C9orf163 Antibody

What is C9orf16 and why is it important in research?

C9orf16 (Chromosome 9 Open Reading Frame 16) is an 83 amino acid protein belonging to the UPF0184 family encoded by a gene mapping to human chromosome 9q34.11. This protein has recently gained significance in cancer research as its expression is associated with the development and progression of pancreatic ductal adenocarcinoma (PDAC) . The protein, whose functions were largely unknown until recently, has been identified as part of a crucial gene regulation system (MYC-C9orf16) actively involved in PDAC pathogenesis . Understanding C9orf16 is important because it represents aberrant genetic programs in cancer and could serve as both a diagnostic biomarker and therapeutic target, particularly in pancreatic cancer research .

What experimental applications are C9orf16 antibodies suitable for?

C9orf16 antibodies are validated for multiple experimental applications in research settings:

These applications have been validated through experimental procedures described in research studies, including the characterization of C9orf16 expression in pancreatic cancer tissues .

How should C9orf16 antibodies be stored and handled for optimal performance?

For optimal performance and stability, C9orf16 antibodies should be stored at -20°C in aliquots to avoid repeated freeze-thaw cycles that can degrade antibody quality . The antibody formulations typically contain aqueous buffered solutions (0.01M TBS, pH 7.4) with 1% BSA, 0.03% Proclin300 as preservative, and 50% Glycerol for stability . When working with these antibodies, it's important to note that some formulations contain ProClin, which is classified as hazardous and should be handled by trained personnel using appropriate safety precautions . For long-term storage exceeding 12 months, monitoring antibody performance with positive controls is recommended before using in critical experiments.

What are the best practices for using C9orf16 antibodies in immunohistochemistry analysis?

When conducting immunohistochemistry with C9orf16 antibodies, researchers should follow these methodological steps for optimal results:

• Sample preparation: Deparaffinize and rehydrate tissue sections properly if using paraffin-embedded samples.

• Antigen retrieval: Perform heat-induced epitope retrieval using citrate buffer (pH 6.0) to expose antigenic sites that may be masked during fixation .

• Blocking endogenous peroxidases: Treat with 3% hydrogen peroxide to inactivate endogenous peroxidases that could cause background staining .

• Primary antibody incubation: Apply C9orf16 antibody at appropriate dilution (typically 1:500 for IHC-P as validated in pancreatic cancer studies) .

• Secondary antibody application: Use appropriate HRP-conjugated secondary antibodies (e.g., goat anti-rabbit IgG H&L at 1:1000 dilution) .

• Visualization: Develop signal using DAB substrate kit according to manufacturer's instructions .

• Quantification: Evaluate C9orf16 protein expression by counting stained cells across multiple tumor/benign fields (recommended minimum of 500 cells across 5 different fields) .

This protocol has been successfully implemented in studies examining C9orf16 expression differences between normal pancreatic tissue and PDAC samples .

How can researchers validate C9orf16 antibody specificity?

Validating antibody specificity is crucial for ensuring reliable experimental results. For C9orf16 antibodies, researchers should employ multiple validation strategies:

• Positive and negative control tissues: Include tissues known to express C9orf16 (e.g., PDAC samples) and tissues with minimal expression (normal pancreatic epithelial cells) .

• Genetic manipulation controls: Compare staining between wild-type cells and those with C9orf16 knockdown or overexpression . Lentiviral particle systems used for C9orf16 knockdown or activation provide excellent validation controls .

• Western blot analysis: Confirm antibody specificity by detecting a band of appropriate molecular weight, comparing with recombinant protein standards when available .

• Cross-reactivity assessment: Test antibody against samples from multiple species if cross-reactivity is claimed (e.g., human, mouse, rat) .

• Peptide competition assay: Pre-incubate the antibody with immunizing peptide (synthetic peptide derived from human C9orf16) to confirm binding specificity .

These validation steps help ensure that experimental observations are truly related to C9orf16 and not to non-specific binding or cross-reactivity.

What considerations should be made when selecting between different conjugated forms of C9orf16 antibodies?

The choice between different conjugated forms of C9orf16 antibodies depends on experimental design, detection systems available, and multiplexing requirements:

When designing multiplex experiments, consider spectral overlap between fluorophores and select conjugates that minimize bleed-through. For tissue samples with high autofluorescence, far-red conjugates (Cy7, AbBy Fluor® 647/680/750) often provide better signal-to-noise ratios .

How can C9orf16 antibodies be utilized to study its role in cancer progression mechanisms?

C9orf16 antibodies serve as powerful tools for investigating the mechanistic role of this protein in cancer progression through several advanced research applications:

• Expression profiling: Quantifying C9orf16 expression across normal tissues, primary tumors, and metastatic sites using immunohistochemistry and western blotting reveals its stepwise upregulation during cancer progression . In PDAC, C9orf16 shows minimal expression in normal epithelial cells, increased expression in primary tumors, and highest expression in metastatic cells .

• Functional pathway analysis: Combined with RNA-seq or proteomics data, C9orf16 immunostaining can help identify associated signaling networks. Research has demonstrated that MYC signaling pathways are the most activated pathways regulating C9orf16 expression in PDAC .

• Treatment response prediction: Monitoring C9orf16 expression before and after chemotherapy can help evaluate its potential as a predictive biomarker for treatment resistance, as functional studies have shown its involvement in chemotherapy resistance .

• Metastatic potential assessment: Correlating C9orf16 expression levels with invasion assays and metastatic outcomes helps determine its value as a prognostic marker. Higher expression correlates with increased cell migration and invasion capabilities in experimental models .

• Therapeutic target validation: Using C9orf16 antibodies in combination with inhibitors of related pathways (e.g., MYC inhibitors) can help validate therapeutic approaches targeting this axis .

These applications collectively provide insights into how C9orf16 contributes to cancer biology and its potential as a therapeutic target.

What methodological approaches can be used to study the MYC-C9orf16 axis in cancer?

The MYC-C9orf16 regulatory axis represents a promising area for cancer research. Investigators can employ these methodological approaches to explore this relationship:

• Chromatin immunoprecipitation (ChIP): Determine if MYC directly binds to the C9orf16 promoter region using anti-MYC antibodies followed by qPCR or sequencing.

• Dual immunostaining: Perform co-localization studies with both MYC and C9orf16 antibodies to evaluate their spatial relationship in tissue samples .

• Genetic manipulation studies: Employ MYC knockdown/overexpression systems to observe consequent changes in C9orf16 expression. This approach has revealed MYC as a key regulator of C9orf16 expression in PDAC .

• Pharmacological inhibition: Treat cells with MYC pathway inhibitors and monitor C9orf16 expression using western blotting and qPCR to confirm pathway connections.

• Reporter assays: Construct C9orf16 promoter-reporter systems to quantify the impact of MYC modulation on transcriptional activity.

• Single-cell analysis: Apply single-cell RNA sequencing combined with computational analyses to identify correlations between MYC and C9orf16 expression at single-cell resolution. This approach has been valuable in identifying C9orf16 as a PDAC biomarker through analysis of normal, primary, and metastatic PDAC scRNA-seq datasets .

• Functional phenotyping: Conduct cell proliferation (MTT assays), migration, and invasion assays following manipulation of the MYC-C9orf16 axis to determine functional outcomes .

These methodological approaches provide a comprehensive framework for investigating the mechanistic relationships and functional significance of the MYC-C9orf16 axis in cancer.

How can researchers optimize C9orf16 antibody-based detection in heterogeneous tumor samples?

Detecting C9orf16 in heterogeneous tumor samples presents unique challenges that require optimization strategies:

• Sample microdissection: For highly heterogeneous samples, laser capture microdissection can isolate specific cell populations before immunostaining or western blot analysis.

• Multiplexing with lineage markers: Co-stain samples with C9orf16 antibodies and epithelial markers (e.g., EpCAM, E-cadherin) to distinguish cancer cells from stromal components in complex tumor microenvironments .

• Single-cell resolution techniques: Employ imaging mass cytometry or multiplexed immunofluorescence to simultaneously detect C9orf16 and multiple cell-type markers at single-cell resolution.

• Quantitative image analysis: Utilize digital pathology platforms with machine learning algorithms to quantify C9orf16 expression across different cellular compartments within heterogeneous samples.

• Sequential immunostaining: Apply multispectral imaging with sequential antibody stripping and reprobing to evaluate multiple markers on the same tissue section.

• Reference standard inclusion: Include calibration standards of known C9orf16 concentration to enable accurate quantification across heterogeneous samples.

• Pre-analytical variable control: Standardize fixation time, antigen retrieval conditions, and staining protocols to minimize technical variability that might obscure biological differences.

These optimizations have proven valuable in studies characterizing C9orf16 expression across normal pancreatic tissues and heterogeneous PDAC samples .

What are common challenges when working with C9orf16 antibodies and how can they be addressed?

Researchers may encounter several technical challenges when working with C9orf16 antibodies:

For C9orf16 specifically, optimizing antigen retrieval is critical as studies have shown that heat-induced epitope retrieval with citrate buffer (pH 6.0) significantly improves detection in PDAC samples .

How should researchers interpret C9orf16 expression data in relation to clinical outcomes?

Interpreting C9orf16 expression data in relation to clinical outcomes requires careful methodological considerations:

• Quantification methodology: Define clear scoring systems for C9orf16 positivity. Studies examining PDAC have counted stained cells across multiple fields (>500 cells across 5 different fields) to establish reliable quantification .

• Expression thresholds: Establish clinically relevant cutoff values based on receiver operating characteristic (ROC) curve analysis comparing expression levels with outcomes.

• Multivariate analysis: Combine C9orf16 expression data with established prognostic factors (tumor stage, grade, etc.) in multivariate models to determine independent prognostic value.

• Correlation with molecular subtypes: Integrate C9orf16 expression with molecular subtyping data, as its relationship with MYC suggests it may be particularly relevant in specific molecular subtypes of cancer .

• Temporal dynamics consideration: When possible, analyze expression changes over disease course using sequential samples, as C9orf16 expression increases from normal tissue to primary cancer to metastasis .

• Technical validation: Cross-validate findings using multiple antibody clones or detection methods (IHC, IF, western blot) to ensure robust clinical correlations.

• Functional context: Interpret expression data in light of functional studies demonstrating C9orf16's roles in proliferation, invasion, and chemotherapy resistance .

These methodological approaches help ensure that correlations between C9orf16 expression and clinical outcomes reflect true biological relationships rather than technical artifacts.

What emerging technologies might enhance C9orf16 antibody-based research?

Several emerging technologies hold promise for advancing C9orf16 antibody-based research:

• Spatial transcriptomics integrated with immunohistochemistry: Combining C9orf16 protein detection with spatial gene expression analysis would provide insights into how its expression relates to the broader transcriptional landscape in different tissue microenvironments.

• Proximity ligation assays: These could reveal protein-protein interactions between C9orf16 and other molecules in the MYC pathway, helping to elucidate its functional mechanisms .

• Mass spectrometry immunohistochemistry: This emerging technique could provide absolute quantification of C9orf16 protein with spatial resolution in tissue samples.

• CRISPR-based functional screens: Combined with C9orf16 antibody detection, these screens could identify genes that modulate C9orf16 expression or function beyond the established MYC connection .

• Live-cell imaging with fluorescent antibody fragments: This approach could track C9orf16 dynamics in real-time during cancer cell processes such as invasion and mitosis.

• Antibody-drug conjugates: Exploring C9orf16 antibodies as targeting moieties for therapeutic delivery could translate basic research findings into potential clinical applications.

• Single-molecule localization microscopy: Super-resolution imaging with C9orf16 antibodies could reveal previously unknown subcellular localization patterns relevant to its function.

These technologies would extend beyond current applications that have established C9orf16 as a biomarker with functional roles in cancer progression .

How might C9orf16 antibodies contribute to developing new therapeutic approaches for PDAC?

C9orf16 antibodies can facilitate therapeutic development for PDAC through several research applications:

• Target validation: Using antibodies to confirm C9orf16 expression in patient-derived xenografts and organoid models can help validate it as a therapeutic target .

• Response biomarker development: C9orf16 antibodies can be used to develop immunohistochemistry-based companion diagnostics to identify patients most likely to respond to therapies targeting the MYC-C9orf16 axis .

• Therapeutic antibody development: The identified epitopes in current research antibodies could guide the development of therapeutic antibodies if C9orf16 has accessible extracellular domains.

• Mechanistic studies: C9orf16 antibodies can help elucidate the protein's role in chemoresistance mechanisms, potentially identifying combination therapy approaches to overcome treatment resistance .

• Drug screening: High-content screening using C9orf16 antibodies can identify compounds that modulate its expression or function, potentially revealing new therapeutic candidates.

• Circulating tumor cell detection: Developing sensitive detection methods for C9orf16-expressing circulating tumor cells could enable improved monitoring of treatment response and recurrence.

• Evaluating pathway inhibitors: C9orf16 antibodies can be used to assess the efficacy of MYC pathway inhibitors in downregulating this downstream target in preclinical models .