s100A11 Antibody

What is S100A11 and why is it important in research?

S100A11 (also known as calgizzarin or S100C) is a 10-12 kDa calcium-binding protein belonging to the S100 family, characterized by a unique helix-loop-helix EF hand motif. It exists in multiple forms - as monomers, homodimers, and heterodimers with S100B. S100A11 functions both intracellularly as a calcium sensor/binding protein and extracellularly where it binds to receptors like RAGE to influence cellular responses. Its importance stems from its involvement in various diseases including cancers, metabolic diseases, neurological disorders, and vascular calcification, making it a valuable research target for understanding disease mechanisms and developing therapeutic approaches .

How does S100A11 expression differ between normal and cancerous tissues?

Immunohistochemistry (IHC) analyses from the Human Protein Atlas database reveal that S100A11 protein is highly expressed in ER+ breast cancer tissues but not detected in normal breast tissues . This differential expression pattern makes S100A11 a potential biomarker for cancer diagnosis and prognosis. Transcriptomic and proteomic analyses have consistently demonstrated S100A11 overexpression in breast cancer tissues compared to normal breast tissues . Researchers should consider this expression pattern when designing experiments to investigate S100A11's role in cancer pathogenesis.

What are the validated applications for S100A11 antibodies in research?

Based on published research, S100A11 antibodies have been successfully employed in several experimental applications:

• Western blot analysis - Detected in human cell lines such as JEG-3 (epithelial choriocarcinoma) and HeLa (cervical epithelial carcinoma) at approximately 10 kDa under reducing conditions

• Immunohistochemistry (IHC) - Used to compare expression between normal and cancerous tissues

• Neutralization experiments - Applied in 3D tumor-macrophage experimental platforms to block S100A11 function and observe effects on macrophage infiltration

• ELISA - Quantification of S100A11 levels in conditioned media from macrophage cultures

How can I optimize Western blot protocols for S100A11 detection?

For optimal S100A11 detection by Western blot, the following protocol parameters have been validated:

• Sample preparation: PVDF membrane with cell lysates from epithelial cancer cell lines (JEG-3, HeLa) under reducing conditions

• Antibody concentration: 1 μg/mL of Goat Anti-Human S100A11 Antigen Affinity-purified Polyclonal Antibody

• Secondary antibody: HRP-conjugated Anti-Goat IgG Secondary Antibody

• Buffer system: Use Immunoblot Buffer Group 8

• Expected band size: Approximately 10 kDa

For troubleshooting, ensure proper sample preparation and consider that S100A11 may form dimers or multimers that could affect migration patterns if your sample preparation doesn't fully reduce the protein.

What experimental models are suitable for studying S100A11 function?

Several experimental models have been validated for S100A11 research:

• Human cell lines: T47D, JEG-3, and HeLa cells show high S100A11 expression and are suitable for knockdown studies

• 3D matrix models: Effective for studying macrophage infiltration and migration in response to S100A11

• Patient-derived organoids: Clinically relevant model for assessing S100A11 neutralization effects

• Primary PBMC-derived macrophages: Useful for investigating S100A11's effects on immune cell function

• THP1 macrophage cell line: Alternative to primary macrophages for studying S100A11-mediated recruitment

How can I study S100A11's role in macrophage recruitment in cancer models?

To investigate S100A11's role in macrophage recruitment, researchers have successfully employed the following methodological approaches:

• 3D co-culture systems: Establish co-cultures of cancer cells (e.g., T47D) with macrophages (either primary PBMC-derived or THP1 cell line) in a 3D matrix environment.

• S100A11 neutralization: Add S100A11 blocking antibody to the co-culture system (compare with IgG control).

• Genetic silencing: Use siRNA or CRISPR/Cas9 to knockdown S100A11 in cancer cells.

• Quantitative assessment: Measure macrophage infiltration by counting the number of recruited macrophages in the 3D environment.

• Functional validation: Assess macrophage phenotypic markers (e.g., CD206) to evaluate the immunosuppressive capacity of recruited macrophages .

This experimental design allows for comprehensive analysis of both the direct effects of S100A11 on macrophage recruitment and the subsequent functional consequences.

What techniques can be used to quantify S100A11 secretion from cells?

To quantify S100A11 secretion from various cell types, researchers can employ:

• ELISA: Use commercial ELISA kits (e.g., RayBio® ELH-S100A11) to measure S100A11 concentration in conditioned media from cell cultures .

• Western blot analysis: Perform quantitative Western blot on concentrated conditioned media.

• Mass spectrometry: For unbiased proteomics approach to identify and quantify S100A11 in complex secretomes.

• Experimental design considerations:

  • Collect conditioned media after culturing cells in serum-free media to avoid interference

  • Include positive controls such as macrophages stimulated with IFN-γ and LPS (M1-like) or IL-4 and IL-13 (M2-like)

  • Normalize measurements to cell number or total protein content

How can I assess the effects of S100A11 on macrophage migration and infiltration?

Two complementary approaches have been validated for studying S100A11's effects on macrophage motility:

• Time-lapse imaging to evaluate migration speed:

  • Expose primary PBMC-derived macrophages to recombinant S100A11 (test different concentrations, e.g., 1 and 10 ng/ml)

  • Track individual cell trajectories over time

  • Calculate migration speed and compare to control conditions

• Inverted migration assay to assess 3D infiltration:

  • Establish a concentration gradient of S100A11 in a 3D extracellular matrix

  • Track number and position of macrophages infiltrating the 3D environment

  • Quantify recruitment in response to different S100A11 concentrations

These methods provide quantitative measurements of both the speed and extent of macrophage responses to S100A11 stimulation.

How can I determine if S100A11 is a prognostic biomarker in cancer?

To evaluate S100A11 as a prognostic biomarker, researchers should follow this methodological approach:

• Transcriptomic analysis:

  • Utilize public datasets (e.g., METABRIC for breast cancer) with RNA-seq and clinical outcome data

  • Define patient groups based on S100A11 expression levels (e.g., top 25% vs. bottom 25%)

  • Perform survival analysis using Kaplan-Meier curves and Cox proportional hazards regression models

• Correlation with immune infiltration:

  • Analyze single-cell RNA sequencing data to correlate S100A11 expression in cancer cells with macrophage density

  • Compare macrophage infiltration patterns between S100A11-high and S100A11-low tumors

• Protein-level validation:

  • Compare immunohistochemistry (IHC) staining of S100A11 between normal and tumor tissues

  • Correlate expression with clinical outcomes

Research has demonstrated that ER+ breast tumors overexpressing S100A11 are associated with worse survival outcomes, supporting its potential as a prognostic biomarker .

What are the mechanisms by which S100A11 influences the tumor microenvironment?

S100A11 modulates the tumor microenvironment through several mechanisms:

• Direct effects on macrophage recruitment:

  • Increases macrophage migration speed in a dose-dependent manner

  • Enhances macrophage infiltration in 3D extracellular matrices

  • Functions as a paracrine factor secreted by cancer cells

• Phenotypic modulation of macrophages:

  • Influences expression of immunosuppressive markers (e.g., CD206) on macrophages

  • Genetic silencing of S100A11 in cancer cells results in reduced expression of these markers

• Receptor-mediated signaling:

  • May interact with receptors like RAGE to trigger downstream signaling pathways

  • Induces EGF and promotes cell growth extracellularly

• Intracellular vs. extracellular functions:

  • Intracellularly suppresses growth

  • Extracellularly promotes cell growth and crosstalk with immune cells

Understanding these mechanisms provides opportunities for therapeutic intervention targeting the S100A11-macrophage axis.

How does S100A11 neutralization affect different cancer models?

The effects of S100A11 neutralization have been investigated across multiple cancer models with consistent findings:

• Established ER+ breast cancer cell lines:

  • S100A11 neutralization with blocking antibody significantly decreased macrophage recruitment in co-cultures with T47D cells

  • No significant differences in cancer cell viability were observed after treatment with control (IgG) or anti-S100A11 antibodies

• Patient-derived organoids:

  • Treatment with S100A11 blocking antibody reduced macrophage infiltration by approximately 45% compared to IgG control (p<0.05)

• THP1 macrophage model:

  • S100A11 neutralization decreased recruitment of THP1 macrophages when co-cultured with cancer cells

These findings across different models strengthen the evidence for S100A11 as a therapeutic target to modulate the macrophage-rich tumor microenvironment.

What are the critical factors for proper storage and handling of S100A11 antibodies?

For optimal preservation of antibody activity, follow these storage and handling guidelines:

• Long-term storage:

  • Store at -20 to -70°C for up to 12 months from date of receipt

  • Use a manual defrost freezer and avoid repeated freeze-thaw cycles

• Short-term storage:

  • After reconstitution, store at 2 to 8°C under sterile conditions for up to 1 month

  • For extended periods, aliquot and store at -20 to -70°C for up to 6 months under sterile conditions

• Handling precautions:

  • Reconstitute according to manufacturer's instructions

  • Minimize freeze-thaw cycles by preparing working aliquots

  • Keep antibody solutions on ice during experiments

  • Avoid contamination by using sterile techniques

How can I validate the specificity of my S100A11 antibody?

Validating antibody specificity is crucial for reliable research results. Implement these validation strategies:

• Positive controls:

  • Use cell lines with confirmed S100A11 expression (e.g., JEG-3, HeLa, T47D)

  • Include recombinant S100A11 protein as a standard

• Negative controls:

  • Utilize S100A11 knockout/knockdown cells generated via CRISPR/Cas9 or siRNA

  • Include isotype control antibodies

• Cross-reactivity assessment:

  • Test against related S100 family members, particularly S100B which can form heterodimers with S100A11

  • Consider that human S100A11 shares 78% and 82% amino acid identity with mouse and porcine S100A11, respectively, when working with different species

• Multi-technique validation:

  • Confirm results across different applications (Western blot, IHC, ELISA)

  • Verify with antibodies targeting different epitopes of S100A11

What are common pitfalls in S100A11 functional studies and how can they be avoided?

Researchers should be aware of these potential pitfalls and implement appropriate controls:

• Cellular localization considerations:

  • S100A11 functions both intracellularly and extracellularly, requiring careful experimental design to distinguish these roles

  • In human keratinocytes, S100A11 redistributes from uniform cytoplasmic distribution to the inner membrane upon calcium stimulation

• Heterogeneity in cancer models:

  • Expression levels of S100A11 vary across cell lines; screen multiple lines before selecting experimental models

  • Patient-derived models may show variable expression patterns requiring characterization

• Neutralization efficiency:

  • Verify the neutralizing capacity of antibodies using recombinant protein assays before cell-based experiments

  • Include dose-response studies to determine optimal antibody concentrations

• Confounding factors in co-culture systems:

  • Control for indirect effects by including appropriate mono-culture controls

  • Consider that S100A11 may impact both cancer cells and immune cells simultaneously

How can I integrate S100A11 findings with broader tumor microenvironment research?

To contextualize S100A11 research within the broader tumor microenvironment field:

• Correlation with other immune cell populations:

  • Beyond macrophages, investigate how S100A11 affects other immune cell types

  • In glioblastoma, high S100A11 expression predicts infiltration of multiple immune cell types

• Integration with existing TME models:

  • Connect findings to established models of tumor-immune interactions

  • Consider how S100A11 fits into the spectrum of tumor-associated macrophage recruitment factors

• Multi-omics approach:

  • Combine transcriptomics, proteomics, and functional assays for comprehensive understanding

  • Utilize single-cell RNA sequencing to delineate cell type-specific effects of S100A11

• Translational relevance:

  • Evaluate S100A11 expression in treatment-resistant tumors

  • Investigate potential combination approaches targeting S100A11 alongside established therapies

What are promising future research directions for S100A11 antibodies in cancer immunotherapy?

Several promising research directions emerge from current S100A11 findings:

• Therapeutic antibody development:

  • Optimize S100A11-neutralizing antibodies for in vivo applications

  • Develop antibody formats with enhanced tumor penetration

• Combination therapy approaches:

  • Investigate synergy between S100A11 neutralization and immune checkpoint inhibitors

  • Explore combination with macrophage-reprogramming agents to modify the tumor immune microenvironment

• Biomarker development:

  • Validate S100A11 as a predictive biomarker for response to immunotherapies

  • Develop companion diagnostics for S100A11-targeting approaches

• Expanded cancer types:

  • Extend research beyond ER+ breast cancer to other tumor types

  • Investigate S100A11's role in glioblastoma and other cancers where initial associations have been found

How can computational approaches enhance S100A11 research?

Computational methods offer powerful tools to accelerate S100A11 research:

• Structural biology predictions:

  • Model S100A11-receptor interactions to guide antibody development

  • Predict effects of mutations or splice variants on protein function

• Systems biology analysis:

  • Map S100A11 within signaling networks to identify potential synergistic targets

  • Infer causal relationships from multi-omics data

• Machine learning applications:

  • Develop algorithms to predict patient populations likely to benefit from S100A11-targeting therapies

  • Use image analysis to quantify S100A11 expression and macrophage infiltration in tissue samples

• Drug repurposing:

  • Screen existing compounds for potential S100A11 inhibitory activity

  • Identify molecules that may indirectly modulate S100A11 expression or function