NRSN2 Antibody

What is NRSN2 and what are its known biological functions?

NRSN2 (Neurensin-2) is a 204 amino acid protein with a calculated molecular weight of approximately 22 kDa (though observed at ~26 kDa on SDS-PAGE) that belongs to the vesicular membrane protein (VMP) family . It shows high sequence homology to Neurensin-1. Based on this similarity, NRSN2 is thought to play a role in the maintenance and/or transport of vesicles .

The protein is encoded by the NRSN2 gene (also known as C20orf98), which is located on chromosome 20 in humans . While its exact function remains to be fully characterized, NRSN2 has been implicated in cancer progression through various signaling pathways, including PI3K/AKT/mTOR, in multiple cancer types .

Which antibody applications are most reliable for NRSN2 detection?

Based on validated research protocols, NRSN2 antibodies have been successfully used in:

• Western Blot (WB): Most commercially available NRSN2 antibodies are validated for WB applications . Typical working dilutions range from 1:500 to 1:2000.

• Immunocytochemistry/Immunofluorescence (ICC/IF): Several antibodies show good results at dilutions around 1:100 .

• Immunohistochemistry (IHC): Validated for tissue sections with recommended dilutions between 1:20 and 1:200 .

For optimal results, researchers should use antibodies that have been validated for their specific application and sample type. Western blotting appears to be the most commonly used and reliable method for NRSN2 detection across different studies .

What is the expected band pattern for NRSN2 in Western blot?

The calculated molecular weight of NRSN2 is 22 kDa, but it is typically observed at approximately 26 kDa on SDS-PAGE . This discrepancy may be due to post-translational modifications. In Western blot analysis:

• Abcam's antibody (ab237739) detected bands at 34 kDa and 68 kDa in mouse liver tissue lysate .

• Proteintech's antibody (17574-1-AP) reports an observed molecular weight of 26 kDa .

Researchers should be aware that band patterns may vary depending on the tissue/cell type, sample preparation method, and the specific antibody used. When working with a new antibody or sample type, positive controls (e.g., human brain tissue, which is known to express NRSN2) are essential for band identification and validation .

What are the optimal protocols for NRSN2 immunohistochemical detection in tissues?

For optimal IHC detection of NRSN2, researchers should consider the following protocol elements:

• Fixation: Standard 10% neutral buffered formalin fixation is typically sufficient.

• Antigen retrieval: As suggested by Proteintech for antibody 17574-1-AP:

  • Primary recommendation: TE buffer pH 9.0

  • Alternative method: Citrate buffer pH 6.0

• Antibody dilution: Start with a range of 1:20 to 1:200 and optimize based on signal-to-noise ratio .

• Detection system: A polymer-based detection system is recommended for optimal sensitivity and reduced background.

• Scoring system: For semi-quantitative analysis of NRSN2 expression, researchers can adopt the scoring method described by Ma et al.:

  • Percent positivity scored as: "0" (< 5%, negative), "1" (5%-25%, sporadic), "2" (25%-50%, focal), "3" (> 50%, diffuse)

  • Staining intensity scored as: "0" (no staining), "1" (weakly stained), "2" (moderately stained), "3" (strongly stained)

  • Final score calculated as: percent positivity score × staining intensity score (range: 0-9)

  • Expression levels defined as: "-" (score 0-1), "+" (score 2-3), "++" (score 4-6), "+++" (score > 6)

How should researchers validate the specificity of NRSN2 antibodies?

Antibody validation is crucial for generating reliable data. For NRSN2 antibodies, consider these validation approaches:

• Positive and negative controls:

  • Positive tissue controls: Human brain tissue is known to express NRSN2 .

  • Cell line controls: A549 cells express moderate levels of NRSN2 and can be used for validation .

• siRNA knockdown experiments: Transfect cells with NRSN2-specific siRNAs and confirm reduced band intensity in Western blot compared to non-targeting control siRNAs .

• Overexpression studies: Transfect cells with NRSN2 expression vectors (e.g., pRK5-NRSN2) and confirm increased band intensity .

• Treatment with NRSN2 inducers: Treating cells with known inducers of NRSN2 expression can serve as positive controls.

• Full-blot analysis: Always examine the full-length blot with molecular weight markers to identify potential non-specific bands .

The study by Lau et al. emphasizes the importance of validating antibodies in each assay of interest and reporting detailed antibody usage information to improve reproducibility .

What controls should be included in NRSN2 Western blot experiments?

For rigorous NRSN2 Western blot experiments, include the following controls:

• Positive tissue/cell controls:

  • Human brain tissue lysate (known to express NRSN2)

  • A549 lung carcinoma cells (moderate NRSN2 expression)

  • HepG2 liver cancer cells (altered NRSN2 expression)

• Loading controls:

  • Standard housekeeping proteins (GAPDH, β-actin, tubulin)

  • For subcellular fraction analysis, include fraction-specific markers

• Specificity controls:

  • Lysates from cells with NRSN2 knockdown (using siRNA or shRNA)

  • Lysates from cells overexpressing NRSN2

• Treatment controls:

  • Untreated vs. treated samples to demonstrate regulation of NRSN2 expression

• Molecular weight markers:

  • Include markers that span the expected range (20-70 kDa) to accurately identify NRSN2 bands

Remember to include full blots in publications or supplementary materials to demonstrate antibody specificity, as emphasized in recent calls to improve research reproducibility .

How do you explain the seemingly contradictory roles of NRSN2 in different cancer types?

The contradictory roles of NRSN2 across different cancer types represent an intriguing research question. Based on the available literature:

These contradictions may be explained by:

• Tissue-specific signaling contexts: NRSN2 may interact with different signaling networks in different tissues

• Cancer-specific genetic backgrounds: Mutations in partner proteins may alter NRSN2 function

• Differential regulation of downstream targets: NRSN2 may regulate different sets of genes in different cancer types

• Methodological differences: Variations in detection methods, scoring systems, and sample preparation

Further research using consistent methodologies across multiple cancer types and mechanistic studies to identify tissue-specific interaction partners would help resolve these apparent contradictions.

What signaling pathways does NRSN2 interact with and how does this affect experimental design?

NRSN2 has been implicated in several signaling pathways, which should inform experimental design:

• PI3K/AKT/mTOR pathway:

  • In NSCLC, NRSN2 silencing reduced phosphorylation levels of Akt and mTOR

  • NRSN2 overexpression increased Akt and mTOR phosphorylation

  • Experimental implications: Include measurements of p-Akt and p-mTOR levels; consider combination experiments with PI3K/mTOR inhibitors

• AMPK/ULK1 pathway:

  • In HPV-transfected laryngeal cancer, NRSN2 regulates autophagy through the AMPK/ULK1 pathway

  • Knockdown of NRSN2 suppressed this pathway and inhibited autophagy

  • Experimental implications: Monitor autophagy markers (LC3B, p62); include AMPK activators/inhibitors in experiments

• Wnt/β-catenin signaling:

  • Mentioned in a publication title as being regulated by NRSN2 in osteosarcoma

  • Experimental implications: Include β-catenin localization and Wnt target gene expression analyses

When designing experiments to study NRSN2 function, researchers should:

• Include pathway-specific readouts (phosphorylation status of key proteins)

• Consider combinatorial approaches with pathway activators/inhibitors

• Design rescue experiments to confirm pathway involvement (e.g., using rapamycin to activate autophagy after NRSN2 knockdown)

• Integrate multi-omics approaches to capture the broader impact on cellular signaling

A comprehensive experimental design would include time-course analyses to distinguish between immediate and secondary effects of NRSN2 modulation on these pathways.

What technical challenges are commonly encountered when working with NRSN2 antibodies?

Researchers working with NRSN2 antibodies should be aware of several technical challenges:

• Antibody specificity issues:

  • Even highly specific monoclonal antibodies may detect non-NRSN2 proteins that co-migrate with NRSN2

  • Example: The EP1808Y monoclonal antibody from Abcam detected another protein in HepG2 cells that could be mistaken for NRSN2

  • Solution: Validate antibodies using siRNA knockdown and include appropriate controls

• Inconsistent band patterns:

  • Observed molecular weights (26-68 kDa) often differ from the calculated weight (22 kDa)

  • Different antibodies may detect different band patterns

  • Solution: Use positive controls and full-length blots with molecular weight markers

• Variable expression across tissues:

  • NRSN2 expression varies significantly across tissue types

  • Solution: Include tissue-specific positive controls in each experiment

• Limited antibody characterization:

  • Many commercial antibodies lack thorough validation across applications

  • Solution: Perform in-house validation for your specific application and tissue/cell type

• Incomplete reporting in literature:

  • Publications often list the company without specifying product numbers, making it difficult to reproduce results

  • Solution: Always report complete antibody information including catalog numbers

Lau et al. advocate for specifying antibody product numbers for each experiment and validating antibodies in each assay by treatment with inducers or knockdown, showing full-length blots with markers to demonstrate unambiguous identification of NRSN2 .

How can NRSN2 expression analysis be used as a prognostic biomarker in cancer research?

NRSN2 expression has shown potential as a prognostic biomarker in several cancers:

For researchers interested in using NRSN2 as a prognostic biomarker:

• Standardized scoring system: Adopt the semi-quantitative scoring method described by Ma et al., combining percent positivity and staining intensity

• Tissue microarray analysis: Efficient for analyzing large cohorts

• Multivariate analysis: Always adjust for established prognostic factors

• Correlation with molecular subtypes: Determine if NRSN2's prognostic value varies by cancer subtype

• Combination with other markers: Explore if NRSN2 adds value to existing prognostic panels

The apparent dichotomy of NRSN2's role in different cancers (tumor suppressive in HCC vs. oncogenic in others) suggests that its prognostic value may be context-dependent, requiring careful validation in each cancer type.

What are the recommended methods for modulating NRSN2 expression in functional studies?

Researchers have successfully employed several approaches to modulate NRSN2 expression:

• RNA interference (RNAi):

  • siRNA approach: Pooled siRNAs showed more effective knockdown than single siRNAs in A549 cells

  • shRNA approach: Used successfully in HPV-transfected laryngeal cancer cells (TU212/HPV and TU138/HPV)

  • Evaluation: Monitor knockdown efficiency by qRT-PCR and Western blot

• Overexpression systems:

  • Plasmid vectors: pRK5-NRSN2 constructs have been used for overexpression studies

  • Cloning strategy: Forward primer 5′-CTAGCTAGCATGATGCCGAGCTGCAATC-3′ and reverse primer 5′-CGCGGATCCTCAGGAGTCCCTCTTGGG-3′

  • Transfection: Lipofectamine 2000 has been used successfully for transfection at 37°C

  • Validation: qRT-PCR using primers 5′-GATGGCAAGTGGTATGGGGTC-3′ (forward) and 5′-CGAGGACAGGCTGATCTTCC-3′ (reverse)

• Pathway modulators:

  • Autophagy activator rapamycin (RAP) has been used to rescue phenotypes caused by NRSN2 knockdown

  • PI3K/Akt/mTOR inhibitors can be used to block NRSN2-mediated signaling

• CRISPR/Cas9 gene editing:

  • While not explicitly mentioned in the search results, CRISPR/Cas9 would be an advanced approach for complete knockout studies

For optimal functional studies, researchers should:

• Include appropriate controls (empty vector, non-targeting siRNA)

• Validate expression changes at both mRNA and protein levels

• Perform rescue experiments to confirm specificity

• Consider time-course experiments to distinguish between immediate and adaptive effects

How should researchers interpret contradictory experimental results when studying NRSN2?

When faced with contradictory results in NRSN2 research, consider these methodological approaches:

• Evaluate antibody specificity:

  • Different antibodies may detect different isoforms or cross-react with similar proteins

  • Example: The EP1808Y monoclonal antibody detected a non-NRSN2 protein in HepG2 cells that co-migrated with Nrf2

  • Action: Validate antibodies using siRNA knockdown and multiple detection methods

• Consider cellular context:

  • NRSN2 functions differently across cancer types:

    • Tumor suppressor in HCC

    • Oncogene in NSCLC , laryngeal cancer , and CRC

  • Action: Analyze pathway activation status in your specific model

• Examine experimental conditions:

  • Cell confluence can affect protein expression levels

  • Example: The intensity of a non-specific band detected by EP1808Y increased when THLE-2 cells were grown to overconfluency

  • Action: Standardize cell culture conditions and document thoroughly

• Analyze genetic background:

  • Different cell lines may have mutations affecting NRSN2 function

  • Action: Sequence NRSN2 and key pathway components in your model

• Investigate post-translational modifications:

  • The calculated molecular weight (22 kDa) differs from observed weights (26-68 kDa)

  • Action: Use phosphatase treatments or mass spectrometry to identify modifications

• Integrate multiple experimental approaches:

  • Combine genetic modulation (siRNA, overexpression) with pharmacological approaches

  • Use multiple detection methods (WB, IHC, IF, qRT-PCR)

  • Perform in vitro and in vivo studies when possible

When reporting contradictory results, thoroughly document all experimental conditions, antibody details (including catalog numbers), and consider publishing full blots as supplementary data to aid in reproducibility efforts .

What are the emerging techniques for studying NRSN2 protein-protein interactions?

While the search results don't specifically mention protein-protein interaction studies for NRSN2, researchers can apply these cutting-edge approaches:

• Proximity-dependent biotinylation (BioID or TurboID):

  • Fuse NRSN2 to a biotin ligase to identify proximal proteins

  • Advantages: Captures transient interactions; works in native cellular environments

  • Applications: Could help identify vesicular transport partners of NRSN2

• CRISPR-based approaches:

  • CRISPR activation/inhibition to modulate NRSN2 expression

  • CRISPR knock-in of tags for endogenous protein studies

  • Applications: Study NRSN2 function without overexpression artifacts

• Live-cell imaging techniques:

  • FRET/BRET to study dynamic interactions

  • Split fluorescent/luminescent reporters

  • Applications: Monitor NRSN2 interactions with vesicular transport machinery in real-time

• Mass spectrometry-based interactomics:

  • Immunoprecipitation coupled with LC-MS/MS

  • Crosslinking mass spectrometry (XL-MS)

  • Applications: Identify interaction partners in different cancer contexts to explain differential functions

• Single-cell analysis:

  • Single-cell RNA-seq combined with protein analysis

  • Applications: Understand heterogeneity of NRSN2 expression and function within tumors

Given that NRSN2 appears to function differently across cancer types , applying these techniques in multiple cellular contexts would help elucidate the molecular basis for its context-dependent roles.

How can researchers better standardize NRSN2 antibody validation to improve reproducibility?

To improve reproducibility in NRSN2 research, researchers should implement these standardization practices:

• Comprehensive antibody reporting:

  • Always specify complete antibody information: manufacturer, catalog number, lot number, clone (for monoclonals), and host species

  • Include detailed methods: dilutions, incubation times/temperatures, blocking reagents

• Multi-method validation:

  • Validate each antibody using:

    • Genetic approaches: siRNA/shRNA knockdown, CRISPR knockout

    • Overexpression systems: Transfection with NRSN2 expression vectors

    • Treatment controls: Use known modulators of NRSN2 expression

• Standardized controls:

  • Positive controls: Human brain tissue for NRSN2 expression

  • Negative controls: Knockdown samples, irrelevant tissues

  • Multiple cell lines: Test across different cellular contexts

• Full blot documentation:

  • Include full-length blots with molecular weight markers in publications or supplementary materials

  • Document all observed bands, not just the expected one

• Independent validation:

  • Validate findings with multiple antibodies targeting different epitopes

  • Compare monoclonal and polyclonal antibodies

• Antibody characterization databases:

  • Submit validation data to public repositories

  • Reference previous validation studies

Following the example of the Nrf2 antibody characterization study by Lau et al. , researchers should conduct comparative studies of available NRSN2 antibodies across multiple applications and cell types to establish community standards for antibody selection and validation.

What integrated approaches could resolve the context-dependent roles of NRSN2 in cancer?

To resolve the apparent contradictions in NRSN2's role across cancer types, an integrated research approach is necessary:

• Multi-cancer comparative studies:

  • Apply identical methodologies across multiple cancer types

  • Compare NRSN2 expression, regulation, and function in:

    • Hepatocellular carcinoma (tumor suppressor context)

    • Lung cancer (oncogenic context)

    • Laryngeal cancer (oncogenic context)

    • Colorectal cancer (oncogenic context)

• Pathway analysis integration:

  • Systematically analyze NRSN2's interaction with:

    • PI3K/AKT/mTOR pathway

    • AMPK/ULK1 pathway and autophagy

    • Wnt/β-catenin signaling

  • Use pathway inhibitors/activators to determine context-dependent outcomes

• Multi-omics approach:

  • Integrate transcriptomics, proteomics, and metabolomics

  • Perform ChIP-seq to identify differential transcriptional targets

  • Analyze NRSN2 interactome across cancer types

• Genetic background characterization:

  • Sequence NRSN2 and key pathway genes

  • Identify cancer-specific mutations that might alter NRSN2 function

  • Create isogenic cell lines with specific genetic alterations

• Structural biology studies:

  • Determine if NRSN2 undergoes different post-translational modifications

  • Identify cancer-specific protein conformations

• In vivo models:

  • Develop tissue-specific NRSN2 knockout/overexpression models

  • Compare phenotypes across multiple tissue types

This integrated approach would help determine whether NRSN2's opposing roles are due to true biological differences, cellular context, or methodological variations, advancing our understanding of this complex protein.