FAM118A Antibody

What is FAM118A and what are the available antibody options for its detection?

FAM118A (Family with Sequence Similarity 118 Member A) is a protein-coding gene that encodes a single-pass transmembrane protein with largely unknown function . Current antibody options include:

• Polyclonal antibodies: Predominantly rabbit-derived polyclonal antibodies against human FAM118A, such as HPA003902 from Atlas Antibodies/Sigma-Aldrich

• Recombinant protein antigens: Available as controls for antibody validation (e.g., NBP1-88590PEP)

• Conjugated antibodies: Including FITC-conjugated, HRP-conjugated, and biotin-conjugated variants

Most commercially available antibodies target human FAM118A, though some cross-react with mouse, rat, and other species. These antibodies have been validated for various applications including Western blot, immunohistochemistry, immunofluorescence, and ELISA .

What are the validated applications for FAM118A antibodies in experimental research?

FAM118A antibodies have been validated for multiple experimental applications with specific recommended dilutions:

Most antibodies have undergone enhanced validation including recombinant expression validation . When using these antibodies for specific applications, researchers should optimize dilutions based on their specific experimental conditions.

How is FAM118A expression altered in cancer models, and what methodological considerations should be taken when using FAM118A antibodies for cancer research?

Research has shown variable FAM118A expression patterns across cancer types:

• In glioblastoma stem cell (GSC) cultures, FAM118A was significantly down-regulated compared to neural stem cell cultures

• Western blot analysis confirmed FAM118A protein up-regulation in 15 tested GSC cultures

When using FAM118A antibodies for cancer research, consider:

• Antibody validation: Confirm specificity in your specific cancer model using positive and negative controls

• Expression context: FAM118A was part of a gene signature that included 20 genes differentially expressed in GSCs vs. NSCs

• Correlation with survival: Although FAM118A wasn't specifically identified as a survival predictor, it was part of gene clusters that showed significant correlation with survival in mesenchymal GBM subtypes

• Multiple detection methods: Combine protein detection (via antibodies) with mRNA analysis to confirm expression patterns as sometimes protein and mRNA levels don't correlate

Methodologically, researchers should use multiple antibody-based techniques (WB, IHC, IF) to corroborate findings and account for potential differences between protein expression and mRNA levels .

What technical challenges exist in distinguishing between FAM118A and other members of the FAM protein family, and how can these be addressed?

Distinguishing between FAM protein family members presents several challenges:

• Sequence homology: FAM proteins often share sequence similarities

• Cross-reactivity risk: Antibodies may cross-react with related family proteins

• Similar molecular weights: Some FAM proteins have comparable molecular weights, complicating Western blot interpretation

To address these challenges:

• Epitope selection: Choose antibodies targeting unique epitopes. The immunogen sequence provided for HPA003902 (VTQDAEVMEVLQNLYRTKSFLFVGCGETLRDQIFQALFLYSVPNKVDLEHYMLVLKENEDHFFKHQADMLLHGIKVVSYGDCFDHFPGYVQDLATQICKQQSPDADRVDSTTLLGNACQDCAKRKLEENGIE) targets a specific region of FAM118A

• Knockout validation: Use FAM118A knockout/knockdown controls via available esiRNA options (e.g., EMU011021 for mouse, EHU018311 for human)

• Multiple antibody approach: Employ multiple antibodies targeting different epitopes to confirm specificity

• Immunoprecipitation followed by mass spectrometry: This can definitively confirm antibody targets in complex samples

Recent studies have developed sophisticated approaches for antibody specificity design and validation that could be applied to FAM family proteins .

What are the optimal sample preparation protocols for detecting FAM118A in different cellular compartments and tissue types?

Based on available research, optimal sample preparation varies by application:

For Western Blot:

• Use RIPA or NP-40 based lysis buffers with protease inhibitors

• Include phosphatase inhibitors if studying post-translational modifications

• Recommended protein loading: 20-50 μg of total protein

• Detection range: 0.04-0.4 μg/mL antibody concentration

For Immunohistochemistry:

• Formalin-fixed paraffin-embedded (FFPE) sections (4-6 μm)

• Antigen retrieval: Heat-induced epitope retrieval in citrate buffer (pH 6.0)

• Recommended dilution: 1:50-1:200

• Include appropriate positive tissue controls (based on Human Protein Atlas data)

For Immunofluorescence:

• PFA-fixed cells (4% paraformaldehyde)

• Permeabilization with 0.1-0.5% Triton X-100

• Recommended antibody dilution: 0.25-2 μg/mL

For subcellular localization studies, consider cellular fractionation protocols prior to Western blotting to separate membrane, cytoplasmic, and nuclear fractions, as FAM118A is described as a transmembrane protein .

How should researchers address experimental variability when using different lots or sources of FAM118A antibodies?

To address experimental variability across antibody lots and sources:

• Lot validation protocol:

  • Perform side-by-side comparison with previous lot

  • Document antibody performance metrics (signal-to-noise ratio, specificity patterns)

  • Maintain detailed records of antibody performance by lot

• Standardization measures:

  • Use recombinant FAM118A protein as a positive control (e.g., NBP1-88590PEP)

  • Include consistent positive and negative tissue/cell controls

  • Establish standard curves for quantitative applications

  • Maintain consistent blocking, incubation, and washing protocols

• Cross-validation approaches:

  • Validate findings with antibodies from multiple vendors

  • Correlate antibody-based detection with orthogonal methods (RNA-seq, mass spectrometry)

  • For critical experiments, use antibodies validated through enhanced methods

• Documentation practices:

  • Record complete antibody information (catalog number, lot, dilution, incubation conditions)

  • For published research, include validation evidence and detailed methodologies

When switching antibody sources, conduct thorough validation studies comparing the new antibody to previously established results before proceeding with critical experiments.

How do researchers interpret contradictory data regarding FAM118A expression across different cancer types and experimental models?

Contradictory data regarding FAM118A expression requires careful interpretation:

• Context-specific regulation:

  • Research indicates FAM118A may be down-regulated in some contexts (e.g., GSC cultures) while up-regulated in others

  • Consider cancer subtype differences - expression patterns may vary across molecular subtypes

• Methodological reconciliation:

  • Compare detection methods (antibody-based vs. mRNA-based)

  • In one study, microarray data showed FAM118A was up-regulated 3.2-fold in GSCs, while qPCR showed it was down-regulated 0.3-fold, suggesting method-dependent results

  • Protein levels (Western blot) showed FAM118A was up-regulated in 15 GSC cultures tested

• Analytical framework:

  • Hierarchical clustering can help identify co-expression patterns (FAM118A clustered with other genes in GBM studies)

  • Correlation with clinical outcomes may reveal functional significance

• Integration strategies:

  • Combine data from multiple techniques (WB, IHC, IF, qPCR)

  • Consider pathway analysis that places FAM118A in broader biological context

  • Evaluate protein-protein interactions that may affect detection or function

What are the current hypotheses regarding FAM118A function based on antibody-based research findings?

Current hypotheses about FAM118A function derived from antibody-based research include:

• Potential role in cancer biology:

  • Differential expression in glioblastoma stem cells suggests involvement in cancer pathways

  • Correlation with other cancer-associated genes indicates possible participation in oncogenic networks

• Transmembrane signaling:

  • As a single-pass transmembrane protein , FAM118A may function in signal transduction

  • Antibody-detected localization patterns could inform membrane-associated functions

• Genetic associations:

  • FAM118A contains a missense mutation (rs6007594) associated with ankylosing spondylitis that changes arginine to histidine, predicted to be "probably damaging" with a PolyPhen-2 score of 0.999

  • This suggests potential involvement in immunological or inflammatory processes

• Expression regulation:

  • FAM118A expression in lymphoblastoid cell lines and human osteoblasts showed major SNP effects on expression levels

  • This indicates genetic regulation of expression with potential functional consequences

While definitive function remains unclear, antibody-based research continues to elucidate FAM118A's potential roles by identifying its expression patterns, localization, and associations with disease states.

What advanced validation techniques can researchers employ to ensure FAM118A antibody specificity beyond standard methods?

Advanced validation techniques for ensuring FAM118A antibody specificity include:

• Genetic validation approaches:

  • CRISPR/Cas9 knockout validation: Generate FAM118A knockout cell lines as negative controls

  • siRNA/shRNA knockdown: Use available esiRNA products (EMU011021 for mouse, EHU018311 for human) to validate signal reduction

  • Overexpression systems: Create FAM118A-overexpressing cell lines as positive controls

• Orthogonal validation:

  • Mass spectrometry validation: Immunoprecipitate with FAM118A antibody followed by MS identification

  • Proximity ligation assay (PLA): Confirm protein interactions using two different antibodies targeting different FAM118A epitopes

  • Super-resolution microscopy: Compare subcellular localization patterns using multiple antibodies

• Biophysical validation:

  • Surface plasmon resonance (SPR): Measure binding kinetics to recombinant FAM118A

  • Epitope mapping: Identify precise binding sites using peptide arrays or hydrogen-deuterium exchange MS

  • Competitive binding assays: Verify epitope specificity using synthetic peptides

• Enhanced contextual validation:

  • Multi-tissue profiling: Compare staining patterns across tissue panels against known expression data

  • Antibody performance in multiple applications: Cross-validate across WB, IHC, IF, and IP

  • Signal pattern analysis: Confirm expected molecular weight, subcellular localization, and tissue distribution

The biophysics-informed modeling approach described in result offers advanced methods for designing antibodies with customized specificity profiles that could be applied to FAM118A antibody development.

How can researchers differentiate between specific and non-specific binding when using FAM118A antibodies in complex samples?

To differentiate between specific and non-specific binding in complex samples:

• Comprehensive control strategy:

  • Negative controls: Include secondary-only controls, isotype controls, and pre-immune serum controls

  • Blocking controls: Pre-incubate antibody with recombinant FAM118A protein or immunogenic peptide

  • Tissue/cell controls: Use known positive and negative tissues/cell lines based on transcriptomic data

  • Genetic controls: Compare wildtype to FAM118A knockdown/knockout samples

• Signal validation techniques:

  • Multiple antibody approach: Use antibodies targeting different FAM118A epitopes

  • Signal quantification: Analyze signal-to-noise ratios and compare to background

  • Expected pattern analysis: Confirm expected molecular weight (WB), subcellular localization (IF), and tissue distribution (IHC)

• Advanced analytical methods:

  • Titration experiments: Perform antibody dilution series to identify optimal signal-to-noise ratio

  • Sequential extraction: Compare antibody performance across different extraction methods

  • Two-dimensional electrophoresis: Assess specificity based on both molecular weight and isoelectric point

• Protocol optimization:

  • Blocking optimization: Test different blocking agents (BSA, milk, normal serum)

  • Buffer composition adjustments: Modify salt concentration, detergents, pH to reduce non-specific binding

  • Incubation parameters: Optimize temperature, time, and concentration

When publishing results, clearly document all validation steps taken to distinguish specific from non-specific binding to enhance reproducibility and reliability of FAM118A antibody-based findings.

What is the significance of FAM118A in cancer research based on antibody detection studies, and how should researchers interpret changes in its expression?

Based on antibody detection studies, FAM118A shows notable significance in cancer research:

• Expression patterns in cancer:

  • In glioblastoma studies, FAM118A was identified as part of a signature of 20 genes differentially expressed between glioblastoma stem cells (GSCs) and neural stem cells (NSCs)

  • Western blot analysis confirmed that 15 proteins encoded by these genes, including FAM118A, were up-regulated in all tested GSC cultures

  • Interestingly, while protein levels were up-regulated, qPCR showed FAM118A was down-regulated (0.3-fold) at the mRNA level

• Interpretation framework:

  • Post-transcriptional regulation: Discrepancies between mRNA and protein levels suggest potential post-transcriptional regulation mechanisms

  • Context-dependent expression: FAM118A expression may vary across cancer subtypes and experimental models

  • Co-expression networks: Hierarchical clustering showed FAM118A was co-expressed with other cancer-associated genes

• Methodological considerations:

  • Use multiple detection methods (WB, IHC, qPCR) to comprehensively assess expression

  • Include appropriate controls for each experimental system

  • Consider pathway analysis to place FAM118A alterations in biological context

• Clinical correlations:

  • While the direct correlation between FAM118A alone and patient survival was not detailed, it was part of gene clusters showing correlation with survival in GBM subtypes

  • Researchers should analyze correlation with clinical parameters including survival, tumor grade, and treatment response

When interpreting changes in FAM118A expression, researchers should consider the technical approach, cancer context, and potential functional implications within broader molecular networks.

What methodological approaches should researchers use when studying FAM118A in relation to immune responses and inflammation, particularly given its genetic association with ankylosing spondylitis?

Given FAM118A's genetic association with ankylosing spondylitis , researchers studying its role in immune responses should employ these methodological approaches:

• Genetic analysis framework:

  • SNP analysis: Examine the functional impact of the rs6007594 missense mutation (arginine to histidine) associated with ankylosing spondylitis

  • Expression quantitative trait loci (eQTL) studies: Investigate how FAM118A variants affect expression in relevant cell types

  • Haplotype analysis: Study linkage disequilibrium patterns around the FAM118A locus

• Cellular models for immunological investigation:

  • Primary immune cell cultures: Analyze FAM118A expression in lymphocytes, monocytes, and dendritic cells

  • Osteoblast models: Study FAM118A in human osteoblasts, where SNP effects on expression have been demonstrated

  • Inflammation models: Examine FAM118A expression changes during inflammatory stimulation

• Protein interaction studies:

  • Co-immunoprecipitation: Identify FAM118A binding partners in immune cells

  • Proximity labeling: Use BioID or APEX2 approaches to map the FAM118A interactome

  • Pathway analysis: Investigate FAM118A's relationship to established inflammatory pathways

• Functional assays:

  • Cytokine profiling: Measure impact of FAM118A modulation on inflammatory cytokine production

  • Signal transduction analysis: Assess effects on NF-κB, JAK-STAT, and other immune signaling pathways

  • Cell migration and adhesion: Evaluate FAM118A's role in immune cell trafficking

• Antibody-based visualization techniques:

  • Multiplex immunofluorescence: Co-localize FAM118A with immune markers in tissue sections

  • Flow cytometry: Quantify FAM118A expression across immune cell populations

  • Intracellular cytokine staining: Correlate FAM118A expression with cytokine production

When conducting these studies, researchers should carefully validate antibody specificity in each immune cell type and experimental condition, and consider genetic background when interpreting results.

What emerging technologies could improve the specificity and utility of FAM118A antibodies for advanced research applications?

Several emerging technologies could enhance FAM118A antibody specificity and utility:

• Next-generation antibody engineering:

  • Biophysics-informed modeling: Apply computational approaches that identify multiple binding modes for enhanced specificity, as described in recent research

  • Structure-guided antibody design: Utilize structural data to design antibodies targeting specific FAM118A epitopes

  • Nanobodies and single-domain antibodies: Develop smaller antibody formats for improved tissue penetration and epitope access

• Advanced screening technologies:

  • Phage display with high-throughput sequencing: Identify antibodies with customized specificity profiles

  • Microfluidic antibody screening: Rapidly screen thousands of single B cells for FAM118A-specific antibody production

  • Genotype-phenotype linked antibody screening: Apply new methods that link antibody sequences directly to their binding properties

• Multimodal detection systems:

  • Bifunctional antibodies: Develop reagents that simultaneously detect FAM118A and interacting partners

  • Intrabodies with reporter functions: Create FAM118A-targeting antibodies with built-in fluorescent or enzymatic reporters

  • Proximity-dependent labeling antibodies: Generate antibodies conjugated to enzymes that label proximal proteins

• Enhanced validation platforms:

  • Tissue and cell microarrays: Develop comprehensive validation panels across multiple tissues and cell types

  • Automated machine learning validation: Apply AI to analyze antibody staining patterns for specificity validation

  • Standardized reporting frameworks: Implement structured validation data reporting for improved reproducibility

These technologies could significantly advance FAM118A research by providing more specific, sensitive, and versatile detection tools. The biophysics-informed modeling approach mentioned in is particularly promising for designing antibodies with customized specificity profiles.

How might researchers integrate antibody-based detection with 'omics approaches to better understand FAM118A function in health and disease?

Integrating antibody-based FAM118A detection with 'omics approaches offers powerful opportunities for functional discovery:

• Multi-omics integration strategies:

  • Antibody-proteomics pipeline: Combine FAM118A immunoprecipitation with mass spectrometry to identify interaction networks

  • Spatial transcriptomics-immunohistochemistry correlation: Map FAM118A protein expression against spatial transcriptomic profiles

  • ChIP-seq coupling: Use FAM118A antibodies for chromatin immunoprecipitation paired with sequencing to identify associated DNA regions if FAM118A has nuclear functions

• Single-cell multi-parameter analysis:

  • Single-cell proteogenomics: Correlate FAM118A protein levels with transcriptomic profiles at single-cell resolution

  • Imaging mass cytometry: Map FAM118A expression alongside dozens of other proteins in tissue sections

  • Multi-epitope ligand cartography (MELC): Create high-dimensional maps of FAM118A localization relative to other cellular markers

• Systems biology approaches:

  • Network analysis: Place FAM118A in protein-protein interaction networks using antibody-based interactome data

  • Pathway modeling: Integrate FAM118A expression data from antibody studies with pathway analysis

  • Multi-parameter perturbation studies: Assess system-wide effects of FAM118A modulation

• Translational research applications:

  • Clinical sample profiling: Correlate FAM118A protein expression with patient genomic/transcriptomic profiles

  • Biomarker discovery pipeline: Evaluate FAM118A as a potential biomarker across disease contexts

  • Therapeutic target assessment: Use antibody-based detection to validate FAM118A as a potential drug target

• Data integration frameworks:

  • Machine learning approaches: Develop predictive models integrating antibody-based FAM118A data with other 'omics datasets

  • Knowledge graphs: Create comprehensive relationship networks connecting FAM118A to biological pathways and disease mechanisms

  • Cloud-based collaborative platforms: Establish integrated databases combining antibody validation, expression, and functional data