olfml3a Antibody

What is OLFML3 and what biological functions has it been associated with?

OLFML3 is a secreted scaffold protein that plays several critical roles in biological processes. Primarily, it functions in dorsoventral patterning during early development by stabilizing axial formation. It restricts chordin (CHRD) activity on the dorsal side by facilitating the association between tolloid proteases and their substrate chordin, which enhances chordin degradation. Beyond developmental roles, OLFML3 likely has matrix-related functions involved in placental and embryonic development, and may perform similar roles in other physiological processes . OLFML3 is also known by several alternative names including PSEC0035, PSEC0173, PSEC0244, UNQ663/PRO1294, HNOEL-iso, and hOLF44 .

What detection methods are available for OLFML3 in research applications?

Several validated methods exist for detecting OLFML3 in research samples:

Researchers should always include proper controls when using these methods, including positive and negative tissue controls, especially when working with new antibody lots or experimental conditions.

How does OLFML3 expression vary across normal and pathological tissues?

OLFML3 exhibits differentiated expression patterns across tissues. In normal physiology, it has developmental roles, but in pathological conditions—particularly cancer—OLFML3 shows altered expression patterns. It is expressed at high levels, mainly in blood vessels, across multiple human cancer types . In colorectal cancer specifically, elevated expression of OLFML3 mRNA correlates with shorter relapse-free survival, higher tumor grade, and the angiogenic microsatellite stable consensus molecular subtype 4 (CMS4) . This expression pattern suggests a role in tumor vasculature development and potential utility as a prognostic marker.

What role does OLFML3 play in cancer progression and how can antibodies help elucidate these mechanisms?

OLFML3 has emerged as a significant player in cancer biology, particularly in colorectal cancer. Research indicates that OLFML3 contributes to tumor growth through multiple mechanisms:

• Angiogenesis promotion: OLFML3 is highly expressed in blood vessels of multiple human cancers, suggesting a pro-angiogenic function .

• Immunomodulation: Antibody-mediated blockade of OLFML3 and genetic deletion of host Olfml3 reduces recruitment of tumor-promoting tumor-associated macrophages, indicating OLFML3 helps create an immunosuppressive microenvironment .

• Lymphangiogenesis and pericyte coverage: Treatment with OLFML3-blocking antibodies and Olfml3 gene deletion decreases lymphangiogenesis and pericyte coverage, which can limit tumor growth and metastatic potential .

These findings position OLFML3-targeting antibodies as valuable research tools for mechanistic studies and potential therapeutic development. When designing experiments to study these mechanisms, researchers should consider using both antibody blockade and genetic approaches (such as CRISPR/Cas9) to validate findings through complementary methods.

How can specificity of OLFML3 antibodies be validated across different experimental applications?

Validating antibody specificity is crucial for reliable research outcomes. For OLFML3 antibodies, a multi-layered validation approach is recommended:

• Genetic validation: Compare antibody staining/detection between wild-type and Olfml3 knockout models or cells with CRISPR-mediated OLFML3 deletion.

• Peptide competition assays: Pre-incubate the antibody with the immunizing peptide to confirm signal elimination.

• Orthogonal method comparison: Compare protein expression using multiple antibodies targeting different epitopes of OLFML3 .

• Cross-reactivity assessment: Test the antibody against closely related olfactomedin family proteins to ensure it doesn't cross-react.

• Application-specific validation: Even when an antibody works well in Western blot, it requires separate validation for IHC or ICC applications due to differences in protein conformation and epitope accessibility .

Researchers should document and report validation methods alongside their experimental findings to enhance reproducibility.

What computational approaches can improve OLFML3 antibody design and specificity?

Recent advances in computational modeling can significantly enhance antibody design for improved specificity:

• Biophysics-informed modeling: This approach identifies distinct binding modes associated with specific ligands, enabling prediction and generation of antibody variants with desired specificity profiles. Such models can be trained on experimentally selected antibodies to predict outcomes beyond the experimental dataset .

• Active learning strategies: For antibody-antigen binding prediction, active learning approaches can efficiently expand labeled datasets by starting with small subsets and iteratively adding more data. This is particularly valuable for library-on-library screening approaches where generating comprehensive experimental data is cost-prohibitive .

• High avidity, low affinity (HALA) antibody design: Computational kinetic models can simulate competition between antibodies and guide the design of HALA antibodies that adjust competition based on target expression. Such approaches utilize dimensionless numbers that capture the ratio between antibody competition and internalization to achieve maximum efficacy independent of target expression levels .

These computational approaches can help researchers develop OLFML3 antibodies with customized specificity profiles, either with high affinity for a particular target or with cross-specificity for multiple targets .

What are the optimal conditions for OLFML3 antibody use in Western blotting?

Based on validated protocols, the following conditions are recommended for optimal OLFML3 detection by Western blot:

For optimal results, include positive control samples (such as A549 whole cell lysate) and negative controls (such as lysates from cells with OLFML3 knockdown) .

What immunohistochemistry protocols yield optimal OLFML3 staining in tissue sections?

For effective IHC detection of OLFML3 in tissue sections, the following protocol elements are crucial:

• Tissue preparation: Formalin fixation followed by paraffin embedding (FFPE) preserves OLFML3 antigenicity effectively .

• Antigen retrieval: Heat-induced epitope retrieval in citrate buffer (pH 6.0) for 20 minutes is recommended to expose OLFML3 epitopes after formalin fixation.

• Antibody concentration: A 1/500 dilution of rabbit polyclonal anti-OLFML3 antibody shows optimal signal-to-noise ratio in FFPE tissues .

• Detection system: Polymer-based detection systems with DAB (3,3'-diaminobenzidine) substrate provide good sensitivity for OLFML3 visualization.

• Counterstaining: Light hematoxylin counterstaining allows visualization of tissue architecture without obscuring OLFML3 staining.

When analyzing OLFML3 staining patterns, researchers should pay particular attention to blood vessels, as OLFML3 shows high expression in tumor vasculature .

How can researchers design experiments to study OLFML3 function in tumor models?

To effectively investigate OLFML3 function in tumor models, consider the following experimental design strategies:

• Antibody-mediated blockade: Treat tumor-bearing animals with anti-OLFML3 blocking antibodies to assess effects on tumor growth, angiogenesis, and immune cell infiltration .

• Genetic approaches: Use Olfml3 knockout mice or CRISPR/Cas9-mediated deletion in specific cell types to determine cell-specific contributions of OLFML3 to tumor progression .

• Combination studies: Assess OLFML3 blockade in combination with other therapies, such as anti-PD1 immunotherapy, to identify potential synergistic effects .

• Mechanistic readouts: Include analyses of:

  • Angiogenesis (CD31 staining)

  • Lymphangiogenesis (LYVE1 staining)

  • Pericyte coverage (α-SMA staining)

  • Macrophage infiltration (F4/80 or CD68 staining)

• Patient-derived xenografts: Establish PDX models from human tumors with varying OLFML3 expression levels to assess clinical relevance of experimental findings.

These approaches provide complementary insights into OLFML3 biology and its potential as a therapeutic target.

How should contradictory findings regarding OLFML3 expression in different tissue types be interpreted?

When faced with contradictory findings regarding OLFML3 expression across different studies or tissue types, consider the following analytical approach:

• Methodology assessment: Different detection methods (IHC, Western blot, RNA-seq) may yield divergent results due to varying sensitivity and specificity. Antibody clones, epitope accessibility, and post-translational modifications can significantly impact detection .

• Cellular context: OLFML3 expression may be cell-type specific within tissues. For example, high expression in blood vessels but low expression in parenchymal cells could yield different results depending on the proportion of these cell types in the analyzed sample .

• Disease stage consideration: OLFML3 expression may change during disease progression. In cancer, expression patterns may differ between early and late stages, or between primary tumors and metastatic sites .

• Isoform analysis: Check whether studies are detecting all OLFML3 isoforms or specific variants. Design experiments to specifically distinguish between isoforms if necessary.

• Meta-analysis approach: When possible, conduct systematic meta-analyses of multiple studies to identify consistent patterns and potential sources of variation.

Researchers should clearly report the specific methodologies used for OLFML3 detection to facilitate cross-study comparisons.

What machine learning approaches are applicable for optimizing OLFML3 antibody design?

Machine learning offers powerful tools for antibody design that researchers studying OLFML3 can leverage:

• Binding mode identification: Biophysics-informed models can identify different binding modes for antibodies, each associated with particular ligands. This enables the prediction and generation of antibody variants with customized specificity profiles beyond those observed experimentally .

• Active learning for binding prediction: When predicting antibody-antigen binding, active learning strategies can reduce the cost of generating experimental data by starting with a small labeled subset and iteratively expanding the dataset. This is particularly valuable for library-on-library screening approaches .

• Out-of-distribution prediction: Advanced machine learning models can predict interactions when test antibodies and antigens are not represented in the training data, a scenario known as out-of-distribution prediction .

• Cross-specificity design: Computational models can be employed to design antibody sequences with predefined binding profiles—either cross-specific (interacting with several distinct ligands) or specific (interacting with a single ligand while excluding others) .

When implementing these approaches, researchers should consider collaborating with computational biologists to develop custom models suited to their specific research questions about OLFML3.

How can researchers integrate OLFML3 antibody data with other -omics approaches for comprehensive functional analysis?

Integrating antibody-based OLFML3 data with other -omics approaches provides a comprehensive view of its biological functions:

• Transcriptomics integration: Correlate OLFML3 protein levels (detected by antibodies) with mRNA expression to identify post-transcriptional regulation mechanisms. In colorectal cancer, elevated OLFML3 mRNA expression correlates with clinical outcomes, but protein-level validation strengthens these findings .

• Proteomics complementation: Combine antibody-based methods with mass spectrometry to identify OLFML3 interaction partners and post-translational modifications that may affect its function.

• Spatial transcriptomics/proteomics: Correlate OLFML3 antibody staining patterns with spatial -omics data to understand its expression in the tissue microenvironment context, particularly in relation to blood vessels and immune cell infiltration .

• Single-cell analysis: Use OLFML3 antibodies for flow cytometry or mass cytometry (CyTOF) combined with single-cell RNA-seq to identify specific cell populations expressing OLFML3 and their transcriptional profiles.

• Systems biology approach: Integrate all data types into network models to predict OLFML3's role in broader signaling pathways and biological processes.

This multi-omics approach provides robust validation across platforms and reveals functional insights that might be missed by any single method.