RTKN Antibody

What is RTKN/Rhotekin and what are its primary biological functions?

Rhotekin (RTKN) is a scaffolding protein that functions as part of the Rho GTPase signaling complex, playing critical roles in cellular morphology regulation and cell movement. In humans, the canonical RTKN protein has 609 amino acid residues with a molecular mass of approximately 63 kDa .

Functionally, RTKN:

• Mediates Rho signaling to activate NF-kappa-B

• May confer increased resistance to apoptosis to cells, particularly relevant in gastric tumorigenesis

• Potentially plays a novel role in the organization of septin structures

• Is involved in lymphopoiesis when considering its paralog RTKN2

RTKN expression patterns show high levels in liver and kidney tissues, with variable expression across other tissue types .

What are the common applications for RTKN antibodies in research?

RTKN antibodies are employed in multiple research applications, with the most common being:

Research indicates that RTKN antibodies have been validated across multiple cell lines including HEK293T, Raw264.7, PC12, and HeLa, making them versatile tools for comparative studies .

What are the key considerations when selecting an RTKN antibody for experimental procedures?

When selecting an RTKN antibody, researchers should consider:

• Target epitope: Antibodies targeting different regions (N-terminal vs. C-terminal) may provide different results. For example, some antibodies are raised against residues within the first 150 amino acids, while others target regions beyond position 300 .

• Specificity validation: Ensure the antibody has been validated using proper controls:

  • Positive controls: Cell lines known to express RTKN (e.g., Jurkat cells)

  • Negative controls: Knockout cells or competitive peptide blocking

• Cross-reactivity: Verify species reactivity is appropriate for your model system. Many RTKN antibodies react with human, mouse, and rat samples, but cross-reactivity should be confirmed .

• Application compatibility: Some antibodies perform better in certain applications than others. For example, antibody ab154954 is recommended for WB and ICC/IF applications, while ab234866 is optimized for WB and IHC-P .

How can researchers differentiate between RTKN and its paralog RTKN2 in experimental systems?

Distinguishing between RTKN and RTKN2 presents a significant challenge due to their structural similarities. Methodologically, researchers should:

• Use paralog-specific antibodies: Select antibodies that specifically recognize unique epitopes in either RTKN or RTKN2. For RTKN2, the canonical protein has 609 amino acids with a mass of 69.3 kDa, with up to 3 different isoforms reported .

• Employ molecular approaches:

  • RT-qPCR using primer sets that specifically amplify either RTKN or RTKN2 transcripts

  • RNA interference experiments targeting unique sequences in each paralog

  • CRISPR-Cas9 knockout of specific paralogs to confirm antibody specificity

• Consider tissue-specific expression patterns: RTKN2 is predominantly expressed in lymphocytes, CD4 positive T-cells, and bone marrow-derived cells, whereas RTKN shows higher expression in liver and kidney tissues .

• Examine functional differences: RTKN2 plays an important role in lymphopoiesis and can be used as a marker to identify Alveolar Type I Cells and Regulatory T Cells .

What are the recommended validation strategies for confirming RTKN antibody specificity in novel experimental contexts?

Comprehensive validation of RTKN antibodies in new experimental contexts should include:

• Multi-technique validation: Confirm consistent results across different methods:

  • Western blot showing bands at the expected molecular weight (63 kDa)

  • IHC/IF showing expected subcellular localization patterns

  • Peptide competition assays to demonstrate specificity

• Signal correlation with expression levels:

  • Compare signal intensities across cell lines with known differential expression of RTKN

  • Include tissues known to have high (liver, kidney) and low RTKN expression

• Genetic validation approaches:

  • siRNA/shRNA knockdown of RTKN to demonstrate corresponding reduction in antibody signal

  • Overexpression studies showing increased signal intensity

  • CRISPR-Cas9 knockout controls where applicable

• Cross-platform validation: If studying protein-protein interactions, confirm results using orthogonal methods:

  • Co-immunoprecipitation followed by mass spectrometry

  • Proximity ligation assays

  • FRET/BRET studies for close interactions

These approaches collectively provide strong evidence for antibody specificity and reliability .

How do experimental conditions affect RTKN detection in Western blot applications?

Several experimental variables can significantly impact RTKN detection in Western blot applications:

• Sample preparation factors:

  • Lysis buffer composition: RTKN is a scaffolding protein that interacts with membrane-associated Rho GTPases, so detergent selection is critical

  • Phosphatase inhibitors: Required to preserve phosphorylation states that may affect antibody recognition

  • Protease inhibitors: Essential to prevent degradation of RTKN during sample preparation

• Electrophoresis and transfer conditions:

  • Gel percentage: 7.5% SDS-PAGE has been reported as optimal for RTKN separation

  • Transfer time and buffer: Extended transfer times may be necessary for complete transfer of the 63 kDa protein

• Antibody incubation parameters:

  • Optimal dilutions range from 1:500-1:10,000 depending on the specific antibody

  • Primary antibody incubation temperature and duration significantly affect signal quality

  • Blocking conditions must be optimized to minimize background without masking epitopes

• Detection system selection:

  • Enhanced chemiluminescence (ECL) versus fluorescence-based detection systems

  • Signal amplification methods for low abundance detection

Published protocols have demonstrated successful RTKN detection in various cell lines including Jurkat, HEK293T, Raw264.7, PC12, and HeLa cell lysates .

What methodological approaches are recommended for investigating RTKN's role in Rho GTPase signaling pathways?

To investigate RTKN's function in Rho GTPase signaling, researchers should consider these methodological approaches:

• Protein-protein interaction assays:

  • Pull-down assays using GST-tagged RhoA, RhoB, or RhoC to capture RTKN from cell lysates

  • Co-immunoprecipitation with RTKN antibodies followed by immunoblotting for Rho proteins

  • Proximity ligation assays to visualize RTKN-Rho interactions in situ

• Signaling cascade analysis:

  • Phospho-specific antibodies to monitor activation of downstream effectors (e.g., NF-κB pathway components)

  • Luciferase reporter assays for NF-κB activation in response to Rho-RTKN signaling

  • Calcium flux measurements to assess rapid signaling events

• Cellular function studies:

  • Migration and invasion assays following RTKN knockdown or overexpression

  • Apoptosis assays to assess RTKN's reported anti-apoptotic functions

  • Cytoskeletal remodeling visualization using fluorescently labeled actin or septin structures

• Advanced imaging techniques:

  • Live cell imaging with fluorescently tagged RTKN to monitor dynamic localization

  • FRET-based biosensors to measure Rho activation in relation to RTKN localization

  • Super-resolution microscopy to visualize RTKN-mediated septin structure organization

These approaches can be complemented with pharmacological inhibitors of Rho signaling to dissect pathway dependencies .

How can researchers effectively study RTKN's potential role in cancer progression?

To investigate RTKN's involvement in cancer progression, researchers should implement a multi-faceted approach:

• Expression analysis in clinical samples:

  • IHC analysis of RTKN expression in tumor versus matched normal tissues

  • Tissue microarray analysis across cancer types and stages

  • Correlation of RTKN expression with patient outcome data

• Functional assays in cancer models:

  • Stable knockdown or overexpression of RTKN in cancer cell lines

  • Assessment of proliferation, migration, invasion, and apoptosis resistance

  • Soft agar colony formation and spheroid growth assays

  • In vivo xenograft models to assess tumor growth and metastatic potential

• Mechanistic investigations:

  • Analysis of NF-κB pathway activation states in relation to RTKN expression

  • Investigation of RTKN's impact on apoptotic signaling cascades

  • Assessment of RTKN's influence on epithelial-mesenchymal transition markers

• Therapeutic targeting strategies:

  • Evaluation of RTKN as a biomarker for response to Rho pathway inhibitors

  • Investigation of synthetic lethality approaches targeting RTKN-dependent pathways

  • Development of strategies to disrupt RTKN-Rho interactions

Given RTKN's reported role in conferring resistance to apoptosis in gastric tumorigenesis, special attention should be paid to gastric cancer models when designing these experiments .

What are common issues with RTKN antibodies in Western blot applications and how can they be resolved?

Researchers frequently encounter several challenges when working with RTKN antibodies in Western blot applications:

Researchers have reported successful detection using 7.5% SDS-PAGE gels with antibody dilutions ranging from 1:1000 to 1:10000 depending on the specific antibody .

How can researchers optimize immunohistochemistry protocols for RTKN detection in different tissue types?

Optimizing IHC protocols for RTKN detection requires careful consideration of tissue-specific factors:

• Fixation and processing:

  • Formalin-fixed paraffin-embedded (FFPE) tissues typically yield good results with RTKN antibodies

  • Optimize fixation time to balance antigen preservation and tissue morphology

  • For frozen sections, test both acetone and paraformaldehyde fixation methods

• Antigen retrieval methods:

  • Heat-induced epitope retrieval using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0)

  • Enzymatic retrieval using proteinase K for certain tissue types

  • Determine optimal retrieval duration through time-course experiments

• Tissue-specific considerations:

  • For liver and kidney tissues (high RTKN expression), reduce antibody concentration to avoid oversaturation

  • For tissues with lower expression, consider signal amplification methods

  • Use chromogens appropriate for tissue autofluorescence characteristics

• Controls and validation:

  • Include tissue-matched negative controls (primary antibody omission)

  • Use tissues with known RTKN expression patterns as positive controls

  • For human tissues, testis and prostate have demonstrated successful RTKN detection

• Detection system selection:

  • For low abundance detection, use polymer-based detection systems

  • For co-localization studies, consider fluorescence-based multiplexing

  • For quantitative analysis, calibrate digital imaging parameters across samples

Researchers have successfully detected RTKN in human testis and prostate cancer tissues using antibody dilutions of approximately 1:100 .

What strategies can be employed when encountering contradictory results with different RTKN antibodies?

When faced with conflicting results from different RTKN antibodies, employ these systematic resolution strategies:

• Epitope mapping analysis:

  • Compare epitope locations of different antibodies (N-terminal vs. C-terminal regions)

  • Consider the accessibility of epitopes in different experimental conditions

  • Evaluate whether discrepancies might reflect detection of different isoforms

• Comprehensive validation:

  • Implement orthogonal approaches (RT-PCR, mass spectrometry) to confirm protein identity

  • Conduct side-by-side testing with standardized protocols and identical samples

  • Perform peptide competition assays with each antibody's immunogen

• Genetic approaches for resolution:

  • Use CRISPR-Cas9 or siRNA knockdown to confirm specificity

  • Overexpress tagged RTKN constructs as positive controls

  • Create domain deletion mutants to map epitope requirements

• Systematic analysis of variables:

  • Document all protocol differences (buffers, incubation times, detection methods)

  • Standardize critical parameters across antibodies

  • Test antibodies across multiple applications (WB, IHC, IF) to identify pattern consistency

• Literature and database cross-referencing:

  • Compare results with published studies using the same antibodies

  • Consult antibody validation databases for reported specificity issues

  • Contact antibody manufacturers for technical support and validation data

When comparing contradictory results, evaluate whether differences might reflect biological variables rather than technical artifacts, such as RTKN's differential expression across tissues or cell types .

How can researchers effectively utilize RTKN antibodies in multiplex immunofluorescence experiments?

Optimizing multiplex immunofluorescence with RTKN antibodies requires careful experimental design:

• Antibody selection and validation:

  • Choose RTKN antibodies raised in different host species than other target antibodies

  • Validate all antibodies individually before multiplexing

  • Confirm that secondary antibodies do not cross-react with primaries from other species

• Panel design considerations:

  • Pair RTKN with functionally related proteins (e.g., Rho GTPases, NF-κB pathway components)

  • Include cytoskeletal markers to assess co-localization with structural elements

  • Consider subcellular compartment markers to determine precise localization

• Technical optimization:

  • Determine optimal antibody dilutions in the multiplex context (typically more dilute than single staining)

  • Optimize sequential staining protocols if using same-species antibodies

  • Select fluorophores with minimal spectral overlap and appropriate brightness for each target

• Controls for multiplexing:

  • Include single-stained controls for spectral unmixing

  • Prepare controls with one primary antibody omitted to assess cross-reactivity

  • Use blocking steps between sequential antibody applications

• Advanced multiplexing approaches:

  • Consider tyramide signal amplification for low-abundance targets

  • Implement sequential multiplexing with antibody stripping for same-species antibodies

  • Utilize spectral imaging for separating closely overlapping fluorophores

Successful multiplex immunofluorescence has been achieved with RTKN antibodies at dilutions around 1:200, with paraformaldehyde fixation yielding good results in A431 cells .

What are the methodological considerations for studying RTKN in immune cell populations?

When investigating RTKN in immune cells, researchers should address these specific methodological considerations:

• Immune cell isolation and preparation:

  • Use density gradient centrifugation or magnetic separation for pure populations

  • Consider the impact of isolation methods on signaling pathways and protein expression

  • Standardize resting periods post-isolation before analysis

• Activation state considerations:

  • Compare RTKN expression/localization between resting and activated immune cells

  • Document activation protocols precisely, as signaling dynamics are time-dependent

  • Consider the role of RTKN2 in lymphopoiesis when studying lymphocyte populations

• Flow cytometry applications:

  • Optimize fixation and permeabilization protocols for intracellular RTKN detection

  • Include appropriate isotype controls matched to RTKN antibody

  • Consider phospho-flow approaches to correlate RTKN with active signaling states

• Imaging approaches for immune cells:

  • Implement adherence strategies for non-adherent cells (poly-L-lysine coating)

  • Consider the small cytoplasmic volume when interpreting localization

  • Use confocal microscopy for accurate subcellular localization determination

• Functional assays specific to immune contexts:

  • Assess impact of RTKN modulation on immune cell migration and adhesion

  • Evaluate RTKN's role in immune synapse formation

  • Investigate potential roles in cytokine production and secretion

Since RTKN2 (a paralog of RTKN) is expressed in lymphocytes, CD4 positive T-cells, and bone marrow-derived cells, researchers should be careful to distinguish between these related proteins in immune cell studies .

What approaches can be used to study the potential interactions between RTKN and receptor tyrosine kinases (RTKs)?

Investigating potential crosstalk between RTKN and receptor tyrosine kinases requires sophisticated methodological approaches:

• Co-immunoprecipitation strategies:

  • Immunoprecipitate RTKN and blot for specific RTKs of interest

  • Perform reverse co-IP (immunoprecipitate RTKs and blot for RTKN)

  • Use crosslinking approaches for transient interactions

  • Include appropriate negative controls (IgG, knockout samples)

• Proximity-based interaction assays:

  • Implement proximity ligation assays (PLA) to visualize potential interactions in situ

  • Consider FRET/BRET approaches with fluorescently tagged proteins

  • Use BioID or APEX2 proximity labeling to identify interaction networks

• Functional crosstalk assessment:

  • Modulate RTKN expression and assess impact on RTK phosphorylation/activation

  • Use specific RTK inhibitors and evaluate effects on RTKN-mediated functions

  • Explore the impact of RTK activation on RTKN localization and complex formation

• Signaling pathway integration:

  • Map shared downstream signaling nodes between RTKN and RTK pathways

  • Use phospho-specific antibodies to track signaling dynamics

  • Implement proteomics approaches to identify phosphorylation changes

• Advanced imaging approaches:

  • Use live-cell imaging with fluorescently tagged proteins to track dynamic interactions

  • Implement super-resolution microscopy to visualize nanoscale associations

  • Consider single-molecule tracking to assess interaction kinetics

While direct interactions between RTKN and RTKs have not been extensively documented in the provided search results, these approaches would be effective for investigating potential functional relationships, particularly since both are involved in key signaling pathways .

How can researchers effectively employ RTKN antibodies in developing bispecific antibody therapeutics?

When considering RTKN antibodies for bispecific antibody development, researchers should consider these methodological approaches:

• Antibody fragment generation and characterization:

  • Generate and validate Fab or scFv fragments from RTKN antibodies

  • Assess epitope accessibility in the target context

  • Determine binding kinetics (kon, koff) and affinity constants (KD) using surface plasmon resonance

• Bispecific format selection:

  • Consider various bispecific formats based on therapeutic goals:

    • IgG-like formats for extended half-life

    • Small bispecific formats for tissue penetration

    • Multivalent formats for enhanced avidity

  • Reference established bispecific platforms such as knobs-into-holes, DNL method, or CrossMAb technology

• Production and purification strategies:

  • Optimize expression systems (mammalian, insect, bacterial)

  • Implement purification strategies to ensure homogeneity

  • Confirm correct assembly using analytical techniques (SEC, mass spectrometry)

• Functional validation:

  • Verify binding to both targets simultaneously using BLI or other suitable methods

  • Assess impact on target cell populations using flow cytometry

  • Evaluate functional effects in relevant cell-based assays

• Stability and developability assessment:

  • Conduct thermal stability studies (DSC, nanoDSF)

  • Assess aggregation propensity

  • Evaluate pH and buffer condition sensitivities

The knobs-into-holes Fc construct has been validated as a viable approach for ensuring correct heavy chain pairing in bispecific antibodies, which would be applicable to RTKN-targeting bispecifics .

What are the cutting-edge applications of RTKN antibodies in single-cell analysis techniques?

RTKN antibodies can be integrated into emerging single-cell technologies through these innovative approaches:

• Single-cell proteomics applications:

  • Mass cytometry (CyTOF) integration using metal-conjugated RTKN antibodies

  • CITE-seq approaches combining transcriptomics with RTKN protein detection

  • Microfluidic antibody capture for single-cell western blotting

• Spatial biology applications:

  • Multiplex immunofluorescence with RTKN antibodies in spatial transcriptomics workflows

  • Integration with technologies like GeoMx DSP or Visium for spatial context

  • Custom antibody panels including RTKN for imaging mass cytometry

• Live-cell dynamics at single-cell level:

  • Development of non-perturbing antibody fragments for live imaging

  • Integration with optogenetic approaches to modulate RTKN function

  • Correlation of RTKN dynamics with cellular behaviors at single-cell resolution

• Algorithmic approaches for data integration:

  • Machine learning algorithms to correlate RTKN expression with cellular phenotypes

  • Trajectory analysis incorporating RTKN protein levels with transcriptomic states

  • Network analysis connecting RTKN to broader signaling ecosystems

• Single-cell functional genomics:

  • Combining CRISPR perturbations with RTKN antibody detection

  • Correlation of genetic backgrounds with RTKN protein levels

  • Assessment of genetic dependencies on RTKN functionality

These approaches expand the utility of RTKN antibodies beyond traditional bulk analyses, providing unprecedented resolution into cellular heterogeneity and function.

How might RTKN antibodies be incorporated into advanced antibody-drug conjugate (ADC) research?

While RTKN is primarily an intracellular protein making it challenging as a direct ADC target, innovative approaches could leverage RTKN biology in ADC development:

• Target selection strategies:

  • Identify cell surface proteins that correlate with RTKN expression in cancer

  • Screen for tumor types with RTKN dependency to prioritize ADC applications

  • Consider RTKN-dependent tumors for specific ADC targeting strategies

• Mechanistic considerations:

  • Design ADCs that synergize with RTKN-dependent pathways

  • Create bispecific antibody-based PROTACs (AbTACs) to potentially target RTKN for degradation

  • Consider intracellular antibody delivery approaches for direct RTKN targeting

• Response monitoring applications:

  • Use RTKN antibodies to monitor treatment effects of ADCs

  • Incorporate as biomarkers in ADC clinical trials

  • Develop RTKN-based companion diagnostics for ADC therapies

• Technical development approaches:

  • Explore antibody engineering strategies including camelid antibodies or other specialized formats

  • Consider novel linker chemistries for targeted payload delivery

  • Evaluate combination approaches with signaling pathway inhibitors

• Validation methodologies:

  • Implement multiparameter analytics to assess ADC effects on RTKN-dependent pathways

  • Develop organoid models to evaluate ADC efficacy in RTKN-expressing tumors

  • Utilize patient-derived xenografts to assess clinical translation potential

The AbTAC approach, which uses bispecific antibodies to recruit E3 ligases for target protein degradation, represents an innovative strategy that could potentially be applied to RTKN research contexts .

What are the future directions for integrating artificial intelligence and machine learning with RTKN antibody research?

The integration of AI/ML approaches with RTKN antibody research offers transformative potential:

• Epitope prediction and antibody design:

  • Machine learning algorithms to predict optimal RTKN epitopes for antibody development

  • AI-guided antibody engineering for enhanced specificity and affinity

  • Deep learning approaches to predict cross-reactivity with related proteins

• Image analysis enhancement:

  • Automated quantification of RTKN expression in immunohistochemistry

  • Deep learning for subcellular localization pattern recognition

  • Convolutional neural networks for multiplexed IF image analysis

• Multi-omics data integration:

  • Machine learning algorithms to correlate RTKN protein levels with genomic and transcriptomic data

  • Network analysis tools to position RTKN within broader signaling networks

  • Predictive modeling of RTKN involvement in disease progression

• Clinical translation applications:

  • AI-based patient stratification using RTKN expression patterns

  • Machine learning for predicting treatment responses based on RTKN-related biomarkers

  • Development of digital pathology algorithms incorporating RTKN detection

• Literature mining and knowledge discovery:

  • Natural language processing to extract RTKN-related insights from scientific literature

  • Automated hypothesis generation for unexplored functions of RTKN

  • Knowledge graph approaches to identify novel RTKN interaction partners