PCDHGC3 Antibody

What is PCDHGC3 and what is its function in normal physiology?

PCDHGC3 is a member of the protocadherin gamma gene cluster, which belongs to the cadherin superfamily - the largest subgroup of calcium-dependent adhesion molecules. It functions as a neural cadherin-like cell adhesion protein that plays a critical role in establishing and maintaining specific cell-cell connections in the brain . The protein is primarily expressed in the nervous system, but also found in liver and skin tissues . In the central nervous system, PCDHGC3 appears to be involved in regulating major signaling pathways and inflammatory responses, particularly in brain microvascular endothelial cells (BMECs) which constitute part of the blood-brain barrier .

How are PCDHGC3 antibodies generated and validated?

PCDHGC3 antibodies are typically generated using either recombinant protein fragments or synthetic peptides corresponding to specific regions of the PCDHGC3 protein. For monoclonal antibodies, fusion proteins representing specific amino acid regions (such as amino acids 720-804 of the variable cytoplasmic domain) are often produced recombinantly in E. coli . For polyclonal antibodies, synthetic peptides corresponding to defined regions (e.g., amino acids 518-547 of human PCDHGC3) are conjugated to carrier proteins like KLH .

Validation typically involves multiple steps including Western blot confirmation of specificity against cells overexpressing the target protein, verification of expected staining patterns in known positive tissues, and testing for cross-reactivity against other related protocadherin proteins . Crucially, knockout validation using CRISPR/Cas9-engineered cell lines lacking PCDHGC3 expression provides the most definitive confirmation of antibody specificity .

What are the common applications for PCDHGC3 antibodies in research?

PCDHGC3 antibodies have multiple research applications based on their specificity and binding characteristics. They are commonly used in Western Blotting (WB) for detecting and quantifying PCDHGC3 protein expression in tissue or cell lysates, with typical dilutions ranging from 1:500-1:1000 . Immunohistochemistry (IHC) applications allow examination of tissue distribution and localization of PCDHGC3, particularly in brain tissues and tumors .

Additional applications include immunocytochemistry (ICC) for subcellular localization studies, flow cytometry (FACS) for analyzing PCDHGC3 expression in specific cell populations, and enzyme immunoassays (EIA) for quantitative measurement of PCDHGC3 in biological samples . The specific application dictates optimal antibody concentration, incubation conditions, and detection methods.

What tissue types show significant PCDHGC3 expression?

PCDHGC3 shows differential expression across tissues, with highest expression in neural tissues, particularly in the brain . Research has revealed that PCDHGC3 is significantly overexpressed in glioma tissue compared to non-cancerous brain specimens . Within glioblastoma multiforme (GBM) tumors, expression varies by region - higher in cellular tumor areas compared to leading edge, pseudopalisading cells, and perivascular regions .

Moderate expression levels have been detected in liver and skin . Brain microvascular endothelial cells also express PCDHGC3, which is relevant to blood-brain barrier function . This tissue-specific expression pattern suggests specialized functions in different cellular contexts and potential utility as a biomarker in certain pathological conditions.

What are the optimal fixation and antigen retrieval methods for PCDHGC3 immunohistochemistry?

For PCDHGC3 immunohistochemistry, tissue fixation and antigen retrieval are critical for preserving protein structure while enabling antibody access to epitopes. Optimal protocols typically include fixation in 10% neutral buffered formalin for 24-48 hours, though overfixation should be avoided as it may mask PCDHGC3 epitopes .

Heat-induced epitope retrieval (HIER) using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) is recommended to reverse protein cross-linking introduced during fixation . A standardized protocol would include deparaffinization and rehydration of tissue sections, HIER using citrate buffer (pH 6.0) for 20 minutes, cooling to room temperature, blocking endogenous peroxidase activity, and blocking non-specific binding with appropriate serum. Primary PCDHGC3 antibody is typically applied at optimized dilution (1:500-1:1000) overnight at 4°C, followed by detection using appropriate secondary antibody and visualization system .

How should researchers design proper controls for PCDHGC3 antibody experiments?

Rigorous control design is essential for reliable PCDHGC3 antibody experiments. Positive controls should include tissues known to express PCDHGC3 (e.g., normal brain tissue) and cell lines with confirmed PCDHGC3 expression (e.g., U343 glioma cells) . Recombinant PCDHGC3 protein can serve as a positive control for Western blot applications.

Negative controls are equally important and should include PCDHGC3 knockout cell lines (such as CRISPR/Cas9-generated PCDHGC3 KO U343 cells) , primary antibody omission controls, isotype controls matching the primary antibody, and tissues known not to express PCDHGC3. Specificity controls should include peptide competition assays where the antibody is pre-incubated with the immunizing peptide, as well as testing against other protocadherin family members to ensure no cross-reactivity . Commercial PCDHGC3 antibodies often specify "no cross-reactivity against other Gamma-protocadherin-A, -B or -C proteins," which should be independently verified .

What are the optimal techniques for quantifying PCDHGC3 expression in tissue samples?

Accurate quantification of PCDHGC3 expression requires appropriate methodological approaches depending on the research question. For protein-level quantification, Western blot analysis with densitometry allows relative comparison between samples, as demonstrated in studies comparing PCDHGC3 protein levels in GBM versus non-cancerous brain samples . Standardization using housekeeping proteins like β-actin or GAPDH is essential for reliable quantification.

For tissue-level expression patterns, immunohistochemistry with systematic scoring methods provides spatial information. Digital image analysis of immunostained sections enables objective quantification through measurement of staining intensity and percentage of positive cells . For mRNA quantification, RT-qPCR remains the gold standard, allowing precise measurement of PCDHGC3 transcript levels .

How can specificity of PCDHGC3 antibodies be verified across different protocadherin family members?

Verifying specificity of PCDHGC3 antibodies across the protocadherin family is crucial due to high sequence homology. Epitope analysis should prioritize antibodies targeting unique regions of PCDHGC3 not conserved in other protocadherins, such as the variable cytoplasmic domain (e.g., amino acids 720-804) . This domain is typically more distinct than the extracellular domains shared among protocadherin family members.

Cross-reactivity testing should include Western blot analysis using recombinant proteins from multiple protocadherin family members, testing on cells overexpressing different protocadherin family members, and verifying no signal in PCDHGC3 knockout samples while retaining signals for other family members . More sophisticated approaches include immunoprecipitation followed by mass spectrometry to confirm specificity for PCDHGC3 versus other family members, and epitope-blocking experiments where the antibody is pre-incubated with peptides from PCDHGC3 and related family members to observe selective blocking.

How does PCDHGC3 expression differ between glioma tissues and normal brain tissue?

PCDHGC3 shows significant differential expression between glioma and normal brain tissue based on multiple lines of evidence. Both PCDHGC3 mRNA and protein are significantly overexpressed in glioma tissue compared to non-cancerous brain specimens, as demonstrated by Western blot and RT-qPCR analyses . This overexpression pattern has been confirmed in multiple glioma cell lines including GaMG, U87, U138, and U343 .

Within glioblastoma multiforme (GBM) tumors, PCDHGC3 expression varies by region - it is significantly enhanced in the cellular part of GBMs but decreased in regions with pseudopalisading cells or hyperplastic blood vessels . Expression is also lower in the leading edge regions compared to cellular tumor areas. Western blot analysis of protein lysates from randomly selected GBM samples confirmed a statistically significant increase in PCDHGC3 protein levels compared to non-cancerous brain samples . This differential expression pattern suggests potential diagnostic utility and functional significance in glioma biology.

What is the correlation between PCDHGC3 expression and patient prognosis in gliomas?

Research has revealed important correlations between PCDHGC3 expression and clinical outcomes in glioma patients. High PCDHGC3 mRNA expression correlates with longer progression-free survival (PFS) in glioma patients . Specifically, in GBM patients, PCDHGC3 mRNA expression significantly correlated with PFS (p = 0.018) .

Interestingly, no further correlations were found between PCDHGC3 expression and other clinical parameters in GBM patients, including sex, MGMT promoter methylation, tumor growth pattern, localization, tumor volume, Ki-67-staining, or type of therapy . These findings suggest PCDHGC3 could have specific utility as a prognostic biomarker in gliomas, independent of other established factors.

What molecular mechanisms might explain PCDHGC3's role in tumor progression?

Several molecular mechanisms have been implicated in PCDHGC3's role in tumor biology, revealing complex and sometimes seemingly contradictory functions. PCDHGC3 appears to regulate the WNT signaling pathway, with knockout in U343 glioma cells decreasing the expression of several genes involved in WNT signaling . Given WNT signaling's key role in cancer development and progression, this represents a potential mechanism for PCDHGC3's effects.

Functionally, PCDHGC3 knockout resulted in slower growth rate but significantly faster migration in wound healing assays . This suggests a complex role where PCDHGC3 may promote proliferation but inhibit migration/invasion - critical processes in tumor progression. Some studies have described PCDHGC3 as a tumor suppressor that promotes apoptosis of tumor cells and suppresses both Wnt- and mTOR-signaling pathways, affecting growth of various tumors including Wilms tumors, breast cancer, and prostate cancer .

Epigenetic regulation may also be important, as asynchronous hypermethylation of PCDH family members (including PCDHGC3) has been observed in colorectal adenomas and carcinomas . The contradictory effects observed (promoting growth but inhibiting migration) highlight the complex and possibly context-dependent role of PCDHGC3 in tumor biology.

How can CRISPR/Cas9-engineered PCDHGC3 knockout models contribute to understanding its function in cancer?

CRISPR/Cas9-engineered PCDHGC3 knockout models provide powerful tools for elucidating this protein's function in cancer biology. In the research literature, PCDHGC3 knockout in U343 glioma cells has already yielded valuable insights . This approach allows direct assessment of phenotypic changes resulting from PCDHGC3 loss, including alterations in growth rate, migration capacity, and gene expression patterns.

The knockout model revealed that PCDHGC3 deletion results in slower growth but enhanced migration in wound healing assays, suggesting a dual role in tumor cell behavior . At the molecular level, PCDHGC3 knockout decreased expression of several genes involved in WNT signaling, highlighting its role in regulating this cancer-relevant pathway .

Similarly, PCDHGC3 knockout in brain microvascular endothelial cells revealed its importance in barrier integrity, inflammatory responses, and signaling pathway regulation . Such models allow systematic investigation of PCDHGC3's role in specific aspects of tumor biology, including proliferation, migration, invasion, angiogenesis, and immune interactions. By comparing knockout phenotypes across different cell types and combining with in vivo models, researchers can develop a comprehensive understanding of PCDHGC3's complex functions in cancer.

How does PCDHGC3 influence cellular signaling pathways relevant to cancer and neurological disorders?

PCDHGC3 exerts influence over multiple signaling pathways critical to both cancer progression and neurological function. Research has shown that PCDHGC3 deletion affects the MAPK/ERK pathway, with serum starvation leading to significantly higher phosphorylation of extracellular signal-regulated kinases (Erk) in PCDHGC3 knockout cells . The enhanced migration of PCDHGC3 knockout cells was significantly inhibited by the MAPK pathway inhibitor SL327, confirming this pathway's involvement .

PCDHGC3 also regulates WNT/β-catenin signaling, with knockout decreasing expression of WNT pathway genes . Migration in PCDHGC3 knockout cells was inhibited by the β-catenin/Wnt pathway inhibitor XAV939 . Additionally, PCDHGC3 appears to interact with the mTOR pathway, as knockout cell migration was inhibited by the mTOR inhibitor Torin 2 . Previous studies have described PCDHGC3 as a suppressor of both Wnt and mTOR signaling pathways .

In the context of inflammatory signaling, particularly relevant to neurological disorders, PCDHGC3 knockout cells responded more strongly to oxygen/glucose deprivation and TNFα treatment, with significantly higher induction of interleukin 6 mRNA than wild type cells . This multi-pathway regulatory role positions PCDHGC3 as a potential therapeutic target in both cancer and neurological conditions.

What techniques are optimal for studying PCDHGC3 in blood-brain barrier function?

Studying PCDHGC3 in blood-brain barrier (BBB) function requires specialized techniques based on its demonstrated role in barrier integrity. In vitro BBB models using Transwell culture systems with PCDHGC3 wild-type or knockout brain microvascular endothelial cells (BMECs) allow measurement of paracellular permeability using fluorescent tracers of different molecular weights . Electrical resistance measurement (TEER) can quantify barrier integrity changes resulting from PCDHGC3 manipulation.

Molecular transport studies should examine expression of efflux pumps and transporters in PCDHGC3 knockout versus wild-type BMECs, as PCDHGC3 deletion alters expression of these critical BBB components . Functional transport assays for specific substrates can determine which transport pathways are most affected by PCDHGC3 deletion.

Inflammatory challenge models using TNFα treatment or oxygen/glucose deprivation (OGD) as mentioned in research findings can reveal PCDHGC3's role in BBB response to pathological conditions . Measuring cytokine production (e.g., IL-6) and inflammatory signaling pathway activation provides insight into mechanisms. For signaling pathway analysis, phosphorylation studies of key molecules (ERK, Akt) and inhibitor studies with pathway-specific compounds (SL327, XAV939, Torin 2) have proven informative in existing research .

How can researchers effectively use PCDHGC3 antibodies for co-localization studies with other proteins?

Co-localization studies require careful methodological approaches to accurately determine spatial relationships between PCDHGC3 and other proteins of interest. Immunofluorescence double or triple labeling techniques using antibodies against PCDHGC3 and potential interacting partners can reveal co-localization patterns at the subcellular level. When selecting antibody combinations, researchers should ensure primary antibodies are raised in different host species to allow differential detection with species-specific secondary antibodies.

Confocal microscopy with Z-stack acquisition enables three-dimensional assessment of protein co-localization, which is particularly important for membrane proteins like PCDHGC3. Super-resolution microscopy techniques (STED, PALM, STORM) can further refine co-localization analysis beyond the diffraction limit, revealing nanoscale spatial relationships.

Quantitative co-localization analysis should employ established metrics such as Pearson's correlation coefficient, Manders' overlap coefficient, or object-based co-localization analysis. Proximity ligation assay (PLA) offers a powerful alternative that detects proteins in close proximity (< 40 nm), providing greater specificity than standard co-localization. For potential protein-protein interactions, co-immunoprecipitation using PCDHGC3 antibodies followed by mass spectrometry or Western blotting for specific partners can complement microscopy approaches.

What approaches can be used to study the epigenetic regulation of PCDHGC3 expression?

Studying epigenetic regulation of PCDHGC3 requires multiple complementary approaches. DNA methylation analysis using bisulfite sequencing or methylation-specific PCR can identify CpG island methylation status in the PCDHGC3 promoter region, which may correlate with expression levels . Pyrosequencing or next-generation sequencing approaches allow quantitative assessment of methylation at specific CpG sites.

Chromatin immunoprecipitation (ChIP) assays using antibodies against specific histone modifications (H3K4me3, H3K27me3, H3K27ac) can reveal the chromatin state around the PCDHGC3 locus. ChIP-seq provides genome-wide maps of these modifications, placing PCDHGC3 regulation in broader epigenetic context.

Treatment of cells with epigenetic modifying drugs such as 5-aza-2'-deoxycytidine (DNA methyltransferase inhibitor) or histone deacetylase inhibitors can help determine if PCDHGC3 expression is responsive to epigenetic modulation. This has particular relevance given the observed hypermethylation of protocadherin family members in some cancers .

Chromosome conformation capture techniques (3C, 4C, Hi-C) can reveal three-dimensional chromatin interactions affecting PCDHGC3 expression, particularly important given its location within a gene cluster. Integration of these approaches with expression data enables comprehensive understanding of PCDHGC3's epigenetic regulation in normal and pathological states.

Why might Western blots with PCDHGC3 antibodies show multiple bands?

Western blots with PCDHGC3 antibodies frequently show multiple bands, which can complicate data interpretation. Post-translational modifications represent a common cause, as PCDHGC3 contains potential glycosylation sites in its extracellular domain and phosphorylation sites in its cytoplasmic domain that can create bands of different molecular weights . The expected molecular weight of PCDHGC3 is approximately 100 kDa, but modified forms may migrate differently .

Alternative splicing within the protocadherin gene family, including PCDHGC3, can generate protein isoforms of different sizes. The protocadherin gamma cluster has complex transcriptional regulation that may produce multiple protein variants . Proteolytic processing is another consideration, as cadherins and protocadherins can undergo cleavage, generating fragments detectable by antibodies targeting different domains.

Despite validation, cross-reactivity remains a concern due to high sequence homology in the protocadherin family, particularly with polyclonal antibodies . Technical factors including incomplete denaturation, sample degradation, or non-specific binding may also contribute to multiple bands. Verification approaches include comparing patterns in wild-type versus PCDHGC3 knockout samples, using multiple antibodies targeting different epitopes, and including recombinant PCDHGC3 protein as a positive control .

How can contradictory results between mRNA and protein expression of PCDHGC3 be reconciled?

Reconciling contradictory results between PCDHGC3 mRNA and protein expression requires consideration of biological regulation and methodological factors. Post-transcriptional regulation through microRNAs or RNA-binding proteins may affect translation efficiency without changing detected mRNA levels. Protein stability and turnover rates may differ across cell types or conditions, with ubiquitin-proteasome or lysosomal degradation pathways potentially affecting protein levels independent of transcription.

Methodological considerations include sensitivity differences between RT-qPCR (for mRNA) and Western blot (for protein), antibody specificity issues that may not accurately reflect true protein levels, and sample preparation differences that affect mRNA or protein recovery . Spatial and temporal dynamics, including time lag between transcription and translation, subcellular localization changes, or cell-specific translation efficiency differences may also contribute to apparent discrepancies.

To address these issues, researchers should employ multiple methods to measure both mRNA (RT-qPCR, RNA-seq, in situ hybridization) and protein (Western blot, immunohistochemistry, mass spectrometry) . Time-course experiments can capture dynamic relationships between transcription and translation. Using translation inhibitors or proteasome inhibitors helps evaluate protein synthesis and degradation rates. These comprehensive approaches allow researchers to determine whether discrepancies represent biological regulation or technical artifacts.

What factors affect antibody selection for different PCDHGC3 research applications?

Antibody selection for PCDHGC3 research should be tailored to specific applications based on several critical factors. Epitope location is paramount - antibodies targeting the extracellular domain are optimal for detecting native protein in flow cytometry or cell-surface immunofluorescence, while those targeting intracellular domains may be better for Western blot and fixed-cell applications . The variable cytoplasmic domain (amino acids 720-804) has proven useful for generating specific antibodies with minimal cross-reactivity .

Antibody format influences application suitability - monoclonal antibodies offer high specificity and batch-to-batch consistency ideal for quantitative applications, while polyclonal antibodies recognize multiple epitopes, potentially providing higher sensitivity but variable specificity . Host species selection affects compatibility with other antibodies in multi-labeling experiments and available secondary detection systems.

Validation for specific applications is crucial - antibodies validated for Western blot may not perform optimally in immunohistochemistry or flow cytometry . Commercial antibodies should provide validation data for intended applications. Species cross-reactivity is another consideration, as antibodies raised against human PCDHGC3 may not recognize mouse or rat orthologs with equal affinity .

Finally, researchers should consider whether post-translational modifications or specific isoforms are relevant to their research question, and select antibodies capable of distinguishing these variants when necessary.

How should researchers address batch-to-batch variation in PCDHGC3 antibody performance?

Addressing batch-to-batch variation in PCDHGC3 antibody performance requires systematic validation and standardization approaches. Each new antibody batch should undergo validation through Western blot on standard positive controls (e.g., U343 cells, brain tissue) , side-by-side comparison with previous antibody batches, and titration experiments to determine optimal working dilution for each application. Verification of specificity using PCDHGC3 knockout samples provides the most definitive control .

Implementing standard operating procedures is essential - maintain detailed records of antibody lot numbers, dilutions, and performance; use standardized positive and negative controls with each experiment; document storage conditions and avoid freeze-thaw cycles; and establish quality control thresholds for acceptable performance.

Technical strategies to minimize variation include purchasing larger amounts of a single lot for long-term studies, aliquoting antibodies to minimize freeze-thaw cycles, including internal normalization controls in each experiment, and considering pooling multiple validated batches for large studies . Alternative approaches include maintaining multiple validated antibodies targeting different PCDHGC3 epitopes, generating site-specific recombinant antibodies for consistent performance, and validating findings with orthogonal methods not relying on antibodies.

Data analysis adjustments may be necessary, including normalizing data within batches before comparing across batches, including batch as a covariate in statistical analyses, and using bridging samples processed with both old and new antibody batches .