ret Antibody

What is RET and why is it a significant research target?

RET (rearranged during transfection) is a transmembrane receptor tyrosine kinase that plays crucial roles in cell growth, differentiation, and survival. It functions as a receptor for glial cell line-derived neurotrophic factor (GDNF) family ligands. RET is particularly significant because mutations in this proto-oncogene are associated with multiple endocrine neoplasia type 2 (MEN2B), medullary thyroid carcinoma (MTC1), and other cancers. The protein is approximately 124.3 kilodaltons in mass and can be found in several isoforms, including RET51 . In neurological research, RET is notable for its expression in motor neurons and its role in neuronal development and function, making it an important target for studies on neurodegenerative diseases .

What types of RET antibodies are available for research purposes?

Research-grade RET antibodies are available in several formats with distinct specificities:

• Total RET antibodies - Recognize all forms of the RET protein regardless of phosphorylation status

• Phospho-specific antibodies - Target specific phosphorylation sites (e.g., Tyr905) that indicate active signaling

• Isoform-specific antibodies - Distinguish between RET9 and RET51 splice variants

• Domain-specific antibodies - Target extracellular, transmembrane, or cytoplasmic regions

Available species reactivity includes human, mouse, rat, and some non-human primates, with human RET antibodies showing varying degrees of cross-reactivity with mouse RET . The antibodies are produced in various host species including goat and mouse, which should be considered when designing multi-color immunofluorescence experiments to avoid secondary antibody cross-reactivity .

How can I validate RET antibody specificity for my experimental system?

Methodological validation of RET antibodies should follow these steps:

• Positive and negative control tissues/cells: For RET, human spinal cord sections showing motor neuron staining serve as excellent positive controls . Conversely, tissues known not to express RET can serve as negative controls.

• Western blot validation: Confirm the antibody detects a band of appropriate molecular weight (~124 kDa for full-length RET). Compare with lysates from cells with RET knockdown or knockout.

• Phospho-antibody validation: When using phospho-specific RET antibodies (e.g., pTyr905), include samples treated with phosphatase or samples from cells treated with RET kinase inhibitors .

• Cross-reactivity testing: If working with multiple species, test the antibody against recombinant proteins or cell lysates from each species. Some RET antibodies show 100% cross-reactivity between human and mouse RET in direct ELISAs and Western blots .

• Immunoprecipitation followed by mass spectrometry: For ultimate validation, perform IP with the RET antibody followed by mass spectrometry identification of the precipitated proteins.

How should I design a flow cytometry panel that includes RET antibody?

When incorporating RET antibody into a flow cytometry panel, follow these methodological principles:

• Prioritize marker placement based on expression levels: Since RET may be expressed at varying levels depending on the cell type, match antibody brightness with expected expression. For low RET expression, use bright fluorophores (like PE or APC); for high expression, dimmer fluorophores may suffice .

• Consider autofluorescence interference: Neuronal cells often have high autofluorescence. Choose fluorochromes with emission spectra distinct from cellular autofluorescence patterns. The Cytek Aurora system may be preferable for samples with high autofluorescence .

• Avoid spectral overlap with co-expressed markers: If examining markers co-expressed with RET (e.g., GFRα co-receptors), ensure their fluorophores have minimal spectral overlap to prevent false positives due to compensation issues .

• Implement proper gating strategy:

  • Begin with size/shape discrimination (FSC vs SSC)

  • Exclude doublets using Area vs Height parameters

  • Apply dead cell exclusion dye

  • Gate on population of interest (e.g., CD45+ for immune cells)

  • Then analyze RET expression

• Include fluorescence minus one (FMO) controls: Essential for setting gates accurately, especially when RET expression might form a continuum rather than discrete positive/negative populations.

A staining index calculation can help objectively compare fluorophore brightness options:
Staining Index = (MFI positive - MFI negative) / (2 × SD of negative)

What are the optimal conditions for immunohistochemical detection of RET in tissue samples?

For optimal immunohistochemical detection of RET in tissues, follow this validated protocol:

• Tissue preparation:

  • Fresh tissues should be immediately fixed in 10% neutral buffered formalin

  • Paraffin embedding should follow standard protocols with careful temperature control

  • Cut sections at 4-6 μm thickness

• Antigen retrieval:

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

  • 20 minutes at 95-98°C has been validated for RET epitope exposure

• Antibody incubation:

  • Apply RET antibody at 3 μg/mL concentration

  • Incubate for 1 hour at room temperature in a humidified chamber

  • For human spinal cord tissue, this concentration has been demonstrated to produce specific staining of motor neurons

• Detection system:

  • For goat primary antibodies (like AF1485), use Anti-Goat IgG HRP Polymer

  • For mouse primary antibodies, appropriate anti-mouse detection systems

  • DAB (3,3'-diaminobenzidine) works well as a chromogen, producing brown staining

  • Counterstain with hematoxylin for nuclear visualization

• Controls:

  • Include a section without primary antibody (negative control)

  • Include known RET-positive tissue (like spinal cord motor neurons)

What are the key considerations for western blot analysis using RET antibodies?

When performing western blot analysis with RET antibodies, follow these methodological guidelines:

• Sample preparation:

  • Use RIPA buffer supplemented with phosphatase inhibitors (especially critical for phospho-RET detection)

  • Include protease inhibitors to prevent degradation

  • Sonicate briefly to shear DNA and reduce sample viscosity

• Gel selection:

  • Use 7.5% or 4-12% gradient gels due to RET's large size (~124 kDa)

  • Run at lower voltage (80-100V) to improve resolution of high molecular weight proteins

• Transfer conditions:

  • Use wet transfer for large proteins like RET

  • Transfer at 30V overnight at 4°C or 100V for 2 hours with cooling

  • PVDF membrane is recommended over nitrocellulose for better protein retention

• Blocking and antibody incubation:

  • Block with 5% BSA in TBST for phospho-specific antibodies

  • For total RET detection, 5% non-fat dry milk in TBST is suitable

  • Primary antibody dilutions will vary by product (1:1000 is common)

• Detection considerations:

  • Enhanced chemiluminescence (ECL) with film exposure or digital imaging

  • For weakly expressed RET, consider signal amplification systems

• Positive controls:

  • SH-SY5Y human neuroblastoma cells express detectable levels of RET

  • Recombinant RET protein can serve as a positive control

Why might I observe multiple bands when using RET antibodies in western blot?

Multiple bands in RET western blots can occur for several legitimate biological and technical reasons:

• Alternative splicing: RET has multiple isoforms including RET9 (≈120 kDa) and RET51 (≈124 kDa) that differ in their C-terminal tails. Depending on the epitope recognized by the antibody, you may see one or both isoforms .

• Post-translational modifications:

  • Glycosylation: RET undergoes extensive N-glycosylation, producing bands at ≈150-170 kDa (mature) and ≈120 kDa (immature)

  • Phosphorylation: Multiple phosphorylation states can cause slight mobility shifts

  • Ubiquitination: Higher molecular weight smears may indicate ubiquitinated RET

• Proteolytic processing: RET can undergo proteolytic cleavage, generating fragments of various sizes. Calpain-mediated cleavage produces a ≈100 kDa fragment.

• Technical issues:

  • Incomplete reduction: Ensure fresh DTT or β-mercaptoethanol in sample buffer

  • Sample degradation: Always use protease inhibitors during lysis

  • Non-specific binding: Try different blocking agents or increase washing stringency

To confirm band identity, compare staining patterns with antibodies targeting different RET epitopes or use genetic approaches (siRNA knockdown, CRISPR knockout) .

How do I interpret variable RET staining intensities in immunohistochemistry?

Variation in RET immunostaining intensity is common and requires careful interpretation:

• Biological variables affecting RET expression:

  • Developmental stage: RET expression changes dramatically during development

  • Cell activation state: RET may be upregulated upon specific signaling events

  • Microenvironment: GDNF family ligands can alter RET expression and localization

  • Disease state: Mutations or chromosomal rearrangements can alter expression levels

• Technical variables affecting staining intensity:

  • Fixation time: Overfixation can mask epitopes

  • Antigen retrieval efficiency: Critical for consistent results

  • Antibody concentration: Titrate carefully for optimal signal-to-noise ratio

  • Detection system sensitivity: Enhanced detection systems may be needed for low expression

• Quantification approaches:

  • H-score method: Combines intensity (0-3+) and percentage of positive cells

  • Digital image analysis: Software-based quantification of DAB intensity

  • Cell-by-cell analysis: Important when expression is heterogeneous

• Validation strategies:

  • Multi-antibody confirmation: Use antibodies against different RET epitopes

  • Correlation with mRNA expression: In situ hybridization or RT-PCR from microdissected samples

  • Functional correlation: Phospho-RET staining should correlate with downstream pathway activation

When examining RET in human spinal cord, motor neurons typically show moderate to strong staining as demonstrated in published data . Variations from this pattern may indicate technical issues or biological alterations worthy of further investigation.

What controls should I include when studying phosphorylated RET?

When studying phosphorylated RET (e.g., pTyr905), implement these essential controls:

• Positive controls:

  • Cell lines treated with RET ligands (e.g., GDNF plus GFRα1)

  • Tissues known to contain activated RET (e.g., developing enteric nervous system)

  • Cells transfected with constitutively active RET mutants (e.g., RET-MEN2A)

• Negative controls:

  • Samples treated with lambda phosphatase to remove phosphorylation

  • RET kinase inhibitor-treated samples (e.g., vandetanib or cabozantinib)

  • RET knockout or knockdown samples

• Specificity controls:

  • Pre-absorption with phospho-peptide vs. non-phospho-peptide

  • Parallel blots with total RET antibody to normalize phospho-signal

  • Combined IP-Western approach: IP with total RET, then blot with phospho-specific

• Technical considerations:

  • Always include phosphatase inhibitors (sodium orthovanadate, sodium fluoride)

  • Maintain cold conditions during sample preparation

  • Process all comparable samples simultaneously

• Quantification approach:

  • Express results as phospho-RET/total RET ratio

  • Include time-course studies when examining dynamic phosphorylation events

  • For immunohistochemistry, use phospho-specific antibodies on serial sections

Phospho-RET (Tyr905) antibodies have been validated in multiple studies, showing 58 citations and 119 figures according to supplier data, demonstrating their reliability for studying RET activation .

How can I design experiments to study RET isoform-specific signaling using antibodies?

Designing experiments to distinguish between RET9 and RET51 isoform signaling requires a strategic approach:

• Isoform-specific antibody selection:

  • Choose antibodies targeting the unique C-terminal sequences of RET9 (9 residues) or RET51 (51 residues)

  • Validate specificity using cells expressing only one isoform through genetic engineering

  • Consider using epitope-tagged RET constructs (HA-RET9, FLAG-RET51) with well-characterized tag antibodies

• Combinatorial immunoprecipitation approach:

  • Immunoprecipitate with isoform-specific antibodies

  • Blot for co-precipitating proteins to identify isoform-specific binding partners

  • Alternatively, IP with antibodies against suspected binding partners and blot for RET isoforms

• Phosphorylation analysis workflow:

  • IP with isoform-specific antibodies

  • Blot with phospho-specific antibodies to compare activation patterns

  • Examine downstream pathway activation (ERK, AKT, STAT3) following isoform-specific IP

• Proximity ligation assay (PLA) strategy:

  • Use isoform-specific antibodies paired with antibodies against putative interactors

  • This allows visualization of protein-protein interactions in situ with isoform specificity

  • Quantify PLA signals to measure relative interaction strengths

• Functional readouts:

  • Couple antibody-based detection with functional assays

  • Measure neurite outgrowth, cell survival, or proliferation after isoform-specific perturbation

  • Correlate antibody-detected expression patterns with functional outcomes

This methodological approach has been validated in studies examining the differential roles of RET isoforms in development and disease, revealing isoform-specific signaling complexes and biological outcomes .

What are the optimal approaches for studying RET in flow cytometry for rare cell populations?

When studying RET expression in rare cell populations by flow cytometry, implement these advanced methodological approaches:

• Sample enrichment strategies:

  • Magnetic bead pre-enrichment using markers co-expressed with RET

  • Density gradient separation to remove unwanted cell populations

  • Depletion of abundant negative populations prior to staining

• High-dimensional panel design:

  • Place RET antibody in the brightest channel available (PE, APC, or BV421)

  • Use spectral flow cytometry (e.g., Cytek Aurora) for better resolution of rare populations

  • Implement a dump channel with markers of irrelevant cells labeled with the same fluorophore

• Acquisition parameters optimization:

  • Collect more events (minimum 1-5 million) to capture sufficient rare cells

  • Reduce flow rate to improve signal resolution

  • Use threshold triggering on parameters relevant to your population

• Analysis considerations:

  • Apply sequential gating strategy beginning with viability and singlets

  • Consider using probability contour plots rather than dot plots for rare events

  • Implement dimensionality reduction algorithms (tSNE, UMAP) to identify populations

  • Use SH-SY5Y cells as a positive control for setting up RET detection parameters

• Validation approach:

  • Back-sorting of putative RET+ populations for functional or molecular validation

  • Correlation of flow cytometry results with immunohistochemistry of the same tissue

  • Single-cell RNA-seq of sorted populations to confirm RET mRNA expression

This approach has been validated for detecting RET in neuroblastoma cell lines, where intracellular staining following fixation and permeabilization with saponin proved effective .

How can I use RET antibodies to investigate RET splicing variants in disease models?

To investigate RET splicing variants (particularly RET9 vs. RET51) in disease models, employ this comprehensive methodology:

• Antibody-based splicing variant detection:

  • Western blot analysis using antibodies that either:
    a) Recognize both variants but resolve them by size difference
    b) Specifically target unique C-terminal sequences

  • Quantify the ratio of variants using densitometry with normalization to loading controls

• Immunohistochemical localization:

  • Use isoform-specific antibodies on serial sections

  • Implement multiplexed immunofluorescence to co-localize with disease markers

  • Quantify relative expression in different cell types within diseased tissue

• Co-immunoprecipitation for variant-specific interactomes:

  • IP with isoform-specific antibodies

  • Identify differential binding partners using mass spectrometry

  • Validate key interactions with reverse co-IP and proximity ligation assays

• Correlation with disease parameters:

  • Create a scoring system for variant expression levels

  • Correlate with clinical data (survival, treatment response)

  • Track changes in variant ratios during disease progression

• Functional validation approach:

  • Combine antibody detection with genetic manipulation

  • Create cells expressing only one variant through CRISPR-mediated editing

  • Use antibodies to confirm expression and track signaling differences

This approach has been successfully applied in cancer research, particularly in thyroid carcinomas where RET splicing variants show differential oncogenic potential .

What methodologies can I use to study RET trafficking and membrane localization?

To study RET trafficking and membrane localization, implement these advanced antibody-based approaches:

• Surface biotinylation coupled with immunoprecipitation:

  • Biotinylate surface proteins on live cells

  • Immunoprecipitate with RET antibodies

  • Blot with streptavidin-HRP to detect surface fraction

  • Blot separate aliquot with RET antibody to determine total RET

  • Calculate surface/total ratio to quantify membrane localization

• Antibody internalization assay workflow:

  • Incubate live cells with RET antibodies that recognize extracellular domain

  • Allow internalization at 37°C for various time points

  • Strip remaining surface antibodies with acid wash

  • Detect internalized antibody-RET complexes by microscopy or flow cytometry

• Immunofluorescence co-localization analysis:

  • Co-stain RET with markers of specific cellular compartments:

    • Na+/K+ ATPase (plasma membrane)

    • EEA1 (early endosomes)

    • Rab11 (recycling endosomes)

    • LAMP1 (lysosomes)

    • GM130 (Golgi)

  • Calculate Pearson's correlation coefficients to quantify co-localization

  • Track changes following ligand stimulation or inhibitor treatment

• TIRF microscopy approach:

  • Use RET antibodies or fluorescent protein-tagged RET

  • Visualize only molecules within ~100 nm of the plasma membrane

  • Quantify dwelling time at the membrane under different conditions

• Antibody-based RUSH system implementation:

  • Combine retention using selective hooks (RUSH) with antibody detection

  • Track synchronized protein trafficking from ER to plasma membrane

  • Use pulse-chase approach with antibody detection at fixed timepoints

This multi-faceted approach has been validated in neuronal cell models, where RET trafficking dynamics are critical for proper signaling in response to neurotrophic factors .

How can I combine RET antibody-based detection with functional assays of RET signaling?

To integrate RET antibody detection with functional signaling assays, implement this comprehensive workflow:

• Split-sample approach:

  • Divide each sample for parallel antibody-based and functional analyses

  • Use antibodies to quantify RET expression/phosphorylation levels

  • Simultaneously measure functional outcomes in the matched sample

  • Correlate expression/activation with function on a sample-by-sample basis

• Sequential analysis workflow:

  • Perform functional assays on live cells (e.g., calcium flux, neurite outgrowth)

  • Fix and immunostain the same cells for RET expression/activation

  • Use image registration to correlate functional readouts with antibody staining at single-cell level

• Reporter system integration:

  • Generate cell lines with RET-dependent transcriptional reporters

  • Validate reporter activity correlates with antibody-detected RET activation

  • Use reporter for live monitoring and antibodies for endpoint validation

• Multiplexed signaling analysis:

  • Perform phospho-flow cytometry with antibodies against:
    a) Phospho-RET (e.g., pTyr905)
    b) Downstream effectors (pERK, pAKT, pSTAT3)

  • Analyze correlation between receptor activation and pathway activation

  • Gate on different expression levels to determine signaling thresholds

• In vivo correlation approach:

  • Use functional imaging (PET, SPECT) to assess activity in animal models

  • Perform post-mortem antibody-based analysis on the same tissues

  • Map functional data to molecular expression patterns

This integrated approach has been validated in studies examining RET signaling in neuronal populations, demonstrating clear correlation between antibody-detected activation states and functional outcomes in neural development and maintenance .

What are the best methods for combining RET antibody detection with genetic manipulation of RET?

To effectively combine RET antibody detection with genetic manipulation, implement this methodological framework:

• CRISPR/Cas9 modification validation:

  • Design genetic modifications (knockout, point mutations, tagged insertions)

  • Use RET antibodies to confirm successful editing:
    a) Total RET antibodies to confirm knockout
    b) Phospho-specific antibodies to validate functional impact of point mutations
    c) Epitope tag antibodies to detect inserted tags

  • Compare antibody signals between wild-type and edited cells

• Overexpression system analysis:

  • Transfect cells with RET variant constructs

  • Use antibodies to:
    a) Quantify expression levels for normalization
    b) Detect subcellular localization changes
    c) Measure phosphorylation status as readout of activity

  • Compare antibody staining patterns between endogenous and overexpressed RET

• RNA interference correlation:

  • Implement siRNA or shRNA against RET

  • Use antibodies to verify knockdown efficiency at protein level

  • Quantify relationship between mRNA reduction and protein reduction

  • Establish time course of protein depletion following RNA interference

• Rescue experiment design:

  • Knockout endogenous RET

  • Re-express specific variants or mutants

  • Use antibodies to:
    a) Confirm absence of endogenous protein
    b) Verify expression of the rescue construct
    c) Measure restoration of downstream signaling

• Domain-swapping analysis:

  • Create chimeric receptors with domains from other RTKs

  • Use domain-specific antibodies to verify chimera expression

  • Examine altered signaling using phospho-specific antibodies

This approach has been validated in multiple studies examining the functional consequences of RET mutations found in cancer and developmental disorders .

How can I use RET antibodies in single-cell analysis techniques?

For integrating RET antibodies into single-cell analysis workflows, implement these cutting-edge approaches:

• Single-cell Western blotting protocol:

  • Separate single cells in microwell arrays

  • Lyse in situ and separate proteins by size

  • Probe with RET antibodies and normalization controls

  • Quantify expression level heterogeneity across individual cells

  • Compare with bulk population averages to identify rare subpopulations

• Mass cytometry (CyTOF) integration:

  • Conjugate RET antibodies to rare earth metals

  • Combine with 30-40 other antibodies for comprehensive phenotyping

  • Analyze RET expression in relation to cell lineage and activation markers

  • Implement unsupervised clustering to identify novel RET-expressing populations

• Single-cell immunofluorescence quantification:

  • Immunostain for RET and co-markers

  • Image using high-content microscopy

  • Extract quantitative features:
    a) Expression level (intensity)
    b) Subcellular localization (spatial distribution)
    c) Morphological parameters (cell shape, neurite length)

  • Correlate RET expression patterns with morphological phenotypes

• Imaging mass cytometry approach:

  • Use metal-tagged RET antibodies on tissue sections

  • Ablate tissue with laser and analyze released metals

  • Generate high-dimensional spatial maps of RET expression

  • Preserve tissue architecture context while obtaining single-cell resolution

• CODEX multiplexed imaging integration:

  • Incorporate RET antibodies into DNA-barcoded antibody panels

  • Perform iterative imaging cycles with fluorescent reporters

  • Achieve 40+ marker detection on the same tissue section

  • Map RET expression to complex cellular neighborhoods

These approaches enable unprecedented resolution of RET biology at the single-cell level, revealing heterogeneity masked by bulk analyses and providing insight into rare cell populations with unique RET expression or activation patterns .

What approaches can I use to study the RET interactome using antibody-based methods?

To comprehensively map the RET interactome using antibody-based methods, implement this advanced methodological framework:

• Co-immunoprecipitation with mass spectrometry:

  • Immunoprecipitate RET using validated antibodies under different conditions

  • Perform LC-MS/MS analysis of co-precipitated proteins

  • Implement SILAC or TMT labeling for quantitative comparison

  • Filter against control IPs to remove non-specific interactions

  • Validate key interactions by reverse IP and western blotting

• BioID proximity labeling approach:

  • Generate RET-BioID fusion proteins

  • Use antibodies to confirm expression and proper localization

  • Purify biotinylated proteins and identify by mass spectrometry

  • Compare interactome of wild-type RET versus mutant forms

  • Validate spatial proximity of identified partners using antibodies

• APEX2 proximity labeling strategy:

  • Create RET-APEX2 fusions

  • Validate using antibodies against RET and the APEX2 tag

  • Perform rapid biotin labeling of proximal proteins

  • Compare interaction landscapes across different cellular compartments

  • Confirm key interactions with conventional antibody-based methods

• Multiplex co-immunoprecipitation array:

  • IP RET under various conditions

  • Probe co-precipitates with antibody arrays targeting RTK signaling proteins

  • Quantify relative binding across different experimental conditions

  • Create dynamic interactome maps in response to ligands or inhibitors

• Proximity ligation assay (PLA) screening:

  • Perform systematic PLA between RET and candidate interactors

  • Quantify interaction signals at subcellular resolution

  • Map interaction networks to specific cellular compartments

  • Track dynamic changes in interactions following stimulation

This integrated approach reveals context-dependent interactions that may be missed by single methods and has been validated in studies of RET signaling complexes in both physiological signaling and pathological contexts .

How should I analyze large-scale phospho-RET antibody data in the context of other -omics data?

For integrating phospho-RET antibody data with other -omics datasets, implement this comprehensive analytical framework:

• Multi-omics data normalization strategy:

  • Convert phospho-RET antibody signals to standardized scores

  • Apply batch correction when combining datasets from different experiments

  • Normalize against total RET levels to focus on activation rather than expression

  • Create integrated data matrices suitable for multi-omics analysis

• Correlation analysis approach:

  • Calculate Spearman or Pearson correlations between:
    a) Phospho-RET levels and transcriptomic signatures
    b) Phospho-RET and other phospho-proteins (phospho-proteomics)
    c) Phospho-RET and metabolomic profiles

  • Visualize correlation networks using force-directed layouts

  • Identify modules of co-regulated biomolecules

• Pathway enrichment integration:

  • Map phospho-RET levels to known signaling pathways

  • Perform gene set enrichment analysis (GSEA) on genes correlating with phospho-RET

  • Integrate with pathway databases (KEGG, Reactome, WikiPathways)

  • Identify pathway-level consequences of RET activation

• Causal network inference:

  • Apply Bayesian network algorithms to infer directionality

  • Test causal relationships with intervention data (RET inhibitors, knockdown)

  • Build predictive models of downstream effects of RET activation

  • Validate key network connections experimentally

• Visualization and representation:

  • Create multi-level network visualizations

  • Develop interactive dashboards for exploring relationships

  • Implement dimensionality reduction (PCA, t-SNE) for sample clustering

  • Generate publication-quality figures showing key associations

This analytical framework has been applied to understand RET signaling in the context of neural development and cancer biology, revealing novel connections between RET activation and broader cellular processes .

What are the most effective methods for quantifying and statistically analyzing RET antibody signals in tissue microarrays?

For rigorous quantification and statistical analysis of RET antibody signals in tissue microarrays (TMAs), implement this methodological framework: