OFUT24 Antibody

How can I verify the specificity of an OFUT24 antibody before using it in my experiments?

Antibody validation is critical to ensure experimental reproducibility and reliability. A robust validation approach involves generating a knockout (KO) cell line for your protein of interest using CRISPR/Cas9 technology. This allows direct comparison between wild-type and KO cells by immunoblot, providing clear evidence of antibody specificity.

The recommended validation workflow includes:

• Consulting protein abundance databases like PaxDB to identify cell lines with relatively high expression of your target protein

• Using CRISPR/Cas9 to generate knockout controls in these cells

• Screening antibodies by immunoblot using both parental and KO cell lines

• Further validating promising candidates through additional applications like immunoprecipitation and immunofluorescence

This systematic approach addresses the reproducibility crisis resulting from non-specific antibodies and provides confidence in experimental results.

What applications is the OFUT24 antibody suitable for?

While specific applications for OFUT24 antibody aren't detailed in the search results, antibodies are generally characterized for suitability across various applications. For example, the OTUB2 antibody (ab74371) is suitable for Western blot (WB) and has been validated with human samples .

When evaluating an antibody for specific applications, consider:

• Validated applications listed by the manufacturer

• Available literature demonstrating successful use

• Species reactivity and validated sample types

• Required dilutions for each application

• Supporting validation data including images of positive and negative controls

Many manufacturers classify applications as:

• Tested and working (covered by product promise)

• Expected to work based on testing

• Predicted to work based on homology

• Not recommended

Always perform your own validation for your specific experimental conditions.

How can I optimize immunoprecipitation protocols with OFUT24 antibody for downstream mass spectrometry analysis?

Optimizing immunoprecipitation (IP) for mass spectrometry requires careful consideration of multiple factors to maintain specificity while minimizing contaminants. Based on protocols used for other antibodies:

• Sample preparation:

  • Collect cells in HEPES lysis buffer (20 mM HEPES, pH 7.5, 150 mM NaCl, 1 mM EDTA, 0.5% Triton X-100) supplemented with protease inhibitors

  • Incubate on ice for 30 minutes before ultracentrifugation (238,700×g for 15 min at 4°C)

• Pre-clearing:

  • Incubate lysates with protein G Sepharose beads for 30 minutes to reduce non-specific binding

• Immunoprecipitation:

  • Couple your OFUT24 antibody to protein G Sepharose (optimal antibody amount typically 1-5 μg)

  • Incubate pre-cleared lysates with antibody-conjugated beads for 4-18 hours at 4°C

  • Wash beads thoroughly (3-4 times) with lysis buffer

• Sample processing for mass spectrometry:

  • Remove detergents and salts by running samples into a stacking gel

  • Perform in-gel digestion with trypsin after reduction and alkylation

  • Extract peptides and analyze by LC-MS/MS

Always include appropriate controls, such as samples from knockout cell lines or isotype control antibodies, to confidently identify specific interactors.

What strategies should I implement when using OFUT24 antibody for multiplex immunofluorescence imaging?

Multiplex immunofluorescence allows visualization of multiple proteins simultaneously, but requires careful optimization to prevent cross-reactivity and signal interference:

• Antibody selection and validation:

  • Validate OFUT24 antibody specificity using knockout controls in your imaging system

  • Test fixation conditions (4% PFA vs. methanol) as they significantly impact epitope accessibility

  • Determine optimal antibody concentration through titration experiments

  • Verify absence of signal in knockout controls using identical imaging parameters

• Multiplexing strategy:

  • For co-localization studies, combine OFUT24 antibody with organelle markers (e.g., LAMP1-YFP for lysosomes)

  • Use secondary antibodies with minimal spectral overlap

  • Include single-color controls to assess bleed-through

• Image acquisition and analysis:

  • Use appropriate filter sets to minimize spectral overlap

  • Maintain consistent imaging parameters across experimental conditions

  • Employ quantitative analysis methods like Pearson's correlation coefficient for co-localization studies

Resources like the IBEX repository can provide additional protocols for multiplex tissue imaging applications .

How do I design experiments to definitively demonstrate OFUT24 antibody specificity across multiple applications?

A comprehensive validation strategy across multiple applications ensures reliable experimental outcomes:

• Western blot validation:

  • Compare signal between wild-type and knockout cell lines

  • Test multiple cell lines expressing different levels of the target protein

  • Use gradient gels (e.g., 5-16%) to maximize separation

  • Include loading controls and total protein staining (e.g., REVERT stain)

  • Quantify signal using fluorescent secondary antibodies and imaging systems like LI-COR Odyssey

• Immunoprecipitation validation:

  • Perform IP from both wild-type and knockout lysates

  • Analyze by mass spectrometry to confirm target enrichment

  • Quantify specific vs. non-specific binding

• Immunofluorescence validation:

  • Create mosaic cultures of labeled wild-type and knockout cells

  • Test multiple fixation methods (PFA, methanol)

  • Compare antibody staining with GFP-tagged protein localization

  • Use high-resolution confocal microscopy to assess subcellular localization

• Flow cytometry validation:

  • Compare staining profiles between expressing and non-expressing cells

  • Use appropriate isotype controls

  • Perform blocking experiments with recombinant protein

Document all validation results systematically to provide confidence in antibody specificity across applications.

What are the critical controls needed when using OFUT24 antibody for quantitative immunoblotting?

Quantitative immunoblotting requires rigorous controls to ensure reliable and reproducible results:

• Essential controls:

  • Genetic knockout or knockdown samples

  • Recombinant protein standards for absolute quantification

  • Technical replicates (minimum of three)

  • Total protein normalization rather than single housekeeping proteins

  • Concentration series to ensure linearity of signal

• Sample preparation considerations:

  • Consistent lysis conditions

  • Protease and phosphatase inhibitors

  • Equal protein loading confirmed by total protein stain

  • Denaturation conditions optimized for your target

• Technical considerations:

  • Use fluorescent secondary antibodies for wider linear range

  • Run gradient gels for optimal separation

  • Include intercalated standards for normalization across blots

  • Perform antibody titration to determine optimal concentration

• Quantification approach:

  • Normalize to total protein rather than single reference proteins

  • Use digital imaging systems (e.g., LI-COR Odyssey) rather than film

  • Apply consistent analysis parameters across experiments

  • Report both raw and normalized values

This approach maximizes reproducibility and allows meaningful comparison across experimental conditions.

How should I address contradictory results when comparing OFUT24 antibody data with RNA-seq or proteomics datasets?

Discrepancies between antibody-based protein detection and other methodologies are common in research. A systematic approach to resolving these contradictions includes:

• Validate antibody specificity:

  • Confirm antibody specificity using knockout controls

  • Test multiple antibodies targeting different epitopes

  • Consider post-translational modifications that might affect epitope recognition

• Examine methodological differences:

  • RNA levels often don't directly correlate with protein levels due to translational regulation and protein stability

  • Proteomics may miss proteins with certain characteristics (hydrophobicity, low abundance)

  • Different extraction methods may yield different protein subpopulations

• Investigate biological explanations:

  • Protein may be expressed in specific subcellular compartments

  • Context-dependent expression (cell cycle, stress, etc.)

  • Potential presence of isoforms with different antibody reactivity

• Complementary approaches:

  • Tagged protein expression

  • Alternative detection methods

  • Functional assays to confirm protein activity

When presenting contradictory results, clearly document all experimental conditions and propose biological hypotheses that might explain the discrepancies.

What approaches should I use to troubleshoot unexpected subcellular localization patterns of proteins detected with OFUT24 antibody?

Unexpected localization patterns require systematic investigation to determine whether they represent genuine biological insights or technical artifacts:

• Technical validation:

  • Test multiple fixation and permeabilization conditions

  • Compare different antibody concentrations

  • Use epitope-tagged versions of the protein

  • Perform super-resolution microscopy for detailed localization

  • Test antibody in knockout/knockdown cells to confirm specificity

• Biological considerations:

  • Investigate if localization changes under different conditions (stress, cell cycle)

  • Consider potential protein isoforms with different localization patterns

  • Examine co-localization with organelle markers

  • Review literature for reported moonlighting functions or shuttling behavior

• Experimental approaches to resolve discrepancies:

  • Biochemical fractionation followed by immunoblotting

  • Live-cell imaging with fluorescently tagged proteins

  • Proximity labeling approaches (BioID, APEX)

  • Correlative light and electron microscopy for high-resolution localization

Unexpected localization patterns often lead to new biological insights when thoroughly investigated and validated.

How can I determine the most appropriate antibody repositories to find validated antibodies for my research targets?

With numerous antibody sources available, selecting the most appropriate repositories requires consideration of multiple factors:

• Types of antibody resources:

  • Data repositories: Share validation data for antibodies

  • Search engines: Allow searching across multiple vendor catalogs

  • Target-specific resources: Focus on particular protein families or applications

• Selection criteria for repositories:

  • Validation stringency (knockout controls, multiple applications)

  • Relevance to your research area (cancer, neuroscience, immunology)

  • Types of applications covered (imaging, western blot, flow cytometry)

  • Quantity and quality of supporting data

• Recommended repositories by application:

  Repository Type	Focus	Applications	Notes
  Human Protein Atlas	Human proteins	Immunoblot, IP, IF	Extensive validation data
  Cell Atlas	Healthy human cells	Imaging (IHC, ICC, IF)	Subcellular localization
  Cancer Atlas	Cancer tissues	Various	Cancer-specific expression
  BD Cytometry resources	Immune cells	Flow cytometry	Panel design tools
  Antibodypedia	Any	Any	Aggregates data across sources
  IBEX repository	Any	Multiplex tissue imaging	Optimized protocols

• Validation strategy:

  • Cross-reference antibodies across multiple repositories

  • Prioritize antibodies with validation in your application of interest

  • Consider generating your own validation data for community benefit

Utilizing these resources effectively can save significant time and resources by identifying pre-validated reagents.

How might new antibody engineering technologies enhance the specificity and utility of tools like the OFUT24 antibody?

Emerging technologies are revolutionizing antibody development and applications:

• Technological advances:

  • Single B-cell sequencing for rapid antibody discovery

  • Phage display with synthetic libraries for difficult targets

  • CRISPR-based epitope tagging for antibody validation

  • Machine learning approaches to predict cross-reactivity

• Engineering improvements:

  • Nanobodies and single-domain antibodies for improved tissue penetration

  • Site-specific conjugation for precise labeling

  • Fc engineering to eliminate unwanted effector functions

  • Bi-specific and multi-specific formats for complex applications

• Application enhancements:

  • Intrabodies for live-cell applications

  • Split antibody complementation for proximity detection

  • Antibody-based optogenetic tools

  • Cleavable linkers for controlled release applications

These advances promise to address current limitations in antibody specificity, sensitivity, and functionality, enhancing their utility across research applications.

What considerations should guide the integration of OFUT24 antibody data with other -omics datasets?

Integrating antibody-based protein detection with multi-omics approaches requires careful consideration of technical and biological factors:

• Data normalization challenges:

  • Different dynamic ranges between techniques

  • Batch effects and technical variability

  • Sample preparation differences

  • Need for appropriate controls across platforms

• Integration approaches:

  • Correlation analysis between protein levels and transcript abundance

  • Pathway enrichment across multiple data types

  • Network analysis incorporating protein-protein interactions

  • Machine learning methods for multi-omics integration

• Biological interpretation frameworks:

  • Consider time delays between transcription and translation

  • Account for post-translational modifications and protein stability

  • Incorporate spatial information when available

  • Develop testable hypotheses that span multiple data types

• Technical considerations:

  • Ensure matched samples across platforms when possible

  • Document all metadata thoroughly

  • Consider single-cell approaches for heterogeneous systems

  • Validate key findings with orthogonal methods