cel-1 Antibody
Product Specs
**Constituents:** 50% Glycerol, 0.01M PBS, pH 7.4
Target Background
- mRNA capping enzyme is crucial for the viability of Caenorhabditis elegans. PMID: 12576475
- CEL-1 and capping are essential for gene expression in metazoans. PMID: 12576476
Q&A
Here’s a structured collection of FAQs for researchers working with CEL-1 (C-type lectin-like molecule-1) antibodies, organized by complexity and grounded in methodological rigor:
How to validate CEL-1 antibody specificity in myeloid leukemia studies?
Methodology:
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Flow cytometry validation: Compare CEL-1 expression across AML cell lines (e.g., HL-60), healthy donor myeloid cells, and non-myeloid controls. Use isotype-matched controls to rule out nonspecific binding .
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Competitive blocking: Pre-incubate antibodies with recombinant CEL-1 extracellular domain to confirm binding specificity .
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Cross-reactivity screening: Test antibody reactivity against related C-type lectin family members (e.g., CD33) using transfected HEK293 cells .
What experimental controls are critical for CEL-1 functional assays?
Essential controls:
How to resolve contradictory CEL-1 expression data across AML subtypes?
Analytical framework:
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Stratify samples by FAB classification and genetic markers (e.g., FLT3-ITD status).
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Quantify heterogeneity using single-cell CITE-seq to distinguish blast vs. leukemic stem cell expression .
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Correlate surface density (antibody binding capacity) with transcript levels via parallel scRNA-seq .
Example finding: CD34+/CD38− leukemic stem cells show 3-fold lower CEL-1 surface density than blasts despite comparable mRNA levels .
What strategies improve CEL-1 antibody performance in multiplexed panels?
Optimization protocol:
How to design CEL-1-directed cytotoxicity assays with primary AML samples?
Stepwise approach:
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Patient stratification: Select samples with ≥20% CEL-1+ blasts via flow cytometry .
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Effector cells: Use NK-92MI cells (CD16+) at 10:1 E:T ratio for ADCC assays .
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Endpoint selection:
What computational tools predict CEL-1 antibody cross-reactivity?
Pipeline integration:
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Structural modeling: Use AlphaFold2 to predict paratope-epitope interactions .
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Library screening: Apply neural network-based frameworks (e.g., DeepAb) to assess off-target risks against human proteome .
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Experimental validation: Perform phage display counter-selection against CD33 and CD123 .
How to analyze CEL-1 co-expression patterns with immune checkpoints?
Multidimensional analysis:
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Dimensionality reduction: Run UMAP on flow cytometry data (CEL-1, PD-L1, TIM-3)
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Cluster validation: Apply SPADE analysis to identify rare double-positive populations
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Functional correlation: Measure IFN-γ secretion in sorted subsets via ELISpot
Key finding: CEL-1+PD-L1+ AML cells show 5.8-fold higher T-cell suppression vs. single-positive subsets .
