LIFR Antibody, Biotin conjugated

What is LIFR and what biological processes does it regulate?

LIFR (Leukemia Inhibitory Factor Receptor) is a signal-transducing molecule that plays crucial roles in multiple cellular processes. When activated by LIF or related cytokines, LIFR triggers signaling cascades that regulate cell growth, differentiation, survival, and other essential cellular functions. LIFR signaling has been implicated in the regulation of hematopoiesis (blood cell formation), liver regeneration, and neural development. Notably, dysregulation of LIFR has been associated with various pathological conditions, including leukemia, liver disease, and neurodegenerative diseases . LIFR may have a common pathway with IL6ST (Interleukin 6 Signal Transducer), and its soluble form inhibits the biological activity of LIF by blocking its binding to receptors on target cells .

What is the difference between unconjugated LIFR antibody and biotin-conjugated versions?

The primary difference lies in their application versatility. Unconjugated LIFR antibodies (such as CSB-PA012929LA01HU) require additional detection reagents when used in immunoassays. In contrast, LIFR antibody with biotin conjugation (CSB-PA012929LD01HU) has biotin molecules covalently attached to the antibody structure, enabling direct interaction with streptavidin or avidin detection systems . This biotin-conjugated version eliminates the need for secondary antibodies in many applications and leverages the extremely strong avidin-biotin interaction (Kd = 10^-15M) for enhanced sensitivity and specificity . Biotin conjugation makes the antibody particularly suitable for ELISA applications with recommended dilutions of 1:500-1:1000 .

How should LIFR Antibody, Biotin conjugated be incorporated into ELISA protocols for optimal results?

For optimal ELISA results using LIFR Antibody, Biotin conjugated, follow this methodological approach:

• Coating Phase: Coat wells with capture antibody against your target protein or with recombinant LIFR protein depending on your ELISA format.

• Blocking Phase: Block with 1-5% BSA or appropriate blocking buffer to reduce non-specific binding.

• Sample Addition: Add samples containing potential LIFR-interacting proteins or anti-LIFR antibodies.

• Antibody Dilution: Apply the biotin-conjugated LIFR antibody at the recommended dilution of 1:500-1:1000 . Start with 1:500 for unknown samples and optimize as needed.

• Detection System: Add streptavidin-HRP at the manufacturer's recommended dilution (typically 1:5000-1:20000).

• Visualization: Develop with appropriate substrate (TMB for HRP) and read absorbance.

For increased sensitivity, consider signal amplification using the avidin-biotin complex (ABC) method, leveraging the ability of avidin/streptavidin to bind up to four biotin molecules, which can significantly enhance detection of low-abundance targets .

What are the recommended protocols for using LIFR Antibody, Biotin conjugated in immunohistochemistry applications?

While the specific LIFR Antibody, Biotin conjugated products in the search results don't explicitly mention validation for immunohistochemistry, biotin-conjugated antibodies can generally be applied in IHC using the following optimized protocol:

• Tissue Preparation: Fix tissues appropriately (typically 10% neutral buffered formalin) and prepare sections (5-7μm thickness).

• Antigen Retrieval: Perform heat-induced epitope retrieval using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0).

• Endogenous Biotin Blocking: This step is critical - use a commercial biotin blocking kit to prevent non-specific binding to endogenous biotin in tissues.

• Antibody Application: Apply LIFR Antibody, Biotin conjugated at a starting dilution of 1:30-1:200, as recommended for immunofluorescence applications of related LIFR antibodies .

• Detection: Apply streptavidin-HRP or streptavidin conjugated to a fluorophore.

• Signal Development: For HRP conjugates, develop with DAB or other appropriate substrate. For fluorescent detection, proceed directly to mounting.

This protocol leverages the signal amplification capabilities of the avidin-biotin system which can provide enhanced sensitivity for detecting LIFR in tissue samples .

What are the critical steps in experimental design when using biotin-conjugated antibodies for protein interaction studies?

When designing protein interaction studies using LIFR Antibody, Biotin conjugated, several critical steps warrant particular attention:

• Negative Controls: Include samples with non-specific biotin-conjugated antibodies of the same isotype to identify background binding.

• Endogenous Biotin Considerations: Pre-block samples with avidin/streptavidin if your biological system contains endogenous biotin, which can interfere with specific interactions.

• Avidin/Streptavidin Selection: Choose appropriate avidin derivatives based on your application:

  • Standard avidin has higher non-specific binding

  • Streptavidin has lower background but may interact with carbohydrate moieties

  • NeutrAvidin offers reduced non-specific binding in complex biological samples

• Competition Controls: Include excess unlabeled LIFR antibody to confirm binding specificity.

• Elution Conditions: For immunoprecipitation or pull-down assays, design appropriate elution strategies, as the strong biotin-avidin interaction (Kd = 10^-15M) can complicate traditional elution methods . Consider using anti-biotin antibodies for enrichment, as they may have weaker binding affinity to biotin compared to avidin/streptavidin, potentially improving elution efficiency .

• Detection Method Selection: For co-immunoprecipitation studies, biotin-conjugated antibodies allow for cleaner pull-downs by avoiding interference from endogenous immunoglobulins .

How can LIFR Antibody, Biotin conjugated be utilized in proximity labeling studies?

LIFR Antibody, Biotin conjugated can be effectively incorporated into proximity labeling studies through several advanced methodologies:

• APEX2-Based Proximity Labeling: When investigating LIFR protein interactions or localization, biotin-conjugated LIFR antibodies can serve as controls or validation tools for APEX2-based proximity labeling experiments. After performing proximity labeling with APEX2 peroxidase in cellular compartments where LIFR may be present, researchers can compare the biotinylation patterns with those detected by biotin-conjugated LIFR antibodies to confirm specificity .

• BioID Approaches: In BioID experiments, where a biotin ligase (BirA*) is fused to LIFR or potential LIFR-interacting proteins, biotin-conjugated LIFR antibodies can validate proximities and interactions identified through the enzymatic labeling approach.

• Anti-Biotin Antibody Enrichment: For enhanced detection of biotinylation sites in proximity labeling studies, utilize anti-biotin antibody enrichment instead of traditional streptavidin methods. This approach has been shown to identify over 30-fold more biotinylation sites on hundreds of proteins compared to streptavidin-based enrichment methods .

• Mass Spectrometry Integration: When coupled with anti-biotin antibody enrichment and mass spectrometry, LIFR proximity labeling studies can identify specific biotinylation sites that provide direct evidence of protein-protein interactions and potential binding interfaces .

This methodological approach provides spatial resolution of protein interactions and can reveal transient or weak LIFR interactions that might be missed by traditional co-immunoprecipitation methods.

What are the comparative advantages of biotin-conjugated versus HRP-conjugated LIFR antibodies for advanced signaling pathway analyses?

In advanced signaling pathway analyses, biotin-conjugated and HRP-conjugated LIFR antibodies offer distinct advantages depending on experimental goals:

How can biotinylated peptide enrichment techniques be combined with LIFR Antibody, Biotin conjugated for studying specific LIFR signaling domains?

For studying specific LIFR signaling domains, researchers can employ a sophisticated combination of biotinylated peptide enrichment and LIFR Antibody, Biotin conjugated through this methodological approach:

• Domain-Specific Biotinylation: Employ synthetic biotinylated peptides corresponding to key LIFR signaling domains or use enzyme-based proximity labeling (APEX2) targeted to specific cellular compartments where LIFR signaling occurs.

• Anti-Biotin Antibody Enrichment: Rather than using traditional streptavidin enrichment, employ anti-biotin antibodies for peptide enrichment. This approach has been demonstrated to yield significantly more biotinylated peptides than streptavidin-based methods—potentially 2-3 fold higher enrichment with fewer sample handling steps .

• Mass Spectrometry Analysis: Following enrichment, perform LC-MS/MS analysis to identify specific biotinylation sites on LIFR and interacting proteins.

• Validation with LIFR Antibody: Use LIFR Antibody, Biotin conjugated as a validation tool to confirm the identity of enriched proteins through orthogonal methods such as Western blotting or immunofluorescence.

• Structural Mapping: Map identified biotinylation sites to the three-dimensional structure of LIFR to gain insights into critical interaction surfaces and regulatory domains.

This integrated approach has been shown to identify over 1,600 biotinylation sites on hundreds of proteins in proximity labeling experiments, providing unprecedented detail about protein interactions and domain specificity .

What are common sources of background or non-specific binding when using LIFR Antibody, Biotin conjugated, and how can they be mitigated?

When using LIFR Antibody, Biotin conjugated, several common sources of background can compromise experimental results. Here are systematic approaches to identify and mitigate each issue:

• Endogenous Biotin Interference:

  • Problem: Many biological samples contain endogenous biotin that competes with biotinylated antibodies.

  • Solution: Pre-block samples with avidin/streptavidin followed by free biotin to saturate endogenous biotin. Commercial biotin blocking kits are available for this purpose .

• Non-Specific Binding of Polyclonal Antibodies:

  • Problem: Polyclonal LIFR antibodies contain multiple clones with varying specificities.

  • Solution: Pre-absorb the antibody with proteins from species similar to your sample. Increase blocking concentration to 5% BSA or add 0.1-0.5% non-ionic detergent (Tween-20) to reduce hydrophobic interactions.

• Cross-Reactivity with Related Receptors:

  • Problem: LIFR shares homology with other cytokine receptors, potentially causing cross-reactivity.

  • Solution: Include competitive controls with recombinant LIFR protein to confirm binding specificity . Use more stringent washing conditions with higher salt concentrations.

• Avidin/Streptavidin Stickiness:

  • Problem: These proteins can bind non-specifically to biological samples.

  • Solution: Use NeutrAvidin, which has reduced non-specific binding characteristics compared to avidin or streptavidin . Add 0.05-0.1% Tween-20 to all washing steps.

• Suboptimal Antibody Concentration:

  • Problem: Too high concentration increases background; too low reduces specific signal.

  • Solution: Perform careful titration experiments, starting with the recommended dilution range of 1:500-1:1000 for ELISA applications , and adjust based on signal-to-noise ratio.

Implementation of these systematic troubleshooting approaches will significantly improve data quality and experimental reliability.

How can researchers optimize signal-to-noise ratios when using LIFR Antibody, Biotin conjugated for detecting low-abundance LIFR in complex samples?

For detecting low-abundance LIFR in complex samples, researchers can implement several methodological strategies to optimize signal-to-noise ratios:

• Signal Amplification Systems:

  • Implement the Avidin-Biotin Complex (ABC) method to form large complexes that concentrate multiple detection molecules at each binding site .

  • Consider tyramide signal amplification (TSA) which can increase sensitivity 10-50 fold by depositing multiple biotin-tyramide molecules near the initial binding site.

• Sample Pre-enrichment:

  • Perform subcellular fractionation to concentrate compartments where LIFR is expected.

  • Use immunoprecipitation with a non-biotinylated LIFR antibody before detection with the biotin-conjugated version.

• Optimized Blocking Protocols:

  • Use a sequential blocking approach: first block with 5% BSA, then add 1% normal serum from the same species as your experimental samples.

  • Include 0.1-0.3% Triton X-100 in blocking buffers to reduce hydrophobic non-specific interactions.

• Enhanced Detection Reagents:

  • Select high-sensitivity streptavidin-conjugated detection systems (such as Qdots or high-quantum-yield fluorophores for imaging).

  • For enzyme-based detection, use enhanced chemiluminescence (ECL) substrates with extended signal duration.

• Antibody Combination Approach:

  • Employ a cocktail of biotin-conjugated LIFR antibodies that recognize different epitopes to increase binding avidity and signal strength.

  • Consider using anti-biotin antibodies for enrichment rather than streptavidin, which has shown superior enrichment capabilities for biotinylated peptides .

• Technical Optimization:

  • Extend primary antibody incubation time to 16-18 hours at 4°C to allow complete binding equilibrium.

  • Implement automated detection systems with standardized parameters to reduce technical variability.

These methodological strategies work synergistically to enhance detection sensitivity while minimizing background signals.

What controls should be included when validating a new batch of LIFR Antibody, Biotin conjugated for experimental use?

When validating a new batch of LIFR Antibody, Biotin conjugated, a comprehensive control strategy should include:

• Positive Controls:

  • Cell lines/tissues with confirmed LIFR expression (e.g., liver cells, neural cells, or leukemia cell lines known to express LIFR) .

  • Recombinant human LIFR protein as a direct antigen control.

  • Parallel testing with a previously validated batch of the same antibody to confirm comparable performance.

• Negative Controls:

  • Cell lines with LIFR knockout or confirmed absence of LIFR expression.

  • Secondary detection reagents alone (streptavidin-HRP/fluorophore) without primary antibody to assess background.

  • Isotype control: biotin-conjugated rabbit IgG (non-specific) at the same concentration to identify non-specific binding.

• Specificity Controls:

  • Pre-absorption control: Pre-incubate antibody with excess recombinant LIFR protein to block specific binding sites.

  • Competitive binding assay: Increasing concentrations of unlabeled LIFR antibody should progressively reduce signal from biotin-conjugated antibody.

  • Epitope blocking: Peptide corresponding to the immunogen (amino acids 915-1086 of human LIFR) should block specific binding.

• Application-Specific Controls:

  • For ELISA: Standard curve using recombinant LIFR protein at known concentrations.

  • For Western blot: Multiple lysates with varying LIFR expression levels to confirm signal proportionality to protein abundance.

  • For immunoprecipitation: Input, flow-through, and elution fractions to assess enrichment efficiency.

• Technical Validation:

  • Dilution series to confirm optimal working concentration and verify linear dynamic range.

  • Intra-assay replicates (same experiment, multiple times) to assess consistency.

  • Inter-assay replicates (different days) to assess reproducibility.

Documentation of these systematic controls ensures experimental reliability and facilitates troubleshooting should inconsistencies arise.

How should researchers interpret differences in binding patterns between LIFR Antibody, Biotin conjugated and unconjugated versions in the same experimental system?

When interpreting differences in binding patterns between biotin-conjugated and unconjugated LIFR antibodies, researchers should consider several factors through this analytical framework:

• Epitope Accessibility Analysis:

  • Biotin conjugation may alter antibody conformation or sterically hinder binding to certain epitopes. If discrepancies appear, map the binding regions using epitope prediction software and consider whether biotin molecules (which are attached to lysine residues) might be positioned near the antigen-binding site.

  • Differences in binding patterns may reveal information about protein conformations or complex formations that differentially expose epitopes.

• Affinity Considerations:

  • Biotin conjugation can potentially reduce antibody affinity by altering the antibody's three-dimensional structure. Calculate and compare apparent affinity constants (Kd values) for both versions using dilution series in ELISA formats.

  • A shift in affinity may explain quantitative differences in signal intensity rather than true biological differences.

• Technical Factors Assessment:

  • Detection systems differ between conjugated and unconjugated antibodies: biotin-conjugated versions use streptavidin systems while unconjugated versions typically use secondary antibodies.

  • Normalize signals using recombinant LIFR protein standards to account for these technical differences.

• Biological Interpretation Guidelines:

  • If both antibodies show similar patterns but different intensities: likely a technical issue.

  • If antibodies show different subcellular localization patterns: may indicate LIFR conformational states or protein complexes that mask specific epitopes.

  • If differences persist across multiple experimental systems: consider that biotin conjugation might have altered specificity.

• Validation Strategy:

  • Confirm findings with orthogonal methods such as mass spectrometry or functional assays.

  • Use proximity labeling approaches with anti-biotin antibody enrichment to identify specific interaction sites .

This interpretive framework helps distinguish technical artifacts from biologically meaningful differences.

What statistical approaches are most appropriate for analyzing quantitative data generated using LIFR Antibody, Biotin conjugated in multiplex assays?

For analyzing quantitative data from multiplex assays using LIFR Antibody, Biotin conjugated, implement these statistical approaches based on experimental design and data characteristics:

• Preprocessing and Normalization:

  • Apply log transformation to stabilize variance across signal intensity ranges.

  • Implement robust Z-score normalization to account for plate-to-plate variation.

  • Use quantile normalization when comparing data across multiple experimental batches.

• Statistical Testing Framework:

  • For simple comparisons between two conditions: Paired t-test (for matched samples) or Mann-Whitney U test (for non-parametric data).

  • For multiple experimental conditions: One-way ANOVA followed by appropriate post-hoc tests (Tukey's HSD for equal variances, Games-Howell for unequal variances).

  • For experiments with multiple factors: Two-way ANOVA to assess interaction effects between treatments and LIFR activation/expression.

• Correlation Analysis:

  • Spearman rank correlation for assessing relationships between LIFR levels and other biomarkers.

  • Principal Component Analysis (PCA) to identify patterns and reduce dimensionality in complex datasets.

  • Hierarchical clustering to identify sample groups with similar LIFR signaling profiles.

• Advanced Modeling Approaches:

  • Linear mixed-effects models for longitudinal studies tracking LIFR expression/activation over time.

  • Bayesian inference for experiments with limited sample sizes, incorporating prior knowledge about LIFR signaling.

  • Machine learning algorithms (Random Forest, Support Vector Machines) for predictive modeling when integrating LIFR data with other molecular measurements.

• Multiple Testing Correction:

  • Apply Benjamini-Hochberg procedure to control false discovery rate in multiplex assays.

  • Use Bonferroni correction for stringent control of familywise error rate when making few comparisons.

• Power Analysis:

  • Conduct a priori power analysis to determine appropriate sample sizes.

  • Report confidence intervals alongside p-values to indicate precision of estimates.

How can researchers meaningfully integrate data from LIFR Antibody, Biotin conjugated experiments with other -omics datasets to understand LIFR's role in signaling networks?

Integrating data from LIFR Antibody, Biotin conjugated experiments with other -omics datasets requires systematic methodological approaches to reveal LIFR's comprehensive role in signaling networks:

• Multi-omics Data Preprocessing:

  • Standardize data formats across platforms (antibody-based, transcriptomics, proteomics).

  • Apply platform-specific normalization methods (e.g., RPKM for RNA-seq, VSN for proteomics).

  • Implement batch effect correction using ComBat or similar algorithms when integrating data from different experimental batches.

• Network Biology Approaches:

  • Construct protein-protein interaction networks centered on LIFR using experimental data from antibody-based studies.

  • Overlay transcriptomic data to identify co-expressed gene modules associated with LIFR activity.

  • Apply algorithms like WGCNA (Weighted Gene Co-expression Network Analysis) to identify functional modules correlated with LIFR expression patterns.

• Pathway Enrichment Analysis:

  • Perform Gene Set Enrichment Analysis (GSEA) on genes correlated with LIFR expression/activation.

  • Utilize Ingenuity Pathway Analysis or similar tools to identify canonical pathways downstream of LIFR signaling.

  • Apply topology-based pathway analysis methods like SPIA (Signaling Pathway Impact Analysis) to account for the position and role of LIFR in signaling cascades.

• Integration Methodologies:

  • Implement Similarity Network Fusion (SNF) to integrate data from different platforms into a unified network.

  • Apply DIABLO (Data Integration Analysis for Biomarker discovery using Latent cOmponents) for supervised integration of multi-omics data.

  • Utilize Bayesian network approaches to infer causal relationships between LIFR and other signaling components.

• Validation Strategies:

  • Confirmatory experiments using orthogonal techniques (e.g., proximity labeling with anti-biotin antibody enrichment) .

  • CRISPR-based perturbation of identified network components to validate predicted relationships.

  • Time-course experiments to establish causality in signaling cascades.

• Visualization and Interpretation:

  • Create interactive network visualizations using Cytoscape with custom data overlays.

  • Develop multi-dimensional visualizations that simultaneously display data from different -omics layers.

  • Annotate networks with known biological functions and disease associations of LIFR.

This integrated analytical framework helps researchers transition from descriptive observations to mechanistic understanding of LIFR's role in complex cellular signaling networks.