lmo1 Antibody

What is LMO1 and why is it significant in research?

LMO1 encodes a protein containing cysteine-rich LIM domains involved in protein-protein interactions. It functions as a transcriptional regulator mapped to an area of consistent chromosomal translocation in chromosome 11. LMO1 has been identified as an oncogene in several cancer types, including T-cell acute lymphoblastic leukemia, neuroblastoma, glioma, and lung cancer . The significance of LMO1 in research lies in its role in tumor progression, invasion, and migration, making it a valuable target for studying cancer mechanisms and potential therapeutic interventions.

LMO1 protein has a molecular mass of approximately 17.8 kilodaltons and contains two LIM domains that facilitate interactions with other proteins, particularly transcription factors . Its expression is significantly elevated in certain cancer types, especially those with neuroendocrine differentiation like small cell lung cancer (SCLC) .

What types of LMO1 antibodies are available for research applications?

LMO1 antibodies are available in multiple formats designed for different experimental applications:

When selecting an LMO1 antibody, researchers should consider the specific experimental requirements including target species, application type, and epitope specificity .

How should LMO1 antibodies be stored and handled to maintain optimal activity?

Proper storage and handling of LMO1 antibodies is crucial for maintaining their functionality:

Following these guidelines will help ensure consistent experimental results and extend the shelf life of your LMO1 antibodies.

What are the optimal protocols for using LMO1 antibodies in Western blotting applications?

For optimal Western blotting with LMO1 antibodies, follow these evidence-based protocols:

• Sample preparation: Lyse cells in RIPA buffer containing protease inhibitors. For LMO1 detection, 20 μg of protein lysate is typically sufficient .

• Gel electrophoresis: Separate proteins on SDS-PAGE gels (10-12% typically works well for LMO1's 17.8 kDa size).

• Transfer: Transfer proteins to PVDF membranes, which have shown good results with LMO1 antibodies .

• Blocking: Block membranes with 5% bovine serum albumin (BSA) in TBST for 1 hour at room temperature.

• Primary antibody incubation: Dilute LMO1 antibody according to manufacturer's recommendations (typically 1:500-1:2000) and incubate overnight at 4°C. Some validated antibodies include Abcam ab137599 and Proteintech 55014-1-AP, which have been successfully used in published research .

• Detection: Use HRP-linked secondary antibody and enhanced chemiluminescence (ECL) for visualization. Normalize band intensity to GAPDH or another appropriate housekeeping protein .

• Validation controls: Include positive controls such as 293T cell lysate, mouse brain, or rat brain lysates, which express detectable levels of LMO1 .

This protocol has been validated in multiple studies investigating LMO1's role in cancer progression and can be modified based on specific experimental requirements .

How can researchers effectively use LMO1 antibodies for immunohistochemistry studies?

For successful immunohistochemistry (IHC) with LMO1 antibodies:

• Tissue preparation: Fix tissues in formalin and embed in paraffin. Cut sections at 4-μm thickness and mount on charged slides .

• Deparaffinization and rehydration: Process sections through xylene and graded alcohols to water.

• Antigen retrieval: Perform pressure cooking for 5 minutes in citrate buffer (pH 6.0), which has been shown to effectively unmask LMO1 epitopes .

• Endogenous peroxidase blocking: Block with 0.3% H₂O₂ to reduce background staining.

• Protein blocking: Block with 5% BSA for 1 hour to minimize non-specific binding.

• Primary antibody incubation: Apply validated LMO1 antibody (such as Abcam ab137599) at manufacturer-recommended dilution and incubate according to established protocols .

• Detection system: Use horseradish peroxidase-linked secondary antibody followed by diaminobenzidine (DAB) for visualization. Counterstain with hematoxylin.

• Scoring system: For semi-quantitative analysis, use a combined scoring system where staining intensity is recorded as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), and the percentage of positively stained cells is recorded as 0 (0-25%), 1 (25-50%), 2 (50-75%), and 3 (75-100%). Final IHC scores are obtained by multiplying these two scores, with a median score of 6 typically used as the cutoff for distinguishing "high expression" from "low expression" .

This methodology has been successfully employed in studies examining LMO1 expression in glioma tissues and correlating expression levels with clinical outcomes .

What approaches are recommended for optimizing LMO1 antibody specificity and minimizing background in immunoassays?

To enhance LMO1 antibody specificity and reduce background:

• Antibody validation: Validate antibody specificity using positive controls (e.g., cell lines with known LMO1 expression) and negative controls (knockdown/knockout samples) . Western blot confirmation of the antibody's specificity prior to other applications is strongly recommended.

• Titration optimization: Perform antibody titration experiments to determine the optimal working concentration that provides specific signal with minimal background.

• Blocking optimization: Test different blocking agents (BSA, normal serum, commercial blockers) to identify the most effective for your specific tissue or cell type.

• Incubation conditions: Optimize temperature and duration of antibody incubation. For some LMO1 antibodies, overnight incubation at 4°C yields better results than shorter incubations at room temperature.

• Washing stringency: Implement stringent washing steps using buffers with appropriate detergent concentrations to remove unbound or weakly bound antibodies.

• Signal amplification selection: Choose appropriate detection systems based on expression levels. For low-abundance LMO1, consider using amplification systems like TSA (tyramide signal amplification).

• Cross-reactivity assessment: When working with tissues containing multiple LIM domain proteins, verify that your selected LMO1 antibody doesn't cross-react with related proteins like LMO2 or LMO3 .

• Immunoaffinity purification: Consider using immunoaffinity-purified antibodies, which generally show higher specificity than crude antisera .

These approaches are critical for generating reliable data, especially when studying LMO1 in complex tissues or in quantitative applications.

How can LMO1 antibodies be effectively utilized in the study of LMO1's role in cancer progression?

LMO1 antibodies have been instrumental in elucidating LMO1's role in cancer progression through several methodological approaches:

• Expression correlation studies: Using LMO1 antibodies for immunohistochemistry on tissue microarrays allows correlation of LMO1 expression with clinical parameters. In glioma studies, high LMO1 expression was associated with high tumor grade and poor prognosis, particularly in patients with isocitrate dehydrogenase (IDH)-wild-type and 1p/19q non-codeletion tumors .

• Functional validation through knockdown experiments: After siRNA-mediated knockdown of LMO1, antibodies can confirm protein depletion via Western blot before assessing functional changes. This approach revealed that LMO1 silencing inhibits tumor growth, invasion, and migration in glioma cell lines .

• Mechanistic pathway investigation: LMO1 antibodies have been used to investigate downstream effects of LMO1 expression on other proteins. For example, Western blotting with relevant antibodies showed that LMO1 positively regulates NGFR expression and activates the NF-κB pathway in glioma cells .

• In vivo tumor models: In xenograft models, LMO1 antibodies can assess tumor protein expression through immunohistochemistry. This approach demonstrated reduced expression of proliferation and invasion markers (Ki67, vimentin) in tumors with downregulated LMO1 .

• Neuroendocrine differentiation studies: LMO1 antibodies have been used to show that LMO1 expression correlates with neuroendocrine markers in lung cancer, with significantly higher expression in small cell lung cancer compared to non-small cell lung cancer .

These methodologies collectively provide a comprehensive approach to studying LMO1's oncogenic functions across different cancer types.

What strategies can be employed to study the interactions between LMO1 and other proteins using antibody-based techniques?

To investigate LMO1's protein-protein interactions:

• Co-immunoprecipitation (Co-IP): Use LMO1 antibodies to pull down LMO1 complexes, followed by Western blotting with antibodies against suspected interaction partners. This approach helped identify the LMO1-NGFR interaction in glioma cells .

• Proximity ligation assay (PLA): This technique allows visualization of protein interactions in situ with high specificity and sensitivity. By combining LMO1 antibodies with antibodies against potential binding partners (like transcription factors), researchers can detect interactions as fluorescent spots when proteins are in close proximity.

• Chromatin immunoprecipitation (ChIP): For studying LMO1's role in transcriptional complexes, ChIP with LMO1 antibodies can identify DNA regions where LMO1-containing complexes bind, revealing genes directly regulated by LMO1.

• Immunofluorescence co-localization: Double immunofluorescence staining with LMO1 antibodies and antibodies against potential interaction partners can provide evidence of co-localization in cellular compartments.

• Protein complementation assays: These split-reporter systems, when combined with immunoblotting validation using LMO1 antibodies, can confirm direct protein interactions.

• Immunoprecipitation followed by mass spectrometry: This unbiased approach can identify novel LMO1 interaction partners that can then be validated by targeted Co-IP experiments.

• ELISA-based interaction studies: Developing assays using immobilized LMO1 antibodies can quantitatively measure binding affinities between LMO1 and partner proteins.

When implementing these techniques, it's critical to include appropriate controls, such as IgG control for Co-IP and isotype controls for immunofluorescence, to ensure specificity of detected interactions .

How can researchers accurately interpret LMO1 expression data from antibody-based assays in the context of cancer heterogeneity?

Interpreting LMO1 expression in heterogeneous cancer samples requires careful methodological considerations:

• Standardized scoring systems: Implement rigorous semi-quantitative scoring systems for IHC as described in published studies. For example, combining intensity scores (0-3) with percentage of positive cells (0-3) yields a final score that can be dichotomized using median values as cutoffs .

• Cellular localization analysis: LMO1 can have different functions depending on subcellular localization. Carefully assess not just presence/absence but also whether the protein is nuclear, cytoplasmic, or both.

• Integration with molecular subtyping: Correlate LMO1 expression with molecular subtypes of the cancer being studied. For example, in gliomas, LMO1 expression relates to IDH mutation status and 1p/19q codeletion status .

• Single-cell approaches: Consider using laser capture microdissection to isolate specific cell populations before Western blot analysis, or employ single-cell immunohistochemistry techniques to account for intratumoral heterogeneity.

• Multi-marker assessment: Evaluate LMO1 in conjunction with other markers. In lung cancer, LMO1 expression correlates with neuroendocrine markers (CHGA, SYP, ENO2), providing context for interpretation .

• Validation across techniques: Confirm expression patterns using orthogonal methods. If IHC shows high LMO1 expression, validate with Western blot or qPCR when possible.

• Quantitative image analysis: Employ digital pathology and automated image analysis to obtain more objective quantification of LMO1 staining.

• Clinical correlation: Interpret LMO1 expression in relation to clinical outcomes through Kaplan-Meier survival analysis and multivariate Cox regression, as demonstrated in studies showing LMO1 as an independent predictor of poor survival in certain cancers .

What are common technical challenges when using LMO1 antibodies and how can they be addressed?

Researchers frequently encounter these challenges when working with LMO1 antibodies:

• Inconsistent detection sensitivity:

  • Problem: Variable detection of LMO1 despite consistent expression.

  • Solution: Optimize protein extraction methods specifically for nuclear proteins. Use fresh lysates and include protease inhibitors to prevent degradation. Consider nuclear extraction protocols since LMO1 is a transcriptional regulator .

• Discrepancies between mRNA and protein levels:

  • Problem: Some cell lines (e.g., HCC827, HCC78, HCC44) show low LMO1 mRNA levels but comparable protein expression to high-mRNA cell lines .

  • Solution: Always validate findings at both RNA and protein levels. Post-transcriptional regulation may affect LMO1 expression.

• Non-specific binding:

  • Problem: Cross-reactivity with other LIM-domain proteins.

  • Solution: Use antibodies that target unique regions of LMO1 rather than conserved LIM domains. C-terminal targeting antibodies often provide better specificity .

• Variable transfection efficiency in knockdown experiments:

  • Problem: Inconsistent LMO1 knockdown across cell lines (as observed with HCC827 cells) .

  • Solution: Optimize transfection protocols for each cell line; consider stable knockdown using lentiviral transduction of shLMO1 for consistent results .

• Epitope masking in fixed tissues:

  • Problem: Poor antibody binding in formalin-fixed tissues.

  • Solution: Implement optimized antigen retrieval protocols, such as pressure cooking in citrate buffer (pH 6.0) .

• Batch-to-batch variability:

  • Problem: Inconsistent results between antibody lots.

  • Solution: Maintain lot records, include consistent positive controls across experiments, and consider purchasing larger quantities of a single lot for long-term studies.

• Low signal in IHC:

  • Problem: Weak staining despite proper technique.

  • Solution: Try signal amplification methods, optimize antibody concentration, extend incubation times, or test alternative LMO1 antibodies validated for IHC applications .

Implementing these solutions can significantly improve experimental outcomes when working with LMO1 antibodies.

How should researchers validate LMO1 antibody specificity for their particular experimental system?

Comprehensive validation of LMO1 antibodies should include:

• Positive and negative control samples:

  • Use cell lines or tissues with known LMO1 expression status (e.g., 293T cells, mouse brain, rat brain for positive controls) .

  • Include samples where LMO1 has been knocked down via siRNA or CRISPR-Cas9 as negative controls .

• Cross-platform validation:

  • Compare antibody reactivity across multiple techniques (Western blot, IHC, IF) to ensure consistent detection.

  • Verify that the antibody detects the correct molecular weight protein (approximately 17.8 kDa for LMO1) .

• Peptide competition assay:

  • Pre-incubate the antibody with the immunizing peptide and confirm signal reduction in subsequent assays.

  • For recombinant protein-raised antibodies (like CSB-PA02055A0Rb), use the specific recombinant fragment (AA 5-156) for competition .

• Orthogonal method comparison:

  • Correlate protein detection with mRNA expression by qRT-PCR.

  • Be aware that discrepancies may exist, as seen in some lung cancer cell lines where protein levels didn't match mRNA expression patterns .

• Cross-reactivity assessment:

  • Test the antibody against related proteins, particularly other LMO family members (LMO2, LMO3), to confirm specificity.

  • Use cell lines that differentially express LMO family members.

• Immunoprecipitation followed by mass spectrometry:

  • Perform IP with the LMO1 antibody and verify the presence of LMO1 peptides by mass spectrometry.

• Batch testing:

  • When receiving a new lot, perform side-by-side comparison with previous lots using consistent positive controls.

• Species reactivity validation:

  • If working with non-human models, verify cross-reactivity by aligning the antibody's immunogen sequence with the target species LMO1 sequence.

  • Some antibodies (like ABIN6749813) have predicted reactivity based on sequence homology but require experimental validation .

These validation steps are essential for ensuring reliable and reproducible results in LMO1 research.

How can LMO1 antibodies be utilized to investigate the role of LMO1 in neuroendocrine differentiation of cancer?

LMO1 antibodies are valuable tools for studying neuroendocrine differentiation through the following methodological approaches:

• Correlation analysis with neuroendocrine markers: Use LMO1 antibodies alongside established neuroendocrine markers (CHGA, SYP, ENO2) in multiplex immunohistochemistry or sequential Western blotting to establish associations between LMO1 and neuroendocrine phenotype .

• Cell line panel screening: Apply LMO1 antibodies in Western blot analysis across diverse cancer cell lines to identify differential expression patterns. Research has shown significantly higher LMO1 expression in small cell lung cancer (SCLC) cells compared to non-small cell lung cancer (NSCLC) and normal lung cells, suggesting LMO1 as a potential biomarker for neuroendocrine differentiation .

• Mechanistic studies of neuroendocrine differentiation: Use LMO1 antibodies to monitor protein expression changes during induced neuroendocrine differentiation in cancer cell models. This can help establish whether LMO1 is a driver or consequence of neuroendocrine transdifferentiation.

• Co-immunoprecipitation studies: Employ LMO1 antibodies in Co-IP experiments to identify binding partners specific to neuroendocrine-differentiated cancer cells, potentially uncovering unique transcriptional complexes.

• Chromatin immunoprecipitation sequencing (ChIP-seq): Use LMO1 antibodies in ChIP-seq to map genomic binding sites in neuroendocrine versus non-neuroendocrine cancer cells, revealing differential gene regulation programs.

• Tissue microarray analysis: Apply validated LMO1 antibodies to large cohorts of cancer tissues with varying degrees of neuroendocrine differentiation to establish clinical correlations and potential diagnostic utility.

• Functional studies with readouts: Following LMO1 modulation (overexpression or knockdown), use antibodies to confirm expression changes before assessing effects on neuroendocrine marker expression, providing evidence for causal relationships .

These approaches collectively provide a comprehensive framework for investigating LMO1's role in neuroendocrine cancer biology, potentially leading to new diagnostic markers or therapeutic targets.

What methodological considerations should be addressed when using LMO1 antibodies in the study of glioma progression and invasion?

When studying glioma progression and invasion with LMO1 antibodies, researchers should address these methodological considerations:

• Tumor heterogeneity assessment: Implement multi-region sampling and analysis to account for intratumoral heterogeneity in gliomas. LMO1 expression may vary across different regions of the same tumor .

• IDH mutation status stratification: Always stratify glioma samples by IDH mutation status, as LMO1 expression has shown significant association with prognosis specifically in IDH-wild-type gliomas .

• Invasion assay optimization: When using in vitro invasion assays after LMO1 knockdown or overexpression, quantify the expression of invasion-associated proteins (Vimentin, Slug, Snail, MMP2) by Western blotting with specific antibodies to correlate molecular changes with phenotypic effects .

• 3D culture systems: Consider using 3D invasion models that better recapitulate the brain extracellular matrix rather than simple transwell assays when studying LMO1's effects on invasion.

• In vivo model selection: For xenograft studies, both subcutaneous and orthotopic (intracranial) models should be considered, with the latter being more physiologically relevant. Validated antibodies for immunohistochemistry (like Abcam ab137599) can be used to confirm LMO1 expression in xenograft tissues .

• Pathway analysis integration: When investigating mechanisms of LMO1-driven invasion, use antibodies against components of relevant signaling pathways (e.g., NGFR-NF-κB axis) to establish mechanistic links. Phospho-specific antibodies (like anti-p-p65) are particularly useful for assessing pathway activation .

• Patient-derived models: Consider using patient-derived xenografts or organoids that maintain the molecular characteristics of the original tumor, including LMO1 expression patterns, for more clinically relevant studies.

• Correlation with imaging features: For clinical translation, correlate LMO1 expression (determined by IHC) with radiological features of invasion using pre-operative MRI scans.

How can LMO1 antibodies be used to develop prognostic or predictive biomarkers in cancer research?

Development of LMO1-based biomarkers for cancer prognosis requires systematic methodological approaches:

These methodological approaches provide a framework for translating LMO1 antibody-based research findings into clinically useful prognostic or predictive biomarkers.

What are the emerging applications of LMO1 antibodies in cancer immunotherapy research?

While LMO1 has not been directly targeted in current immunotherapy approaches, several emerging research directions utilize LMO1 antibodies in immunotherapy-related investigations:

• Neoantigen identification: LMO1 antibodies can help validate protein expression from mutations or alternative splicing events in LMO1 that might generate tumor-specific neoantigens for personalized cancer vaccines.

• CAR-T cell therapy development: Although not directly used therapeutically, LMO1 antibodies are essential for validating target expression in CAR-T development pipelines, especially for neuroblastoma and SCLC where LMO1 overexpression has been documented .

• Tumor microenvironment studies: Using multiplex immunohistochemistry with LMO1 antibodies alongside immune cell markers can help characterize the relationship between LMO1 expression and tumor immune infiltration.

• Checkpoint inhibitor response prediction: LMO1 expression analysis using validated antibodies could potentially serve as a biomarker for immunotherapy response, particularly in neuroendocrine tumors where LMO1 expression is elevated .

• Post-immunotherapy monitoring: LMO1 antibody-based assays might detect changes in tumor expression patterns following immunotherapy, providing insights into resistance mechanisms.

• Antibody-drug conjugate (ADC) research: While not currently developed as ADC targets, research using LMO1 antibodies helps characterize expression patterns that could inform future therapeutic approaches targeting LMO1-expressing cells.

• Immuno-PET imaging: Development of radiolabeled LMO1 antibody fragments for immuno-PET imaging could enable non-invasive detection and monitoring of LMO1-expressing tumors.

• Combination therapy biomarkers: LMO1 expression analysis may help predict responses to combinations of immunotherapy with targeted agents, particularly those affecting pathways regulated by LMO1 such as NF-κB signaling .

These emerging applications highlight the importance of continued development and validation of LMO1 antibodies with high specificity and sensitivity for advancing cancer immunotherapy research.

How can researchers design experiments to resolve conflicting data regarding LMO1 expression and function across different cancer types?

To address contradictory findings in LMO1 research, implement these methodological approaches:

• Standardized antibody validation protocol:

  • Establish a multi-step validation process for LMO1 antibodies before use in comparative studies

  • Include knockout/knockdown controls, peptide competition assays, and cross-platform validation

  • Document antibody details (catalog number, lot, clone, epitope) in all publications to enable direct comparison

• Harmonized expression analysis methodology:

  • Design expression studies using multiple technical approaches (IHC, Western blot, qPCR)

  • Apply consistent scoring systems for semi-quantitative analysis

  • Use absolute quantification methods when possible (digital PCR, mass spectrometry)

  • Account for potential discrepancies between mRNA and protein levels as observed in some lung cancer cell lines

• Comprehensive cancer type profiling:

  • Analyze LMO1 expression across comprehensive tissue panels representing multiple cancer types and subtypes

  • Include matched normal tissues as controls

  • Stratify results by relevant molecular subtypes within each cancer type

• Context-dependent functional analysis:

  • Design parallel functional studies in multiple cell lines representing different cancer types

  • Use identical experimental conditions, knockdown/overexpression constructs, and phenotypic assays

  • Investigate cell-type-specific binding partners through comparative IP-MS studies

  • Consider the tissue microenvironment's influence on LMO1 function

• Multi-omics integration:

  • Correlate LMO1 protein levels with mRNA expression, genomic alterations, and epigenetic modifications

  • Identify tissue-specific regulatory mechanisms that might explain differential expression or function

  • Use systems biology approaches to map LMO1-associated networks in different cancer contexts

• Clinical correlation with molecular subtyping:

  • Stratify survival analyses by cancer type, stage, and molecular subtypes

  • Develop multivariate models that account for cancer-specific prognostic factors

  • Investigate treatment-specific effects of LMO1 expression

These approaches can resolve apparently conflicting data by revealing context-dependent mechanisms of LMO1 regulation and function across cancer types.

What are the methodological challenges in developing therapeutic approaches targeting LMO1, and how can antibodies contribute to overcoming these challenges?

Developing therapeutics targeting LMO1 presents several methodological challenges that antibody-based approaches can help address:

• Target accessibility challenges:

  • Challenge: LMO1 is an intracellular protein, making it difficult to target with conventional antibody therapeutics.

  • Antibody-based solutions: Use LMO1 antibodies to develop cell-penetrating antibody derivatives, antibody-drug conjugates targeting surface markers co-expressed with LMO1, or to validate intracellular protein delivery systems.

• Target validation uncertainties:

  • Challenge: Establishing LMO1 as a viable therapeutic target requires robust validation across multiple models.

  • Antibody-based solutions: Deploy LMO1 antibodies in tissue microarrays across diverse cancer cohorts to identify patient populations with high expression. Use antibodies to confirm knockdown efficiency in preclinical models before interpreting phenotypic effects .

• Functional redundancy concerns:

  • Challenge: Other LIM-domain proteins may compensate for LMO1 inhibition.

  • Antibody-based solutions: Use antibodies against multiple LMO family members to assess compensatory upregulation after LMO1 inhibition. Develop multiplexed assays to monitor the entire LMO family simultaneously.

• Pathway complexity:

  • Challenge: LMO1 functions within complex protein interaction networks that vary by cancer type.

  • Antibody-based solutions: Use LMO1 antibodies in co-immunoprecipitation studies to map cancer-specific protein interaction networks. Identify critical downstream effectors (like NGFR in glioma) that might serve as alternative therapeutic targets .

• Pharmacodynamic marker development:

  • Challenge: Monitoring LMO1 inhibition in clinical trials requires reliable pharmacodynamic markers.

  • Antibody-based solutions: Develop IHC protocols using validated LMO1 antibodies for patient biopsies. Create assays measuring downstream targets (MMP2, Vimentin, p-p65) as surrogate markers of LMO1 inhibition .

• Biomarker identification:

  • Challenge: Identifying patients most likely to benefit from LMO1-targeted therapy.

  • Antibody-based solutions: Develop standardized LMO1 IHC assays with clear scoring criteria to stratify patients. Integrate with other prognostic markers to create comprehensive prediction models .

• Resistance mechanism understanding:

  • Challenge: Anticipating and overcoming resistance to LMO1-targeted therapies.

  • Antibody-based solutions: Use antibodies to monitor changes in expression and localization of LMO1 and related pathways during treatment and resistance development.