NOMO1 Antibody, HRP conjugated

What is NOMO1 and what biological processes does it regulate?

NOMO1 (Nodal Modulator 1) is a 134 kDa protein consisting of 1222 amino acids that plays a critical role in developmental processes, particularly in heart formation. The protein functions as an antagonist to Nodal signaling, a pathway essential for proper embryonic development . Research demonstrates that NOMO1 significantly influences human cardiomyocyte progenitor cells (hCMPCs) by regulating their proliferation, cell cycle progression, and differentiation into functional cardiomyocytes .

Molecular studies have established that NOMO1 serves as a direct target for miR-33a-5p, with this microRNA-mediated regulation affecting cardiac development. The relationship between NOMO1 expression and cardiac development markers (GATA4, cTnT, and MEF2C) indicates its importance in proper cardiac morphogenesis . Understanding NOMO1's function is particularly relevant for congenital heart disease (CHD) research, as dysregulation of the protein contributes to cardiac developmental abnormalities.

What are the key applications of NOMO1 Antibody, HRP conjugated?

The HRP-conjugated NOMO1 polyclonal antibody has been specifically validated for ELISA applications . The direct conjugation of Horseradish Peroxidase (HRP) to the antibody eliminates the need for secondary antibodies, which significantly streamlines experimental workflows and potentially reduces background signal in appropriate assay systems.

While the HRP-conjugated variant is optimized for ELISA, other NOMO1 antibody formats serve additional experimental purposes:

Researchers investigating NOMO1 expression in cellular contexts should select the appropriate antibody format based on their specific experimental needs, cellular/tissue systems, and detection methods.

What is the specificity and reactivity profile of NOMO1 Antibody, HRP conjugated?

The NOMO1 Polyclonal Antibody, HRP Conjugated (A69632-100) demonstrates specific reactivity against human NOMO1 . The antibody was generated using a recombinant human Nodal modulator 1 protein fragment spanning amino acids 918-1044 as the immunogen . This region represents approximately 12% of the full protein sequence, providing a specific antigenic determinant.

Being a polyclonal antibody, it recognizes multiple epitopes within this region, which can provide enhanced sensitivity for detection. The antibody underwent purification using Protein G affinity chromatography, ensuring high specificity and minimal non-specific binding .

When validating this antibody in experimental systems, researchers should consider both the advantages of polyclonal recognition (multiple epitope detection) and potential limitations (batch-to-batch variation). The documented human specificity makes this reagent particularly suitable for human cell lines and tissue samples, though cross-reactivity testing in specific experimental systems is recommended.

What is the optimal storage and handling protocol for NOMO1 Antibody, HRP conjugated?

To maintain optimal activity of the NOMO1 Polyclonal Antibody, HRP Conjugated, researchers should follow these evidence-based handling protocols:

• Shipping conditions: The antibody is shipped at 4°C

• Short-term storage: Store at -20°C

• Long-term storage: Store at -80°C for extended preservation

• Stability: Avoid repeated freeze-thaw cycles which may compromise antibody function

• Buffer composition: The antibody is provided in a stabilizing buffer containing 0.03% Proclin 300, 50% Glycerol, and 0.01M PBS at pH 7.4

This formulation is designed to maintain both antibody integrity and HRP enzymatic activity. The high glycerol content prevents freeze damage, while Proclin 300 inhibits microbial growth. For optimal results, researchers should aliquot the antibody upon receipt to minimize freeze-thaw cycles that could diminish performance in sensitive applications like ELISA.

How does HRP conjugation affect antibody performance compared to unconjugated versions?

HRP conjugation provides several methodological advantages while introducing certain performance considerations compared to unconjugated NOMO1 antibodies:

Performance advantages:

Performance considerations:

• Conjugation may slightly alter binding kinetics or affinity compared to the native antibody

• HRP conjugates have more restricted application versatility (primarily optimized for ELISA)

• Shelf-life may differ from unconjugated antibodies due to potential degradation of the HRP enzyme

• Buffer requirements may be more stringent to maintain both antibody binding and enzymatic activity

When selecting between conjugated and unconjugated NOMO1 antibodies, researchers should consider their specific application requirements, detection method constraints, and the need for protocol simplification versus application flexibility.

What are the considerations for optimizing ELISA protocols when using NOMO1 Antibody, HRP conjugated?

When developing robust ELISA protocols with NOMO1 Antibody, HRP conjugated, researchers should systematically optimize these critical parameters:

Antigen coating optimization:

• Test a range of recombinant NOMO1 protein concentrations (typically 0.1-10 μg/ml) for standard curve development

• Evaluate different coating buffers (carbonate/bicarbonate pH 9.6 vs. PBS pH 7.4)

• Determine optimal coating temperature and duration (4°C overnight vs. 37°C for 2 hours)

Blocking parameters:

• Compare blocking agents (BSA, casein, non-fat milk) at various concentrations (1-5%)

• Optimize blocking duration (1-3 hours) and temperature (room temperature vs. 37°C)

Antibody titration:

• Perform a dilution series of the HRP-conjugated NOMO1 antibody to determine optimal concentration

• Test various diluents to minimize background while preserving specific signal

Detection optimization:

• Compare HRP substrates (TMB, ABTS, OPD) for optimal signal-to-noise ratio

• Determine optimal development time before signal saturation

• Establish plate reading parameters (wavelength, integration time)

Validation controls:

• Include known NOMO1-positive samples (human brain tissue, A431 cells, COLO 320 cells)

• Incorporate NOMO1-depleted samples as negative controls

• Test assay linearity, recovery, precision, and detection limits

Researchers should systematically document each optimization step and verify assay performance using samples with defined NOMO1 expression levels to ensure reliable and reproducible quantification.

How can researchers verify the specificity of NOMO1 Antibody, HRP conjugated in their experimental systems?

Comprehensive validation of NOMO1 Antibody, HRP conjugated specificity requires multiple complementary approaches:

Positive control validation:

• Test antibody recognition in systems with confirmed NOMO1 expression, such as:

  • Human brain tissue

  • A431 cells

  • COLO 320 cells

  • Human colon tissue

Negative control validation:

• Analyze samples where NOMO1 is absent or depleted:

  • NOMO1 knockdown models created through siRNA/shRNA techniques

  • Cell lines known to lack NOMO1 expression

  • Tissues from species outside the antibody's reactivity profile

Competitive inhibition:

• Pre-incubate the antibody with excess recombinant NOMO1 protein (specifically the 918-1044AA immunogen fragment)

• Compare signal between blocked and unblocked antibody to confirm specificity

Orthogonal validation:

• Correlate protein detection with NOMO1 mRNA levels quantified by qRT-PCR

• Compare results with alternative NOMO1 antibodies targeting different epitopes

• Verify molecular weight consistency with the expected 134-135 kDa size of NOMO1

Experimental manipulation:

• Analyze NOMO1 detection in overexpression systems

• Examine expected biological responses, such as effects on cardiomyocyte differentiation markers (GATA4, cTnT, MEF2C)

This multi-faceted validation approach provides robust evidence for antibody specificity and ensures reliable experimental outcomes when working with NOMO1 Antibody, HRP conjugated.

What are the potential cross-reactivity issues with NOMO1 Antibody, HRP conjugated and how can they be addressed?

As a polyclonal antibody, the NOMO1 Antibody, HRP conjugated may present several potential cross-reactivity challenges that researchers should systematically address:

Potential cross-reactivity sources:

• Recognition of homologous proteins with similar epitopes (especially NOMO2 and NOMO3 family members)

• Non-specific binding to abundant proteins in complex samples

• Interaction with endogenous biotin or peroxidase-like enzymes in certain tissues

• Binding to Fc receptors present in immune cell-rich samples

Methodological approaches to address cross-reactivity:

• Pre-absorption validation:

  • Pre-incubate antibody with excess target antigen (recombinant NOMO1 fragment 918-1044AA)

  • Compare signal between pre-absorbed and non-absorbed samples

  • Signal elimination confirms specific binding

• Depletion experiments:

  • Validate antibody in NOMO1 knockdown systems using siRNA/shRNA approaches

  • Compare signal between wild-type and depleted samples

  • Proportion of signal reduction should correlate with knockdown efficiency

• Blocking strategies:

  • Incorporate additional blocking steps for problematic samples

  • Use specific blockers for potential interfering factors (avidin/biotin blocking, peroxidase quenching)

  • Include carrier proteins (BSA, casein) in antibody diluent

• Orthogonal validation:

  • Compare results with alternative NOMO1 antibodies recognizing different epitopes

  • Correlate protein detection with mRNA expression profiles

  • Confirm expected molecular weight (134-135 kDa) and localization pattern

By implementing these systematic validation approaches, researchers can confidently distinguish between specific NOMO1 detection and potential cross-reactivity artifacts in their experimental systems.

How can NOMO1 Antibody, HRP conjugated be integrated into studies of Nodal signaling pathways?

The NOMO1 Antibody, HRP conjugated provides a valuable tool for investigating Nodal signaling pathways, particularly given NOMO1's documented role as a potential Nodal signaling antagonist . Researchers can implement several methodological approaches:

Quantitative correlation studies:

• Measure NOMO1 protein levels in relation to Nodal pathway components using multiplexed or sequential ELISA approaches

• Correlate NOMO1 expression with Nodal pathway activation markers (phospho-Smad2/3)

• Analyze how NOMO1 expression relates to Nodal target gene expression

Pathway perturbation analysis:

• Quantify NOMO1 expression changes following:

  • Nodal ligand administration

  • Nodal pathway inhibition (SB431542, Lefty)

  • Genetic manipulation of Nodal pathway components

• Determine if NOMO1 shows feedback regulation within the Nodal signaling network

MicroRNA regulatory studies:

• Investigate how miR-33a-5p modulates NOMO1 expression and subsequently affects Nodal signaling

• Analyze relationships between microRNA regulatory networks, NOMO1 levels, and Nodal pathway activity

• Determine if other microRNAs targeting NOMO1 produce similar effects on Nodal signaling

Developmental context analysis:

• Map NOMO1 expression patterns during embryonic development in relation to Nodal signaling gradients

• Correlate NOMO1 levels with developmental outcomes dependent on Nodal signaling

• Examine NOMO1 expression in congenital disorders associated with Nodal pathway dysfunction

By systematically implementing these approaches, researchers can utilize the NOMO1 Antibody, HRP conjugated to dissect the complex regulatory relationships between NOMO1 and Nodal signaling, particularly in developmental and disease contexts.

What are the considerations for using NOMO1 Antibody, HRP conjugated in studies of congenital heart disease or cardiomyocyte function?

When applying NOMO1 Antibody, HRP conjugated to cardiovascular research, researchers should implement specialized methodological approaches based on NOMO1's established role in cardiac development:

Sample selection considerations:

• Prioritize human cardiomyocyte progenitor cells (hCMPCs) as relevant models

• Consider developmental staging when analyzing cardiac tissues

• Include both normal and pathological cardiac samples for comparative analysis

Functional correlation analyses:

• Measure NOMO1 levels in relation to critical cardiac differentiation markers:

  • GATA4 (cardiac transcription factor)

  • cTnT (cardiomyocyte structural protein)

  • MEF2C (myocyte enhancer factor)

Cell cycle and proliferation assessment:

• Correlate NOMO1 expression with markers of cell cycle progression

• Analyze relationship between NOMO1 levels and G0/S transition in cardiomyocytes

• Investigate how NOMO1 expression affects proliferation rates measured by techniques like CCK-8 assay

Apoptosis pathway integration:

• Examine relationships between NOMO1 expression and apoptotic regulators:

  • Bax (pro-apoptotic)

  • Cleaved caspase-3 (apoptosis effector)

  • Bcl-2 (anti-apoptotic)

MicroRNA regulatory circuit analysis:

• Investigate how miR-33a-5p targeting of NOMO1 affects cardiac development and function

• Analyze if NOMO1 levels predict sensitivity to miR-33a-5p-mediated effects

• Explore potential therapeutic applications by modulating the miR-33a-5p/NOMO1 axis

Disease model integration:

• Apply ELISA-based NOMO1 quantification in congenital heart disease models

• Compare NOMO1 expression across different CHD subtypes

• Correlate NOMO1 levels with severity of developmental cardiac abnormalities

By systematically implementing these approaches, researchers can leverage NOMO1 Antibody, HRP conjugated to advance understanding of cardiac development mechanisms and congenital heart disease pathogenesis.

What technical challenges might arise when using NOMO1 Antibody, HRP conjugated in complex tissue samples?

When working with complex tissue samples, researchers using NOMO1 Antibody, HRP conjugated should anticipate and address several technical challenges:

Tissue extraction optimization:

• Develop tissue-specific extraction protocols to ensure complete solubilization of membrane-associated NOMO1

• Test multiple extraction buffers with different detergent compositions

• Validate extraction efficiency by comparing multiple extraction methods

• Include protease and phosphatase inhibitors to prevent NOMO1 degradation or modification

Endogenous peroxidase management:

• Identify tissues with high endogenous peroxidase activity (liver, kidney, blood-rich samples)

• Implement appropriate peroxidase quenching steps before antibody application

• Test multiple quenching protocols to determine optimal conditions that preserve NOMO1 epitopes

Matrix effect mitigation:

• Evaluate signal interference from tissue-specific components

• Develop sample dilution strategies to minimize matrix effects

• Consider spike-recovery experiments to quantify matrix interference

• Test different blocking reagents to minimize non-specific interactions

Sample normalization approaches:

• Establish appropriate loading controls for each tissue type

• Normalize NOMO1 measurements to total protein concentration

• Consider tissue-specific housekeeping proteins for relative quantification

• Implement appropriate statistical approaches for cross-tissue comparisons

Assay validation in tissue context:

• Verify antibody performance in tissue-specific contexts using known NOMO1-expressing tissues:

  • Human brain tissue

  • Human colon cancer tissue

  • Human colon tissue

  • Human pancreas tissue

By systematically addressing these technical challenges, researchers can generate reliable and reproducible data using NOMO1 Antibody, HRP conjugated even in complex tissue environments.

How can researchers quantitatively analyze NOMO1 expression using HRP-conjugated antibodies?

For precise quantitative analysis of NOMO1 expression using HRP-conjugated antibodies, researchers should implement a comprehensive methodological framework:

Standard curve development:

• Generate a recombinant NOMO1 protein standard curve spanning at least 3 orders of magnitude

• Prepare standards in the same matrix as experimental samples when possible

• Verify linearity across the detection range (R² > 0.98)

• Establish lower and upper limits of quantification

Sample preparation standardization:

• Develop consistent protein extraction and processing protocols

• Validate protein recovery rates across different sample types

• Normalize input protein concentration across all samples

• Process standards and samples identically

Assay quality control:

• Calculate intra-assay and inter-assay coefficients of variation (target CV < 15%)

• Include quality control samples at low, medium, and high concentrations

• Validate spike recovery in complex matrices (acceptable range: 80-120%)

• Determine antibody specificity via competitive inhibition with recombinant NOMO1

Data analysis approaches:

• Apply appropriate curve-fitting models (4-parameter logistic regression recommended)

• Transform data if necessary to achieve normal distribution

• Implement statistical approaches appropriate for data distribution

• Correlate protein measurements with NOMO1 mRNA levels from qRT-PCR for orthogonal validation

Comparative analysis framework:

• Establish baseline NOMO1 expression in relevant control samples

• Express experimental results as fold-change from appropriate controls

• Correlate NOMO1 levels with functional outcomes or disease parameters

• Consider multivariate analysis when examining NOMO1 in complex biological contexts

By implementing this rigorous quantitative framework, researchers can generate reliable and reproducible measurements of NOMO1 expression across different experimental conditions and sample types.

What controls should be included when using NOMO1 Antibody, HRP conjugated in different experimental contexts?

A comprehensive control strategy is essential when using NOMO1 Antibody, HRP conjugated across various experimental paradigms:

Positive expression controls:

• Include samples with confirmed NOMO1 expression:

  • Human brain tissue

  • A431 cells

  • COLO 320 cells

  • Human colon tissue

• Use recombinant NOMO1 protein as a reference standard

• Consider samples with experimentally upregulated NOMO1 expression

Negative expression controls:

• Analyze samples with NOMO1 knockdown using siRNA/shRNA approaches

• Include cell lines known to express minimal NOMO1

• Process negative control samples identically to experimental samples

Antibody specificity controls:

• Perform competitive inhibition with recombinant NOMO1 protein (918-1044AA fragment)

• Include isotype control (rabbit IgG-HRP) at equivalent concentration

• Compare multiple antibodies targeting different NOMO1 epitopes when possible

Assay technical controls:

• Include reagent blanks (no sample, no antibody)

• Prepare standard curves with each experimental batch

• Run quality control samples at low, medium, and high concentrations

• Process technical replicates to assess reproducibility

Biological context controls:

• Analyze NOMO1 expression alongside established regulatory factors:

  • miR-33a-5p levels (known NOMO1 regulator)

  • Nodal pathway activation markers

  • Developmental stage markers in relevant systems

• Include pathway perturbation controls (activators/inhibitors) when studying regulatory mechanisms

By systematically implementing this comprehensive control strategy, researchers can generate reliable, reproducible, and contextually meaningful data when using NOMO1 Antibody, HRP conjugated across diverse experimental scenarios.