LCR38 Antibody
Description
Product Overview
The LRRC38 Antibody (HPA038778) is a rabbit-derived polyclonal antibody developed by Sigma-Aldrich as part of the Prestige Antibodies® line, validated for immunohistochemistry (IHC) in human tissues .
| Parameter | Detail |
|---|---|
| Biological Source | Rabbit |
| Antibody Type | Polyclonal, affinity isolated |
| Reactivity | Human |
| Applications | Immunohistochemistry (1:20–1:50 dilution) |
| Target Modification | Unmodified |
| Storage Conditions | −20°C in buffered aqueous glycerol solution |
| Immunogen Sequence | NNLVGVHEDAFETLESLQVLELNDNNLRSLSVAALAALPALRSLRLDGNPWLCDCDFAHLFSWIQENASKLPK |
Immunogen Design and Epitope Specificity
The antibody was generated using a synthetic peptide corresponding to residues in the human LRRC38 protein . This epitope-directed approach aligns with advanced monoclonal antibody production methodologies, where antigenic peptides are displayed on carrier proteins (e.g., thioredoxin) to enhance immunogenicity and specificity .
Key Features of Epitope Selection:
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Peptide length: 70 amino acids
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Sequence uniqueness: Minimizes cross-reactivity with unrelated proteins
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Surface accessibility: Ensures recognition of native protein conformations
Validation and Quality Control
The Prestige Antibodies® line undergoes rigorous validation through the Human Protein Atlas (HPA) project :
Validation Metrics:
Performance Insights:
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Specificity: No cross-reactivity observed in protein array screens .
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Reproducibility: Consistent staining patterns across multiple tissue types .
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Clinical Relevance: Detects LRRC38 in both normal and cancerous tissues, suggesting roles in cellular homeostasis and disease .
Significance in Biomedical Research
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Diagnostic Potential: LRRC38’s tissue-specific expression could serve as a biomarker for cancers or autoimmune disorders .
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Therapeutic Exploration: Antibodies targeting LRR domains are being investigated for immune checkpoint modulation (e.g., relatlimab for LAG-3) .
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Technical Advancements: The integration of KO validation and epitope-directed production sets a benchmark for antibody reliability .
Limitations and Future Directions
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Functional Data Gap: The precise biological role of LRRC38 remains uncharacterized.
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Expanded Validation: Further studies using CRISPR/Cas9-edited LRRC38 KO models are needed to confirm off-target effects.
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Therapeutic Development: Bispecific antibody formats (e.g., anti-LRRC38/PD-1) could enhance efficacy in immune-oncology .
Product Specs
Target Background
Q&A
Given the lack of specific information on "LCR38 Antibody" in the search results, I will provide a general framework for FAQs related to monoclonal antibodies in academic research, focusing on aspects relevant to experimental design, data analysis, and advanced research questions. This approach will help researchers navigate common challenges and considerations in antibody research.
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To evaluate the efficacy of a monoclonal antibody in a preclinical model, researchers should:
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Select Appropriate Models: Choose models that closely mimic the human disease condition.
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Dose Optimization: Perform dose-response studies to determine the optimal dose.
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Control Groups: Include appropriate control groups (e.g., vehicle controls, isotype controls).
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Outcome Measures: Define clear outcome measures (e.g., tumor size reduction, survival rate).
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To resolve data contradictions:
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Re-evaluate Experimental Conditions: Ensure consistency in experimental conditions across studies.
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Statistical Analysis: Use robust statistical methods to compare results.
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Literature Review: Consult existing literature for similar findings or methodological differences.
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Key considerations for humanizing monoclonal antibodies include:
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Humanization Techniques: Use methods like CDR grafting or computational approaches to maintain stability and affinity.
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Immunogenicity Testing: Conduct thorough immunogenicity assessments using HAHA assays.
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Collaborations: Partner with biotech companies for access to advanced humanization technologies.
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Optimization involves:
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High-Throughput Screening: Use high-throughput methods to assess properties like solubility and viscosity.
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Computational Modeling: Employ computational tools to predict and improve antibody stability and affinity.
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Affinity Maturation: Perform iterative rounds of affinity maturation while maintaining stability.
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Target selection criteria include:
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Expression Levels: High expression on target cells and low expression on non-target cells.
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Biological Relevance: The target should play a critical role in disease pathology.
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Preclinical Validation: Validate targets using in vitro and in vivo models before proceeding to clinical trials.
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Bispecific antibodies enhance efficacy by:
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Dual Targeting: Engaging two different antigens or cell types simultaneously.
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Redirecting Immune Cells: Enhancing immune cell recruitment and activation against target cells.
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Improved Pharmacokinetics: Potentially offering better distribution and retention at the target site.
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CD38 is targeted because it is highly expressed on plasma cells, which are key producers of autoantibodies in autoimmune diseases. Depleting these cells can reduce disease activity. Additionally, CD38 expression is elevated in certain autoimmune conditions, making it a promising therapeutic target for diseases like systemic lupus erythematosus (SLE) and others .
Data Table Example: Comparison of Monoclonal Antibodies
| Antibody | Target | Disease Application | Mechanism of Action |
|---|---|---|---|
| Daratumumab | CD38 | Multiple Myeloma | Plasma cell depletion |
| Rituximab | CD20 | Lymphoma, Autoimmune | B-cell depletion |
| Isatuximab | CD38 | Multiple Myeloma | Plasma cell depletion |
