ZNF404 is a nuclear protein with a molecular weight of ~65 kDa, primarily localized to nucleoli and the nuclear rim . Its role in transcriptional regulation suggests potential involvement in cellular processes such as DNA binding and RNA synthesis. Antibodies targeting ZNF404 are polyclonal or monoclonal, validated for techniques like ELISA, Western Blot (WB), Immunohistochemistry (IHC), and Immunocytochemistry (ICC).
ZNF404 exhibits low tissue specificity, with widespread expression across human tissues, including the brain, liver, kidney, and blood . Single-cell RNA sequencing data from the Human Protein Atlas confirms its presence in diverse cell types, though precise subcellular localization (e.g., nucleolar rim) is critical for experimental design .
| Tissue/Cell Type | Expression Level | Key Applications |
|---|---|---|
| Brain (Cerebral Cortex) | Moderate | Neurological disorder studies |
| Liver | High | Metabolic regulation research |
| Kidney | Moderate | Renal disease models |
| Blood (Lymphocytes) | Low | Hematological studies |
ZNF404 antibodies primarily target human proteins but show cross-reactivity with other species:
Studies suggest ZNF404 may interact with chromatin-modifying complexes, though its exact targets remain unclear . Antibodies enable tracking of its nuclear dynamics in real-time imaging studies .
ZNF404 has been implicated in breast cancer, with deleterious SNPs potentially disrupting its regulatory function . Antibodies like HPA032014 are used to map protein expression in tumor microenvironments .
ZNF404 is a zinc finger protein that may play an important role in transcriptional regulation within human cells. As a member of the zinc finger protein family, it likely contributes to gene expression control through DNA binding capabilities. The protein has been implicated in various cellular processes, making it a target of interest for researchers investigating transcriptional networks. While the specific biological pathways it regulates remain under investigation, researchers typically employ antibodies against ZNF404 to detect its expression patterns, localization, and potential interactions with other molecular components .
ZNF404 antibodies have been validated for several detection techniques, with varying efficacy depending on the specific antibody clone and experimental context. Based on current research tools, ZNF404 antibodies can be reliably used in ELISA (Enzyme-Linked Immunosorbent Assay) with recommended dilutions around 1:62500, Western blotting (WB) at approximately 1 μg/mL concentration, and immunohistochemistry (IHC) at dilutions ranging from 1:20-1:200. Additionally, immunocytochemistry/immunofluorescence applications typically employ dilutions between 0.25-2 μg/mL . It's critical to validate each application independently for your specific experimental system.
Sample preparation for ZNF404 detection varies by application. For Western blotting, complete cell lysis using a buffer containing protease inhibitors is crucial to prevent degradation of the target protein. For immunocytochemistry/immunofluorescence, fixation with 4% paraformaldehyde for 10 minutes at room temperature followed by permeabilization with 0.2% saponin for approximately 20 minutes has proven effective for immune cell preparations . For tissue samples in immunohistochemistry, standard formalin fixation and paraffin embedding protocols are compatible with most ZNF404 antibodies, though antigen retrieval steps may be necessary. Special consideration should be given to preservation of protein conformation during sample preparation to maintain epitope accessibility.
Validation of ZNF404 antibody specificity requires a multi-faceted approach. First, conduct positive and negative control experiments using cell lines or tissues with known ZNF404 expression profiles. Second, perform knockdown/knockout validation by comparing antibody signal between wild-type samples and those where ZNF404 expression has been reduced via siRNA or CRISPR-Cas9. Third, epitope competition assays can be conducted by pre-incubating the antibody with the immunizing peptide (when available) to confirm signal specificity. Fourth, compare results across multiple detection methods (e.g., Western blot, immunostaining) to confirm consistent expression patterns. Finally, consider cross-validating with alternative antibodies that target different epitopes of ZNF404. The recent advances in antibody screening using CyTOF and single-cell RNA-sequencing data correlation approaches demonstrate promising validation strategies that could be applied to ZNF404 antibodies .
The optimal blocking conditions for ZNF404 immunodetection depend on the application and specific antibody. For Western blotting, 5% non-fat dry milk or 3-5% BSA in TBS-T (Tris-buffered saline with 0.1% Tween-20) for 1 hour at room temperature typically provides effective blocking. For immunohistochemistry and immunocytochemistry, 1-5% normal serum (matching the species of the secondary antibody) in PBS with 0.1-0.3% Triton X-100 for 1 hour is generally suitable. When working with fluorescent detection methods, it's important to use blocking solutions free of autofluorescent components. Optimization may be necessary based on signal-to-noise ratio, with longer blocking times (up to overnight at 4°C) sometimes beneficial for reducing background in challenging samples.
Optimizing Western blot detection of ZNF404 requires attention to several parameters. First, ensure complete protein extraction using a lysis buffer containing ionic detergents like SDS or strong non-ionic detergents like Triton X-100, along with protease inhibitors. Second, load adequate protein (typically 20-50μg of total protein per lane) and separate on an appropriate percentage gel (10-12% for optimal resolution in the expected ZNF404 molecular weight range). Third, optimize transfer conditions, with semi-dry transfer at 15V for 30-45 minutes or wet transfer at 30V overnight at 4°C often yielding good results for zinc finger proteins. Fourth, based on validated antibody parameters, use the ZNF404 antibody at 1 μg/mL concentration, paired with an HRP-conjugated secondary antibody at a 1:50 dilution . Fifth, optimize exposure times during detection to capture specific signal while minimizing background. Consider testing enhanced chemiluminescence (ECL) reagents of varying sensitivity depending on the expected abundance of ZNF404 in your samples.
Multiplexing ZNF404 with other markers requires careful selection of compatible antibodies and detection systems. For fluorescence-based methods, choose primary antibodies from different host species (e.g., rabbit anti-ZNF404 with mouse anti-KRT5) and pair with spectrally distinct fluorophore-conjugated secondary antibodies. Alternatively, directly conjugated primary antibodies (such as FITC, HRP, or biotin-conjugated ZNF404 antibodies) can simplify multiplexing workflows . For advanced multiplexing, consider mass cytometry (CyTOF) approaches where metal-tagged antibodies allow simultaneous detection of numerous markers without spectral overlap concerns. The emerging antibody panel design approach using single-cell RNA-sequencing data, as described in recent research, can help identify optimal marker combinations that include ZNF404 alongside other proteins of interest . Sequential staining protocols may also be employed when antibody compatibility issues arise.
Inconsistent results with ZNF404 antibodies can stem from multiple factors. First, batch-to-batch variability is common with polyclonal antibodies, so maintaining consistent antibody lots throughout a study is advisable. Second, ZNF404 expression may vary with cell cycle, differentiation state, or stress conditions, leading to biological variability independent of technical factors. Third, epitope masking due to protein-protein interactions or post-translational modifications might affect antibody binding. Fourth, inconsistent sample preparation, particularly inadequate fixation, permeabilization, or antigen retrieval, can significantly impact detection. Fifth, storage conditions of both samples and antibodies affect stability and performance. To address these issues, implement rigorous protocol standardization, include appropriate controls in each experiment, and consider multiple detection methods to cross-validate findings for critical results.
Cross-reactivity with other zinc finger proteins is a significant concern when working with ZNF404 antibodies due to structural similarities within this protein family. To address this challenge, first select antibodies generated against unique regions of ZNF404 rather than conserved zinc finger domains. Antibodies targeting the middle region or specific amino acid sequences (such as AA 467-516 or AA 63-314) may offer improved specificity . Second, validate specificity using knockout or knockdown controls. Third, perform parallel experiments with antibodies recognizing different epitopes of ZNF404 to confirm consistent patterns. Fourth, consider preabsorption controls with related zinc finger proteins to assess cross-reactivity. Finally, complement antibody-based detection with nucleic acid-based methods (RT-PCR, RNA-seq) that can distinguish between related zinc finger genes with greater specificity.
Essential controls for ZNF404 immunohistochemistry include several types. First, positive control tissues with known ZNF404 expression validate staining protocols and antibody functionality. Second, negative control tissues lacking ZNF404 expression help establish background staining levels. Third, technical negative controls (primary antibody omission) identify non-specific secondary antibody binding. Fourth, isotype controls (non-specific IgG matching the primary antibody's host species and concentration) reveal non-specific binding of the primary antibody. Fifth, absorption controls (pre-incubating the antibody with its immunizing peptide) confirm signal specificity. When interpreting results, pay particular attention to subcellular localization patterns, as ZNF404's role in transcriptional regulation suggests nuclear localization should predominate in most contexts.
Interpreting ZNF404 expression in the context of single-cell heterogeneity requires sophisticated analytical approaches. Recent advances in single-cell technologies have revealed that seemingly homogeneous cell populations often contain substantial variation in gene and protein expression. When analyzing ZNF404 expression, consider implementing the following strategies: First, use high-resolution imaging or flow cytometry to quantify expression at the single-cell level rather than relying on population averages. Second, correlate protein expression (antibody-based) with mRNA levels (RNA-seq) at the single-cell level, recognizing that correlation coefficients between 0.38 and 0.58 have been observed for cell surface markers in immune cells . Third, assess co-expression with known cellular subtype markers to identify potential functional relationships. Fourth, when interpreting variable expression, consider whether it represents distinct cellular subpopulations or stochastic biological noise. Novel antibody screening approaches that leverage single-cell RNA-sequencing data can be particularly valuable for validating ZNF404 as a marker of cellular subpopulations .
Incorporating ZNF404 antibodies into single-cell analysis workflows requires integration with contemporary technologies. For flow cytometry or mass cytometry (CyTOF) applications, combine ZNF404 antibodies with established lineage markers to identify cell populations with differential ZNF404 expression. When designing panels, assess antibody compatibility through titration experiments and avoid antibodies competing for the same epitope. For imaging-based single-cell analysis, ZNF404 antibodies can be incorporated into multiplexed immunofluorescence or imaging mass cytometry (IMC) panels. Recent research has demonstrated the value of predictive approaches that integrate single-cell RNA-sequencing data with antibody screening to validate marker selection for highly multiplexed panels . When analyzing resultant data, dimensional reduction techniques like UMAP can help visualize the relationship between ZNF404 expression and other cellular parameters. Software tools like Scanpy can facilitate clustering analysis to identify cell populations with distinctive ZNF404 expression patterns.
Using ZNF404 antibodies in spatial transcriptomics studies requires careful integration of protein and RNA detection methodologies. First, select ZNF404 antibodies with demonstrated performance in tissue sections and compatibility with your spatial analysis platform. Second, optimize tissue processing to preserve both protein epitopes and RNA integrity, typically requiring a balance between fixation strength and duration. Third, when designing multiplex panels, consider the cellular compartmentalization of ZNF404 (likely nuclear) when selecting complementary markers. Fourth, for correlation between protein and RNA levels, acknowledge the expected moderate correlation (r ≈ 0.38-0.58) based on immune cell profiling studies . Fifth, during data analysis, integrate spatial information with expression data to identify tissue regions with distinctive ZNF404 expression patterns. Emerging platforms like Imaging Mass Cytometry combined with spatial transcriptomics can provide complementary data on ZNF404 protein localization and gene expression in the same tissue section.
Investigating protein-protein interactions involving ZNF404 requires specialized applications of antibodies beyond simple detection. For co-immunoprecipitation (Co-IP), use anti-ZNF404 antibodies that have been validated for immunoprecipitation applications to pull down ZNF404 along with its binding partners. Alternatively, perform reverse Co-IP with antibodies against suspected interaction partners and probe for ZNF404 in the precipitate. For proximity ligation assays (PLA), combine ZNF404 antibodies with antibodies against potential interaction partners from different host species, followed by species-specific secondary antibodies conjugated to complementary oligonucleotides. For chromatin immunoprecipitation (ChIP) applications, optimize fixation conditions to preserve DNA-protein interactions and select ChIP-validated ZNF404 antibodies. When designing these experiments, consider the potential impact of antibody binding on protein interactions and include appropriate negative controls such as IgG from the same species as the primary antibody.
Emerging technologies are significantly expanding the applications of ZNF404 antibodies in research. First, comprehensive antibody validation platforms combining single-cell RNA-sequencing with high-throughput antibody screening are improving confidence in marker selection and validation . Second, highly multiplexed imaging technologies like Imaging Mass Cytometry allow simultaneous visualization of ZNF404 alongside dozens of other proteins in tissue contexts . Third, spatial transcriptomics approaches enable correlation of ZNF404 protein expression with gene expression in preserved tissue architecture. Fourth, engineered antibody formats, including nanobodies and single-chain variable fragments, may offer improved tissue penetration and reduced background. Fifth, machine learning approaches are enhancing image analysis capabilities, improving the quantification of subtle changes in ZNF404 expression and localization. These technological advances collectively support more nuanced investigations of ZNF404's role in cellular processes and tissue contexts.
Quantifying ZNF404 expression in imaging data requires standardized approaches to ensure reproducibility and reliability. For immunofluorescence or immunohistochemistry images, begin with proper cell segmentation - recent tools like Mesmer have shown excellent performance for identifying cellular boundaries in multiplex imaging data . After segmentation, extract mean pixel intensity values for ZNF404 staining within each cellular compartment (nucleus, cytoplasm, or whole cell depending on the biological question). For more robust quantification, implement a strategy to remove outliers, such as applying a 99th percentile cutoff to eliminate pixel hotspots that may result from acquisition artifacts . Normalize expression data by dividing each channel by its maximum pixel intensity or using reference markers to account for technical variation between samples. For comparative analyses, maintain identical acquisition settings (exposure time, gain, laser power) across all samples. Advanced analysis can include pixel-based colocalization measurements with other markers or subcellular structures.
| Analysis Step | Method | Parameters | Considerations |
|---|---|---|---|
| Cell Segmentation | Mesmer AI-based approach | Default parameters | Suitable for complex tissue architectures |
| Signal Extraction | Mean pixel intensity | Per cell or subcellular compartment | Adjust for cell size if necessary |
| Outlier Removal | Percentile cutoff | 99th percentile threshold | Removes acquisition artifacts |
| Normalization | Division by maximum intensity | Channel-specific | Accounts for staining efficiency differences |
| Statistical Analysis | Cluster analysis (e.g., Leiden method) | Implemented in Scanpy | Identifies cell populations with similar expression profiles |
Discrepancies between mRNA and protein levels of ZNF404 are common and biologically meaningful. Research correlating RNA and protein expression has found moderate correlations (r = 0.38-0.58) for cell surface markers across immune cell populations . Several factors contribute to these discrepancies: First, post-transcriptional regulation may affect mRNA stability or translation efficiency. Second, post-translational modifications can impact protein stability and half-life independently of mRNA levels. Third, temporal delays between transcription and translation mean that mRNA and protein measurements represent different time points in the regulatory process. Fourth, technical factors including antibody specificity and RNA detection sensitivity contribute to apparent discrepancies. When faced with such discrepancies, consider time-course experiments to capture the temporal relationship between mRNA and protein expression. Additionally, incorporating orthogonal data from paired proteomic and RNA studies can significantly improve prediction accuracy of protein expression from RNA data, particularly by removing genes with consistently low RNA-protein correlation .