STON1 Antibody

What is STON1 and why is it a target for antibody development?

STON1 (Stonin 1) is an endocytic protein involved in cellular trafficking pathways. As demonstrated in recent studies, STON1 plays significant roles in various cellular mechanisms, making it an important research target in both normal physiology and disease states, particularly in cancer biology . Antibodies against STON1 have been developed to enable researchers to detect, quantify, and characterize this protein across multiple experimental platforms. These antibodies recognize specific epitopes, typically in the C-terminal or N-terminal regions of the STON1 protein, allowing for precise detection in research applications .

What are the common applications for STON1 antibodies in research?

STON1 antibodies have been validated for multiple research applications, with the most common being:

• Western Blotting (WB): For detecting and quantifying STON1 in protein lysates

• Enzyme-Linked Immunosorbent Assay (ELISA): For quantitative analysis in solution

• Immunohistochemistry (IHC): For visualizing STON1 expression in tissue sections

• Immunofluorescence (IF): For subcellular localization studies

• Immunocytochemistry (ICC): For cellular expression analysis

The selection of the appropriate application depends on the specific research question being addressed. For instance, when investigating protein expression patterns across tissues, IHC would be most appropriate, while protein-protein interactions might require co-immunoprecipitation approaches using these antibodies.

How does STON1 expression vary across different tissues and cell types?

STON1 expression shows tissue-specific patterns that researchers should consider when designing experiments. Based on immunohistochemical studies, STON1 exhibits variable expression across different tissue types. In kidney tissues specifically, studies have demonstrated that STON1 is significantly downregulated in kidney renal clear cell carcinoma (KIRC) compared to normal kidney tissues . This differential expression has important implications for understanding the protein's role in normal versus pathological states.

Research using various cell lines has similarly shown differential expression patterns. For instance, when comparing normal renal tubular epithelial cell lines (HK-2) with KIRC cell lines (A498, ACHN, and 786-O), significant differences in STON1 expression levels have been documented . These variations highlight the context-dependent nature of STON1 expression and its potential functional significance.

How should researchers select the most appropriate STON1 antibody for their specific application?

Selection of the optimal STON1 antibody depends on several critical factors that researchers must consider:

• Target epitope: Antibodies targeting different regions (C-terminal, N-terminal, specific amino acid sequences) may perform differently depending on the experimental context

• Host species: Consider compatibility with your experimental system to avoid cross-reactivity issues

• Clonality: Polyclonal antibodies offer broader epitope recognition, while monoclonal antibodies provide higher specificity

• Validated applications: Ensure the antibody has been validated for your specific application (WB, IF, IHC, etc.)

• Species reactivity: Verify cross-reactivity with your species of interest (human, mouse, rat, etc.)

For instance, when working with human samples, researchers should select antibodies with confirmed human reactivity, such as the ABIN6265349 antibody that detects endogenous levels of total STON1 in human samples , or the A13140-1 antibody that has been validated for human, mouse, and rat samples .

What are the optimal conditions for STON1 antibody storage and handling to maintain efficacy?

Proper storage and handling of STON1 antibodies is critical for maintaining their performance and extending their usable lifespan:

• Long-term storage: Store at -20°C for up to one year

• Working storage: For frequent use, store at 4°C for up to one month

• Avoid repeated freeze-thaw cycles as they can degrade antibody quality and performance

• Most STON1 antibodies are supplied in buffer solutions containing stabilizers (such as 50% glycerol) and preservatives (like 0.02% sodium azide)

The typical formulation of commercially available STON1 antibodies includes:

• Concentration: ~1mg/ml

• Buffer: PBS (phosphate-buffered saline)

• pH: Maintained around 7.2

• Additives: Glycerol (50%) and sodium azide (0.02%)

These storage conditions ensure optimal antibody stability and performance across multiple experimental uses.

What dilution ratios and incubation conditions are recommended for various STON1 antibody applications?

Optimal working conditions vary by application and specific antibody. Based on validated protocols:

These recommendations serve as starting points, and researchers should optimize conditions for their specific experimental systems. For instance, the A13140-1 antibody has been specifically validated for IHC applications with a recommended dilution range of 1:50-1:200 . Always perform preliminary titration experiments to determine optimal antibody concentration for your specific application.

How can STON1 antibodies be used to investigate the protein's role in tumor immune microenvironment?

STON1 has emerged as a significant factor in tumor immune microenvironments, particularly in kidney renal clear cell carcinoma (KIRC). Researchers can leverage STON1 antibodies to:

• Characterize expression patterns in tumor versus normal tissues using IHC

• Correlate STON1 expression with immune cell infiltration patterns

• Investigate associations with immune checkpoint molecules

• Evaluate potential as a biomarker for immunotherapy response

Recent studies have demonstrated that STON1 expression correlates with immune cell infiltration patterns in KIRC. Specifically, tumors with high STON1 expression showed enriched immune cell populations and better prognosis compared to tumors with low STON1 expression. These findings suggest that STON1 may influence the tumor immune microenvironment, potentially creating an immune non-inflamed phenotype in KIRC .

When designing such studies, researchers should consider using multiplexed immunohistochemistry or immunofluorescence to simultaneously visualize STON1 and immune cell markers, allowing for spatial correlation analysis within the tumor microenvironment.

What validation controls should be included when using STON1 antibodies in experimental workflows?

Rigorous validation is essential for generating reliable results with STON1 antibodies. Recommended controls include:

• Positive control tissues/cells with known STON1 expression (e.g., normal kidney tissues for KIRC studies)

• Negative control tissues/cells with minimal STON1 expression

• Isotype controls using non-immune IgG of the same species and class as the primary antibody

• Blocking peptide controls to confirm antibody specificity

• siRNA or CRISPR knockout validation for definitive specificity assessment

For IHC applications specifically, proper controls should include non-immune IgG as a negative control and validated positive control tissues. The antibody's specificity should be thoroughly verified through blocking peptide competition or other specificity tests .

For Western blotting applications, researchers should validate observed bands against the predicted molecular weight of STON1 (approximately 83 kDa) , and consider including STON1-knockdown or overexpression controls to confirm band identity.

How do different scoring systems impact the interpretation of STON1 expression in tissue microarrays?

When evaluating STON1 expression in tissue microarrays, the choice of scoring methodology significantly impacts data interpretation and reproducibility. Researchers typically employ a composite scoring system that accounts for both staining intensity and percentage of positive cells:

For staining intensity:

• 0: Negative

• 1: Weak

• 2: Moderate

• 3: Strong

For percentage of positive cells:

• 1: 0–25% positive cells

• 2: 26–50% positive cells

• 3: 51–75% positive cells

• 4: 76–100% positive cells

The total immunoreactive score is calculated by combining both parameters, creating a more comprehensive evaluation of STON1 expression. This approach provides greater resolution in distinguishing expression levels across samples compared to binary positive/negative classification.

Researchers should clearly document their scoring methodology and include representative images of different scoring categories to ensure reproducibility. Additionally, multiple independent scorers should evaluate the same samples to establish inter-observer reliability, particularly in clinical correlation studies.

What is the relationship between STON1 expression and clinical parameters in kidney cancer research?

Recent investigations into STON1's role in kidney renal clear cell carcinoma (KIRC) have revealed significant correlations between STON1 expression and various clinical parameters:

• STON1 is significantly downregulated in KIRC compared to normal kidney tissues

• Decreased STON1 expression correlates with:

  • Higher tumor grade

  • Advanced TNM stage

  • Presence of distant metastasis

  • Poorer patient status

These findings highlight STON1's potential as a prognostic biomarker in KIRC, warranting further investigation into its mechanistic role in disease progression and treatment response.

How can STON1 antibodies be used to investigate the relationship between STON1 and immunotherapy response?

STON1 has emerged as a potential predictor of immunotherapy response, particularly in kidney cancer. Researchers can leverage STON1 antibodies to:

• Stratify patient samples based on STON1 expression levels

• Correlate STON1 expression with immune checkpoint marker expression

• Evaluate associations with tumor mutational burden (TMB)

• Analyze relationships with mismatch repair proteins

• Predict potential immunotherapy response based on STON1 expression patterns

Interestingly, research has demonstrated that STON1 is positively correlated with mismatch repair proteins and negatively correlated with tumor mutational burden. Single-sample Gene Set Enrichment Analysis and Pearson correlation analyses have revealed that tumors with low STON1 expression may be more responsive to immune checkpoint blockade therapy, while those with high STON1 expression might be better candidates for targeted therapies .

When designing studies to investigate these relationships, researchers should employ multiplex approaches that simultaneously evaluate STON1, immune checkpoint molecules, and markers of immune cell infiltration to develop comprehensive predictive models.

What methods can be used to quantitatively compare STON1 expression between normal and cancer cell lines?

Researchers investigating STON1 expression differences between normal and cancerous cell lines have several quantitative methodologies at their disposal:

• Quantitative RT-PCR (qRT-PCR):

  • Using validated primers for STON1 (e.g., forward: 5'-GCCCAAATATTTCCTGCAGAGTC-3', reverse: 5'-CTGAGGCCAGGAAGGTTCAG-3')

  • Normalizing to appropriate housekeeping genes (e.g., GAPDH)

  • Calculating relative expression using the 2^(-ΔΔCt) method

• Western Blotting:

  • Using validated STON1 antibodies

  • Normalizing to loading controls (e.g., GAPDH, β-actin)

  • Employing densitometric analysis for quantification

• Immunofluorescence/Immunocytochemistry:

  • Using standardized staining protocols with validated STON1 antibodies

  • Quantifying fluorescence intensity using digital image analysis

  • Comparing subcellular localization patterns

When comparing expression across different cell lines, researchers should maintain consistent experimental conditions, including cell density, passage number, and culture conditions to minimize variability. Additionally, biological replicates from independent passages should be included to account for inherent biological variation.

What are common causes of non-specific staining with STON1 antibodies, and how can these be mitigated?

Non-specific staining is a common challenge when working with antibodies, including those targeting STON1. Key causes and solutions include:

• Insufficient blocking:

  • Extend blocking time (1-2 hours at room temperature)

  • Use protein-rich blocking solutions (5% BSA or 5-10% normal serum)

  • Consider adding 0.1-0.3% Triton X-100 for better penetration

• Excessive antibody concentration:

  • Titrate antibody to determine optimal working concentration

  • Start with manufacturer's recommended dilution range (e.g., 1:50-1:200 for IHC)

  • Perform preliminary experiments with serial dilutions

• Cross-reactivity with similar epitopes:

  • Use more specific monoclonal antibodies when available

  • Perform pre-absorption controls with immunizing peptide

  • Include knockout or knockdown samples as negative controls

• Inadequate washing:

  • Increase number and duration of wash steps

  • Use gentle agitation during washing

  • Ensure appropriate buffer composition (PBS with 0.05-0.1% Tween-20)

By systematically addressing these factors, researchers can significantly improve the signal-to-noise ratio in their STON1 antibody applications, resulting in more reliable and interpretable data.

How can researchers validate the specificity of STON1 antibodies in their experimental systems?

Validating antibody specificity is crucial for generating reliable data. For STON1 antibodies, comprehensive validation approaches include:

• Genetic manipulation controls:

  • siRNA or shRNA knockdown of STON1

  • CRISPR/Cas9-mediated knockout of STON1

  • Overexpression of tagged STON1 constructs

• Peptide competition assays:

  • Pre-incubating the antibody with excess immunizing peptide

  • Comparing staining patterns with and without peptide competition

  • Observing elimination of specific signal while non-specific signal remains

• Multiple antibody validation:

  • Testing different antibodies targeting distinct STON1 epitopes

  • Comparing staining patterns across antibodies

  • Confirming consistent results with antibodies from different sources/clones

• Mass spectrometry correlation:

  • Isolating STON1 via immunoprecipitation

  • Confirming identity by mass spectrometry

  • Correlating MS data with antibody-based detection methods

These validation approaches should be applied to the specific experimental system being used, as antibody performance can vary across applications, fixation methods, and sample types.

What advanced techniques can be used to simultaneously detect STON1 and other proteins in complex tissue samples?

For researchers investigating STON1 in complex biological contexts, several advanced multiplexing techniques enable simultaneous detection of multiple markers:

• Multiplex Immunofluorescence (mIF):

  • Sequential staining with different primary antibodies

  • Using species-specific or isotype-specific secondary antibodies with distinct fluorophores

  • Employing nuclear counterstains for cell identification

  • Performing multispectral imaging for signal separation

• Chromogenic Multiplex Immunohistochemistry:

  • Sequential IHC with different chromogens

  • Using antibody stripping or blocking between rounds

  • Digital image analysis for quantification

• Imaging Mass Cytometry (IMC):

  • Metal-tagged antibodies against STON1 and other proteins

  • Laser ablation and mass spectrometry detection

  • Highly multiplexed (30+ markers) spatial protein profiling

• Co-immunoprecipitation followed by Western blotting:

  • Pulling down STON1 and detecting interaction partners

  • Investigating protein complexes involving STON1

When performing these advanced techniques, researchers should carefully validate each antibody individually before combining them in multiplexed assays. Additionally, appropriate controls should be included to account for potential cross-reactivity or spectral overlap in fluorescence-based methods.

How might STON1 antibodies be used to explore the protein's role in predicting treatment response beyond immunotherapy?

While current research has primarily focused on STON1's relationship with immunotherapy response, its utility in predicting responses to other treatment modalities represents an important frontier:

• Targeted therapies:

  • Research indicates that high STON1 expression may predict better response to targeted therapies in KIRC

  • STON1 antibodies can be used to stratify patients in retrospective and prospective clinical studies

  • Correlation analyses between STON1 expression and response to specific targeted agents (e.g., tyrosine kinase inhibitors) can be performed

• Conventional chemotherapy:

  • Investigating whether STON1 expression correlates with sensitivity to standard chemotherapeutic agents

  • Using cell line models with varying STON1 expression to assess drug sensitivity profiles

• Radiation therapy:

  • Exploring whether STON1 expression influences radiosensitivity

  • Correlating STON1 levels with radiation response in preclinical and clinical samples

Future studies should employ STON1 antibodies in multiplex approaches that simultaneously assess STON1 expression and markers of treatment response, potentially identifying STON1 as a component of predictive biomarker panels for personalized treatment selection.

What experimental approaches can be used to investigate the mechanistic role of STON1 in cancer progression?

Understanding STON1's mechanistic role in cancer requires sophisticated experimental approaches:

• Functional genomics:

  • CRISPR/Cas9-mediated knockout or knockdown of STON1

  • Overexpression studies using wild-type and mutant STON1 constructs

  • Rescue experiments to confirm specificity of observed phenotypes

• Proteomic analyses:

  • Immunoprecipitation with STON1 antibodies followed by mass spectrometry

  • Identification of STON1 interacting partners in normal vs. cancer contexts

  • Phosphoproteomic analyses to identify post-translational modifications

• Transcriptomic effects:

  • RNA-seq following STON1 modulation to identify downstream transcriptional changes

  • Integration with protein expression data using STON1 antibodies

  • Pathway analyses to identify affected biological processes

• In vivo models:

  • Xenograft studies with STON1-modulated cell lines

  • Analysis of tumor growth, metastasis, and immune infiltration

  • IHC with STON1 antibodies to confirm expression in tumor models

These approaches should be integrated to develop a comprehensive understanding of STON1's role in cancer biology, potentially identifying novel therapeutic targets or strategies.