RPS11C Antibody

What is RPS11 and what is its function in cellular processes?

RPS11 (Ribosomal protein S11) serves as a component of the small ribosomal subunit (40S) and is essential for protein synthesis in cells. It functions as part of the small subunit (SSU) processome, which is the first precursor of the small eukaryotic ribosomal subunit. During SSU processome assembly in the nucleolus, RPS11 works alongside numerous ribosome biogenesis factors and RNA chaperones to generate RNA folding, modifications, rearrangements, and cleavage, as well as targeted degradation of pre-ribosomal RNA by the RNA exosome .

The protein is also known as "Small ribosomal subunit protein uS17" or "40S ribosomal protein S11" and plays a crucial role in the translation process by aiding in ribosome assembly and promoting protein synthesis . Understanding RPS11's function is vital for investigating its role in disease pathogenesis and identifying potential therapeutic interventions.

What applications are RPS11 antibodies validated for?

RPS11 antibodies have been validated for multiple research applications, with different products showing varying application profiles:

When selecting an antibody for your research, ensure it has been validated for your specific application. For instance, in immunofluorescent studies, RPS11 antibodies have successfully labeled MCF7 cells at 1/100 dilution, with DAPI used for nuclear counterstaining .

What species reactivity is available for RPS11 antibodies?

Different RPS11 antibodies offer varying species reactivity profiles:

When working with non-human samples, select antibodies with confirmed cross-reactivity for your target species. Some manufacturers predict cross-reactivity based on sequence homology but may not have experimentally validated these predictions .

What are the recommended protocols for using RPS11 antibodies in Western blot?

For optimal Western blot results with RPS11 antibodies, follow these methodological guidelines:

• Sample preparation: Prepare whole cell lysates (such as 293T) or tissue homogenates containing the target protein.

• Dilution optimization: Start with the manufacturer's recommended dilution range. For example, use 1:1000-1:5000 for polyclonal antibodies or 2μg/ml as shown in validated protocols .

• Detection system: Use appropriate secondary antibodies, such as goat polyclonal to rabbit IgG at 1/10000 dilution when working with rabbit-derived primary antibodies .

• Expected results: Look for a band at approximately 18 kDa, which is the predicted molecular weight of RPS11 .

• Controls: Include positive controls (cell lines known to express RPS11, such as MCF7 or 293T) and negative controls (primary antibody omission) to validate specificity.

When troubleshooting, remember that RPS11 is abundantly expressed in most cell types due to its fundamental role in ribosome function.

How can I optimize immunohistochemistry protocols for RPS11 detection in tissue samples?

When conducting immunohistochemistry for RPS11 detection in tissues, particularly for cancer research, follow these methodological approaches:

• Tissue processing: Use formalin-fixed, paraffin-embedded (FFPE) tissue sections, as demonstrated in hepatocellular carcinoma studies .

• Antigen retrieval: Perform heat-induced epitope retrieval using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0), depending on your antibody's requirements.

• Antibody dilution: Begin with recommended dilutions (1:20-1:200 for polyclonal antibodies ) and optimize based on signal-to-noise ratio.

• Detection system: Use appropriate visualization systems (HRP/DAB) with hematoxylin counterstaining.

• Scoring system: Implement a standardized scoring system similar to that used in HCC research, where patients were separated into high and low RPS11 expression groups based on staining intensity .

• Tissue microarrays: For high-throughput analysis, consider using tissue microarrays as employed in HCC studies examining RPS11 expression across multiple patient samples .

What controls should be included when validating RPS11 antibody specificity?

To ensure reliable results and validate antibody specificity, incorporate these essential controls:

• Positive tissue controls: Include tissues known to express RPS11, such as liver tissues for hepatocellular carcinoma studies .

• Cell line controls: Use cell lines with confirmed RPS11 expression (such as MHCC97H, MHCCLM3, SK-Hep1, PLC/PRF/5, Hep3B) and compare with normal cell lines (e.g., L02 for liver studies) .

• Knockdown validation: Perform siRNA knockdown of RPS11 to confirm antibody specificity by demonstrating reduced signal in knockdown samples.

• Blocking peptide controls: Use specific blocking peptides corresponding to the immunogen to confirm binding specificity.

• Multiple antibody validation: When possible, confirm findings using different antibodies raised against different epitopes of RPS11.

• Multiple detection methods: Validate expression using complementary techniques (WB, IHC, qRT-PCR) as demonstrated in HCC research where both protein and mRNA levels were assessed .

How can RPS11 expression be correlated with clinical parameters in cancer research?

RPS11 expression has been significantly correlated with several clinical parameters in cancer research, particularly in hepatocellular carcinoma (HCC). When designing studies to investigate these correlations:

• Clinical parameter collection: Gather comprehensive patient data including:

  • Serum biomarkers (AFP, CA19-9, CEA)

  • Liver function tests (ALP)

  • Tumor characteristics (differentiation, microvascular invasion)

  • Demographic data (age, gender)

• Statistical analysis: Perform appropriate statistical tests to determine correlations:

  • Chi-square tests for categorical variables

  • t-tests or non-parametric tests for continuous variables

• Findings from HCC research:

  • High RPS11 expression positively correlates with elevated AFP levels (P=0.021 in training cohort, P=0.010 in validation cohort)

  • High RPS11 expression correlates with elevated CA19-9 levels (P=0.002 in training cohort, P=0.021 in validation cohort)

  • Association with poor tumor differentiation (P=0.022)

  • Correlation with presence of microvascular invasion (P=0.018 in validation cohort)

These findings suggest that elevated RPS11 expression may be associated with more aggressive tumor phenotypes, providing a basis for further mechanistic investigations.

What prognostic value does RPS11 expression have in cancer, and how should this be analyzed?

Research has demonstrated significant prognostic value of RPS11 expression, particularly in hepatocellular carcinoma. To investigate this aspect:

What molecular pathways are associated with RPS11 and how can they be investigated?

To explore the molecular pathways associated with RPS11, implement these advanced research approaches:

• Bioinformatic analyses:

  • Perform single-sample gene-set enrichment analysis (ssGSEA) as conducted in HCC research

  • Identify pathways enriched in samples with high RPS11 expression

  • Focus on pathways related to tumor resistance, survival, and recurrence

• Functional studies:

  • Design RPS11 knockdown and overexpression experiments to directly assess impact on identified pathways

  • Evaluate effects on cell proliferation, migration, invasion, and apoptosis

  • Assess impact on drug sensitivity and resistance mechanisms

• Co-immunoprecipitation studies:

  • Identify RPS11 interaction partners within the ribosome and potential extraribosomal functions

  • Use RPS11 antibodies for pull-down experiments followed by mass spectrometry analysis

• Translation efficiency assays:

  • Investigate whether RPS11 dysregulation affects global translation or specific mRNA subsets

  • Use ribosome profiling to identify differentially translated mRNAs

• In vivo models:

  • Develop animal models with RPS11 manipulation to assess effects on tumor growth and metastasis

  • Evaluate response to therapies in the context of RPS11 expression levels

How should I address non-specific binding when using RPS11 antibodies?

Non-specific binding is a common challenge when working with antibodies. For RPS11 antibodies, implement these solutions:

• Optimization strategies:

  • Titrate antibody concentration using the recommended dilution ranges (e.g., 1:1000-1:5000 for WB, 1:20-1:200 for IHC)

  • Increase blocking duration and concentration (5% BSA or 5% non-fat milk)

  • Add 0.1-0.3% Triton X-100 to reduce hydrophobic interactions

• Buffer modifications:

  • Use the recommended storage buffer components (e.g., 50% Glycerol, 0.01M PBS, PH 7.4)

  • Add 0.03% Proclin 300 as a preservative if specified by the manufacturer

  • Consider adding 0.1-0.2% Tween-20 to washing buffers

• Secondary antibody controls:

  • Include secondary-only controls to identify non-specific binding

  • Use isotype controls to distinguish between specific binding and Fc receptor interactions

• Purification considerations:

  • Select highly purified antibodies (e.g., Protein G purified with >95% purity)

  • Consider using monoclonal antibodies for higher specificity in challenging applications

How can I reconcile discrepancies between protein and mRNA expression levels of RPS11?

When faced with discrepancies between protein and mRNA expression data for RPS11:

• Technical considerations:

  • Verify primer specificity for qRT-PCR to ensure they specifically amplify RPS11

  • Confirm antibody specificity for protein detection

  • Consider the sensitivity differences between techniques

• Biological explanations:

  • Post-transcriptional regulation: RPS11 mRNA may be subject to regulation by microRNAs or RNA-binding proteins

  • Protein stability differences: Variations in protein half-life can cause discrepancies

  • Feedback mechanisms: Ribosomal proteins often have autoregulatory feedback loops

• Methodological approach:

  • Perform both analyses on the same samples as demonstrated in HCC research

  • Use multiple cell lines to establish patterns (e.g., MHCC97H, MHCCLM3, SK-Hep1, PLC/PRF/5, Hep3B versus normal L02)

  • Quantify and normalize data appropriately for each technique

• Verification strategies:

  • Use protein synthesis inhibitors to determine protein stability

  • Employ transcription inhibitors to assess mRNA stability

  • Consider polysome profiling to assess translation efficiency

What are the best practices for quantifying RPS11 expression in tissue samples?

For accurate quantification of RPS11 expression in tissue samples:

• Immunohistochemistry scoring systems:

  • Implement standardized scoring based on staining intensity and percentage of positive cells

  • Use a 4-tier system (negative, weak, moderate, strong) as implemented in HCC research

  • Define clear cutoff values for high versus low expression groups

• Digital pathology approaches:

  • Employ image analysis software for objective quantification

  • Measure both staining intensity and area of positivity

  • Normalize to appropriate controls

• Western blot quantification:

  • Use housekeeping proteins (β-actin, GAPDH) for normalization

  • Employ densitometry software for accurate band intensity measurement

  • Run standard curves with known quantities of recombinant protein

• qRT-PCR methodology:

  • Use multiple reference genes for normalization

  • Apply the 2^-ΔΔCt method for relative quantification

  • Include standard curves for absolute quantification when necessary

• Statistical considerations:

  • Apply appropriate statistical tests based on data distribution

  • Use paired tests when comparing tumor and adjacent normal tissues

  • Report both continuous measurements and categorical classifications