ARC3 Antibody

What is ARH3/ARC3 antibody and what cellular functions does its target protein regulate?

ARH3 (also known as ADPRHL2 or ADPRS) is an ADP-ribosylhydrolase that plays critical roles in cellular metabolism. The protein functions as an ADP-ribose glycohydrolase and O-acetyl-ADP-ribose deacetylase. Specifically, it catalyzes the hydrolysis of the 1'-O-acetyl-ADP-D-ribose isomer rather than the 2''-O-acetyl-ADP-D-ribose or 3''-O-acetyl-ADP-D-ribose isomers . Anti-ARH3 antibodies are immunological reagents designed to specifically detect this protein in various experimental applications, allowing researchers to investigate its expression, localization, and function in biological systems.

What applications are ARH3/ARC3 antibodies validated for in research?

Based on available data, ARH3/ARC3 antibodies have been validated for multiple research applications:

The antibodies have been confirmed to detect both endogenous levels of the protein and overexpressed ARH3 in experimental systems .

What species reactivity do commercially available ARH3/ARC3 antibodies demonstrate?

Most commercially available ARH3/ARC3 antibodies have been primarily validated for human samples . While some antibodies may cross-react with other species due to sequence homology, researchers should verify specificity for their particular species of interest. For example, the rabbit polyclonal ARH3 antibody (ab224751) from Abcam is specifically validated for human samples, though it may work with other species with strong sequence homology .

How should researchers design validation experiments for ARH3/ARC3 antibodies in novel applications?

When validating ARH3/ARC3 antibodies for new applications, researchers should implement the following methodological approach:

• Positive and negative controls: Use cell lines with known ARH3 expression levels. Overexpression systems (as shown in Western blot data where ARH3 was overexpressed in HEK-293T cells) can serve as positive controls .

• Knockdown/knockout validation: Employ siRNA knockdown or CRISPR-Cas9 knockout of ARH3 to confirm antibody specificity.

• Epitope mapping: Consider the epitope recognized by the antibody. For instance, some ARH3 antibodies target a recombinant fragment within human ADPRS amino acids 1-150 .

• Cross-application validation: If validating for a new application, compare results with established applications (e.g., if validating for flow cytometry, compare with Western blot patterns).

• Batch testing: Test multiple antibody lots if possible to ensure reproducibility.

What are the optimal experimental conditions for using ARH3/ARC3 antibodies in Western blotting?

For optimal Western blot results with ARH3/ARC3 antibodies, researchers should consider:

• Sample preparation:

  • Prepare total protein lysates in RIPA or similar buffer with protease inhibitors

  • Include phosphatase inhibitors if studying post-translational modifications

  • Denature samples at 95°C for 5 minutes in reducing sample buffer

• Electrophoresis conditions:

  • Use 10-12% SDS-PAGE gels for optimal separation

  • Load 20-40 μg of total protein per lane

• Transfer parameters:

  • Semi-dry or wet transfer at 100V for 60-90 minutes

  • Use PVDF membranes for better protein retention

• Antibody incubation:

  • Block in 5% non-fat milk or BSA for 1 hour at room temperature

  • Dilute primary ARH3 antibody 1:100 in blocking buffer

  • Incubate overnight at 4°C with gentle agitation

  • Use appropriate HRP-conjugated secondary antibody (typically 1:2000-1:5000)

• Detection:

  • Enhanced chemiluminescence (ECL) is suitable for most applications

  • Expected molecular weight of ARH3 is approximately 39 kDa

How can researchers integrate structural knowledge of antibody-antigen interactions into ARH3/ARC3 experimental design?

Recent advances in antibody design technology provide insights for researchers working with ARH3/ARC3 antibodies. Machine learning approaches, particularly large language models (LLMs), have demonstrated effectiveness in capturing fundamental rules of protein sequence and function . When designing experiments with ARH3/ARC3 antibodies, researchers can leverage structural knowledge by:

• Epitope awareness: Understanding the specific epitope recognized by the antibody helps predict potential cross-reactivity and informs experimental design. For instance, antibodies targeting the amino acid region 1-150 of human ADPRS may have different specificity profiles than those targeting other regions.

• Structural complementarity: Consider the structural complementarity between the antibody's complementarity determining regions (CDRs) and the target epitope. This knowledge can inform decisions about antibody selection for specific applications.

• Computational prediction: Use computational tools to predict antibody-antigen interactions and potential cross-reactivity with other proteins, especially when working with novel research systems.

• Affinity considerations: Higher affinity antibodies may be preferred for applications requiring detection of low-abundance targets, while moderate affinity antibodies might be superior for differential expression studies.

What are the recommended protocols for immunohistochemistry with ARH3/ARC3 antibodies?

Based on validated protocols, researchers should follow these methodological guidelines for immunohistochemistry:

• Tissue preparation:

  • Fix tissues in 10% neutral buffered formalin

  • Process and embed in paraffin following standard protocols

  • Section at 4-6 μm thickness

• Antigen retrieval:

  • Heat-induced epitope retrieval using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0)

  • Pressure cook or microwave for 15-20 minutes

• Staining protocol:

  • Block endogenous peroxidase with 3% H₂O₂

  • Block non-specific binding with 5% normal serum

  • Dilute primary ARH3/ARC3 antibody 1:50 in blocking buffer

  • Incubate overnight at 4°C or 1-2 hours at room temperature

  • Use appropriate detection system (e.g., HRP-polymer)

  • Counterstain with hematoxylin

• Controls:

  • Include positive control tissues (testis and Fallopian tube have shown positive staining)

  • Include negative controls (primary antibody omission)

ARH3/ARC3 antibody has been successfully used to detect the protein in paraffin-embedded human testis and Fallopian tube tissues .

What troubleshooting approaches can resolve common issues with ARH3/ARC3 antibody experiments?

When encountering problems with ARH3/ARC3 antibody experiments, consider these methodological troubleshooting approaches:

• No signal or weak signal:

  • Increase antibody concentration (try 2-5× the recommended dilution)

  • Extend incubation time or switch to overnight incubation at 4°C

  • Optimize antigen retrieval (for IHC) or protein extraction method

  • Check protein transfer efficiency (for WB) using reversible staining

  • Verify target protein expression in your sample (use positive control)

• High background:

  • Increase blocking time or concentration

  • Reduce primary antibody concentration

  • Add 0.1-0.3% Triton X-100 to wash buffer

  • Increase wash duration and frequency

  • Use more specific secondary antibody

• Non-specific bands (WB):

  • Optimize lysate preparation (include appropriate protease inhibitors)

  • Increase gel percentage for better resolution

  • Use gradient gels for complex samples

  • Increase blocking stringency

  • Consider using monoclonal antibodies for higher specificity

• Inconsistent results:

  • Standardize sample preparation and experimental conditions

  • Aliquot antibodies to avoid freeze-thaw cycles

  • Verify antibody storage conditions (typically 4°C short-term, -20°C long-term)

  • Prepare fresh working solutions for each experiment

How should researchers quantify and normalize ARH3/ARC3 expression data across different experimental conditions?

For accurate quantification and normalization of ARH3/ARC3 expression data:

• Western blot quantification:

  • Use digital image capture and analysis software

  • Measure band intensity within the linear range of detection

  • Normalize to appropriate loading controls (β-actin, GAPDH, or total protein)

  • Always include biological and technical replicates (minimum n=3)

• Immunohistochemistry quantification:

  • Use digital pathology systems for automated quantification

  • Score staining intensity on a defined scale (0-3+)

  • Calculate H-score (percentage of positive cells × intensity)

  • Use internal controls for normalization

  • Consider multi-observer scoring for subjective assessments

• Statistical analysis:

  • Apply appropriate statistical tests based on data distribution

  • Report both biological and technical variability

  • Use ANOVA with post-hoc tests for multiple comparisons

  • Consider non-parametric tests for IHC scoring data

• Reporting standards:

  • Report antibody catalog number, lot, dilution, and incubation conditions

  • Describe normalization method in detail

  • Include representative images of all experimental conditions

  • Present data with appropriate error bars and significance indicators

What cell and tissue lysis protocols optimize ARH3/ARC3 protein detection?

The choice of lysis protocol significantly impacts ARH3/ARC3 detection sensitivity and specificity:

• Cell lysis buffers:

  • RIPA buffer: Effective for most applications, contains 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS in PBS

  • NP-40 buffer: Gentler option (1% NP-40, 150 mM NaCl, 50 mM Tris-HCl pH 8.0)

  • Add protease inhibitor cocktail freshly before use

• Tissue homogenization:

  • Mechanical disruption in cold lysis buffer using tissue homogenizer

  • For fibrous tissues, consider using ceramic or metal beads with homogenizer

  • Maintain samples on ice throughout processing

• Protein preservation:

  • Process samples immediately after collection

  • Snap-freeze tissues in liquid nitrogen before storage at -80°C

  • Avoid repeated freeze-thaw cycles

• Protein quantification:

  • Use BCA or Bradford assay compatible with lysis buffer

  • Prepare standard curves in the same buffer as samples

  • Load equal amounts of protein for comparative analysis

Based on published protocols, HEK-293T cells transfected with ARH3 overexpression vector have been successfully lysed and analyzed by Western blot, showing clear detection of the target protein .

How does ARH3/ARC3, antibody performance compare in detecting endogenous versus overexpressed protein?

Research data demonstrates important differences in antibody performance when detecting endogenous versus overexpressed ARH3/ARC3:

• Sensitivity considerations:

  • Endogenous detection typically requires higher antibody concentrations or enhanced detection systems

  • Overexpression systems show stronger signals but may not reflect physiological conditions

  • Western blot data shows clear difference in band intensity between vector-only and ARH3-overexpressing HEK-293T cells

• Specificity profile:

  • Non-specific binding may be more apparent in endogenous detection due to lower target-to-background ratio

  • Overexpression can sometimes cause protein mislocalization or aggregation

  • Tag-directed antibodies (against expressed tags like myc-DDK) can verify overexpression

• Experimental recommendations:

  • For endogenous detection: Use longer incubation times, optimize blocking conditions

  • For overexpression studies: Titrate expression levels to avoid artifacts

  • Always include appropriate controls (vector-only transfected cells)

  • Consider dual validation with another detection method

How can ARH3/ARC3 antibodies be integrated into multiplexed imaging techniques?

Researchers can leverage ARH3/ARC3 antibodies in advanced multiplexed imaging applications through the following methodological approaches:

• Multiplex immunofluorescence:

  • Use ARH3/ARC3 antibodies with spectrally distinct fluorophores

  • Apply sequential staining with antibodies from different host species

  • Consider tyramide signal amplification for weak signals

  • Use multispectral imaging systems for signal separation

• Mass cytometry/imaging mass cytometry:

  • Conjugate ARH3/ARC3 antibodies with rare earth metals

  • Combine with other metal-labeled antibodies for simultaneous detection

  • Design panel with minimal signal overlap

  • Apply appropriate compensation and data analysis algorithms

• Proximity ligation assay (PLA):

  • Combine ARH3/ARC3 antibody with antibodies against potential interaction partners

  • Use species-specific secondary antibodies with oligonucleotide probes

  • Detect protein-protein interactions within 40 nm proximity

  • Quantify interaction signals in different cellular compartments

• Super-resolution microscopy:

  • Optimize fixation to preserve epitope accessibility and ultrastructure

  • Use directly labeled primary antibodies when possible

  • Consider photoactivatable fluorophores for PALM/STORM applications

  • For STED microscopy, select bright and photostable fluorophores

What are the emerging applications of AI-based technologies in antibody research relevant to ARH3/ARC3 studies?

Recent advancements in AI-based technologies offer new opportunities for ARH3/ARC3 antibody research:

• De novo antibody design:

  • AI-based generation of antigen-specific antibody CDRH3 sequences can potentially create novel ARH3/ARC3-targeting antibodies

  • Machine learning models like IgLM can generate diverse antibody sequences with substantial sequence diversity and structural variability

  • These approaches may bypass traditional experimental approaches for antibody discovery, offering more efficient alternatives

• Structural prediction and epitope mapping:

  • AI tools can predict antibody-antigen interactions and binding interfaces

  • Models like ImmuneBuilder can assess structural similarity between generated antibodies and known reference antibodies

  • These predictions can guide epitope-specific antibody selection for particular applications

• Performance prediction:

  • AI algorithms can potentially predict antibody performance across different applications

  • Computational approaches may help identify optimal conditions for specific experiments

  • Machine learning models trained on experimental data could predict cross-reactivity profiles

• Validation efficiency:

  • AI-guided approaches for antibody validation could reduce the experimental burden

  • Computational pre-screening of candidates may increase hit rates in experimental validation

  • In one study, AI-generated antibodies achieved a notable hit rate of approximately 15% when experimentally validated

The integration of AI technologies represents a promising frontier for improving specificity, affinity, and application range of ARH3/ARC3 antibodies in research contexts.