si:ch211-147a11.3 Antibody

What is si:ch211-147a11.3 and what is known about its biological function?

si:ch211-147a11.3 is a gene expressed in zebrafish (Danio rerio) that shows significant correlation patterns with visual system development genes. Based on gene expression correlation data, si:ch211-147a11.3 appears to have functional relationships with several crucial developmental genes, particularly those involved in retinal and neural development. The gene shows strongest positive correlation with crx (r=0.165) and otx5 (r=0.159), both of which are essential transcription factors in eye development and photoreceptor differentiation . The functional significance of si:ch211-147a11.3 can be inferred from its expression pattern relationships, suggesting potential roles in retinal development, neurogenesis, or photoreceptor formation. Recent research has also identified si:ch211-147a11.3 as one of several genes whose expression is significantly increased following knockout of miR-184, a condition associated with ocular abnormalities .

What expression patterns does si:ch211-147a11.3 exhibit across zebrafish developmental stages?

si:ch211-147a11.3 expression follows a specific developmental trajectory in zebrafish, with expression patterns that correlate with genes involved in retinal development and neurogenesis. Expression data from Daniocell indicates that si:ch211-147a11.3 shows stage-specific expression patterns that parallel several developmental genes . The gene's expression appears to coincide with critical developmental windows for eye formation and neuronal differentiation. Notable co-expression patterns with neurod1 (r=0.123) and neurod4 (r=0.101) suggest participation in neural differentiation pathways . Additionally, correlations with foxg1b (r=0.102), a gene involved in forebrain and retinal development, further support its developmental significance. Understanding these temporal expression patterns is essential for researchers designing stage-specific experiments with si:ch211-147a11.3 antibodies.

What genes show significant correlation with si:ch211-147a11.3 expression?

si:ch211-147a11.3 expression demonstrates both positive and negative correlations with numerous genes, providing insights into its potential functional networks. The table below summarizes the most significant correlations:

The strongest positive correlations are with crx and otx5, both critical for photoreceptor development and function. Additional correlations with gngt2a (r=0.135), a G protein involved in phototransduction, and opn6a (r=0.117), an opsin gene, further suggest roles in visual system development . Negative correlations with genes like aldob and hspb1 indicate potential regulatory relationships where si:ch211-147a11.3 might function in opposition to these genes. These correlation patterns provide crucial context for interpreting antibody-based expression studies.

What are the recommended approaches for generating and validating si:ch211-147a11.3 antibodies?

When generating si:ch211-147a11.3 antibodies, researchers should employ epitope analysis to select unique protein regions that minimize cross-reactivity with other zebrafish proteins. Antibody generation typically follows either monoclonal or polyclonal approaches, with each offering distinct advantages. Monoclonal antibodies provide high specificity but may recognize limited epitopes, while polyclonal antibodies offer broader epitope recognition but potentially increased cross-reactivity .

For validation, a multi-tiered approach is essential: (1) Western blot analysis to confirm antibody specificity at the expected molecular weight; (2) immunohistochemistry pattern analysis compared with known expression data; (3) peptide competition assays to verify epitope specificity; and (4) knockout/knockdown validation using CRISPR-Cas9 or morpholino methods to confirm signal absence when the target is depleted. Additionally, researchers should consider cross-species validation if the protein sequence is conserved. Proper validation is particularly important given the functional relationships between si:ch211-147a11.3 and visual system genes, as immunostaining patterns should theoretically overlap with expression domains of genes like crx and otx5 .

How should researchers optimize immunohistochemistry protocols for si:ch211-147a11.3 in zebrafish tissues?

Optimizing immunohistochemistry protocols for si:ch211-147a11.3 detection requires careful consideration of tissue preparation, fixation methods, and detection systems. Begin by testing multiple fixation protocols, as overfixation can mask epitopes while underfixation compromises tissue morphology. For zebrafish ocular tissues, a 4% paraformaldehyde fixation for 2-4 hours typically provides a good balance.

Antigen retrieval methods should be systematically evaluated, testing citrate buffer (pH 6.0), Tris-EDTA (pH 9.0), and enzymatic retrieval approaches to determine optimal epitope exposure. Blocking parameters require optimization with 5-10% normal serum from the secondary antibody host species, supplemented with 0.1-0.3% Triton X-100 for membrane permeabilization.

Primary antibody concentration should be titrated between 1:100 to 1:1000, with extended incubation periods (overnight at 4°C) typically yielding better results. For detection, fluorescent secondary antibodies often provide superior signal-to-noise ratios compared to chromogenic methods when examining retinal tissues where si:ch211-147a11.3 is likely expressed based on its correlation with visual system genes . Controls must include secondary-only samples and, ideally, tissues from si:ch211-147a11.3 knockdown models.

What controls are essential when performing Western blot analysis with si:ch211-147a11.3 antibodies?

Western blot analysis with si:ch211-147a11.3 antibodies requires rigorous controls to ensure data reliability. Essential positive controls include recombinant si:ch211-147a11.3 protein (if available) and tissue lysates from developmental stages or tissues with known high expression, particularly those with established visual system development . Negative controls should include lysates from morpholino knockdown or CRISPR knockout zebrafish to verify signal specificity.

Loading controls must be carefully selected; beta-actin may be suitable for whole-embryo lysates, but tissue-specific loading controls should be considered for experiments using isolated eyes or neural tissues. A peptide competition assay should be performed by pre-incubating the antibody with excess immunizing peptide, which should abolish specific signals. Cross-reactivity assessment using closely related proteins determined through bioinformatic analysis can further validate specificity.

Technical controls must include gradient gels to resolve potentially close molecular weight proteins, and transfer efficiency verification using reversible staining methods like Ponceau S. When analyzing developmental stages, researchers should include a temporal series of samples to track expression changes, which can be correlated with the developmental expression patterns of known marker genes like crx and otx5 .

How can si:ch211-147a11.3 antibodies be utilized in co-localization studies with correlated gene products?

Co-localization studies with si:ch211-147a11.3 and its correlated gene products require sophisticated multi-channel immunofluorescence approaches with careful consideration of antibody compatibility. Based on the correlation data, primary targets for co-localization should include CRX (r=0.165), OTX5 (r=0.159), and NEUROD1 (r=0.123) . When designing these experiments, researchers must select antibodies raised in different host species to enable simultaneous detection without cross-reactivity.

For optimal results, sequential staining protocols should be employed when using antibodies from the same host species, implementing complete blocking steps between detection rounds. Confocal microscopy with spectral unmixing capabilities is recommended to resolve potential signal overlap, especially in densely packed retinal tissues. Analysis should utilize colocalization coefficients including Pearson's correlation coefficient and Manders' overlap coefficient to quantify spatial relationships.

Multiple development timepoints should be examined to track dynamic expression changes, particularly during retinal layer formation (24-72 hpf). To validate biological significance of observed co-localization, functional studies using CRISPR-Cas9 to modify si:ch211-147a11.3 should be conducted to assess impacts on expression patterns of correlated genes. Additionally, proximity ligation assays can provide sub-cellular resolution of protein-protein interactions between si:ch211-147a11.3 and its potential binding partners identified through correlation analysis .

What insights can be gained from chromatin immunoprecipitation (ChIP) experiments using si:ch211-147a11.3 antibodies?

Chromatin immunoprecipitation (ChIP) experiments using si:ch211-147a11.3 antibodies can provide crucial insights into the potential gene regulatory functions of this protein, particularly if it functions as a transcription factor or chromatin-associated protein. Given its strong correlation with known transcription factors like crx (r=0.165) and otx5 (r=0.159), investigating its DNA binding properties could reveal novel regulatory circuits in retinal development .

For optimal ChIP experiments, researchers should first confirm nuclear localization of si:ch211-147a11.3 protein through subcellular fractionation and immunofluorescence. Crosslinking conditions must be optimized specifically for zebrafish tissues, typically using 1-1.5% formaldehyde for 10-15 minutes. Sonication parameters should be carefully calibrated to achieve 200-500bp DNA fragments, verified by gel electrophoresis of pre-immunoprecipitation samples.

ChIP-seq analysis should focus on identifying enrichment patterns near genes with strong expression correlations, particularly examining promoter regions of visual system genes like gngt2a (r=0.135) and opn6a (r=0.117) . For data analysis, researchers should implement MACS2 or similar peak-calling algorithms with appropriate input controls. Motif discovery using MEME or similar tools can identify consensus binding sequences, which should be validated through reporter assays. Integration with publicly available datasets for correlated transcription factors like CRX can reveal potential cooperative or competitive binding relationships.

How does miR-184 knockout affect si:ch211-147a11.3 expression in ocular development models?

Research has demonstrated that knockout of miR-184 significantly increases si:ch211-147a11.3 expression levels, potentially contributing to ocular abnormalities observed in these models . This relationship provides an important experimental paradigm for studying si:ch211-147a11.3 regulation and function in eye development. When investigating this regulatory axis, researchers should employ qRT-PCR with carefully designed primers spanning exon-exon junctions to accurately quantify si:ch211-147a11.3 expression changes.

In situ hybridization should be performed in parallel with immunohistochemistry using si:ch211-147a11.3 antibodies to correlate transcript and protein level changes in specific ocular tissues. Temporal analysis across developmental stages is essential, focusing on key timepoints for eye field specification, retinal layer formation, and photoreceptor differentiation. Spatial expression pattern analysis should specifically examine regions expressing correlated genes like crx, otx5, and neurod1 .

For mechanistic investigations, researchers should perform luciferase reporter assays with the si:ch211-147a11.3 3'UTR to confirm direct miR-184 targeting. CRISPR-Cas9 mediated mutation of predicted miR-184 binding sites can verify the specific regulatory elements. Phenotypic rescue experiments where si:ch211-147a11.3 is knocked down in miR-184 knockout models can determine if elevated si:ch211-147a11.3 levels are causally related to observed ocular abnormalities. Additionally, RNA immunoprecipitation of miRNA processing components can confirm physical association of miR-184 with si:ch211-147a11.3 transcripts .

How can researchers address cross-reactivity issues with si:ch211-147a11.3 antibodies?

Pre-adsorption testing using recombinant proteins of potential cross-reactants can identify and eliminate problematic antibody populations. For polyclonal antibodies, affinity purification against the specific si:ch211-147a11.3 epitope can significantly improve specificity. Western blot analysis across multiple tissues should be performed to identify any unexpected bands that might indicate cross-reactivity. When cross-reactivity persists, epitope mapping can identify unique regions for generating more specific antibodies.

Competition assays comparing staining patterns between antibodies targeting different epitopes of si:ch211-147a11.3 can confirm signal specificity. Importantly, researchers should validate antibody specificity in CRISPR knockout models where the si:ch211-147a11.3 gene has been disrupted – any remaining signal in these samples indicates cross-reactivity. Finally, consider using orthogonal detection methods like RNA-scope in conjunction with immunostaining to correlate protein and mRNA localization patterns, which can help distinguish true signal from cross-reactivity .

What approaches can resolve inconsistent results when using si:ch211-147a11.3 antibodies across different detection methods?

Inconsistencies between detection methods using si:ch211-147a11.3 antibodies require systematic troubleshooting and methodological reconciliation. Begin by examining fundamental differences in sample preparation between techniques – fixation conditions suitable for immunohistochemistry may compromise epitopes differently than preparation methods for Western blotting or flow cytometry. Perform antibody validation across multiple lots and sources, as antibody variation can significantly impact results.

Epitope accessibility varies dramatically between methods: denatured proteins in Western blots expose different epitopes than partially-fixed proteins in immunohistochemistry. Test multiple antibody clones targeting different epitopes of si:ch211-147a11.3 to identify method-specific optimal antibodies. Consider native versus reducing conditions in Western blots, as some epitopes may be conformation-dependent.

When discrepancies persist between protein and RNA expression data, investigate post-transcriptional regulation mechanisms, particularly given the known interaction between si:ch211-147a11.3 and miR-184 . Implement absolute quantification methods like quantitative fluorescence with calibration standards across techniques to enable direct comparison of results. Additionally, correlate expression patterns with known marker genes that show strong correlation with si:ch211-147a11.3, such as crx (r=0.165) and otx5 (r=0.159), as these can serve as internal validation controls . Finally, consider examining protein turnover rates and stability, as these can cause discrepancies between transcript and protein levels.

How should researchers analyze si:ch211-147a11.3 expression in relation to ocular development stages?

Analysis of si:ch211-147a11.3 expression throughout ocular development requires integrated approaches that combine quantitative and spatial expression data. Researchers should establish a standardized staging series for zebrafish eye development, capturing key developmental transitions from optic vesicle formation through retinal layer differentiation and maturation. Quantitative PCR should be performed across these stages, normalizing si:ch211-147a11.3 expression to stable reference genes verified specifically for ocular tissues.

Immunohistochemistry should be conducted with consistent sectioning planes to enable accurate comparison between developmental stages. Quantitative image analysis must implement standardized protocols for signal quantification, using automated cell segmentation and intensity measurement software to minimize subjective interpretation. Colocalization analysis with developmental stage-specific markers provides crucial context – particularly with proteins showing strong correlation like CRX (r=0.165) and OTX5 (r=0.159) .

Comparative analysis between wild-type and miR-184 knockout models can reveal stage-specific regulatory relationships, as miR-184 knockout increases si:ch211-147a11.3 expression . Cell type-specific expression should be determined using co-staining with markers for photoreceptors, ganglion cells, and other retinal cell types. For comprehensive understanding, integrate expression data with functional assays at each developmental stage, using targeted knockdown approaches to assess stage-specific functional requirements. Finally, computational modeling of expression dynamics can identify critical transition points in si:ch211-147a11.3 regulation during ocular development.

How can active learning approaches improve antibody-antigen binding prediction for si:ch211-147a11.3 antibody development?

Active learning strategies offer promising approaches to enhance si:ch211-147a11.3 antibody development by optimizing antibody-antigen binding prediction, particularly valuable given the limited experimental data available for this zebrafish protein. Recent advances in computational methods have demonstrated that active learning can reduce the number of required experimental variants by up to 35% while accelerating the learning process significantly compared to random sampling approaches . For si:ch211-147a11.3 antibody development, researchers should implement library-on-library screening approaches where multiple antibody candidates are tested against various si:ch211-147a11.3 epitopes.

Machine learning models can analyze these many-to-many relationships to predict optimal binding pairs, focusing particularly on out-of-distribution predictions where test antibodies and antigens differ from training data. Researchers should start with a small labeled dataset and iteratively expand it based on uncertainty sampling, where the model identifies the most informative experimental tests to perform next. This approach is particularly valuable for zebrafish-specific antibodies where commercial options may be limited .

To maximize efficiency, implement ensemble learning approaches that combine multiple predictive models, as recent research shows that the three best-performing active learning algorithms significantly outperformed random sampling baselines . For si:ch211-147a11.3 specifically, epitope selection should consider regions with minimal similarity to proteins showing negative correlation in expression data, potentially reducing cross-reactivity issues . Simulation frameworks like Absolut! can evaluate algorithm performance before committing to expensive experimental validation, potentially saving substantial research resources.

What potential functional relationships exist between si:ch211-147a11.3 and visual system development genes?

The strong positive correlations between si:ch211-147a11.3 expression and multiple visual system genes suggest important functional relationships that warrant detailed investigation. The highest correlations with crx (r=0.165) and otx5 (r=0.159), both essential transcription factors in photoreceptor development, suggest si:ch211-147a11.3 may participate in transcriptional regulatory networks governing retinal cell fate determination . Additional correlations with gngt2a (r=0.135), a G-protein subunit involved in phototransduction, and opn6a (r=0.117), an opsin gene, further support potential roles in visual function pathways .

To elucidate these relationships, researchers should conduct transcriptome analysis following si:ch211-147a11.3 knockdown/knockout, focusing specifically on effects on correlated visual genes. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) can identify direct binding interactions if si:ch211-147a11.3 functions as a transcription factor or chromatin-associated protein. Protein-protein interaction studies using co-immunoprecipitation with si:ch211-147a11.3 antibodies followed by mass spectrometry can identify physical interactions with products of correlated genes.

Developmental phenotyping of si:ch211-147a11.3 mutants should assess visual system formation, electroretinogram responses, and visual behavioral assays. Genetic interaction studies combining partial knockdown of si:ch211-147a11.3 with correlated genes can reveal functional redundancy or cooperation. Additionally, investigating the regulatory relationship with miR-184 is essential, as knockout of this microRNA increases si:ch211-147a11.3 expression and causes ocular abnormalities, suggesting potential involvement in eye development pathology .

How can researchers leverage correlated gene expression data to predict si:ch211-147a11.3 function?

The comprehensive correlation data available for si:ch211-147a11.3 provides a powerful foundation for computational prediction of its biological function. Gene ontology enrichment analysis of positively correlated genes reveals significant overrepresentation of visual system development, neurogenesis, and photoreceptor differentiation pathways, with crx (r=0.165), otx5 (r=0.159), and neurod1 (r=0.123) among the top correlations . Conversely, negatively correlated genes like aldob (r=-0.077) and hspb1 (r=-0.071) suggest potential antagonistic relationships with metabolic and stress response pathways .

To leverage this data effectively, researchers should implement network analysis approaches like weighted gene co-expression network analysis (WGCNA) to position si:ch211-147a11.3 within functional modules. Comparative analysis across species can identify evolutionarily conserved co-expression relationships, potentially revealing fundamental biological roles. Machine learning algorithms trained on known gene function datasets can predict si:ch211-147a11.3 function based on its correlation pattern fingerprint.

Pathway analysis should focus on visual transduction and retinal development, given correlations with gngt2a (r=0.135) and opn6a (r=0.117) . Protein domain prediction and structural modeling can provide insights into molecular function, which should be validated through targeted mutagenesis of predicted functional domains. Integration with spatial transcriptomic data from zebrafish retinal development can confirm co-expression relationships in specific cell populations. Finally, researchers should develop testable hypotheses based on predicted functions and evaluate them through CRISPR-Cas9 gene editing coupled with phenotypic analysis focusing on visual system development and function.