ARSH Antibody

What is Arylsulfatase H (ARSH) and what cellular functions does it serve?

Arylsulfatase H (ARSH) is a membrane-bound, multi-pass membrane protein involved in cellular sulfate metabolism pathways. ARSH functions within specific biological pathways including Reactome pathways R-HSA-1663150 and R-HSA-9840310 . The protein has a calculated molecular weight of approximately 63,525 Da and contributes to hydrolysis of sulfate esters from various substrates . As a member of the sulfatase family, ARSH participates in post-translational modification processes and potentially influences cellular signaling through the regulation of sulfated compounds. Research investigating its precise biological function continues to evolve, with evidence suggesting roles in both normal cellular physiology and potential implications in pathological conditions where sulfate metabolism is disrupted.

What applications are commercially available ARSH antibodies validated for?

Currently available ARSH antibodies have been validated for Western Blot (WB) and Enzyme-Linked Immunosorbent Assay (ELISA) applications . The commercially available antibodies demonstrate reactivity with human ARSH protein and, in some cases, cross-reactivity with mouse and rat homologs . These antibodies are typically supplied in liquid form in PBS containing 50% glycerol and 0.02% sodium azide, with recommended dilution ranges of 1:500-2000 for Western Blot applications and 1:5000-20000 for ELISA procedures . It's important to note that while these applications have been validated, researchers should conduct their own validation tests when applying these antibodies to new experimental systems or additional techniques such as immunohistochemistry or immunofluorescence where formal validation data may not yet be available.

What are the optimal storage conditions for ARSH antibodies to maintain reactivity?

For long-term storage and maximum antibody stability, ARSH antibodies should be stored at -20°C for up to one year from receipt . For frequent use and short-term storage (up to one month), 4°C storage is recommended to minimize damage from freeze-thaw cycles . The antibody formulation, typically containing 50% glycerol and 0.02% sodium azide in PBS, helps maintain stability during storage . Researchers should strictly avoid repeated freeze-thaw cycles as these can progressively degrade antibody quality and diminish binding efficacy . When working with the antibody, it's advisable to aliquot the stock solution into smaller volumes upon initial thawing to minimize the number of freeze-thaw cycles each portion undergoes. Additionally, all handling should occur under sterile conditions to prevent microbial contamination, despite the presence of sodium azide as a preservative in the formulation.

What is the appropriate epitope region for ARSH antibody selection?

The commercially available ARSH antibodies are typically designed against the amino acid region 450-530 of the human ARSH protein . This region has been validated to produce antibodies capable of detecting endogenous levels of the protein in experimental systems . When selecting an ARSH antibody for specific research applications, consideration should be given to this epitope region to ensure proper antigen recognition. The antibodies generated against this region are produced by immunizing rabbits with synthesized peptides derived from this amino acid sequence . This approach yields polyclonal antibodies with multiple binding sites across the target region, which can enhance detection sensitivity in various applications. For experiments requiring detection of specific ARSH variants or particular domains of the protein, researchers should carefully evaluate whether antibodies targeting the 450-530 region will adequately recognize their protein of interest or if custom antibody development against alternative epitopes might be necessary.

What are the recommended protocols for ARSH antibody validation in Western Blot applications?

For robust ARSH antibody validation in Western Blot applications, researchers should implement a comprehensive approach starting with proper sample preparation. Cell or tissue lysates should be prepared with complete protease inhibitor cocktails to preserve protein integrity. The recommended dilution range for ARSH antibodies in Western Blot applications is 1:500-2000 , though optimization is advised for each experimental system. A standardized validation protocol should include:

• Positive and negative control samples (tissues/cells known to express or lack ARSH)

• Molecular weight verification (expected band at approximately 63.5 kDa)

• Signal specificity assessment using:

  • Blocking peptide competition assays

  • Genetic knockdown/knockout samples where available

  • Comparison with alternative ARSH antibodies targeting different epitopes

The electrophoresis conditions should be optimized with 8-12% SDS-PAGE gels, and transfer efficiency to membranes should be verified using reversible protein stains. Blocking should be performed with 5% non-fat milk or BSA in TBST, with overnight primary antibody incubation at 4°C. Detection systems should be selected based on required sensitivity, with chemiluminescence often providing optimal results for endogenous ARSH detection. All validation experiments should include technical replicates and appropriate loading controls to ensure reproducibility and accuracy of results.

How can researchers optimize ELISA protocols for ARSH protein quantification?

Optimizing ELISA protocols for ARSH protein quantification requires systematic parameter adjustment. Begin with the recommended antibody dilution range of 1:5000-20000 , but conduct preliminary experiments to determine the optimal concentration for your specific sample type. A standard sandwich ELISA protocol for ARSH detection should include:

For accurate quantification, develop a standard curve using recombinant ARSH protein at concentrations spanning the expected range in your samples (typically 0-1000 ng/ml). Validate assay performance by assessing:

• Intra-assay variation (duplicate or triplicate wells)

• Inter-assay variation (different days/operators)

• Spike-and-recovery experiments to evaluate matrix effects

• Limit of detection and quantification

• Linear dynamic range

Signal development time should be standardized across experiments, and absorbance should be measured at the appropriate wavelength (450 nm for TMB with 570 nm reference). Data analysis should incorporate blank subtraction and standard curve interpolation using appropriate regression models (typically 4-parameter logistic curve fitting).

How can computational approaches enhance ARSH antibody specificity and binding affinity?

Recent advances in computational antibody design can be applied to enhance ARSH antibody development. Deep learning models like IgDesign and AbMAP offer powerful frameworks for optimizing antibody complementarity-determining regions (CDRs) . For ARSH-specific antibody development, these approaches can be implemented in a structured workflow:

• Initial structure prediction: Use AlphaFold or RoseTTAFold to predict the ARSH protein structure if crystallographic data is unavailable.

• Epitope mapping: Employ computational tools to identify optimal epitope regions beyond the standard 450-530 amino acid range, focusing on accessible surface regions with high antigenicity scores.

• CDR optimization: Apply AbMAP's contrastive augmentation approach to refine antibody binding domains . This technique performs in silico mutagenesis of CDRs and analyzes embedding differences between original and mutated sequences to highlight critical binding residues.

• Iterative affinity maturation: Using the methodology described by AbMAP researchers, implement:

  • Predictive modeling of binding affinity for candidate sequences

  • Clustering of high-scoring candidates

  • Selection of diverse representatives for experimental validation

• Experimental validation: Employ surface plasmon resonance (SPR) to validate computational predictions, as demonstrated in the AbMAP study where predicted antibody variants showed "many-fold improvement in binding efficacy" .

The AbMAP approach has demonstrated particular efficacy through its ability to "capture antibody structure and function" through specialized embedding techniques . By applying similar methodologies to ARSH antibody development, researchers can potentially achieve significant improvements in specificity and binding characteristics compared to traditionally developed antibodies.

What cross-reactivity considerations are important when using ARSH antibodies across different species?

When employing ARSH antibodies across species, researchers must address several cross-reactivity considerations to ensure experimental validity. While some commercial ARSH antibodies claim reactivity with human, rat, and mouse ARSH proteins , sequence homology analysis reveals important inter-species variations that may affect epitope recognition. Researchers should consider:

• Sequence conservation analysis: Compare the antibody epitope region (typically 450-530 aa) across target species using multiple sequence alignment tools. Areas of high conservation suggest better cross-reactivity potential, while divergent regions may compromise binding.

• Domain-specific recognition: Evaluate whether the antibody targets functionally conserved domains that maintain structural similarity despite sequence variations.

• Validation hierarchy: Implement a stepwise validation approach:

  • Begin with Western blot analysis using recombinant proteins from each species

  • Confirm with endogenous protein detection in species-specific tissue lysates

  • Validate using immunoprecipitation to confirm native protein recognition

  • Consider knockout/knockdown controls in the non-primary species to verify specificity

• Quantitative binding assessment: When precise quantification is required across species, determine relative binding efficiencies through:

  • Parallel standard curves using recombinant proteins from each species

  • Competitive binding assays to assess relative affinities

  • Surface plasmon resonance to measure binding kinetics for each species variant

• Alternative strategies: When cross-reactivity is insufficient, consider:

  • Developing species-specific antibodies against conserved epitopes

  • Employing computational approaches like AbMAP to design multi-species reactive antibodies

  • Using epitope tagging strategies for consistent detection across experimental models

Cross-reactivity validation is especially critical for comparative studies across model organisms, where false equivalencies due to differential antibody affinity can lead to misinterpretation of biological differences between species.

What are the current methodological approaches for studying ARSH protein-protein interactions?

Investigating ARSH protein-protein interactions requires a multi-faceted approach combining traditional biochemical methods with emerging technologies. Current methodological strategies include:

• Co-immunoprecipitation (Co-IP) optimization: When using ARSH antibodies for Co-IP, researchers should:

  • Test both native and crosslinked conditions to preserve transient interactions

  • Optimize lysis buffers to maintain membrane protein complexes (typically containing 1% digitonin or 0.5-1% NP-40)

  • Employ proper controls including IgG controls, reverse Co-IP validation, and RNase treatment to eliminate RNA-mediated interactions

• Proximity-based labeling techniques:

  • BioID or TurboID fusion with ARSH to identify proximal interacting partners

  • APEX2-based proximity labeling for temporal resolution of interaction dynamics

  • Split-BioID approaches to identify context-specific interactions in different cellular compartments

• Fluorescence-based interaction analysis:

  • Förster Resonance Energy Transfer (FRET) to assess direct protein interactions

  • Fluorescence Lifetime Imaging Microscopy (FLIM) for improved sensitivity in detecting FRET

  • Fluorescence Correlation Spectroscopy (FCS) to analyze diffusion characteristics of protein complexes

• Mass spectrometry integration:

  • Crosslinking Mass Spectrometry (XL-MS) to map interaction interfaces

  • Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) to identify conformational changes upon binding

  • Parallel Reaction Monitoring (PRM) for targeted quantification of interaction stoichiometry

• Computational prediction and validation:

  • Structure-based docking simulations incorporating AbMAP-derived protein structure predictions

  • Network analysis integrating proteomics data with pathway databases (including Reactome pathways R-HSA-1663150 and R-HSA-9840310)

  • Molecular dynamics simulations to assess interaction stability

These approaches can be applied systematically to build a comprehensive interactome map for ARSH, providing insights into its functional role within cellular pathways and potential involvement in disease mechanisms.

How can researchers address non-specific binding issues with ARSH antibodies?

Non-specific binding is a common challenge when working with ARSH antibodies. To systematically address this issue, researchers should implement a comprehensive troubleshooting approach:

• Blocking optimization:

  • Test alternative blocking agents (BSA, non-fat milk, commercial blocking buffers)

  • Increase blocking concentration (3-5%)

  • Extend blocking time (1-2 hours at room temperature or overnight at 4°C)

  • Add 0.1-0.3% Triton X-100 or Tween-20 to reduce hydrophobic interactions

• Antibody dilution refinement:

  • Perform titration experiments beyond the standard 1:500-2000 range for Western blot or 1:5000-20000 for ELISA

  • Consider using higher dilutions with extended incubation times

  • Implement two-step dilution protocols where antibody is pre-diluted in blocking buffer

• Stringency adjustment:

  • Increase salt concentration in wash buffers (150-500 mM NaCl)

  • Add low concentrations of SDS (0.01-0.05%) to wash buffers

  • Increase number and duration of washing steps

  • Consider temperature modification during incubation steps

• Pre-adsorption techniques:

  • Pre-incubate diluted antibody with known non-specific binders (e.g., membrane extracts from knockout cells)

  • Implement immunoaffinity purification against the immunizing peptide

  • Consider using commercially available antibody pre-adsorption kits

• Validation controls:

  • Include blocking peptide competition assays

  • Test antibody on knockout or knockdown samples

  • Compare results across different antibody lots and sources

  • Implement isotype-matched control antibodies

Through systematic optimization of these parameters, researchers can significantly improve signal-to-noise ratios in ARSH detection while maintaining sensitivity. Documentation of optimization steps is critical for experimental reproducibility and should be included in research methodologies.

What approaches can resolve data inconsistencies when different ARSH antibodies yield contradictory results?

• Comprehensive antibody characterization:

  • Map epitope regions of each antibody, noting that most commercial ARSH antibodies target the 450-530 aa region

  • Determine if antibodies recognize different isoforms, post-translational modifications, or conformational states

  • Evaluate antibody isotypes and clonality (polyclonal vs. monoclonal) that may influence detection properties

• Multi-method validation:

  • Implement orthogonal detection methods beyond Western blot and ELISA

  • Correlate protein detection with mRNA expression data

  • Employ mass spectrometry to confirm protein identity in antibody-positive samples

  • Use CRISPR/Cas9-mediated knockout models as definitive controls

• Antibody-independent approaches:

  • Generate epitope-tagged ARSH constructs for detection with well-validated tag antibodies

  • Apply CRISPR-based endogenous tagging strategies

  • Consider proximity-labeling approaches to verify localization and interaction data

• Computational resolution strategies:

  • Apply machine learning techniques similar to AbMAP to predict antibody-epitope interactions

  • Conduct molecular modeling of antibody-antigen complexes to evaluate potential binding mechanisms

  • Integrate these predictions with experimental data to identify potential causes of discrepancy

• Systematic documentation and reporting:

  • Create detailed comparison tables documenting antibody characteristics and experimental conditions

  • Maintain transparency regarding discrepancies in publications

  • Consider collaborative validation with other laboratories

When discrepancies persist despite rigorous investigation, researchers should acknowledge limitations in current ARSH detection tools and consider:

• Developing new antibodies targeting alternative epitopes

• Applying computational antibody design approaches like those described in references and

• Establishing community standards for ARSH detection and quantification

How might emerging antibody technologies enhance ARSH research beyond current methodological limitations?

Emerging antibody technologies offer transformative potential for advancing ARSH research beyond current methodological constraints. Several cutting-edge approaches show particular promise:

• Computationally optimized antibodies:

  • Application of deep learning models like IgDesign and AbMAP specifically to ARSH epitopes

  • Development of structure-based antibody design targeting conformational epitopes using the AbMAP framework that has demonstrated "many-fold improvement in binding efficacy"

  • Integration of molecular dynamics simulations to design antibodies recognizing specific functional states of ARSH

• Next-generation antibody formats:

  • Single-domain antibodies (nanobodies) for improved tissue penetration and access to sterically hindered epitopes

  • Bispecific antibodies recognizing both ARSH and interaction partners to study protein complexes in situ

  • Intrabodies with subcellular targeting signals to study ARSH in specific cellular compartments

• Innovative detection systems:

  • Antibody-based proximity sensors to visualize ARSH interactions in live cells

  • Split-fluorescent protein complementation systems for monitoring dynamic ARSH associations

  • Antibody-DNA conjugates for highly multiplexed detection using DNA-PAINT or sequence-based readouts

• Therapeutic and diagnostic applications:

  • Development of antibody-based modulators of ARSH activity for functional studies

  • Antibody-drug conjugates targeting ARSH in relevant disease models

  • Diagnostic applications in conditions where ARSH expression or function is altered

• Integration with complementary technologies:

  • Combination with CRISPR-based genetic tools for simultaneous perturbation and detection

  • Integration with spatial transcriptomics to correlate ARSH protein localization with gene expression patterns

  • Application with cryo-electron tomography for structural studies of ARSH in native membrane environments

These emerging technologies represent a significant departure from conventional antibody applications and could provide unprecedented insights into ARSH biology, particularly when combined with the computational approaches described in the literature that enable rational design of high-affinity, highly-specific antibody reagents.

What are the current knowledge gaps in ARSH protein function and how might improved antibody tools address them?

Significant knowledge gaps persist in our understanding of ARSH protein biology, with improved antibody tools offering potential pathways to address these limitations. Current knowledge gaps include:

• Physiological substrate specificity:

  • The precise endogenous substrates of ARSH remain poorly characterized

  • Improved antibodies could enable immunoprecipitation of ARSH-substrate complexes

  • AbMAP-designed antibodies with enhanced affinity could facilitate capture of transient enzyme-substrate interactions

• Regulatory mechanisms:

  • Factors controlling ARSH expression, localization, and activity are incompletely understood

  • Antibodies specifically recognizing post-translationally modified ARSH could reveal regulatory pathways

  • Conformation-specific antibodies designed using computational approaches could distinguish active vs. inactive enzyme states

• Tissue-specific functions:

  • ARSH may serve different roles across tissues, but comprehensive profiling is lacking

  • Highly sensitive antibodies optimized through computational design would enable detection of low-abundance ARSH in various tissues

  • Multi-epitope antibody panels could distinguish potential isoforms with tissue-specific expression patterns

• Disease associations:

  • Potential roles of ARSH in pathological conditions remain largely unexplored

  • Quantitative antibody-based assays with improved sensitivity could detect disease-associated alterations in ARSH levels

  • Antibodies recognizing disease-specific modifications or conformations could serve as biomarkers

• Interactome characterization:

  • The protein-protein interaction network of ARSH is incompletely mapped

  • Proximity-labeling approaches using optimized antibodies could reveal context-specific interaction partners

  • Antibodies targeting interaction interfaces could help validate and functionally characterize predicted interactions

To address these knowledge gaps, next-generation antibody development should focus on creating reagents that go beyond simple detection to provide functional insights. This might include antibodies that modulate ARSH activity, distinguish between functional states, or enable high-resolution localization studies. The application of computational design approaches like those described in references and would be particularly valuable for generating such specialized reagents that could systematically address the current limitations in our understanding of ARSH biology.