RNASE13 Antibody

What is RNASE13 and what is its biological significance?

RNASE13 (Ribonuclease, RNase A Family, 13) is classified as a "non-active" member of the RNase A superfamily. Despite this classification, it consists of a 124-amino acid polypeptide with a molecular weight of approximately 13.7-18 kDa (with some variation in observed versus calculated weight). The protein contains four disulfide bonds that stabilize its three-dimensional structure, which includes a characteristic alpha-helical fold and beta-sheet arrangement. RNASE13 is predicted to enable nucleic acid binding activity and is thought to be localized primarily in the extracellular region. It shows potential involvement in RNA processing pathways and may possess antiviral and antibacterial properties as part of the innate immune system, despite its "non-active" designation .

What types of RNASE13 antibodies are available for research?

Researchers can access several types of RNASE13 antibodies for experimental applications. The most common include polyclonal antibodies raised in rabbits against defined immunogenic regions (such as amino acids 1-156 or 20-156 of human RNASE13). These are available in various formats including unconjugated versions and fluorophore-conjugated variants (e.g., Alexa Fluor 594, 647, and 750 conjugates). Additionally, some suppliers offer recombinant RNASE13 proteins (such as N-His tagged versions) for use as standards or immunogens. The antibodies differ in their specific binding epitopes, cross-reactivity profiles (some react with human only, while others recognize human, mouse, and rat RNASE13), and validated applications .

What are the standard applications for RNASE13 antibodies?

RNASE13 antibodies have been validated for multiple standard laboratory techniques. These applications typically include Western Blotting (WB) for protein detection in lysates with recommended dilutions ranging from 1:500 to 1:2000, Immunohistochemistry on paraffin-embedded sections (IHC-P) at dilutions of 1:100 to 1:200 (often requiring microwave antigen retrieval for optimal results), Immunofluorescence on fixed cells (IF/ICC), and Enzyme-Linked Immunosorbent Assay (ELISA). Some antibodies are also validated for use in Immunoprecipitation (IP) procedures to isolate RNASE13 from complex samples. The specific applications depend on the antibody clone and preparation method .

How should I design validation experiments for a new RNASE13 antibody?

Comprehensive validation of a new RNASE13 antibody should follow a multi-step approach. Begin with Western blot analysis using recombinant RNASE13 protein as a positive control alongside negative controls (pre-immune serum or isotype control). The expected molecular weight observation should be approximately 18 kDa, though post-translational modifications may cause slight variations. Next, validate specificity using siRNA/shRNA knockdown or CRISPR-Cas9 knockout models, where signal reduction should correlate with decreased RNASE13 expression. For IHC applications, compare staining patterns in tissues known to express RNASE13 (such as prostate tissue) with negative controls. Include peptide competition assays where pre-incubation of the antibody with immunizing peptide should abolish specific signals. Finally, cross-validate using multiple antibodies targeting different epitopes of RNASE13 to confirm consistent localization and expression patterns .

What are the optimal storage and handling conditions for RNASE13 antibodies?

To maintain optimal antibody performance, RNASE13 antibodies should be stored at -20°C in appropriate buffer conditions, typically PBS with 0.02% sodium azide and 50% glycerol at pH 7.3. Aliquoting the antibody upon first thaw is strongly recommended to prevent repeated freeze-thaw cycles, which can lead to denaturation and loss of binding activity. For working solutions, store at 4°C for short periods (1-2 weeks) or re-freeze aliquots for longer storage. Prior to experiments, antibodies should be gently mixed rather than vigorously vortexed to prevent protein damage. When diluting, use fresh, high-quality buffers appropriate for the intended application (such as TBST with 5% BSA or milk for Western blotting, or PBS with appropriate blocking agents for immunostaining). Document lot numbers and perform regular validation tests when using a new lot, as lot-to-lot variability can affect experimental outcomes .

How can I optimize IHC-P protocols for RNASE13 detection in tissue samples?

Optimizing IHC-P protocols for RNASE13 detection requires careful attention to several key parameters. Begin with proper fixation—10% neutral buffered formalin for 24-48 hours provides consistent results. Paraffin-embedded sections should be cut at 4-5 μm thickness for optimal antibody penetration. Antigen retrieval is critical; microwave-based methods using citrate buffer (pH 6.0) have proven effective according to validated protocols. Optimal antibody dilutions range from 1:100 to 1:200, but titration experiments are recommended for each new lot. Use a polymer-based detection system rather than biotin-streptavidin to minimize background, especially in tissues with endogenous biotin. Include positive control tissues (e.g., human prostate) and negative controls (primary antibody omission and isotype controls) in each experimental run. Signal development time should be optimized by visual monitoring to prevent oversaturation while ensuring detection of low-abundance signals. For multiplexing with other markers, sequential rather than simultaneous staining often yields cleaner results .

How can I develop a high-throughput screening assay using RNASE13 antibodies?

Developing a high-throughput screening (HTS) assay using RNASE13 antibodies requires integration of advanced molecular techniques. Based on recent methodological advances, implement a Golden Gate-based dual-expression vector system for in-vivo expression of membrane-bound antibodies, which enables rapid screening of recombinant monoclonal antibodies. This system can be adapted to RNASE13 by constructing paired B-cell repertoire amplicons with BsaI restriction sites for assembly with appropriate destination and donor vectors. The assembly mix (containing 1× T4 DNA ligase buffer, 1× BSA, BsaI restriction enzyme, T4 DNA ligase, heavy and light chain amplicons, destination vector, and donor vector) should undergo a specific thermal cycling protocol: 25 cycles at 37°C for 3 min, 16°C for 4 min, 50°C for 5 min, and 80°C for 5 min. Express the antibodies using FreeStyle 293 cells transfected with the 293fectin Transfection Reagent in appropriate media. For screening, fluorescently label RNASE13 protein and analyze binding using flow cytometry, enabling rapid identification of high-affinity RNASE13-binding antibodies .

What are the approaches for characterizing antibody-RNASE13 binding kinetics?

Characterization of antibody-RNASE13 binding kinetics requires sophisticated biophysical methods. Surface Plasmon Resonance (SPR) using platforms like BIAcore 3000 provides detailed kinetic parameters. Immobilize purified RNASE13 antibodies on a CM5 sensor chip using an amine-coupling kit according to manufacturer protocols. Prepare RNASE13 protein in a concentration series (typically 5-fold dilutions ranging from 1-100 nM) and inject at a flow rate of 30 μL/min for 3 minutes with an extended dissociation phase of 7 minutes in HBS-EP buffer (10 mM HEPES, pH 7.4, 150 mM NaCl, 3.4 mM EDTA, 0.005% Surfactant P20). Regenerate the chip between runs using 10 mM glycine (pH 2.5). For multi-parameter analysis, combine SPR with isothermal titration calorimetry (ITC) to obtain thermodynamic parameters complementing kinetic data. Alternatively, bio-layer interferometry (BLI) can provide similar kinetic data with the advantage of using less protein and simpler experimental setup. Analysis should determine association (ka), dissociation (kd) rate constants, and equilibrium dissociation constant (KD), which indicates antibody affinity .

How can I perform epitope mapping to characterize RNASE13 antibody binding sites?

Epitope mapping of RNASE13 antibodies requires a multi-faceted approach. Begin with overlapping peptide arrays covering the entire RNASE13 sequence (amino acids 1-156), with peptides of 15-20 residues and 5-residue overlaps. Screen these arrays with your antibody to identify reactive peptides, focusing on regions like the Val20-Ile156 segment used as immunogens in commercial antibodies. For higher resolution mapping, employ hydrogen-deuterium exchange mass spectrometry (HDX-MS), comparing deuterium incorporation patterns of RNASE13 alone versus antibody-bound RNASE13 – regions protected from exchange upon antibody binding indicate the epitope. Alternatively, use X-ray crystallography of antibody-RNASE13 complexes for atomic-level resolution, though this requires milligram quantities of purified protein complex. For conformational epitopes, alanine-scanning mutagenesis is effective – systematically replace surface-exposed residues with alanine and assess antibody binding to identify critical interaction residues. Compare results against RNASE13's known structural features including its alpha-helical fold and beta-sheet to contextualize the epitope within the protein's tertiary structure .

What are common sources of false positives/negatives in RNASE13 immunodetection?

False positives in RNASE13 immunodetection commonly arise from antibody cross-reactivity with other RNase family members, which share structural similarities despite sequence divergence. To mitigate this, always validate antibody specificity using recombinant RNASE13 alongside other RNase family members in Western blots. Background signal in IHC/IF often results from insufficient blocking or secondary antibody cross-reactivity—optimize using 3-5% BSA or appropriate serum and include secondary-only controls. False negatives frequently stem from inadequate antigen retrieval, particularly for formalin-fixed tissues. Different epitopes require specific retrieval methods: for RNASE13, microwave-based citrate buffer (pH 6.0) retrieval has proven most effective. Antibody concentration is critical—too dilute causes signal loss while overconcentration increases background. For Western blotting, false negatives can result from protein denaturation destroying conformational epitopes; try native conditions if denatured samples yield no signal. Finally, consider RNASE13's variable expression levels across tissues and developmental stages—include positive control samples with confirmed expression and optimize exposure settings for low-abundance detection .

How can I use RNASE13 antibodies in multiplex immunofluorescence applications?

Implementing multiplex immunofluorescence with RNASE13 antibodies requires careful panel design and protocol optimization. Begin by selecting primary antibodies raised in different host species to avoid cross-reactivity of secondary antibodies. If using multiple rabbit-derived antibodies including anti-RNASE13, employ tyramide signal amplification (TSA) with sequential staining, stripping, and re-probing. For optimal results, determine the staining order empirically—typically start with the lowest abundance target (often RNASE13) using TSA amplification. When using fluorophore-conjugated RNASE13 antibodies (available with Alexa Fluor 594, 647, or 750), design panels considering spectral overlap and utilize linear unmixing algorithms during image acquisition. For multi-round staining protocols, validate antibody stripping efficiency between rounds using no-primary controls. Critical controls include single-color references for spectral unmixing and fluorescence minus one (FMO) controls for accurate gating in quantitative analyses. For tissue samples, implement autofluorescence reduction through Sudan Black B (0.1% in 70% ethanol) treatment post-staining or computational removal during image processing .

What approaches can identify post-translational modifications of RNASE13 using specific antibodies?

Identifying post-translational modifications (PTMs) of RNASE13 requires specialized antibody-based approaches combined with advanced analytical techniques. First, enrich for RNASE13 through immunoprecipitation using validated antibodies targeting amino acids 20-156, which allows for subsequent PTM analysis. For phosphorylation analysis, perform immunoprecipitation followed by Western blotting with phospho-specific antibodies targeting common motifs (Ser/Thr/Tyr), combined with phosphatase treatment controls to confirm specificity. Mass spectrometry offers higher sensitivity—immunoprecipitate RNASE13, perform tryptic digestion, and analyze by LC-MS/MS with neutral loss scanning for phosphopeptides. For glycosylation studies, use enzymatic deglycosylation (PNGase F for N-linked, O-glycosidase for O-linked glycans) followed by Western blotting to detect mobility shifts. Alternatively, employ lectin microarrays with immunoprecipitated RNASE13 to profile glycan structures. For ubiquitination and SUMOylation, use two-step immunoprecipitation: first pull down RNASE13, then probe with anti-ubiquitin or anti-SUMO antibodies. When developing PTM-specific antibodies, carefully validate specificity using synthetic peptides containing the modified residue alongside unmodified controls .

How can RNASE13 antibodies be employed in single-cell analysis techniques?

Integrating RNASE13 antibodies into single-cell analysis platforms requires specialized adaptation of immunological methods. For mass cytometry (CyTOF), conjugate purified RNASE13 antibodies with rare earth metals (lanthanides) using commercial conjugation kits, then validate specificity using positive and negative control cell lines before application to heterogeneous samples. In single-cell Western blotting platforms (e.g., Milo), optimize RNASE13 antibody concentration (typically 10-20 μg/mL) and incubation time (4-16 hours at 4°C) for maximum sensitivity with minimal background. For in-situ protein analysis, implement proximity ligation assays (PLA) combining RNASE13 antibodies with antibodies against potential interaction partners—this allows visualization of protein-protein interactions at single-molecule resolution within individual cells. In microfluidic droplet-based systems, functionalize antibodies through biotin-streptavidin chemistry for capture on droplet surfaces, enabling analysis of secreted or membrane-expressed RNASE13 at the single-cell level. For correlation with transcriptomics, perform combined protein-RNA analysis using REAP-seq or CITE-seq protocols, where oligo-tagged RNASE13 antibodies allow simultaneous measurement of protein expression and transcriptome in the same cell .

What considerations are important when using RNASE13 antibodies for examining protein-protein interactions?

When examining RNASE13 protein-protein interactions, several methodological considerations are critical for reliable results. For co-immunoprecipitation experiments, use mild lysis conditions (150 mM NaCl, 1% NP-40 or CHAPS instead of stronger detergents) to preserve native protein complexes, and validate the specificity of the precipitating RNASE13 antibody using knockout/knockdown controls. Consider using reversible crosslinking agents like DSP (dithiobis(succinimidyl propionate)) to stabilize transient interactions before cell lysis. For proximity-based methods such as BioID or APEX2, create fusion proteins with RNASE13 and confirm proper localization and function before inducing biotinylation of proximal proteins. When performing Förster Resonance Energy Transfer (FRET) analyses, carefully select compatible fluorophore-conjugated antibodies with appropriate spectral overlap and minimal bleed-through. For advanced studies, implement co-localization analysis through super-resolution microscopy (STORM, PALM) using directly-conjugated RNASE13 antibodies in combination with antibodies against potential interaction partners. Finally, validate putative interactions through reciprocal pull-downs and confirm biological relevance using functional assays that test the consequences of disrupting the identified interaction .

How can I develop and validate RNASE13 knockout models for antibody specificity testing?

Developing and validating RNASE13 knockout models for antibody specificity testing requires systematic genetic engineering and comprehensive validation. Begin by designing CRISPR-Cas9 guide RNAs targeting early exons of the RNASE13 gene (Gene ID: 440163), prioritizing sequences with high on-target and low off-target scores. For human cell lines, transfect the CRISPR components using optimized protocols for your specific cell type, and after transfection, isolate single-cell clones through limiting dilution or cell sorting. Validate genomic modifications using PCR amplification and Sanger sequencing across the target site to confirm frameshift mutations or deletions. Perform mRNA analysis through RT-qPCR targeting regions both upstream and downstream of the modification site to confirm transcript disruption or nonsense-mediated decay. For protein-level validation, employ multiple RNASE13 antibodies targeting different epitopes in Western blotting, comparing knockout clones with parental cell lines—complete absence of signal across all antibodies strongly supports successful knockout. Additionally, perform immunofluorescence microscopy to confirm loss of protein localization signals. To rule out compensation by other RNase family members, assess expression of related genes using qPCR arrays. Finally, create rescue cell lines by reintroducing RNASE13 cDNA to demonstrate that observed phenotypes are specifically due to RNASE13 loss .

What are appropriate positive and negative controls for RNASE13 antibody experiments?

Implementing rigorous control strategies for RNASE13 antibody experiments is essential for reliable data interpretation. For positive controls, use recombinant human RNASE13 protein (13.7 kDa) with N-terminal His tag in Western blot applications to establish specificity and sensitivity. In tissue-based applications, human prostate tissue has demonstrated consistent RNASE13 expression and serves as an excellent positive control for IHC/IF. For cell-based experiments, validate expression in your model system using qPCR before antibody-based detection. Essential negative controls include: (1) Primary antibody omission control to assess non-specific binding of detection systems, (2) Isotype control (non-specific rabbit IgG at matching concentration) to evaluate non-specific binding due to Fc interactions or hydrophobic effects, (3) Peptide competition/neutralization using the immunizing peptide (e.g., Val20-Ile156 fragment) pre-incubated with primary antibody to confirm epitope specificity, and (4) RNASE13 knockout or knockdown samples as the gold standard negative control. For cross-reactivity assessment, test the antibody against related RNase family members, particularly those with highest sequence homology to RNASE13 .

How should I analyze contradictory results from different RNASE13 antibody clones?

When facing contradictory results from different RNASE13 antibody clones, implement a systematic investigation framework. First, compare the technical specifications of each antibody—focus on epitope regions (antibodies targeting amino acids 1-156 versus 20-156 may yield different results if N-terminal processing occurs), clonality (polyclonal antibodies detect multiple epitopes while monoclonal antibodies target single epitopes), and validation methods used by manufacturers. Next, perform side-by-side comparison experiments using identical samples, protocols, and detection systems to directly compare antibody performance. Assess potential splice variant or isoform detection differences by conducting Western blots under reducing and non-reducing conditions, which may reveal conformational epitope dependencies. For definitive epitope mapping, employ peptide arrays or deletion mutants to precisely identify where each antibody binds. Consider post-translational modifications that might mask epitopes—treat samples with phosphatases, deglycosylation enzymes, or deubiquitinases to reveal whether PTMs affect antibody recognition. Finally, validate results with orthogonal methods such as mass spectrometry or RNA expression analysis to determine which antibody most accurately reflects actual RNASE13 biology. Document all findings methodically to establish a reference for future work with these antibodies .

What statistical approaches are appropriate for quantifying RNASE13 expression in imaging studies?

Quantitative analysis of RNASE13 expression in imaging studies requires rigorous statistical approaches tailored to the specific experimental design. For immunohistochemistry studies, employ QuPath or similar digital pathology platforms to perform automated tissue segmentation followed by DAB intensity quantification using the H-score method (combining intensity and percentage positive cells: H-score = 1×(% weak) + 2×(% moderate) + 3×(% strong)). For immunofluorescence, use CellProfiler or ImageJ with appropriate macros to perform segmentation based on nuclear counterstains, followed by cytoplasmic/membrane intensity measurements of RNASE13 signal. When comparing expression across groups, first assess data normality using Shapiro-Wilk or Kolmogorov-Smirnov tests. For normally distributed data, apply parametric tests (t-test for two groups, ANOVA with post-hoc Tukey for multiple groups); for non-normal distributions, use non-parametric alternatives (Mann-Whitney U or Kruskal-Wallis). In multiplex studies, employ multivariate approaches such as principal component analysis to identify co-expression patterns. For spatial analysis, utilize nearest neighbor analysis or Ripley's K function to assess distribution patterns. Finally, calculate intraclass correlation coefficients to ensure reliability between independent observers, and use power analysis to determine adequate sample sizes (typically n≥20 for each experimental group to detect moderate effect sizes with 80% power) .

How might RNASE13 antibodies be utilized in understanding RNA metabolism pathways?

Despite being classified as "non-active," RNASE13 presents intriguing opportunities for investigating RNA metabolism pathways using specialized antibody-based approaches. Implement RNA immunoprecipitation (RIP) assays using validated RNASE13 antibodies to capture and identify RNA species that associate with RNASE13 in vivo, even if its enzymatic activity is absent or diminished. Optimize crosslinking immunoprecipitation (CLIP) protocols using UV crosslinking (254 nm) to stabilize direct RNA-protein interactions before immunoprecipitation with anti-RNASE13 antibodies, followed by high-throughput sequencing to map interaction sites with single-nucleotide resolution. To assess RNASE13's potential role in RNA granules or processing bodies, perform immunofluorescence co-localization studies with established markers (like DCP1a, GW182, or TIA-1) under various cellular stress conditions. For investigating potential non-canonical functions, utilize proximity labeling approaches by creating RNASE13-BioID fusion proteins to identify the proximal proteome, potentially revealing interactions with active ribonucleases or RNA metabolism machinery. Finally, perform comparative interactome studies across tissues with differential RNASE13 expression to identify tissue-specific roles in RNA processing pathways .

What are promising approaches for developing therapeutic applications targeting RNASE13?

Developing therapeutic applications targeting RNASE13 requires innovative approaches that leverage specific antibodies for both target validation and potential drug development. Begin with comprehensive tissue expression profiling using validated antibodies against amino acids 20-156 to identify disease contexts where RNASE13 is dysregulated, focusing on potential roles in innate immunity based on its predicted extracellular localization and nucleic acid binding capability. For therapeutic antibody development, establish humanized mouse models expressing human RNASE13 to overcome potential species-specific epitope differences, then implement phage display technology to isolate high-affinity antibody fragments with specific functional properties. Characterize antibody candidates using surface plasmon resonance to ensure KD values in the low nanomolar range, and validate functional effects through cell-based assays measuring relevant endpoints. For antibody-drug conjugates, select optimal linker chemistry and cytotoxic payloads based on RNASE13's cellular localization and internalization kinetics. Alternative approaches include developing bispecific antibodies linking RNASE13 recognition with effector functions targeting specific immune cell populations. Throughout development, employ immunotoxicology studies to ensure safety, and develop companion diagnostic assays using validated antibodies to identify patient populations most likely to benefit from RNASE13-targeted therapies .

How can RNASE13 antibodies be integrated into multi-omic research frameworks?

Integrating RNASE13 antibodies into multi-omic research frameworks requires sophisticated methodological approaches that bridge proteomics with other data modalities. Implement cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) by conjugating RNASE13 antibodies to oligonucleotide barcodes, enabling simultaneous protein and transcriptome analysis at single-cell resolution. For spatial multi-omics, employ multiplexed ion beam imaging (MIBI) or imaging mass cytometry (IMC) using metal-conjugated RNASE13 antibodies in conjunction with other protein markers, RNA probes, and DNA labels to create comprehensive tissue maps with subcellular resolution. Develop integrated workflows where cells or tissues are first analyzed by antibody-based methods (flow cytometry or immunohistochemistry) and then subjected to laser capture microdissection for subsequent genomic, transcriptomic, or metabolomic analysis of specific RNASE13-expressing populations. For targeted proteomics, develop multiple reaction monitoring (MRM) mass spectrometry assays for RNASE13, calibrated using immunoaffinity-enriched standards. Finally, implement computational frameworks that integrate protein-level data from antibody-based assays with RNA-seq, ATAC-seq, and metabolomics data to construct regulatory networks and identify potential roles for RNASE13 in broader cellular processes .

What best practices should researchers follow when publishing RNASE13 antibody-based studies?

When publishing RNASE13 antibody-based studies, researchers should adhere to rigorous reporting standards to ensure reproducibility and data integrity. First, provide comprehensive antibody identification including catalog number (e.g., ABIN2924094, A1073), supplier, lot number, RRID (Research Resource Identifier), clonality, host species, and immunogen details (e.g., amino acids 20-156 of human RNASE13). Document validation methods performed, including Western blot with expected molecular weight (13.7-18 kDa), immunoprecipitation efficiency, and specificity controls (especially knockout/knockdown validation). For immunostaining applications, detail the complete protocol including tissue processing, antigen retrieval methods (particularly microwave-based methods for RNASE13), blocking conditions, antibody dilutions (1:100-1:200 for IHC, 1:500-1:2000 for WB), incubation times and temperatures, detection systems, and counterstaining procedures. Include representative images of positive and negative controls alongside experimental samples. For quantitative analyses, describe image acquisition parameters, software used, quantification methods, normalization procedures, and statistical approaches. Finally, deposit raw unprocessed images in public repositories when possible, and consider sharing detailed protocols on platforms like protocols.io to enhance reproducibility across laboratories .