FAM163A Antibody

What is FAM163A and why is it significant in research?

FAM163A, also called neuroblastoma-derived secretory protein (NDSP) or C1orf76, is a secreted protein located on chromosome 1q25.2. It functions as a single-pass membrane protein that plays critical roles in cellular signaling and communication . FAM163A has gained research significance due to its involvement in neuroblastoma, where high expression levels may serve as a marker for bone marrow metastasis . Additionally, FAM163A has been identified as a positive regulator of the ERK signaling pathway, which influences cell proliferation and survival mechanisms, particularly in lung squamous cell carcinoma . Understanding FAM163A's functions provides insights into cancer progression mechanisms and potential therapeutic targets.

What are the available types of FAM163A antibodies and their characteristics?

Several FAM163A antibodies are available for research applications, each with specific characteristics:

The selection of an appropriate antibody depends on your experimental design, required applications, and target species. For example, the E-7 clone from Santa Cruz Biotechnology has been validated for western blotting, immunoprecipitation, immunofluorescence, and ELISA with cross-reactivity across human, mouse, and rat samples .

How is FAM163A expression characterized in different cell types?

FAM163A expression has been characterized through various immunological techniques. In normal bronchial epithelial cells (HBE) and lung squamous cell carcinoma cell lines (LK2 and SK-MES-1), FAM163A protein levels were detected using Western blotting, with higher expression observed in the cancer cell lines compared to normal cells . Immunofluorescence assays revealed that FAM163A is predominantly expressed in the cytoplasm of these cell lines . In neuroblastoma, FAM163A (NDSP) is overexpressed and functions as a secreted protein that may contribute to tumor development . Researchers typically use a combination of Western blotting, immunofluorescence, and immunohistochemistry to characterize FAM163A expression patterns in different tissues and cell types, with antibody dilutions ranging from 1:20-1:50 for immunohistochemistry and 0.04-0.4 μg/mL for immunoblotting .

What are the recommended protocols for FAM163A antibody validation?

Proper antibody validation is crucial for ensuring reliable research results. For FAM163A antibodies, the following validation protocols are recommended:

• Western blotting confirmation: Use overexpression systems alongside endogenous expression to verify specificity, with recommended antibody concentrations of 0.04-0.4 μg/mL .

• Recombinant expression validation: Compare antibody reactivity against recombinant FAM163A protein and negative controls to assess specificity .

• Immunoprecipitation followed by mass spectrometry: This approach can confirm that the antibody is pulling down the correct protein target .

• siRNA/shRNA knockdown: Validate antibody specificity by confirming reduced signal following FAM163A knockdown, as demonstrated in LK2 and SK-MES-1 cell lines .

• Multiple antibody validation: Compare results using different antibodies targeting distinct epitopes of FAM163A to ensure consistent findings.

Enhanced validation techniques, such as testing against protein arrays containing hundreds of human recombinant protein fragments, have been applied to certain commercial antibodies like the Prestige Antibodies from Sigma-Aldrich, providing additional confidence in specificity .

How does FAM163A interact with the ERK signaling pathway in cancer progression?

FAM163A plays a significant role in cancer progression through its interaction with the ERK signaling pathway. Research has identified that FAM163A directly interacts with both 14-3-3β and ERK proteins, facilitating ERK phosphorylation by upregulating the protein level of 14-3-3β . This interaction was confirmed through co-immunoprecipitation experiments in LK2 and SK-MES-1 lung squamous cell carcinoma cells using anti-FAM163A antibody (sc-390936) from Santa Cruz Biotechnology . The pathway analysis revealed that FAM163A overexpression increased the levels of ERK and p90RSK phosphorylation and promoted the expression of cyclin D1, which was reversed when cells were treated with the MEK inhibitor U0126 . This mechanism ultimately leads to enhanced cellular proliferation in LUSC cells through the activation of the ERK pathway. For researchers studying this interaction, it's crucial to employ FAM163A antibodies in combination with antibodies targeting phosphorylated ERK, 14-3-3β, and downstream effectors like cyclin D1 to fully characterize the signaling cascade.

What are the clinical correlations between FAM163A expression and cancer patient outcomes?

Clinical studies have established significant correlations between FAM163A expression and cancer patient outcomes. In lung squamous cell carcinoma patients, immunohistochemistry analysis using FAM163A antibody (sc-390936) revealed that positive FAM163A expression significantly correlated with:

• Larger tumor size (P=0.023)

• Higher TNM staging (P=0.015)

• Regional lymph node metastasis (P=0.016)

Most importantly, Kaplan-Meier survival analysis demonstrated that patients with positive FAM163A expression had a significantly shorter mean survival time (49.72±3.97 months) compared to patients with negative FAM163A expression (63.36±3.14 months, P=0.011) . These findings suggest that FAM163A could serve as a prognostic biomarker in LUSC, with high expression indicating poor patient outcomes. For researchers investigating FAM163A as a prognostic marker, it's essential to standardize immunohistochemistry protocols and scoring methods, with recommended antibody dilutions of 1:200 for the Santa Cruz sc-390936 antibody and a standardized scoring system where scores >4 (on a scale of 0-12) are considered positive for FAM163A expression .

How can researchers optimize co-immunoprecipitation protocols for studying FAM163A protein interactions?

Optimizing co-immunoprecipitation (Co-IP) protocols is crucial for accurately characterizing FAM163A protein interactions. Based on published methodologies, researchers should consider the following optimization steps:

• Cell lysate preparation: Harvest cells in 1X Lysis/Wash Buffer supplemented with protease inhibitors (PMSF) to preserve protein integrity during extraction .

• Pre-clearing step: Incubate lysates with Protein A/G-agarose at 4°C overnight to reduce non-specific binding .

• Antibody selection: Use a validated FAM163A antibody at appropriate dilutions (e.g., 1:50 for sc-390936 from Santa Cruz Biotechnology) along with proper negative controls (e.g., anti-mouse IgG antibody) .

• Incubation conditions: Perform immunoprecipitation overnight at 4°C with the antibody bound to protein G PLUS-agarose to maximize specific interactions .

• Washing stringency: Optimize wash buffer composition and washing steps to minimize background without disrupting specific interactions.

• Detection methods: Use Western blotting with appropriate antibodies to detect co-precipitated proteins.

For studying the interaction between FAM163A and ERK or 14-3-3β specifically, researchers should include reciprocal Co-IPs (using antibodies against each protein) to confirm the interaction. Additionally, including interaction disruptors (such as phosphatase treatments if the interaction is phosphorylation-dependent) can provide mechanistic insights into the nature of the protein-protein interaction.

What are the most effective methods for analyzing FAM163A-mediated signaling changes in real-time?

Analyzing FAM163A-mediated signaling in real-time presents challenges that require sophisticated methodological approaches. Based on current research practices, the following techniques are most effective:

• FRET-based biosensors: Develop fluorescence resonance energy transfer (FRET) biosensors targeting the ERK pathway to monitor FAM163A-induced activation in live cells.

• Live-cell phosphorylation reporters: Utilize reporters that change cellular localization or fluorescence properties upon phosphorylation of key pathway components downstream of FAM163A.

• Real-time PCR following FAM163A manipulation: Monitor expression changes in ERK target genes after FAM163A overexpression or knockdown, with sample collection at multiple time points to capture temporal dynamics .

• Phospho-specific flow cytometry: Quantify phosphorylation levels of ERK and downstream targets at the single-cell level following FAM163A perturbation.

• Time-lapse microscopy with fluorescently tagged proteins: Visualize the dynamic localization and interaction of FAM163A with its binding partners (14-3-3β and ERK) using techniques such as fluorescence recovery after photobleaching (FRAP) or fluorescence correlation spectroscopy (FCS).

These methods should be combined with appropriate controls, including U0126 MEK inhibitor treatment to block ERK pathway activation, as demonstrated in previous studies .

What are the optimal conditions for using FAM163A antibodies in Western blotting applications?

For optimal Western blotting results with FAM163A antibodies, researchers should follow these evidence-based protocols:

• Sample preparation: Extract proteins using standard RIPA buffer supplemented with protease and phosphatase inhibitors to preserve FAM163A integrity and phosphorylation status.

• Protein loading and separation: Load 20-50 μg of total protein per lane and separate using 10-12% SDS-PAGE gels, as FAM163A is a relatively small protein (167 amino acids) .

• Transfer conditions: Use semi-dry or wet transfer methods with PVDF membranes, optimizing transfer time for smaller proteins (typically 1-2 hours at 100V or 30-45 minutes in semi-dry systems).

• Blocking: Block membranes with 5% bovine serum albumin (BSA) for 2 hours at room temperature to minimize background .

• Primary antibody incubation: Dilute FAM163A antibody according to manufacturer recommendations:

  • FAM163A Antibody (E-7): 0.04-0.4 μg/mL

  • Anti-FAM163A (sc-390936): 1:100 dilution

• Incubation conditions: Incubate primary antibody overnight at 4°C for optimal binding specificity .

• Detection: Use appropriate secondary antibodies conjugated to HRP or fluorescent tags, followed by standard chemiluminescence or fluorescence detection systems.

• Controls: Include positive controls (cell lines known to express FAM163A, such as LK2 or SK-MES-1) and loading controls (GAPDH at 1:5000 dilution) .

When troubleshooting Western blots, researchers should consider optimizing antibody concentration, adjusting blocking conditions, and testing different membrane types if background issues arise.

How should immunohistochemistry protocols be optimized for detecting FAM163A in tissue samples?

Optimizing immunohistochemistry (IHC) protocols for FAM163A detection requires attention to several key parameters:

• Tissue preparation: Use formalin-fixed, paraffin-embedded (FFPE) tissue sections of 4-5 μm thickness. Ensure proper antigen retrieval, typically using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) with heat-induced epitope retrieval methods.

• Antibody selection and dilution: For FAM163A detection in human tissues:

  • Anti-FAM163A (sc-390936): Use at 1:200 dilution

  • Anti-FAM163A (HPA010778): Use at 1:20-1:50 dilution

• Incubation conditions: Incubate primary antibody overnight at 4°C or for 1-2 hours at room temperature in a humidified chamber.

• Detection system: Use an appropriate detection system compatible with the primary antibody host species (e.g., mouse or rabbit).

• Counterstaining: Apply hematoxylin counterstaining to visualize tissue architecture.

• Scoring system: Implement a standardized scoring system as described in previous studies:

  • Intensity score (0-3): 0 (no staining), 1 (weak), 2 (moderate), 3 (strong)

  • Percentage score (0-4): 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), 4 (76-100%)

  • Final score (0-12): Multiply intensity by percentage scores

  • Interpretation: >4 is considered positive; 1-4 is weak expression; 0 is no expression

• Controls: Include positive control tissues (lung squamous cell carcinoma or neuroblastoma samples known to express FAM163A) and negative controls (primary antibody omission or isotype controls).

This standardized approach ensures consistent and reproducible FAM163A detection across different tissue samples and research laboratories.

What cell models are most appropriate for studying FAM163A function?

Based on the current literature, the following cell models are most appropriate for studying different aspects of FAM163A function:

When working with these models, researchers should consider:

• Expression verification: Confirm endogenous FAM163A expression levels by Western blot before experimental manipulation.

• Genetic manipulation approaches: Both overexpression (using expression plasmids) and knockdown (using siRNA/shRNA) approaches have been successfully employed to study FAM163A function .

• Functional assays: Cell viability assays and colony formation assays have demonstrated FAM163A's effects on proliferation in the recommended cell lines .

• In vivo models: Xenograft models using FAM163A-overexpressing lung cancer cells have confirmed in vitro findings regarding proliferation enhancement .

These models provide complementary systems for investigating FAM163A's role in different cellular contexts and disease states.

How can researchers address specificity concerns with FAM163A antibodies?

Addressing specificity concerns is critical for generating reliable data with FAM163A antibodies. Researchers should implement the following validation strategies:

• Multiple antibody approach: Compare results using antibodies targeting different epitopes of FAM163A, such as:

  • FAM163A Antibody (E-7) from Santa Cruz (sc-398152)

  • FAM163A Antibody (G-12) from Santa Cruz (sc-unknown)

  • Anti-FAM163A antibody from Sigma-Aldrich (HPA010778)

• Genetic knockdown controls: Validate antibody specificity by confirming signal reduction in FAM163A knockdown samples using siRNA or shRNA approaches .

• Overexpression controls: Confirm increased signal intensity in cells overexpressing FAM163A compared to empty vector controls .

• Peptide competition assays: Pre-incubate the antibody with its specific immunizing peptide (e.g., FAM163A (E-7) Neutralizing Peptide, sc-398152 P) to block specific binding sites .

• Immunoprecipitation-mass spectrometry verification: Confirm that the immunoprecipitated protein is indeed FAM163A through mass spectrometry analysis.

• Cross-reactivity testing: Test the antibody against recombinant proteins with similar sequences or domains to ensure specificity.

• Enhanced validation approaches: Consider antibodies that have undergone enhanced validation, such as testing against protein arrays containing hundreds of human recombinant protein fragments .

By implementing these strategies, researchers can significantly increase confidence in their antibody's specificity and the reliability of their experimental results.

What are common pitfalls in analyzing FAM163A expression data and how can they be avoided?

Researchers analyzing FAM163A expression data should be aware of these common pitfalls and implement the corresponding solutions:

• Pitfall: Inconsistent results across different detection methods

  • Solution: Use multiple techniques (Western blot, qPCR, immunofluorescence) to cross-validate expression findings .

• Pitfall: Non-specific antibody binding leading to false positives

  • Solution: Include appropriate negative controls and validate antibody specificity as described in section 4.1.

• Pitfall: Misinterpretation of subcellular localization

  • Solution: Use proper subcellular fractionation techniques and co-localization studies with established markers for different cellular compartments. FAM163A has been confirmed to localize primarily in the cytoplasm of cells .

• Pitfall: Variability in immunohistochemistry scoring

  • Solution: Implement standardized scoring systems with multiple independent evaluators and appropriate statistical analysis. Use the established 0-12 scoring system for FAM163A with clear cutoffs (>4 considered positive) .

• Pitfall: Overlooking post-translational modifications

  • Solution: Consider using antibodies specific to different forms of FAM163A or complementary techniques like mass spectrometry.

• Pitfall: Misattribution of functional effects

  • Solution: Include pathway inhibitors (e.g., U0126 for MEK/ERK pathway) to confirm the specific signaling pathways through which FAM163A exerts its effects .

• Pitfall: Cell line cross-contamination affecting expression data

  • Solution: Regularly authenticate cell lines used for FAM163A expression studies and maintain rigorous cell culture practices.

By anticipating these challenges, researchers can design more robust experiments and generate more reliable data on FAM163A expression and function.

How should researchers interpret conflicting results between different FAM163A detection methods?

When faced with conflicting results between different FAM163A detection methods, researchers should follow this systematic approach to resolution:

• Evaluate method-specific limitations:

  • Western blotting: May detect denatured epitopes not available in fixed tissues

  • IHC/IF: May be affected by fixation artifacts or epitope masking

  • qPCR: Measures mRNA levels which may not correlate with protein expression due to post-transcriptional regulation

• Consider antibody-specific factors:

  • Different antibodies target different epitopes, which may be differentially accessible in various experimental conditions

  • Clone E-7 is an IgM antibody , while some others are IgG, potentially affecting binding properties

  • Polyclonal antibodies detect multiple epitopes, while monoclonals recognize single epitopes

• Implement resolution strategies:

  • Perform titration experiments to determine optimal antibody concentrations for each method

  • Use recombinant FAM163A as a positive control across methods

  • Employ genetic manipulation (overexpression/knockdown) to validate signal specificity

  • Consider protein modifications that might affect epitope recognition

• Data integration approach:

  • Weigh evidence based on methodological rigor and controls included

  • Look for consistent patterns across methods despite quantitative differences

  • Consider biological context when interpreting results (e.g., cell type-specific expression patterns)

• Reporting recommendations:

  • Transparently report conflicting findings in publications

  • Provide detailed methodological information including antibody catalog numbers, dilutions, and protocols

  • Discuss potential sources of variation and their biological implications

What emerging technologies are advancing FAM163A antibody-based research?

Several cutting-edge technologies are revolutionizing FAM163A antibody-based research:

• Single-cell antibody-based proteomics: Technologies like CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) allow simultaneous measurement of FAM163A protein expression and transcriptome analysis at single-cell resolution.

• Proximity labeling approaches: BioID or APEX2-based proximity labeling coupled with mass spectrometry can identify the FAM163A interactome in living cells with spatial and temporal resolution.

• Super-resolution microscopy: Techniques such as STORM, PALM, or STED microscopy combined with FAM163A antibodies enable visualization of subcellular localization with nanometer precision.

• Antibody engineering: Development of recombinant FAM163A antibody fragments (Fabs, scFvs) with improved specificity and reduced background for advanced imaging applications.

• Quantitative multiplexed immunofluorescence: Simultaneous detection of FAM163A along with multiple signaling pathway components (e.g., phospho-ERK, 14-3-3β) in the same sample through spectral unmixing or sequential staining approaches.

• Tissue clearing techniques: Methods like CLARITY or iDISCO combined with FAM163A antibodies allow 3D visualization of expression patterns in intact tissue specimens.

• Microfluidic antibody-based assays: Lab-on-a-chip platforms for high-throughput screening of FAM163A expression in patient samples with minimal sample requirements.

These emerging technologies are expanding researchers' capabilities to investigate FAM163A's role in normal physiology and disease progression with unprecedented detail and precision.

What are the promising research directions for FAM163A in disease mechanisms and therapeutics?

Based on current knowledge, several promising research directions for FAM163A warrant further investigation:

• FAM163A as a biomarker: Expand clinical correlation studies beyond lung cancer to other malignancies, particularly neuroblastoma where FAM163A/NDSP was originally identified . Standardize detection methods for potential clinical application.

• Therapeutic targeting strategies:

  • Develop blocking antibodies against FAM163A to inhibit its signaling functions

  • Design small molecule inhibitors targeting FAM163A-14-3-3β interaction

  • Explore RNA interference or antisense approaches for FAM163A suppression in cancers with overexpression

• Mechanistic investigations:

  • Characterize the complete FAM163A-mediated signalosome beyond ERK and 14-3-3β

  • Investigate potential roles in other signaling pathways beyond MAPK/ERK

  • Elucidate the structural basis of FAM163A interactions through crystallography studies

• Physiological functions:

  • Define the normal physiological roles of FAM163A in development and tissue homeostasis

  • Investigate potential functions in non-cancer contexts such as inflammation or tissue repair

• Model systems development:

  • Generate FAM163A knockout/knockin animal models to study whole-organism effects

  • Develop patient-derived organoids expressing different levels of FAM163A for personalized medicine approaches

• Combination therapy approaches:

  • Explore FAM163A targeting in combination with existing ERK pathway inhibitors

  • Investigate potential synergies with immunotherapies in cancers expressing FAM163A

These research directions could significantly advance our understanding of FAM163A biology and potentially lead to novel diagnostic and therapeutic approaches for FAM163A-associated diseases.