MLF2 Antibody

What is MLF2 and what cellular functions has it been associated with?

MLF2 (Myeloid Leukemia Factor 2) is a 28 kDa protein (248 amino acids) belonging to the MLF family. The protein has been implicated in several biological processes, including regulation of nitric oxide synthase signaling. MLF2 expression is notably affected by hypoxic conditions, suggesting a role in cellular adaptation to oxygen deprivation. Research indicates that MLF2 plays significant roles in breast cancer tumorigenesis and lung metastasis . Additionally, in avian models, the MLF2 gene has been associated with coccidiosis resistance . Understanding the fundamental biology of MLF2 provides important context for experimental design when using MLF2 antibodies.

What applications are MLF2 antibodies validated for?

MLF2 antibodies have been validated for multiple research applications:

Most commercially available MLF2 antibodies show reactivity with human samples, while some have also been validated with mouse and rat samples . When selecting an MLF2 antibody, researchers should evaluate application-specific validation data provided by manufacturers to ensure compatibility with their experimental system.

What positive controls are recommended for MLF2 antibody validation?

Based on validation data, the following samples serve as reliable positive controls for MLF2 antibody testing:

For immunohistochemistry, it's important to note that antigen retrieval conditions can significantly impact results. For optimal results with MLF2 antibodies, both TE buffer (pH 9.0) and citrate buffer (pH 6.0) have been validated, though specific protocols may vary between antibody clones .

How should MLF2 antibody specificity be evaluated when contradictory results are observed?

When encountering inconsistent or contradictory results with MLF2 antibodies, a systematic validation approach is recommended:

• Epitope comparison analysis: Different MLF2 antibodies target distinct regions of the protein. Some target the C-terminal region, while others target N-terminal epitopes. Antibody 11835-1-AP uses an immunogen consisting of fusion protein Ag2270, while HPA010859 uses a sequence corresponding to amino acids "MIGNMEHMTAGGNCQTFSSSTVISYSNTGDGAPKVYQETSEMRSAPGGIRETRRTVRDSDSGLEQMSIGHHIRDRAHILQRSRNHRTGDQEERQDYINLDESEAAAFDDEWRRETSRFRQQRPLEFRRLESSGA" . Compare your antibody's epitope with the region of interest in your study.

• Cross-validation with multiple antibodies: When possible, confirm results using antibodies that target different epitopes. For example, compare results from a polyclonal antibody (like 11835-1-AP) with a monoclonal antibody (like EPR10245(2)) .

• Genetic validation: Consider using siRNA knockdown or CRISPR-based approaches to reduce MLF2 expression and confirm antibody specificity.

• Orthogonal validation: The enhanced validation approaches mentioned in product data (orthogonal RNAseq, recombinant expression, independent validation) provide additional confidence in antibody specificity .

What is the relationship between MLF2 expression and cancer progression?

MLF2 has been implicated in breast cancer progression through several mechanisms:

• Hormone receptor status correlation: Research indicates MLF2 expression may correlate with ER+ and ERα- breast cancer phenotypes . This suggests potential interactions with hormone signaling pathways.

• Metastatic potential: MLF2 has been associated with lung metastasis in breast cancer models, indicating a potential role in cancer cell migration or invasion .

• Nitric oxide signaling: MLF2 regulates nitric oxide synthase signaling, which may contribute to tumor microenvironment modulation and angiogenesis .

• Hypoxia response: MLF2 expression is affected by hypoxic conditions, suggesting it may play a role in tumor adaptation to the hypoxic microenvironment common in solid tumors .

When investigating MLF2's role in cancer, researchers should consider using tissue microarrays containing multiple cancer types, as several MLF2 antibodies have been validated on arrays containing 20+ common cancer types .

What are the optimal conditions for immunohistochemical detection of MLF2?

For successful immunohistochemical detection of MLF2 in tissue samples, several critical parameters must be optimized:

• Antigen retrieval methods: For MLF2 antibodies, two effective antigen retrieval approaches have been documented:

  • TE buffer (pH 9.0) - Primary recommendation for many MLF2 antibodies

  • Citrate buffer (pH 6.0) - Alternative approach that works for some antibody clones

• Antibody dilution optimization: Recommended dilutions for IHC range from 1:20 to 1:500, depending on the specific antibody clone and sample type . Performing a dilution series is advised for initial optimization.

• Detection system selection: Though not explicitly mentioned in the search results, standard HRP-based detection systems are compatible with the validated MLF2 antibodies.

• Positive control tissues: Human gliomas tissue has been validated as a reliable positive control for MLF2 IHC. Additionally, prostate and colon tissues have shown positive staining with some MLF2 antibody clones .

For paraffin-embedded tissues, heat-mediated antigen retrieval should be performed before commencing with the IHC staining protocol .

How can machine learning approaches be integrated into MLF2 antibody research?

Machine learning (ML) approaches are increasingly being applied to antibody research, including potential applications for MLF2 antibodies:

• Affinity enhancement: ML models like the Random Forest Classifier (AbRFC) can predict non-deleterious mutations that may enhance antibody binding affinity. This approach has demonstrated up to 1000-fold increased binding in antibodies against other targets .

• Epitope optimization: Computational approaches can identify optimal epitope regions for antibody development, potentially improving specificity and reducing cross-reactivity issues that may affect MLF2 detection.

• Protocol optimization: ML models can analyze experimental variables to predict optimal conditions for specific applications like IHC or IF, potentially improving consistency and reducing optimization time.

• Feature engineering: Expert-guided features can be incorporated into ML models to improve prediction accuracy for antibody characteristics. This approach has been successful in optimizing antibody-binding affinity in other systems .

When applying ML to MLF2 antibody research, researchers should consider integrated computational-experimental workflows where ML predictions guide limited experimental screening (typically <10² designs) to efficiently identify optimized antibodies .

What are the critical factors for successful Western blot detection of MLF2?

For optimal Western blot detection of MLF2, consider the following technical recommendations:

Secondary antibody selection should match the host species of the primary antibody, with rabbit being the most common host for available MLF2 antibodies . For recombinant monoclonal antibodies like EPR10245(2), higher dilutions (1:1000) may be effective due to increased specificity .

How can immunofluorescence protocols be optimized for subcellular localization studies of MLF2?

For accurate subcellular localization studies of MLF2 using immunofluorescence:

• Cell fixation methods: Standard PFA fixation (4%) is compatible with MLF2 antibodies, though specific optimization may be required for different cell types.

• Permeabilization optimization: For intracellular detection of MLF2, effective permeabilization is essential. Flow cytometry data indicates successful detection in permeabilized Jurkat cells .

• Antibody dilutions: Recommended dilutions for IF/ICC range from 1:50 to 1:500. HeLa cells have been validated as a reliable positive control for IF/ICC applications with MLF2 antibodies .

• Co-localization studies: Consider using established markers for cellular compartments to determine precise subcellular localization of MLF2.

• Signal amplification: For low-abundance expression, signal amplification methods may be necessary to detect MLF2 in certain cell types or under specific conditions.

The Human Protein Atlas project has characterized many MLF2 antibodies by immunofluorescence to map subcellular localization, providing valuable reference data for experimental design .

What approaches can be used to study MLF2 protein-protein interactions in experimental systems?

To investigate MLF2 protein-protein interactions, consider these methodological approaches:

• Co-immunoprecipitation (Co-IP): While not specifically mentioned in the search results for MLF2, antibodies validated for Western blot applications (like 11835-1-AP, HPA010859, or A89074) may be suitable for immunoprecipitation of MLF2 complexes .

• Proximity ligation assay (PLA): This technique can detect protein interactions in situ with high sensitivity. MLF2 antibodies validated for IF/ICC could potentially be adapted for PLA studies.

• Yeast two-hybrid screening: This approach could identify novel MLF2 interaction partners, providing new insights into its functional networks.

• Bimolecular fluorescence complementation (BiFC): This technique allows visualization of protein interactions in living cells and could be adapted to study MLF2 interactions.

• Proteomic approaches: Mass spectrometry analysis of immunoprecipitated MLF2 complexes can identify interaction partners in an unbiased manner.

Given MLF2's reported roles in nitric oxide signaling and cancer progression, investigating interactions with signaling pathway components and transcriptional regulators would be particularly informative .

How should researchers approach MLF2 antibody selection for multi-species comparisons?

When designing studies that require MLF2 detection across multiple species:

• Sequence homology analysis: Compare MLF2 sequences across target species to identify conserved regions. Antibodies targeting highly conserved epitopes are more likely to show cross-reactivity.

• Validated reactivity: Some MLF2 antibodies have been specifically validated for cross-reactivity with multiple species. For example, antibody A12895 has been validated for human, mouse, and rat samples .

• Testing panel approach: When cross-reactivity is uncertain, testing a panel of MLF2 antibodies with different epitope specificities increases the likelihood of finding a suitable reagent for multi-species studies.

• Epitope mapping: Consider the specific immunogen sequence used to generate the antibody. Antibody A89074 uses a recombinant fusion protein containing amino acids 1-65 of human MLF2, which may have sequence conservation across species .

• Control validation: Always include appropriate positive and negative controls from each species to validate cross-reactivity claims before proceeding with full experimental analysis.

What experimental designs are recommended for studying MLF2 under hypoxic conditions?

Given MLF2's reported sensitivity to hypoxia, the following experimental approaches are recommended:

• Hypoxia gradient analysis: Expose cells to varying oxygen concentrations (e.g., 21%, 5%, 1%, 0.1% O₂) to determine the threshold at which MLF2 expression changes.

• Time-course studies: Monitor MLF2 expression over time following hypoxia induction to distinguish between acute and chronic hypoxia responses.

• Chemical hypoxia mimetics: Compare MLF2 responses between true hypoxia and chemical mimetics (e.g., CoCl₂, DMOG) to determine whether the effect is direct or indirect.

• HIF-1α correlation studies: Since hypoxia responses often involve HIF-1α, co-detection of MLF2 and HIF-1α can provide mechanistic insights.

• Reoxygenation dynamics: Assess whether hypoxia-induced changes in MLF2 expression are reversible upon reoxygenation.

Western blot analysis with antibodies like 11835-1-AP (1:500-1:2000 dilution) would be suitable for quantifying MLF2 protein levels under different hypoxic conditions . Complementary qRT-PCR analysis of MLF2 mRNA would determine whether regulation occurs at the transcriptional or post-transcriptional level.

What considerations should be made when developing new ML-based approaches for MLF2 antibody optimization?

When applying machine learning approaches to MLF2 antibody optimization:

• Training data quality: As demonstrated in the AbRFC case, the quality and diversity of training data is crucial. Researchers should be aware of potential biases in publicly available antibody datasets, such as over-representation of mutations to alanine .

• Feature engineering: Expert-guided features based on prior successes in antibody optimization should be incorporated into ML models for MLF2 antibodies. This approach improved performance in the described AbRFC model .

• Validation strategy: Use out-of-distribution (OOD) validation datasets to test the model's ability to generalize beyond the training data characteristics .

• Integrated workflow design: Design a computational-experimental workflow where ML predictions guide limited experimental screening. The AbRFC approach successfully identified affinity-enhancing mutations with less than 100 designs per round .

• Epitope-specific optimization: Since MLF2 antibodies target different epitopes (N-terminal, C-terminal, etc.), ML approaches should be tailored to the specific epitope region of interest.

These considerations can help researchers develop more efficient approaches to MLF2 antibody optimization, potentially improving specificity, sensitivity, and cross-reactivity profiles.

What are common causes of inconsistent Western blot results with MLF2 antibodies?

When troubleshooting inconsistent Western blot results with MLF2 antibodies:

• Protein extraction method: Different extraction buffers may affect MLF2 detection. Consider comparing RIPA, NP-40, and other extraction buffers to determine optimal conditions.

• Sample storage effects: Repeated freeze-thaw cycles may affect MLF2 stability. Storage recommendations for MLF2 antibodies suggest avoiding repeated freeze-thaw cycles of antibody solutions .

• Blocking optimization: Test different blocking agents (BSA vs. non-fat milk) as these can affect antibody performance and background levels.

• Buffer composition: The storage buffer for many MLF2 antibodies contains PBS with 0.02% sodium azide and 50% glycerol at pH 7.3, which may interact with certain experimental conditions .

• Secondary antibody selection: Ensure appropriate secondary antibody matching. For most MLF2 antibodies, an anti-rabbit secondary is required as they are typically raised in rabbits .

• Expected molecular weight verification: Consistently check that the observed band matches the expected 28 kDa molecular weight of MLF2 .

How can researchers address background issues in immunohistochemistry with MLF2 antibodies?

For reducing background and improving signal-to-noise ratio in MLF2 immunohistochemistry:

• Antigen retrieval optimization: Compare heat-mediated antigen retrieval with TE buffer (pH 9.0) versus citrate buffer (pH 6.0) to determine which produces cleaner results with your specific tissue .

• Antibody titration: Perform careful antibody titration experiments. While recommended dilutions range from 1:50 to 1:500 for IHC, the optimal dilution may vary by tissue type and fixation method .

• Blocking protocol enhancement: Extended blocking steps or alternative blocking reagents may reduce non-specific binding. Consider tissue-specific blocking strategies.

• Detection system selection: Different detection systems may produce varying levels of background. Compare DAB-based versus fluorescent detection systems.

• Tissue preparation factors: Fixation duration, processing methods, and storage conditions of tissue sections can all affect background levels. Freshly prepared sections often provide cleaner results.

• Control experiments: Always include isotype controls (Rabbit IgG) to distinguish between specific and non-specific binding patterns .

For paraffin-embedded tissues, it's essential to perform heat-mediated antigen retrieval before commencing with the IHC staining protocol to ensure optimal MLF2 detection .