mturn Antibody

What is the MTURN protein and what cellular functions does it regulate?

MTURN (Maturin neural progenitor differentiation regulator homolog) is a protein encoded by the MTURN gene (Gene ID: 222166) in humans . This protein plays a crucial role in cell cycle regulation and neural progenitor differentiation. Research indicates that MTurn functions as a cell cycle regulator, controlling cell division and proliferation processes . Understanding MTurn's function is particularly important in neurodevelopmental research and cancer studies, as it provides insights into the mechanisms underlying neural differentiation and potentially cancer progression.

The protein is also known by several aliases including:

• Maturin

• UPF0452 protein C7orf41 homolog

• Protein Ells1

Although the search results primarily discuss polyclonal MTurn antibodies, understanding the general differences between polyclonal and monoclonal antibodies is crucial for researchers:

Polyclonal MTurn antibodies (like PACO60917 and PA5-56177):

• Are produced in rabbits or other host animals against MTurn protein or specific peptide sequences

• Recognize multiple epitopes on the MTurn protein

• Offer high sensitivity due to binding multiple epitopes

• May show batch-to-batch variation

• The MTurn Polyclonal Antibody (PACO60917) has been purified using Protein G, with a purity of >95%

Monoclonal antibodies (general principles):

When selecting an MTurn antibody for research, consider the specific application requirements and whether sensitivity or specificity is more critical for your experimental design.

How should researchers validate MTurn antibody specificity in their experimental systems?

Proper validation of MTurn antibodies is critical for ensuring experimental rigor, especially given concerns about the "antibody crisis" in research . For optimal MTurn antibody validation:

• Knockout/knockdown controls: The gold standard for antibody validation is testing in knockout or knockdown systems. This approach has been demonstrated to be superior to other types of controls, particularly for Western Blots and immunofluorescence imaging .

• Positive and negative tissue controls: Test the antibody on tissues known to express or not express MTurn. Human cerebellum shows strong positivity for MTurn in neuronal cells and serves as an excellent positive control .

• Multiple antibody approach: Use antibodies from different manufacturers or those targeting different epitopes of MTurn to confirm findings.

• Recombinant protein controls: Several MTurn antibodies have been validated against recombinant MTurn protein. The PACO60917 antibody, for example, was validated using recombinant Zebrafish Maturin protein (1-133AA) .

• Specificity testing: Test for cross-reactivity with related proteins or in species the antibody is not designed to target.

Remember that YCharOS found that "an average of ~12 publications per protein target included data from an antibody that failed to recognize the relevant target protein" , highlighting the importance of rigorous validation.

What methodological considerations are important when using MTurn antibodies for Western blotting?

For optimal Western blot results with MTurn antibodies, consider the following methodological details:

• Sample preparation:

  • Use appropriate lysis buffers that preserve protein structure

  • Include protease inhibitors to prevent degradation

  • Determine optimal protein loading amount (typically 20-50 μg)

• Dilution optimization:

  • For PACO60917: Use 1:500-1:5000 dilution range

  • For PA5-112866: Tested at 2 μg/ml concentration for recombinant protein

  • Always perform dilution series to determine optimal concentration for your specific sample

• Incubation conditions:

  • Primary antibody: Typically overnight at 4°C or 2 hours at room temperature

  • Secondary antibody: Usually 1 hour at room temperature

  • Include washing steps with TBST or PBST between incubations

• Detection systems:

  • For fluorescent detection: Use appropriate secondary antibodies conjugated to fluorophores

  • For chemiluminescent detection: Use HRP-conjugated secondary antibodies and suitable detection reagents

• Controls:

  • Positive control: Use recombinant MTurn protein

  • Negative control: Use samples known not to express MTurn

  • Loading control: Use housekeeping proteins (β-actin, GAPDH, etc.)

• Troubleshooting:

  • For high background: Increase blocking time or antibody dilution

  • For weak signal: Decrease antibody dilution or increase exposure time

  • For non-specific bands: Optimize blocking conditions or increase washing steps

How do storage conditions and buffer composition affect MTurn antibody performance?

The performance and shelf-life of MTurn antibodies can be significantly affected by storage conditions and buffer composition:

For maximum shelf-life and performance, researchers should:

• Aliquot antibodies upon receipt to minimize freeze-thaw cycles

• Follow manufacturer's specific storage recommendations

• Check for signs of degradation (precipitation, cloudiness) before use

• Validate antibody performance periodically if stored for extended periods

What approaches can researchers use to analyze antibody data and distinguish between antibody-positive and antibody-negative populations?

When analyzing antibody data, particularly in serological studies, researchers can employ sophisticated statistical approaches to differentiate between antibody-positive and antibody-negative populations:

• Finite Mixture Models (FMMs): These statistical models are widely used in antibody data analysis to classify individuals into antibody-positive or antibody-negative groups . While Gaussian mixture models are commonly used, more flexible approaches using Scale Mixtures of Skew-Normal distributions can better account for asymmetry often observed in antibody data.

• Threshold determination: Establish appropriate cutoff values based on:

  • Control populations

  • Statistical approaches (e.g., 3 standard deviations above negative control mean)

  • ROC curve analysis to optimize sensitivity and specificity

• Median Fluorescence Intensity (MFI) analysis: For bead-based assays, MFI values can be analyzed as both:

  • Categorical data (presence/absence based on established cutoffs)

  • Continuous data (actual MFI values)

• Multi-parameter analysis: When evaluating multiple antibody characteristics, consider using:

  • Machine learning approaches like LASSO (Least Absolute Shrinkage and Selection Operator)

  • Polyfunctionality scores (number of functional readouts exceeding the median across subjects)

  • Area Under the Curve (AUC) measurements for longitudinal data

For complex datasets with multiple antibody measurements, unbiased machine learning approaches can help identify discriminating features and patterns that might not be apparent through conventional analysis.

What methodologies exist for assessing MTurn antibody cross-reactivity and potential false positives?

• Knockout validation: Testing in MTurn knockout systems is the gold standard approach:

  • YCharOS found knockout cell lines to be superior to other controls for Western Blots and immunofluorescence

  • This approach definitively confirms antibody specificity for the target protein

• Epitope mapping: Identify the specific region of MTurn recognized by the antibody:

  • Helps predict potential cross-reactivity with similar epitopes in other proteins

  • Important when working with antibodies raised against specific peptides

• Batch testing: Different antibody lots may have varying specificity profiles:

  • Test new lots against previously validated lots

  • Document lot-specific behavior for reproducibility

• Multiple detection methods: Confirm findings using orthogonal approaches:

  • Combine antibody-based detection with mass spectrometry or other non-antibody methods

  • Use antibodies targeting different MTurn epitopes

• Species cross-reactivity assessment: Important for comparative studies:

  • PACO60917 is reported to have reactivity with Zebrafish

  • PA5-56177 shows highest sequence identity with Mouse (94%) and Rat (94%) orthologs

What sequence validation approaches ensure the reliability of MTurn antibody target recognition?

Ensuring that antibodies accurately recognize their intended MTurn sequence target requires rigorous validation:

• Full sequence validation: This involves confirming the complete amino acid sequence of the target protein and the antibody's recognition region:

  • The "Sequence Validation Percentage" (SVP) has been introduced as a measure for assessing the integrity and validity of results from middle-down approaches

  • Full sequence validation typically requires combining multiple analytical techniques

• Mass spectrometry-based validation:

  • Middle-up LC-QTOF (Liquid Chromatography-Quadrupole Time-of-Flight) approach allows for molecular weight determination of protein domains

  • Middle-down LC-MALDI in-source decay (ISD) provides protein sequencing information

  • Ultra High Resolution (UHR) QTOF mass spectrometry can provide accurate mass and isotopically resolved molecular weight determination

• Immunogen sequence verification:

  • For antibodies like PACO60917, the immunogen (Recombinant Zebrafish Maturin protein, 1-133AA) should be verified

  • PA5-56177 was developed using a specific immunogen sequence: "MDFQQLADVA EKWCSNTPFE LIATEETERR MDFYADPGVS FYVLCPDNGC GDN"

• Orthogonal approaches:

  • Combining epitope mapping with structural analysis

  • Using multiple antibodies targeting different regions of MTurn

  • Comparing antibody recognition patterns with predicted protein domains

These validation approaches are particularly important for MTurn research, as the protein has multiple aliases and potential isoforms that could complicate antibody recognition.

How can MTurn antibodies be utilized in multiplex assays with other neural markers?

Multiplexing allows researchers to simultaneously detect MTurn alongside other neural markers, providing richer data on neural differentiation and development:

• Multicolor immunofluorescence:

  • Use MTurn antibodies with different fluorophore-conjugated secondaries

  • Combine with antibodies against other neural markers (e.g., SOX2, Nestin, DCX)

  • Careful antibody panel design is required to avoid spectral overlap

• Mass cytometry (CyTOF):

  • Label MTurn antibodies with rare earth metals

  • Enables simultaneous detection of 40+ markers without fluorescence spillover

  • Requires specialized equipment but offers high-dimensional data

• Multiplexed immunohistochemistry:

  • Sequential staining with MTurn and other antibodies

  • Tyramide signal amplification allows for multiple rounds of staining

  • Enables co-localization studies in tissue sections

• Single-cell multi-omics:

  • Similar to the approach used with Dextramer® reagents, MTurn antibodies can be integrated with single-cell analysis platforms

  • Combines antibody detection with gene expression and VDJ sequence analysis

  • Provides simultaneous information on protein expression, gene expression, and other cellular characteristics

• Multiplex ELISA and bead-based assays:

  • Conjugate MTurn antibodies to distinct beads

  • Allows quantification alongside other neural markers in solution

  • Enables high-throughput screening in developmental studies

When designing multiplex experiments, consider potential antibody cross-reactivity, optimal fixation conditions for all targets, and appropriate controls for each marker in the panel.

What are the latest advances in antibody technology that might improve MTurn research?

Several cutting-edge antibody technologies could significantly enhance MTurn research:

• Recombinant antibody development:

  • The YCharOS study demonstrated that recombinant antibodies outperformed both monoclonal and polyclonal antibodies across all assays tested

  • Converting hybridoma-derived monoclonal antibodies to recombinant formats improves reproducibility

  • Several initiatives now make antibody sequences publicly available, allowing researchers to produce their own recombinant versions

• Nanobodies and single-domain antibodies:

  • Smaller size allows better tissue penetration

  • Can access epitopes that conventional antibodies cannot reach

  • Middle-down protein sequencing (MDS) using MALDI-ISD has been successfully applied to nanobodies

• Affinity maturation enhancement:

  • Boston Children's Hospital labs have developed methods to enhance affinity maturation and help B cells make more broadly protective antibodies

  • These approaches could potentially be applied to develop more specific MTurn antibodies

  • One method involves introducing antibody genomes into mouse B cells and allowing affinity maturation to happen, generating improved antibodies

• On-target functional modulation:

  • Antibodies that not only bind MTurn but modulate its function

  • Similar to how IgE allergy antibodies can be transformed into reaction blockers

  • Could enable precise manipulation of MTurn activity in neural development studies

• Systems serology approaches:

  • Comprehensive analysis of antibody features beyond simple binding

  • Includes assessment of Fc-effector functions and epitope specificity

  • Machine learning methods can identify discriminating features in complex antibody datasets

These advances promise to improve the specificity, reproducibility, and functional utility of antibodies for MTurn research, potentially overcoming limitations of current reagents.

How can researchers design experiments to study the role of MTurn in neural progenitor differentiation using antibody-based approaches?

To investigate MTurn's role in neural progenitor differentiation, researchers can design targeted experiments using antibody-based approaches:

• Temporal expression analysis:

  • Track MTurn expression across neural differentiation timepoints

  • Correlate with known differentiation markers

  • Use quantitative Western blot or immunofluorescence with MTurn antibodies

• Spatial localization studies:

  • Employ immunohistochemistry to map MTurn distribution in developing neural tissues

  • Co-stain with markers of different neural progenitor populations

  • Use super-resolution microscopy for subcellular localization

• Functional inhibition experiments:

  • Apply neutralizing antibodies to block MTurn function

  • Monitor effects on progenitor proliferation, migration, and differentiation

  • Compare to genetic knockdown/knockout approaches

• Interaction partner identification:

  • Use MTurn antibodies for co-immunoprecipitation

  • Identify binding partners by mass spectrometry

  • Validate interactions with proximity ligation assays

• Patient-derived cell studies:

  • Compare MTurn expression in neural progenitors from patients with neurodevelopmental disorders versus controls

  • Assess correlation between MTurn expression patterns and clinical phenotypes

  • Use patient-specific iPSCs differentiated to neural lineages

Experimental design should include appropriate controls:

• Genetic controls (knockdown/knockout)

• Developmental stage controls

• Regional specificity controls

• Antibody validation controls

By combining these approaches, researchers can build a comprehensive understanding of MTurn's role in neural development and potentially identify therapeutic targets for neurodevelopmental disorders.

What are common pitfalls when working with MTurn antibodies and how can they be addressed?

Researchers commonly encounter several challenges when working with MTurn antibodies:

• Non-specific binding:

  • Problem: Background staining or multiple bands in Western blots

  • Solution: Optimize blocking conditions (try different blocking agents like BSA, milk, or commercial blockers)

  • Increase antibody dilution or perform absorption controls

• Inconsistent results between batches:

  • Problem: Different lots of polyclonal antibodies show varying specificity

  • Solution: Purchase larger quantities of a single lot for long-term studies

  • Consider switching to recombinant antibodies which show better consistency

• Species cross-reactivity issues:

  • Problem: Unexpected or absent staining in different species

  • Solution: Verify species reactivity claims (e.g., PACO60917 is reported for Zebrafish )

  • Check sequence homology between species before selecting antibodies

• Fixation sensitivity:

  • Problem: Loss of epitope recognition after certain fixation methods

  • Solution: Test multiple fixation protocols (PFA, methanol, acetone)

  • Consider epitope retrieval methods for FFPE tissues

• Reproducibility challenges:

  • Problem: Difficulty reproducing published results with the same antibody

  • Solution: Request detailed protocols from authors

  • Consider that approximately 12 publications per protein target include data from antibodies that fail to recognize their target

To improve experimental outcomes, researchers should document all experimental conditions in detail, including:

• Exact antibody catalog number and lot

• Dilution and incubation conditions

• Sample preparation methods

• Blocking and washing conditions

• Detection system specifications

How should researchers integrate antibody-based findings with other techniques when studying MTurn function?

To build a robust understanding of MTurn function, researchers should complement antibody-based approaches with orthogonal techniques:

• Genetic approaches:

  • CRISPR/Cas9 knockout or knockdown of MTurn

  • Overexpression studies with tagged variants

  • Compare phenotypes with antibody neutralization results

• Transcriptomic analysis:

  • RNA-seq to identify genes co-regulated with MTurn

  • Single-cell RNA-seq to map expression in specific cell populations

  • Correlate protein levels (antibody-detected) with mRNA expression

• Functional assays:

  • Cell proliferation, migration, and differentiation assays

  • Electrophysiological measurements in neural systems

  • Behavioral studies in model organisms with MTurn manipulation

• Structural biology:

  • X-ray crystallography or cryo-EM of MTurn protein

  • Use structure to interpret antibody epitope mapping data

  • Structure-function correlation studies

• Mass spectrometry:

  • Proteomic analysis to identify interaction partners

  • Post-translational modification mapping

  • Absolute quantification to validate antibody-based quantification

When integrating these approaches, consider: