CRRSP36 Antibody

What is CRMP3 antibody and what cellular functions does it help study?

CRMP3 antibody is a research tool used to detect and study the Collapsin Response Mediator Protein 3 (also known as DPYSL4, DRP-4, or ULIP4). This protein is necessary for signaling by class 3 semaphorins and subsequent remodeling of the cytoskeleton. It plays crucial roles in axon guidance, neuronal growth cone collapse, and cell migration . When using this antibody, researchers should be aware that while the predicted band size is 62 kDa, the observed band size in western blot applications is typically around 65 kDa, which is important for accurate data interpretation .

What applications are CRMP3 antibodies validated for?

CRMP3 antibodies like ab36217 are validated for multiple research applications including Western Blot (WB) and Immunohistochemistry on paraffin-embedded tissues (IHC-P). The antibody has been tested with human samples and cited in multiple publications . When designing experiments, researchers should consider that polyclonal antibodies like the rabbit anti-CRMP3 provide good sensitivity but may have more batch-to-batch variation compared to monoclonal alternatives. Validation studies show specific reactivity to CRMP3 when tested against other CRMP family members (CRMP1, CRMP2, CRMP4, and CRMP5) .

What is the ProteOn XPR36 system and how does it differ from traditional antibody screening methods?

The ProteOn XPR36 is a surface plasmon resonance (SPR) biosensor platform designed for analyzing label-free biomolecular interactions. Unlike traditional ELISA or flow cytometry-based antibody screening, this system features a unique 6 × 6 interaction array based on a criss-cross fluidic design, allowing immobilization of six ligands into vertical channels while injecting six analytes into horizontal channels . This configuration enables simultaneous analysis of 36 interactions in a single experiment, dramatically increasing throughput compared to sequential methods while providing high-quality kinetic data. The system offers novel referencing options including interspot reference and real-time injection reference to correct for refractive index effects, nonspecific binding, and baseline drift .

How can I optimize CRMP3 antibody dilutions for IHC-P applications in neural tissue samples?

For optimal CRMP3 antibody performance in IHC-P applications with neural tissues such as hippocampus sections, begin with a titration series (1:100 to 1:1000) to determine the optimal signal-to-noise ratio. Published protocols indicate successful staining in human hippocampus formalin-fixed paraffin-embedded tissue . For challenging neural tissues with high background, consider:

• Extending blocking steps (3% BSA in PBS with 0.1% Triton X-100 for 2 hours)

• Implementing antigen retrieval (heat-mediated in citrate buffer pH 6.0)

• Incubating primary antibody at 4°C overnight followed by thorough washing

• Using appropriate detection systems optimized for rabbit polyclonal antibodies

Always include positive controls (human hippocampus sections) and negative controls (primary antibody omission and isotype controls).

What is the "quantikinetics" workflow for antibody characterization and how does it improve research efficiency?

The quantikinetics workflow is an innovative approach using the ProteOn XPR36 system that combines antibody quantification and kinetic analysis in a single cycle (<60 minutes). This workflow significantly improves efficiency by:

• Determining differential antibody concentration across individual culture wells

• Ranking antibodies based on comparative affinities for specific antigens

• Providing detailed kinetic analysis to characterize association (ka, M-1s-1) and dissociation (kd, s-1) rate constants for each antibody-antigen pair

The workflow involves three main steps:

• Step 1: Coupling a capture antibody (e.g., goat anti-mouse IgG) to the chip surface

• Step 2: Generating a standard curve with known antibody concentrations and quantitating unknown samples

• Step 3: Performing One-shot Kinetics analysis

This method is effective across the 10 ng/ml to 100 μg/ml range and provides comprehensive data for antibody selection during development processes.

How can I determine if cross-reactivity will affect my CRMP3 antibody specificity when studying multiple CRMP family members?

Cross-reactivity assessment is critical when studying CRMP family members due to their structural similarities. To determine specificity:

• Perform comparative Western blot analysis with recombinant proteins or E. coli transformed with cDNAs of all CRMP family members (CRMP1-5), as demonstrated with ab36217

• Implement competition assays with free antigen to confirm binding specificity

• Consider peptide array analysis to map epitope recognition across the protein family

• Validate results with knockout/knockdown controls where available

How do you assess and quantify antibody cross-reactivity between related coronavirus antigens?

Assessing antibody cross-reactivity between related coronavirus antigens requires a systematic approach:

• Implement multiplex assays profiling antibody reactivity against multiple viral antigens simultaneously (e.g., whole spike protein, NTD, RBD, and N protein)

• Conduct competition experiments using free antigen cocktails:

  • SARS-CoV-2-specific antigens (RBD and full-length spike proteins)

  • Circulating coronavirus spike proteins (OC43, HKU1, NL63, and 229E)

• Utilize correlation analyses to understand relationships between antibody reactivity against different viral antigens

• Perform SPOT array assays where peptides covering the viral proteome are synthesized on cellulose membranes to map antibody reactivity with high resolution

This approach revealed that approximately 0.60% (95%CI, 0%–2.71%) of uninfected individuals showed evidence of previous SARS-CoV-2 infection, while a majority showed variable antibody reactivity against spike, RBD, or N protein, indicating significant cross-reactivity between coronaviruses .

What experimental controls should be included when evaluating potential cross-reactivity of antibodies in SPR-based kinetic studies?

When conducting SPR-based kinetic studies using systems like the ProteOn XPR36, include these essential controls to accurately evaluate antibody cross-reactivity:

• Interspot references: Utilize the unique 6 × 6 configuration to include dedicated reference spots for immediate proximate correction of refractive index effects and nonspecific binding

• Real-time injection references: Monitor real-time changes of the ligand surface to correct exponential baseline drift when using ligand-capture surface chemistry

• Concentration series: Include a dilution series of the primary analyte to verify concentration-dependent binding

• Negative control analytes: Test structurally similar but non-target proteins to quantify non-specific binding

• Competitive binding assays: Pre-incubate antibodies with soluble antigens before SPR analysis to confirm binding specificity

These controls enable accurate discrimination between specific and non-specific interactions, crucial for interpreting kinetic parameters in complex biological systems.

What are the key validation parameters for ensuring CRMP3 antibody specificity in neurological tissue research?

For rigorous CRMP3 antibody validation in neurological tissue research, implement these key parameters:

• Target selectivity validation:

  • Confirm specificity across all CRMP family members (CRMP1-5) using Western blot analysis

  • Validate correct molecular weight detection (predicted 62 kDa vs. observed 65 kDa for CRMP3)

• Application-specific validation:

  • Verify performance in relevant applications (WB, IHC-P) with appropriate positive controls

  • Document subcellular localization patterns in neuronal tissues that align with known CRMP3 biology

• Reproducibility assessment:

  • Test multiple antibody lots if available

  • Compare results across different sample preparations and experimental conditions

• Biological validation:

  • Correlate antibody staining patterns with expression data from mRNA studies

  • Consider knockout/knockdown controls where available or peptide competition assays

Documentation of these validation parameters should accompany all research findings to ensure data reliability and reproducibility.

How can researchers distinguish between true SARS-CoV-2 antibody reactivity and pre-existing cross-reactivity in serum samples?

Distinguishing between true SARS-CoV-2 antibody reactivity and pre-existing cross-reactivity requires a multi-faceted approach:

• Use multiplex assay profiling against multiple SARS-CoV-2 antigens (spike, NTD, RBD, N protein) and analyze clustering patterns of reactivity

• Implement confirmation testing with orthogonal methods:

  • Commercial diagnostic chemiluminescent (CLIA) assays recognizing spike protein S1 antigen

  • Competition experiments with free SARS-CoV-2 antigens versus circulating coronavirus antigens

• Establish baseline reactivity using appropriate control populations:

  • Pre-pandemic samples or infant sera with limited coronavirus exposure

  • Known COVID-19 convalescent samples as positive controls

• Map epitope-specific reactivity using SPOT arrays covering the full viral proteome to identify true SARS-CoV-2-specific responses versus conserved epitopes shared with circulating coronaviruses

Research has shown that true SARS-CoV-2-specific antibodies typically show high reactivity against all four viral antigens and cluster separately from samples exhibiting cross-reactivity .

How should researchers design experiments to accurately measure antibody-antigen binding kinetics using the ProteOn XPR36 system?

For optimal experimental design when measuring antibody-antigen binding kinetics with the ProteOn XPR36 system:

• Surface preparation:

  • Covalently couple capture antibody (e.g., goat anti-mouse IgG) to the chip surface in the horizontal direction to ensure uniform surface properties including horizontal interspots

  • Confirm high-density capture antibody surface (>7000 RU) for optimal sensitivity

• Quantitation phase:

  • Generate a standard curve using antibody samples of known concentrations

  • Dilute unknown samples appropriately (1:5 or 1:10) in PBST-B buffer

  • Inject at consistent flow rates (25 μl/min) to ensure reproducible results

• Kinetic analysis:

  • Rotate flow channels to the horizontal direction for analyte injection

  • Include buffer injections for equilibration and baseline stabilization

  • Implement proper referencing using the system's unique interspot design

• Data analysis:

  • Use the initial slope for each sample which is linearly dependent on ligand concentration

  • Apply appropriate binding models based on interaction complexity (1:1, heterogeneous ligand, etc.)

This comprehensive approach enables accurate determination of both antibody concentration (effective across 10 ng/ml to 100 μg/ml) and detailed kinetic parameters in a single experimental cycle .

What statistical methods should be used when analyzing antibody cross-reactivity data from population-level studies?

When analyzing antibody cross-reactivity data from population-level studies, researchers should implement these statistical approaches:

• Prevalence estimation:

  • Adjust for assay bias using point estimates of specificity and sensitivity

  • Calculate confidence intervals (e.g., 95%CI) to account for sampling variability

• Correlation analyses:

  • Assess relationships between antibody reactivity against different antigens

  • Apply appropriate statistical corrections for multiple comparisons

• Clustering methods:

  • Implement unsupervised clustering based on antibody reactivity profiles

  • Validate clusters through bootstrap analysis or cross-validation

• Competition experiment analysis:

  • Quantify percent inhibition of antibody binding by competing antigens

  • Compare inhibition patterns between different subject groups (e.g., COVID-19 convalescent vs. uninfected individuals)

• Control for confounding variables:

  • Age-stratified analysis to account for lifetime exposure to circulating coronaviruses

  • Multivariate regression to adjust for demographic and clinical factors

These methods help distinguish true cross-reactivity from non-specific binding and provide rigorous quantification of antibody interactions across different coronavirus antigens.