ORP1C Antibody

What is ORP1C and why is it significant in research?

ORP1C belongs to the oxysterol-binding protein (OSBP) and OSBP-related protein (ORP) family, which plays critical roles in cellular lipid sensing, transfer, and distribution across various membrane compartments. In plants like rice, ORP1C (identified as LOC_Os03g49770) contributes to defense mechanisms against pathogens and influences growth and development . In mammalian systems, related proteins like ORP-1 have been implicated in cancer progression and treatment response, particularly in colorectal cancer with relation to cetuximab sensitivity and immune cell infiltration . The development of specific antibodies against ORP1C is essential for elucidating its localization, interaction partners, and functional roles in various experimental systems.

What are the key characteristics to consider when selecting an ORP1C antibody?

When selecting an ORP1C antibody for research applications, consider these critical characteristics:

• Specificity: Ensure the antibody recognizes ORP1C but not other ORP family members

• Species reactivity: Verify compatibility with your experimental organism (e.g., human, rice)

• Application compatibility: Confirm suitability for your intended applications (Western blot, immunoprecipitation, immunohistochemistry)

• Monoclonal vs. polyclonal: Monoclonal offers greater specificity but polyclonal may provide better detection sensitivity

• Epitope location: Antibodies targeting different protein domains may yield different results

• Validation data: Examine published validation data in relevant experimental systems

Researchers should review existing literature on ORP1C interaction studies, such as those demonstrating XopZ-ORP1C binding in rice cells through co-immunoprecipitation experiments, which successfully used tagged versions (ORP1C-Flag) for detection .

How can I validate the specificity of my ORP1C antibody?

Thorough validation of ORP1C antibody specificity requires a multi-pronged approach:

• Positive controls: Test antibody reactivity against purified recombinant ORP1C protein

• Negative controls: Verify absence of signal in samples lacking ORP1C (e.g., CRISPR knockout cells)

• Competitive binding assay: Pre-incubate antibody with excess ORP1C peptide to confirm specific binding

• Cross-reactivity testing: Test against closely related ORP family members

• Multiple detection methods: Confirm consistent results across different techniques (e.g., Western blot, immunofluorescence)

• siRNA/CRISPR validation: Demonstrate reduction/elimination of signal following ORP1C knockdown/knockout

Research involving ORP1C mutants generated using CRISPR-Cas9 gene editing (as described in rice studies) provides an excellent negative control system for antibody validation .

How can I optimize immunoprecipitation protocols for studying ORP1C protein interactions?

Optimizing immunoprecipitation (IP) for ORP1C requires careful consideration of experimental conditions:

Protocol Optimization Table for ORP1C Immunoprecipitation:

Successful co-immunoprecipitation experiments with ORP1C have been documented, such as the XopZ-ORP1C interaction study in rice where ORP1C-Flag was co-expressed with XopZ-HA in rice protoplasts, allowing specific binding detection .

What approaches are most effective for studying ORP1C localization at the subcellular level?

Effective subcellular localization studies of ORP1C should employ complementary techniques:

• Immunofluorescence microscopy: Use ORP1C-specific antibodies alongside organelle markers (ER, Golgi, endosomes) for co-localization studies

• Cell fractionation and Western blotting: Isolate subcellular fractions and probe for ORP1C distribution

• Proximity labeling approaches: BioID or APEX2 fused to ORP1C can identify proteins in close proximity

• Live-cell imaging: Fluorescently-tagged ORP1C constructs for dynamic localization studies

• Super-resolution microscopy: Techniques like STORM or PALM for nanoscale resolution of ORP1C localization

• Electron microscopy with immunogold labeling: For ultrastructural localization studies

Since ORP1C is implicated in lipid transport between different membrane compartments, combining multiple approaches is essential to fully characterize its dynamic localization patterns.

How can I analyze ORP1C expression in relation to immune cell infiltration in cancer samples?

Analyzing ORP1C expression in relation to immune cell infiltration requires integrated approaches:

• Multiplex immunohistochemistry/immunofluorescence: Use ORP1C antibodies alongside immune cell markers (CD4+, CD8+ T cells, B cells, macrophages) on tissue sections

• Bioinformatic correlation analysis: Utilize tools like Tumor Immune Estimation Resource (TIMER) 2.0 to correlate ORP1C expression with immune cell infiltration signatures

• Single-cell RNA sequencing: Analyze ORP1C expression at single-cell resolution across tumor and immune populations

• Spatial transcriptomics: Map ORP1C expression patterns in relation to immune cell localization within the tumor microenvironment

Studies have demonstrated significant correlations between ORP-1 expression and infiltration of B cells, CD4+ T cells, CD8+ T cells, and macrophages in colorectal cancer, which was validated using immunohistochemistry in clinical samples .

What are the key considerations when designing experiments to study ORP1C's role in treatment resistance?

When investigating ORP1C's role in treatment resistance:

• Model system selection: Choose appropriate cell lines or animal models that demonstrate differential treatment sensitivity

• Resistance induction protocol: Develop standardized protocols for generating resistant models (e.g., cetuximab-resistant colorectal cancer cells)

• ORP1C modulation approaches:

  • Genetic: CRISPR/Cas9 knockout, siRNA knockdown, or overexpression

  • Pharmacological: Small molecule modulators if available

• Comprehensive phenotyping: Assess multiple resistance parameters beyond cell viability

• Mechanistic investigations: Examine downstream signaling pathways, protein-protein interactions, and lipid composition changes

• Clinical correlation: Validate findings using patient-derived samples with known treatment outcomes

Research has shown that patients with advanced colorectal cancer exhibiting high ORP-1 expression demonstrated longer progression-free survival when treated with anti-EGFR therapy compared to those with low ORP-1 expression, highlighting its potential as a predictive biomarker .

What methods are recommended for analyzing contradictory data regarding ORP1C function?

When confronting contradictory data about ORP1C function:

• Systematic variation analysis: Comprehensively document all experimental variables between contradictory studies:

  • Antibody sources, clones, and validation methods

  • Cell types/tissue sources and their baseline characteristics

  • Experimental conditions and methodologies

  • Data analysis approaches

• Independent validation: Reproduce key experiments using multiple methodologies

• Cell/tissue-specific effects: Investigate whether contradictions arise from context-dependent functions

• Isoform-specific analysis: Determine if different ORP1C isoforms might display distinct functions

• Temporal dynamics: Assess whether ORP1C functions differently depending on cell cycle, differentiation state, or stress conditions

• Integration of multi-omics data: Combine transcriptomic, proteomic, and lipidomic approaches to obtain a comprehensive view

The contradictory functions of ORP1C observed in different experimental systems may reflect its diverse roles in various cellular processes, necessitating careful experimental design and thorough controls.

How can I design experiments to investigate ORP1C's role in lipid transport and membrane dynamics?

To investigate ORP1C's role in lipid transport and membrane dynamics:

Experimental Approach Table for ORP1C Lipid Transport Studies:

As OSBPs/ORPs function as non-vesicular lipid transporters sensing and transferring various lipids (especially sterols and phospholipids), experimental designs should focus on ORP1C's specific role in regulating lipid distribution across different cellular membranes .

How can findings from plant ORP1C research inform mammalian ORP1C studies?

Leveraging cross-system ORP1C research requires careful translation:

• Evolutionary conservation analysis: Compare protein domains, motifs, and functional sites between plant and mammalian ORP1C to identify conserved elements

• Functional complementation studies: Express mammalian ORP1C in plant ORP1C mutants (and vice versa) to assess functional conservation

• Comparative interactome mapping: Identify conserved and divergent protein interaction networks

• Parallel pathway analysis: Compare signaling pathways influenced by ORP1C across systems

• Structural biology approaches: Determine if structural features mediating lipid binding/transport are conserved

• Translational experimental design: Design experiments testing hypotheses derived from one system in the other

Research has demonstrated that ORP1C in rice interacts with bacterial effector XopZ and positively regulates resistance to bacterial pathogens , which parallels findings in mammalian systems where ORP-1 influences immune cell infiltration and treatment response in cancer . This suggests conserved roles in pathogen/disease response mechanisms despite evolutionary divergence.

What are the best approaches for quantifying ORP1C expression levels across different experimental systems?

For accurate ORP1C quantification across systems:

• Quantitative PCR (qPCR):

  • Design primers spanning exon-exon junctions

  • Use multiple reference genes for normalization

  • Validate primer efficiency and specificity

• Western blotting quantification:

  • Use purified recombinant ORP1C for standard curves

  • Employ fluorescent secondary antibodies for wider linear detection range

  • Normalize to multiple loading controls

• Mass spectrometry-based proteomics:

  • Use isotope-labeled internal standards

  • Targeted approaches (MRM/PRM) for greater sensitivity

  • Consider both absolute and relative quantification

• Flow cytometry:

  • Optimize fixation and permeabilization for intracellular ORP1C detection

  • Use quantitative beads for standardization

• Cross-platform validation:

  • Confirm consistency between mRNA and protein quantification

  • Document protocol-specific variables affecting quantification

Researchers studying ORP1C in rice successfully used real-time quantitative PCR analysis with appropriate normalization to human GAPDH mRNA expression to examine gene expression changes following various treatments .

What emerging technologies show promise for advancing ORP1C antibody research?

Several cutting-edge technologies offer potential breakthroughs in ORP1C antibody research:

• Single-domain antibodies (nanobodies): Smaller size enables access to sterically hindered epitopes and improved tissue penetration

• Intrabodies: Antibody fragments expressed intracellularly to study ORP1C in live cells

• CRISPR epitope tagging: Endogenous tagging of ORP1C for antibody-independent detection

• Proximity-dependent biotinylation: BioID or APEX2 fusions to map ORP1C's proximal protein environment

• Cryo-electron microscopy: Structural studies of ORP1C-antibody complexes at near-atomic resolution

• AI-assisted epitope prediction: Computational approaches to design antibodies targeting specific functional domains

• Spatially-resolved proteomics: Integration of antibody-based detection with spatial information preservation

These technologies may help resolve current limitations in studying dynamic ORP1C localization, interaction partners, and functional domains in various cellular contexts.

How can systems biology approaches enhance our understanding of ORP1C function?

Systems biology offers powerful frameworks for integrating ORP1C research:

• Multi-omics integration: Combine transcriptomic, proteomic, lipidomic, and metabolomic data to build comprehensive models of ORP1C function

• Network analysis: Map ORP1C-associated protein-protein and gene-gene interaction networks

• Mathematical modeling: Develop quantitative models of ORP1C-mediated lipid transport dynamics

• Machine learning approaches: Identify patterns in large datasets that correlate with ORP1C function or dysfunction

• Pathway enrichment analysis: Identify biological processes and pathways significantly associated with ORP1C

• Comparative systems analysis: Compare ORP1C-associated networks across species, tissues, and disease states

Integrating systems approaches could help reconcile seemingly contradictory findings regarding ORP1C's role in different contexts, such as its positive regulation of resistance to PXO99 strains in rice and its association with treatment response in colorectal cancer .