal antibody dilutions (starting with manufacturer recommendations of 1:50-1:500)

  • Incubation conditions: Extended incubation times (overnight at 4°C) can improve detection

  • Antibody selection: Choose antibodies with validated sensitivity for low-abundance targets

• Sample Preparation Refinements:

  • Optimized antigen retrieval: Test both recommended methods (TE buffer pH 9.0 and citrate buffer pH 6.0)

  • Reduced background: Additional blocking steps (5-10% normal serum, 0.1-0.3% Triton X-100)

  • Fresh tissue samples: Minimize degradation with prompt processing

• Detection Method Selection:

  • Chromogenic vs. fluorescent: Fluorescent detection often provides better sensitivity

  • Digital imaging: Utilize high-resolution imaging and signal quantification

  • Automated platforms: Consider standardized automated IHC platforms for consistent results

• Enrichment Strategies:

  • Cell isolation: Enrich for RETNLB-expressing cell populations

  • Subcellular fractionation: Focus on cytoplasmic fractions where RETNLB is typically localized

  • Concentration methods: For secreted RETNLB in supernatants or biological fluids

When detecting low-abundance RETNLB, it's essential to include appropriate positive controls and implement rigorous optimization to distinguish true signal from background.

How can RETNLB antibodies be utilized in cancer biomarker development?

RETNLB antibodies offer significant potential in cancer biomarker development, particularly for gastrointestinal and oral cancers:

• Tissue Microarray Analysis:

  • IHC screening of large patient cohorts with standardized RETNLB antibody protocols

  • Correlation of expression patterns with clinicopathological parameters

  • Development of scoring systems based on staining intensity and distribution

• Prognostic Biomarker Validation:

  • Longitudinal studies correlating RETNLB expression with patient outcomes

  • Multivariate analysis to determine independent prognostic value

  • This approach is supported by findings that high RETNLB expression correlates with poor survival in oral squamous cell carcinoma patients

• Liquid Biopsy Development:

  • ELISA-based detection of circulating RETNLB in patient serum/plasma

  • Correlation of levels with disease stage, progression, and treatment response

  • Longitudinal monitoring for early detection of recurrence

• Therapeutic Target Identification:

  • Analysis of RETNLB expression across tumor subtypes

  • Investigation of RETNLB's role in tumor growth, invasion, and migration

  • Identification of downstream pathways (such as TLR2/4/ERK) as potential intervention points

• Companion Diagnostic Development:

  • Identification of RETNLB expression thresholds that predict response to specific therapies

  • Development of standardized IHC protocols for clinical implementation

  • Integration with other molecular markers for comprehensive profiling

• Technical Implementation:

  • Adaptation of research-grade antibodies for clinical diagnostic applications

  • Validation across multiple laboratories to ensure reproducibility

  • Automation of staining protocols for clinical implementation

The research showing that RETNLB knockdown significantly reduces cancer cell viability, mobility, and invasiveness demonstrates its potential as both a biomarker and therapeutic target .

What are the emerging applications of RETNLB antibodies in studying disease mechanisms?

RETNLB antibodies are enabling several cutting-edge research applications that illuminate disease mechanisms:

• Single-Cell Analysis:

  • Integration with single-cell technologies to map RETNLB expression at cellular resolution

  • Identification of specific cell populations with altered RETNLB expression in disease states

  • Correlation with single-cell transcriptomics to identify co-expression patterns

• Spatial Biology Applications:

  • Multiplex immunofluorescence to simultaneously visualize RETNLB with other markers

  • Spatial transcriptomics combined with protein detection to map expression landscapes

  • Analysis of RETNLB expression in tissue microenvironments (tumor-stroma interfaces, inflammatory foci)

• Pathway Interaction Mapping:

  • Proximity ligation assays to detect protein-protein interactions in situ

  • RETNLB interaction with TLR2/4 receptors in inflammatory and cancer contexts

  • Co-immunoprecipitation studies to identify novel binding partners

• Dynamic Expression Studies:

  • Live-cell imaging with fluorescently-tagged antibody fragments

  • Temporal analysis of RETNLB expression during disease progression

  • Correlation with metabolic and inflammatory markers in real-time

• Translational Research Applications:

  • Patient-derived organoid models to study RETNLB function in personalized contexts

  • Drug screening platforms incorporating RETNLB as a response biomarker

  • Development of RETNLB-targeting therapeutic modalities based on antibody specificity

• Cross-Disease Comparisons:

  • Comparative analysis of RETNLB expression and function across:

    • Inflammatory bowel diseases

    • Metabolic disorders (particularly insulin resistance)

    • Various cancer types (oral, gastric, colorectal)

These emerging applications leverage RETNLB antibodies to build comprehensive understanding of disease mechanisms, potentially leading to novel diagnostic and therapeutic strategies.

How can gene editing techniques be combined with RETNLB antibody detection for mechanistic studies?

The integration of gene editing technologies with RETNLB antibody-based detection offers powerful approaches for mechanistic investigations:

• CRISPR/Cas9 Knockout and Knockin Models:

  • Generate RETNLB knockout cell lines and animal models

  • Create reporter knockins (fluorescent tags, epitope tags) for live tracking

  • Use RETNLB antibodies to validate editing efficiency by Western blot/IHC

  • Compare protein expression patterns between wild-type and edited models

• Domain-Specific Functional Analysis:

  • Engineer truncation or point mutations in RETNLB functional domains

  • Use RETNLB antibodies to assess expression, localization, and stability of mutants

  • Correlate structural modifications with functional outcomes (secretion, receptor binding)

• Regulatory Element Editing:

  • CRISPR-mediated modification of RETNLB promoter/enhancer regions

  • Analyze consequent changes in protein expression using antibody-based detection

  • Map regulatory networks controlling RETNLB expression in different tissues

• Pathway Validation Studies:

  • Generate TLR2/4 knockouts to validate the proposed RETNLB-TLR2/4-ERK pathway

  • Use RETNLB antibodies to track protein localization in these models

  • Perform rescue experiments with recombinant RETNLB and antibody detection

• Temporal Control Systems:

  • Implement inducible CRISPR systems for temporal regulation of RETNLB

  • Track expression dynamics using antibody-based methods

  • Correlate with phenotypic changes in metabolism or inflammatory responses

• High-Throughput Screening:

  • Combine genome-wide CRISPR screens with RETNLB antibody detection

  • Identify novel regulators of RETNLB expression and function

  • Validate hits through targeted editing and quantitative antibody-based assays

This integrated approach provides mechanistic insights into RETNLB function that cannot be achieved through either technique alone, offering a powerful toolset for investigating RETNLB's roles in inflammation, metabolism, and cancer biology.

How does the detection of RETNLB compare in different tissue contexts?

RETNLB expression exhibits distinctive patterns across tissue types, requiring careful consideration of detection methods and interpretation:

Interpretation Challenges:

• Cellular Heterogeneity: RETNLB expression may be restricted to specific cell subtypes within tissues

• Baseline Variability: Normal expression levels vary significantly between tissue types

• Context-Dependent Regulation: Expression patterns change during inflammation or malignant transformation

Methodological Recommendations:

• Include appropriate positive controls (small intestine) with each experiment

• Evaluate multiple fields and regions within each tissue sample

• Use specific cell type markers in dual-labeling experiments to identify RETNLB-expressing cells

• Consider quantitative analysis methods to objectively compare expression levels across tissues

• Standardize all technical parameters (antibody dilution, incubation time, detection system) when comparing across tissue types

This comparative approach enables reliable assessment of normal versus pathological RETNLB expression across diverse tissue contexts.

How should researchers interpret differences between RETNLB mRNA and protein expression data?

Discrepancies between RETNLB mRNA and protein expression levels are common and require careful interpretation:

• Potential Mechanisms for Discordance:

  • Post-transcriptional regulation (miRNAs, RNA-binding proteins)

  • Translational efficiency variations

  • Protein stability and turnover differences

  • Secretion of RETNLB protein from cells (reducing intracellular detection)

  • Technical variations in detection methodologies

• Methodological Considerations:

  • mRNA detection: qRT-PCR or RNA-Seq measure transcript abundance

  • Protein detection: Antibody-based methods (Western blot, IHC, ELISA) detect protein levels

  • Each technique has different sensitivity, specificity, and dynamic range

• Integrated Analysis Approach:

  • Perform parallel mRNA and protein analysis on the same samples

  • Use multiple methodologies to confirm expression patterns

  • Consider temporal dynamics (mRNA changes may precede protein changes)

  • Account for subcellular localization and secretion of RETNLB

• Functional Validation Strategies:

  • Manipulate expression (knockdown/overexpression) and measure consequences at both mRNA and protein levels

  • In OSCC studies, si-RETNLB reduced both mRNA and protein levels, confirming successful targeting

  • Correlate expression changes with functional outcomes (proliferation, invasion, signaling activation)

• Interpretation Framework:

  • High mRNA/Low protein: Consider protein degradation, inefficient translation, or active secretion

  • Low mRNA/High protein: Consider protein stability, post-transcriptional regulation, or technical artifacts

  • Concordant changes: Stronger evidence for biologically significant regulation

This integrated approach helps researchers distinguish between technical artifacts and biologically meaningful expression patterns when studying RETNLB in disease contexts.

What advanced computational approaches can enhance RETNLB antibody-based research?

Advanced computational methods significantly enhance the depth and rigor of RETNLB antibody-based research:

• Digital Pathology and Image Analysis:

  • Automated quantification of RETNLB IHC staining intensity and distribution

  • Machine learning algorithms for pattern recognition in tissue samples

  • Multiplexed image analysis for co-expression studies with other markers

  • Whole slide imaging for comprehensive tissue analysis beyond selected fields

• Multi-Omics Data Integration:

  • Correlation of antibody-detected RETNLB protein data with:

    • Transcriptomic profiles (RNA-Seq, microarray)

    • Proteomic datasets (mass spectrometry)

    • Genomic alterations (mutations, CNVs)

    • Epigenomic modifications

  • Gene Set Enrichment Analysis to identify pathways associated with RETNLB expression

• Network Analysis Approaches:

  • Protein-protein interaction mapping based on co-immunoprecipitation data

  • Pathway enrichment analysis to contextualize RETNLB in signaling networks

  • Identification of RETNLB-centered regulatory modules in disease states

  • Network-based drug target identification

• Predictive Modeling:

  • Machine learning models to predict patient outcomes based on RETNLB expression

  • Development of integrated biomarker signatures incorporating RETNLB

  • In silico screening for compounds that may modulate RETNLB expression or function

• Spatial Transcriptomics Integration:

  • Correlation of antibody-detected protein localization with spatial transcriptomics data

  • Cell type deconvolution in complex tissues

  • Microenvironment analysis in disease contexts

• Antibody Specificity Computational Validation:

  • Epitope prediction algorithms to assess potential cross-reactivity

  • Structural modeling of antibody-antigen interactions

  • Database mining to identify proteins with similar epitopes

These computational approaches transform antibody-based detection from qualitative observation to quantitative, mechanistic insights that can guide hypothesis generation and experimental design in RETNLB research.