LXN Antibody

What is LXN and why is it important in research?

Latexin (LXN) is the only known mammalian carboxypeptidase inhibitor, first identified in the lateral neocortex of rats. It functions as a hardly reversible, non-competitive, and potent inhibitor of CPA1, CPA2, and CPA4 . LXN has gained significant research interest due to its roles as a tumor suppressor and its involvement in immune regulation and inflammatory responses . The protein may also function as a neuroprotective mechanism against tau toxicity . Understanding LXN biology is particularly valuable for cancer research, immunology, and neurodegenerative disease studies.

What applications can LXN antibodies be used for in laboratory research?

LXN antibodies have been validated for multiple research applications, with specific protocols optimized for each technique. The primary applications include:

For optimal results, it is recommended to titrate the antibody concentration for each specific application and sample type .

What is the molecular characterization of the LXN protein targeted by these antibodies?

LXN protein has the following molecular characteristics that are important for antibody targeting and experimental design:

• Full Name: Latexin

• Calculated Molecular Weight: 222 amino acids, 26 kDa

• Observed Molecular Weight in experiments: 26 kDa

• GenBank Accession Number: BC005346

• Gene Symbol: LXN

• Gene ID (NCBI): 56925

• UNIPROT ID: Q9BS40

This information is crucial for verifying antibody specificity and designing appropriate controls in experiments.

What are the optimal sample preparation protocols for LXN antibody in Western blotting?

For Western blotting with LXN antibody, researchers should follow these methodological considerations:

• Sample preparation: Tissue samples (particularly lung tissue) have been validated for LXN detection. Proteins should be extracted using standard lysis buffers containing protease inhibitors.

• Recommended dilution: Use the antibody at 1:500-1:2000 dilution, with optimization recommended for each experimental system .

• Loading controls: Standard loading controls (β-actin, GAPDH) should be included alongside LXN detection.

• Detection method: Both chemiluminescent and fluorescent secondary antibody approaches work well with LXN antibodies.

• Expected band: Look for a specific band at approximately 26 kDa, which corresponds to the observed molecular weight of LXN .

For enhanced reproducibility, it is advisable to titrate the antibody concentration using positive control samples (such as mouse lung tissue) before proceeding with experimental samples.

How should researchers optimize immunohistochemistry protocols when using LXN antibodies?

For IHC applications using LXN antibodies, follow these methodological recommendations:

• Antigen retrieval: Use TE buffer pH 9.0 for optimal results. Alternatively, citrate buffer pH 6.0 can be used if required .

• Blocking: Use 5-10% normal serum from the same species as the secondary antibody for 1 hour at room temperature to reduce background.

• Dilution optimization: Begin with a 1:100 dilution and adjust based on staining intensity, with recommended ranges of 1:50-1:500 .

• Incubation conditions: Incubate primary antibody overnight at 4°C for best results.

• Controls: Include both positive controls (human intrahepatic cholangiocarcinoma tissue has been validated) and negative controls (omitting primary antibody).

• Visualization: DAB (3,3′-Diaminobenzidine) is an effective substrate for visualization when using 10μL per well .

What are the key considerations for developing high-throughput antibody screening assays that include LXN antibodies?

When integrating LXN antibodies into high-throughput screening workflows, researchers should address these methodological aspects:

• Assay miniaturization: Adapt standard protocols to use smaller sample volumes (<100μL) to conserve material during early screening phases .

• Quality control: Implement rigorous QC measures including assessment of antibody lot-to-lot variability to ensure consistent results.

• Automation compatibility: Ensure protocols are compatible with liquid handling systems and automated plate readers.

• Data management: Implement a robust data management system that can track large numbers of candidates through the screening funnel (hundreds to thousands of molecules) .

• Integrated analysis: Combine LXN antibody-based data with other developability parameters to create comprehensive candidate profiles.

This approach mirrors the integrated, high-throughput developability workflows used in antibody lead discovery campaigns, which accelerate candidate selection and reduce development risks .

How can LXN antibodies be used to investigate macrophage polarization and cancer immunology?

LXN plays a crucial role in macrophage polarization, particularly in relation to M1/M2 phenotypes. Researchers investigating this phenomenon should consider:

• Experimental design: Compare LXN expression between M0, M1, and M2 macrophage populations. Research has shown LXN mRNA is significantly higher in M1-macrophages compared to M0 and M2 macrophages, suggesting it could be a potential M1 macrophage biomarker .

• Functional assays: Use LXN antibodies in combination with flow cytometry to assess macrophage phenotypes, particularly focusing on PD-L2 expression which is enhanced in LXN-deficient macrophages .

• Mechanistic investigations: Implement co-immunoprecipitation experiments using LXN antibodies to investigate the interaction between LXN and JAK1, which inhibits STAT3 activation in macrophages .

• Tumor microenvironment analysis: Apply LXN antibodies in immunohistochemistry of tumor samples to correlate LXN expression with immune cell infiltration patterns.

These approaches can help elucidate how LXN influences the tumor microenvironment through regulation of macrophage phenotypes and T cell function.

What strategies should be employed to validate LXN antibody specificity in knockout/knockdown experimental systems?

For rigorous validation of LXN antibody specificity, researchers should implement these methodological approaches:

• Genetic controls: Utilize LXN knockout (KO) or knockdown (KD) systems as negative controls. The search results reference published applications using KD/KO systems for LXN antibody validation .

• Recombinant protein controls: Use purified recombinant LXN protein as a positive control in Western blotting experiments.

• Cross-reactivity testing: Test the antibody against related carboxypeptidase inhibitors to ensure specificity.

• Multiple detection methods: Validate findings using at least two independent detection methods (e.g., Western blot and immunofluorescence).

• Signal quantification: Implement quantitative image analysis to compare signal intensities between wild-type and KO/KD samples.

This comprehensive validation approach ensures that experimental findings attributed to LXN detection are specific and not due to antibody cross-reactivity with other proteins.

How should researchers interpret conflicting LXN expression data between different experimental techniques?

When faced with discrepant results regarding LXN expression across different experimental approaches, consider these analytical strategies:

• Technique-specific limitations: Different detection methods have inherent biases. For example, IHC may detect localized protein concentrations while WB measures total protein content.

• Epitope accessibility: The antibody epitope may be differentially accessible depending on protein conformation or post-translational modifications in different experimental contexts.

• Sample preparation effects: Different lysate preparation methods may affect protein solubility and extraction efficiency of LXN.

• Quantification approach: Compare relative quantification methods and ensure appropriate normalization strategies across techniques.

• Biological variability: Consider whether discrepancies reflect actual biological differences rather than technical artifacts.

To resolve conflicts, researchers should:

• Use multiple antibodies targeting different LXN epitopes

• Implement complementary nucleic acid-based detection methods (RT-qPCR, RNA-seq)

• Perform sub-cellular fractionation to account for potential compartmentalization effects

How can LXN antibodies be utilized to investigate the protein's role in tumor suppression mechanisms?

To explore LXN's tumor suppressor function, researchers should implement these methodological approaches:

• Expression correlation studies: Use LXN antibodies to analyze protein expression in matched normal and tumor tissues through IHC and Western blot. Studies have shown that LXN deficiency promotes cancer cell growth in subcutaneous tumor models and increases susceptibility to AOM/DSS-induced colorectal cancer .

• Signaling pathway analysis: Investigate the JAK1/STAT3 pathway, which LXN regulates by directly interacting with JAK1 to inhibit STAT3 transcriptional activity. This can be studied using co-immunoprecipitation with LXN antibodies followed by JAK1 detection .

• Immune microenvironment profiling: Apply multiplexed immunofluorescence with LXN antibodies along with immune cell markers to characterize how LXN expression correlates with immune infiltration patterns.

• Functional rescue experiments: In LXN-deficient models, implement add-back experiments with wild-type macrophages and analyze resulting changes in tumor growth and immune function .

This multi-faceted approach can provide mechanistic insights into how LXN functions as a tumor suppressor through both direct effects on cancer cells and modulation of the immune microenvironment.

What are the best practices for analyzing LXN expression in hematopoietic stem cells using specific antibodies?

For investigating LXN in hematopoietic stem cells (HSCs), researchers should consider these specialized methodological approaches:

• Flow cytometry protocol optimization: When analyzing Lin−Sca1+cKit+ (LSK) populations, include LXN antibody staining alongside standard HSC markers. Research has shown that LXN deficiency significantly increases the proportion of LSK cells in bone marrow .

• Sorting strategy: Implement fluorescence-activated cell sorting (FACS) to isolate HSC populations based on LXN expression levels for downstream molecular and functional analyses.

• Immunofluorescence co-localization: Combine LXN antibody staining with markers for stem cell pluripotency, which have been shown to be enriched in LXN-deficient bone marrow through GSEA analysis .

• Single-cell analysis: Apply LXN antibodies in single-cell protein analysis platforms to examine expression heterogeneity within HSC populations.

• Bone marrow transplantation models: Track LXN expression in donor-derived cells following transplantation to study its role in HSC engraftment and differentiation.

These approaches can help elucidate how LXN regulates HSC self-renewal and differentiation, which has implications for understanding both normal hematopoiesis and hematological malignancies.

What are the most common causes of false positive and negative results when using LXN antibodies, and how can they be addressed?

When working with LXN antibodies, researchers should be aware of these potential sources of experimental artifacts and their solutions:

For false positives:

• Cross-reactivity: The antibody may detect proteins with similar epitopes to LXN. Solution: Validate specificity using LXN knockout controls and peptide competition assays.

• Non-specific binding: Particularly in IHC and IF applications. Solution: Optimize blocking procedures using 5-10% normal serum and include detergents like Tween-20 in washing buffers.

• Excessive antibody concentration: Can lead to background signal. Solution: Perform careful titration experiments to determine optimal concentration.

For false negatives:

• Ineffective antigen retrieval: Particularly critical for IHC applications. Solution: Test both recommended methods - TE buffer pH 9.0 and citrate buffer pH 6.0 .

• Protein degradation: LXN may be degraded during sample preparation. Solution: Use fresh samples and include protease inhibitors in all buffers.

• Epitope masking: Post-translational modifications may block antibody binding. Solution: Try multiple antibodies targeting different LXN epitopes.

• Suboptimal storage: Antibody activity may decrease. Solution: Store at recommended temperature (-20°C) and avoid repeated freeze-thaw cycles .

Implementing these troubleshooting approaches will improve experimental reliability and reproducibility when working with LXN antibodies.

How should researchers evaluate antibody lot-to-lot variability when working with LXN antibodies in longitudinal studies?

For maintaining experimental consistency in longitudinal studies using LXN antibodies, implement these quality control measures:

• Reference standard preparation: Create a large batch of positive control lysate (e.g., mouse lung tissue ) that can be aliquoted and used across the entire study duration.

• Comparative lot testing protocol:

  • Run side-by-side Western blots with old and new antibody lots

  • Perform serial dilutions (typically 1:500, 1:1000, 1:2000) with both lots

  • Compare signal intensity and background at each dilution

  • Calculate signal-to-noise ratios for quantitative comparison

• Standardization approach: If signal intensity varies between lots, adjust dilutions to achieve comparable results.

• Documentation: Maintain detailed records of lot numbers, dates of receipt, and comparative testing results.

• Bulk purchasing: When possible, purchase larger quantities of a single lot for critical studies.

This systematic approach to lot testing ensures experimental consistency and allows for appropriate adjustments when lot changes are unavoidable.

How can LXN antibodies be incorporated into multi-omics research approaches?

Researchers aiming to integrate LXN antibody-based data into multi-omics frameworks should consider these methodological strategies:

• Proteogenomic integration: Correlate protein-level LXN expression data from antibody-based techniques with transcriptomic data from RNA-seq. Studies have shown distinct gene expression patterns in LXN-deficient bone marrow, including upregulation of genes like Sqstm1, Sox18, Grem1, and Slit3, and downregulation of genes like Socs3, Thbs1, Fos, and Jun .

• Spatial proteomics approaches: Combine LXN antibody staining with spatial transcriptomics to map protein expression in relation to its regulatory environment within tissues.

• Pathway analysis framework: Integrate LXN protein quantification with phosphoproteomics, particularly focusing on the JAK1/STAT3 pathway, which LXN regulates .

• Single-cell multi-modal analysis: Apply LXN antibodies in single-cell protein detection alongside transcriptomic or epigenomic profiling to understand cellular heterogeneity.

• Systems biology modeling: Incorporate LXN antibody-derived quantitative data into computational models of immune cell networks or tumor microenvironments.

This integrated approach provides a more comprehensive understanding of LXN biology than any single technique, revealing both regulatory mechanisms and functional consequences of LXN expression patterns.

What considerations should researchers take into account when developing immunotherapy strategies targeting the LXN-regulated immune pathways?

When translating LXN research into potential immunotherapeutic approaches, researchers should address these methodological and conceptual considerations:

• Target validation strategy: Use LXN antibodies to characterize expression patterns across diverse patient samples and correlate with clinical outcomes.

• Pathway intersection analysis: Investigate how the LXN-JAK1-STAT3-PD-L2 axis intersects with existing immunotherapy targets. Research has shown that LXN deficiency enhances PD-L2 expression in macrophages through the JAK1/STAT3 pathway .

• Combination therapy assessment: Design in vitro and in vivo studies examining how targeting LXN-regulated pathways might synergize with existing checkpoint inhibitors.

• Biomarker development approach: Evaluate LXN expression as a potential predictive biomarker for response to PD-L2 blockade. Studies have demonstrated that targeted blockade of PD-L2 attenuates tumorigenesis in LXN-deficient mice .

• Experimental model selection: Consider using bone marrow transplantation models with LXN-deficient hematopoietic cells to study effects on tumor immunology, as these have been shown to accelerate AOM/DSS-induced colorectal tumorigenesis .

These considerations provide a framework for translating fundamental insights about LXN biology into potential therapeutic strategies targeting cancer immune evasion.