NMUR1 Antibody

What is NMUR1 and what biological functions does it mediate?

NMUR1 (Neuromedin U Receptor 1) is one of the two G protein-coupled receptors for the neuropeptide Neuromedin U (NMU) . NMUR1 is primarily expressed in peripheral tissues, with particularly high expression in the gastrointestinal tract and lungs . Functionally, NMUR1 plays important roles in modulating smooth muscle contraction (especially in the gastrointestinal tract), stimulating smooth muscles, increasing blood pressure, altering ion transport in the gut, controlling local blood flow, and regulating adrenocortical function . Recent research has also highlighted NMUR1's significant involvement in immune responses and cancer biology, particularly in colorectal cancer (CRC) where it can function as a tumor suppressor and correlate with immune checkpoint expression .

How do I select the appropriate anti-NMUR1 antibody for my research?

When selecting an anti-NMUR1 antibody, consider the following methodological criteria:

• Target epitope location: Determine whether you need an antibody targeting extracellular domains (suitable for flow cytometry of live cells or functional blocking) or intracellular domains (for applications like Western blot or immunohistochemistry of fixed samples) . For instance, antibodies targeting the extracellular third loop (such as peptide DRLMWSMVSHWTDGLR, corresponding to amino acid residues 316-331 of rat NMUR1) are available for research use .

• Cross-reactivity: Verify species reactivity and homology. For example, rat and mouse NMUR1 share only 10 out of 16 amino acid residues in the extracellular third loop region . Human-specific antibodies may not cross-react with rodent samples.

• Validated applications: Confirm that the antibody has been validated for your specific application (Western blot, immunohistochemistry, flow cytometry, etc.) . For example, some anti-NMUR1 antibodies have been validated for Western blot analysis in human cell lines including HT-29 colon adenocarcinoma, Jurkat T-cell leukemia, K562 myelogenous leukemia, and NK-92 natural killer cells .

• Sample type compatibility: Ensure the antibody works with your sample type (fresh-frozen tissue, formalin-fixed paraffin-embedded tissue, cell lysates, etc.) .

What are the recommended positive control samples for validating anti-NMUR1 antibodies?

Based on the expression profile of NMUR1, the following samples can serve as appropriate positive controls:

• Tissue controls: Gastrointestinal tract tissues, particularly colon samples, serve as excellent positive controls due to high NMUR1 expression . Human colon adenocarcinoma cell line HT-29 has been validated as expressing detectable levels of NMUR1 .

• Immune cell populations: CD8+ T cells and NK cells have been identified as expressing significant levels of NMUR1 . Cell lines such as NK-92 (natural killer cell line) and Jurkat (T-cell leukemia) can serve as positive controls .

• Neuronal tissues: Rat cerebellum has been validated for NMUR1 expression by immunohistochemical staining .

• Macrophage cell lines: Mouse J774 macrophage cell line has been validated for cell surface expression of NMUR1 by flow cytometry .

When using these controls, incorporate appropriate blocking peptides (when available) for specificity validation .

What are the recommended protocols for detecting NMUR1 in different sample types?

Western Blot Protocol for NMUR1 Detection:

• Sample preparation: For brain tissue samples, prepare membrane fractions as these better preserve GPCR proteins. For cell lines (HT-29, Jurkat, K562, NK-92), standard whole-cell lysis buffers containing protease inhibitors are suitable .

• Protein separation: Load 20-50μg of protein per lane on 10-12% SDS-PAGE gels.

• Transfer and blocking: Transfer to PVDF membranes and block with 5% non-fat milk or BSA in TBST.

• Primary antibody incubation: Dilute anti-NMUR1 antibody (typically 1:500 to 1:1000) in blocking buffer and incubate overnight at 4°C .

• Detection: Use appropriate HRP-conjugated secondary antibodies and enhanced chemiluminescence detection.

• Specificity control: Include blocking peptide controls where available to validate specificity .

Flow Cytometry Protocol for Surface NMUR1 Detection:

• Cell preparation: Harvest cells (e.g., J774 macrophages) and wash in PBS containing 2% FBS.

• Antibody staining: Incubate 1×10^6 cells with anti-NMUR1 (extracellular) antibody at appropriate dilution for 30 minutes at 4°C.

• Secondary staining: Wash and incubate with fluorophore-conjugated secondary antibody (e.g., goat-anti-rabbit-FITC).

• Analysis: Analyze using standard flow cytometry protocols with appropriate negative and isotype controls .

Immunohistochemistry Protocol for NMUR1 in Tissue Sections:

• Sample preparation: Use perfusion-fixed frozen brain sections or appropriately fixed peripheral tissue samples.

• Antigen retrieval: If using formalin-fixed paraffin-embedded tissues, perform appropriate antigen retrieval.

• Blocking: Block with serum matching the host of the secondary antibody.

• Primary antibody: Incubate with anti-NMUR1 antibody at validated dilution.

• Detection: Apply appropriate detection system and counterstain nuclei with DAPI if desired .

How can I quantify NMUR1 expression in tumor microenvironments using multi-parameter approaches?

For comprehensive analysis of NMUR1 in tumor microenvironments, particularly in relation to immunotherapy responses, implement these methodological approaches:

• Single-cell RNA sequencing analysis: This allows for precise cell type identification and NMUR1 expression profiling across different cell populations. Using platforms like SciBet for cell type identification is recommended for accurate classification of diverse cell populations in tumor samples . This technique has successfully identified elevated NMUR1 expression in CD8+ T cells from colorectal cancer patients who responded to immunotherapy compared to non-responders .

• Correlation with immune markers: Analyze the correlation between NMUR1 expression and established immune cell markers. For instance, NMUR1 shows positive correlation with CD8+ T cell markers CD8A and CD8B in colorectal cancer specimens . Additionally, examine correlations with immune checkpoints such as PDCD1 and CTLA4, which have demonstrated positive associations with NMUR1 across diverse cancer types .

• Pathway analysis: Perform GO enrichment and KEGG pathway analysis to understand the functional implications of NMUR1 in specific cell populations. This approach revealed significant insights into NMUR1's role in T cells from responding versus non-responding patient samples .

• Cellular communication analysis: Examine receptor-ligand interactions at the single-cell level to understand how NMU-NMUR1 signaling contributes to cellular communication within the tumor microenvironment .

• Validation across multiple datasets: Validate findings across independent datasets. The link between NMUR1 expression in CD8+ T cells and immunotherapy response has been consistently observed across three independent single-cell RNA datasets (including GSE205506 and GSE222300) .

What are the technical considerations for ensuring specificity in NMUR1 antibody-based assays?

Ensuring specificity in NMUR1 antibody-based assays requires rigorous controls and validation:

• Blocking peptide validation: Use specific blocking peptides corresponding to the immunogen sequence used to generate the antibody. For example, parallel experiments with the NMUR1 (extracellular) Blocking Peptide (BLP-NR071) should be conducted to confirm signal specificity in Western blot, immunohistochemistry, and flow cytometry applications .

• Knockout/knockdown controls: Where available, include NMUR1 knockout or knockdown samples as negative controls. As demonstrated in functional studies, NMUR1 deletion models provide excellent specificity controls .

• Cross-reactivity assessment: Evaluate potential cross-reactivity with the related receptor NMUR2, which shares sequence homology with NMUR1. Western blot analysis using both rat and mouse brain membranes can help identify potential cross-reactivity issues .

• Multiple antibody validation: When possible, confirm key findings using independently derived antibodies targeting different epitopes of NMUR1.

• Species considerations: Be aware of sequence differences between species. For example, in the 316-331 amino acid region of NMUR1, rat and mouse proteins share only 10 out of 16 amino acid residues, which may affect antibody performance across species .

How does NMUR1 expression correlate with immune cell infiltration and immunotherapy response in colorectal cancer?

NMUR1 expression demonstrates significant correlations with immune cell infiltration and immunotherapy response in colorectal cancer (CRC), making it a potential biomarker for treatment stratification:

• CD8+ T cell correlation: High NMUR1 expression is significantly associated with increased CD8+ T cell infiltration in CRC tumors. This was demonstrated in multiple datasets, including GSE235919 which comprised 23 responders and 11 non-responders to CRC immunotherapy . This correlation suggests that NMUR1 may influence the recruitment or retention of cytotoxic T cells within the tumor microenvironment.

• Immunotherapy response prediction: NMUR1 expression is consistently elevated in patients who respond to immunotherapy compared to non-responders. In comparative evaluations of biomarker potential using AUC, NMUR1 (AUC = 0.626) outperformed established biomarkers like PD-L1 (AUC = 0.555) in predicting response to CRC immunotherapy .

• Immune checkpoint correlation: NMUR1 shows positive associations with immune checkpoint molecules including PDCD1 (PD-1) and CTLA4 across diverse cancer types, suggesting its involvement in regulating immune checkpoint pathways .

• NK cell association: The ratio of NK cells exhibits a positive association with NMUR1 expression, as demonstrated in the CRC_GSE146771_10X dataset . This suggests that NMUR1 may influence multiple components of anti-tumor immunity.

• Chemokine correlation: High NMUR1 expression correlates with increased expression of chemokines and their receptors, potentially facilitating immune cell recruitment to the tumor microenvironment .

These findings collectively indicate that high NMUR1 expression is associated with a tumor-suppressive tumor microenvironment that may benefit more from immunotherapy strategies.

What is the role of NMUR1 in modulating CD8+ T cell function in the context of cancer immunotherapy?

NMUR1 appears to play a critical role in modulating CD8+ T cell function in cancer immunotherapy contexts:

• Metabolic regulation: NMU-NMUR1 signaling has been investigated for its connection with the metabolism and regulation of anti-tumor activity of CD8+ T cells . This signaling pathway may alter CD8+ T cell metabolism in the colorectal cancer microenvironment, potentially enhancing anti-tumor immune responses.

• Enrichment in therapy responders: CD8+ T cells expressing NMUR1 are significantly enriched in patients who respond to immunotherapy. This has been consistently observed across multiple independent datasets (GSE205506, GSE222300), suggesting a functional role for NMUR1 in promoting effective anti-tumor T cell responses .

• Correlation with T cell effector markers: NMUR1 expression positively correlates with CD8+ T cell effector markers, suggesting its potential involvement in the cytotoxic function of these cells .

• Pathway activation: GO enrichment and KEGG pathway analyses of T cells from responders versus non-responders revealed distinct functional signatures associated with NMUR1 expression, providing insights into the molecular mechanisms by which NMUR1 might influence T cell function .

• Cellular communication: At the single-cell level, NMU-NMUR1 interaction appears as a communicative pathway among immune cells within the CRC immune microenvironment, though this interaction may be relatively weak due to the low secretion of NMU in immune cells .

Understanding these mechanisms could potentially lead to novel strategies to enhance immunotherapy efficacy by targeting the NMU-NMUR1 axis.

How can NMUR1 antibodies be used to investigate the relationship between NMUR1 and cancer progression?

NMUR1 antibodies provide powerful tools for investigating the relationship between NMUR1 and cancer progression through multiple methodological approaches:

• Expression profiling across cancer stages: Utilizing anti-NMUR1 antibodies in tissue microarray analysis can help determine how NMUR1 expression patterns change during cancer progression. Research has shown that NMUR1 has significantly lower expression in colorectal adenocarcinoma (COAD) compared to normal tissues, suggesting a potential tumor-suppressive role .

• Functional blocking studies: Antibodies targeting extracellular domains of NMUR1 can be used in functional blocking experiments to assess how disruption of NMU-NMUR1 signaling affects cancer cell proliferation, invasion, and migration. This approach has demonstrated that high NMUR1 expression contributes to the inhibition of colorectal cancer cell proliferation and invasion .

• Co-immunoprecipitation studies: Anti-NMUR1 antibodies can be employed in co-immunoprecipitation experiments to identify protein interaction partners, helping to elucidate the molecular mechanisms by which NMUR1 influences cancer cell behavior and immune responses.

• Immune cell characterization: Using flow cytometry with anti-NMUR1 antibodies, researchers can identify and isolate NMUR1-expressing immune cell populations (particularly CD8+ T cells and NK cells) from tumor samples for further functional characterization .

• Multi-omics integration: Combining antibody-based detection methods with genomic and transcriptomic data can provide a comprehensive understanding of how genetic and epigenetic alterations in NMUR1 correlate with protein expression and function across cancer types .

These approaches have revealed that NMUR1 functions as a suppressor in colorectal cancer and serves as a promising biomarker for immunotherapy response .

What are common challenges in detecting NMUR1 in tissue samples and how can they be overcome?

Researchers often encounter several technical challenges when detecting NMUR1 in tissue samples:

• Low abundance in certain tissues: NMUR1 may be expressed at low levels in some tissues, making detection challenging. To overcome this:

  • Use signal amplification systems such as tyramide signal amplification (TSA) in immunohistochemistry

  • Enrich for membrane fractions in Western blot applications to concentrate GPCR signals

  • Consider more sensitive detection methods such as RNAscope for mRNA detection when protein detection is challenging

• Non-specific binding: G-protein coupled receptors like NMUR1 can be prone to non-specific antibody binding. Mitigate this by:

  • Always including blocking peptide controls in parallel experiments

  • Optimizing blocking conditions with both serum and protein blockers

  • Using multiple antibodies targeting different epitopes when possible

• Receptor internalization: NMUR1 may undergo internalization upon ligand binding, affecting cell surface detection. Address this by:

  • Performing experiments in serum-free conditions to minimize ligand exposure

  • Considering fixation methods that preserve receptor localization

  • Including permeabilization steps to detect both surface and internalized receptors

• Tissue-specific post-translational modifications: NMUR1 may undergo different post-translational modifications in different tissues, affecting antibody recognition. Consider:

  • Using multiple antibodies targeting different epitopes

  • Verifying specificity in each tissue type of interest

  • Including appropriate positive controls for each tissue type

• Cross-reactivity with NMUR2: Due to sequence homology between NMUR1 and NMUR2, antibodies may cross-react. Verify specificity by:

  • Testing in tissues known to express predominantly one receptor subtype

  • Using receptor-specific blocking peptides

  • Validating with knockout/knockdown controls where available

How can I optimize immunohistochemical protocols for NMUR1 detection in different cancer types?

Optimizing immunohistochemical detection of NMUR1 across cancer types requires attention to several key variables:

• Tissue fixation optimization:

  • For FFPE samples: Limit fixation time to 24 hours to prevent epitope masking

  • For frozen sections: Consider light fixation with 4% paraformaldehyde to maintain membrane protein structure while preserving epitopes

  • For each cancer type, compare detection in frozen versus FFPE samples to determine optimal preparation method

• Antigen retrieval method selection:

  • Test multiple antigen retrieval methods (heat-induced in citrate buffer pH 6.0, EDTA buffer pH 9.0, enzymatic retrieval)

  • For colorectal cancer samples, heat-induced epitope retrieval in EDTA buffer pH 9.0 often yields optimal results for membrane proteins

  • Optimize retrieval duration based on tissue type (typically 15-30 minutes)

• Signal amplification strategies:

  • For tissues with lower NMUR1 expression (such as some tumor types), employ polymer-based detection systems or tyramide signal amplification

  • For highly vascularized tumors, consider biotin-free detection systems to minimize background

  • Use automated staining platforms when available to ensure consistency across specimens

• Cancer-specific considerations:

  • For colorectal cancer: Include normal colonic mucosa as internal control

  • For highly necrotic tumors: Focus analysis on tumor margins with viable tissue

  • For immune-rich tumors: Consider dual staining with immune cell markers (CD8, NK markers) to assess co-localization

• Validation strategies:

  • For each cancer type, validate staining patterns using orthogonal methods (RNA-seq data, Western blot)

  • Include tissue microarrays with multiple tumor types to assess staining patterns across cancer types simultaneously

  • Establish scoring criteria appropriate to the subcellular localization pattern observed in each cancer type

Immunohistochemical analysis has successfully visualized NMUR1 expression in rat cerebellum, providing a methodological foundation that can be adapted for cancer tissue analysis .

What are the best approaches for studying NMUR1 in the context of receptor-ligand interactions?

Studying NMUR1 in receptor-ligand interaction contexts requires specialized approaches:

• Single-cell analysis of ligand-receptor pairs:

  • Implement computational tools that analyze single-cell RNA-seq data to identify patterns of ligand-receptor expression

  • The NMU-NMUR1 interaction has been characterized as a communicative pathway among immune cells within the colorectal cancer microenvironment, although relatively weak due to low NMU secretion in immune cells

  • For more robust analysis, consider CellChat, NicheNet, or similar tools that quantify potential cell-cell communication based on expression patterns

• Calcium flux assays:

  • NMUR1 activation by NMU triggers intracellular calcium mobilization

  • Use fluorescent calcium indicators (Fluo-4, Fura-2) to measure NMUR1 activation kinetics in response to NMU

  • This approach can detect the autocrine tumor-promoting pathway activation, including intracellular calcium flux that occurs following NMU-NMUR1 interaction

• Phosphorylation cascade monitoring:

  • NMUR1 activation leads to downstream phosphorylation of ERK1/2 kinases

  • Utilize phospho-specific antibodies in Western blot or ELISA-based assays to track NMUR1 signaling dynamics

  • This methodology has established that NMU-NMUR1 interaction leads to phosphorylation of ERK1/2 kinases in cancer contexts

• Hypoxia-inducible factor activation:

  • NMUR1 signaling has been linked to hypoxia-inducible factor activation

  • Use reporter assays or immunoblotting for HIF-1α to assess this downstream effect of NMUR1 activation

  • This approach helps characterize how NMUR1 contributes to cancer progression through hypoxia signaling pathways

• Competitive binding assays:

  • Employ radiolabeled or fluorescently labeled NMU peptides in competition assays

  • Use anti-NMUR1 antibodies targeting extracellular domains to assess blocking efficiency

  • This methodology can determine binding affinities and identify potential therapeutic antibodies that might disrupt NMU-NMUR1 signaling

These approaches collectively provide a comprehensive toolkit for understanding how NMUR1 interacts with its ligand NMU and initiates downstream signaling events relevant to cancer biology and immune response.

What emerging techniques might enhance our understanding of NMUR1 biology in cancer and immunotherapy?

Several cutting-edge methodological approaches show promise for advancing NMUR1 research:

• Spatial transcriptomics and proteomics: These technologies can map NMUR1 expression within the tumor microenvironment with spatial resolution, revealing how NMUR1-expressing cells are distributed relative to other cell types. This would provide crucial insights into the spatial relationships between NMUR1-expressing CD8+ T cells and other immune or tumor cells, potentially revealing new aspects of NMUR1 biology in cancer immunotherapy .

• CRISPR-Cas9 genome editing for functional studies: Precise genetic manipulation of NMUR1 in immune cells and cancer cell lines can elucidate its causal roles in immunotherapy response. The finding that NMUR1 deletion leads to impaired immunotherapy response provides a foundation for more detailed mechanistic studies using genome editing approaches .

• Single-cell multi-omics integration: Combining single-cell transcriptomics, proteomics, and metabolomics can provide a comprehensive view of how NMUR1 influences cellular phenotypes and functions across different cell types in the tumor microenvironment.

• Mass cytometry (CyTOF) with NMUR1 detection: This approach would allow simultaneous measurement of NMUR1 expression alongside dozens of other proteins at the single-cell level, enabling detailed phenotyping of NMUR1-expressing cells in complex tissues.

• Organoid models incorporating immune components: Patient-derived organoids with co-cultured immune cells can serve as platforms for investigating how NMUR1 modulates interactions between tumor cells and immune cells in a controlled three-dimensional environment.

• In vivo imaging of NMUR1-expressing cells: Developing techniques to track NMUR1-expressing cells longitudinally in animal models would provide insights into their dynamics during tumor progression and immunotherapy response.

These emerging approaches would complement current methodologies and potentially reveal new therapeutic opportunities targeting the NMUR1 pathway.

How might NMUR1 antibodies be utilized in developing novel cancer immunotherapy strategies?

NMUR1 antibodies offer several promising avenues for developing innovative cancer immunotherapy approaches:

• Biomarker-based patient stratification: Anti-NMUR1 antibodies can be used to develop immunohistochemical or flow cytometry-based assays for assessing NMUR1 expression in patient samples. Given that NMUR1 expression is associated with immunotherapy response (AUC = 0.626, outperforming PD-L1 with AUC = 0.555), such assays could help identify patients most likely to benefit from immune checkpoint inhibitors .

• Agonistic antibody development: Designing antibodies that mimic NMU and activate NMUR1 signaling specifically on CD8+ T cells could potentially enhance anti-tumor immune responses. Since high NMUR1 expression in CD8+ T cells correlates with better immunotherapy outcomes, such agonistic antibodies might boost T cell function in patients with low endogenous NMU levels .

• Bispecific antibody engineering: Creating bispecific antibodies that simultaneously target NMUR1 on T cells and tumor-specific antigens could help recruit and activate T cells within the tumor microenvironment, potentially enhancing immune-mediated tumor clearance.

• Antibody-drug conjugates (ADCs): For tumors that express NMUR1, developing ADCs targeting NMUR1 could deliver cytotoxic payloads specifically to cancer cells, though this approach would require careful validation given NMUR1's expression in normal tissues.

• Combination therapy optimization: Anti-NMUR1 antibodies could be used in preclinical studies to identify optimal combinations with existing immunotherapies. For instance, since NMUR1 positively correlates with immune checkpoints like PDCD1 and CTLA4 , combining NMUR1-targeted approaches with checkpoint inhibitors might yield synergistic effects.

• Immune cell engineering: Antibodies against NMUR1 could facilitate the isolation and ex vivo expansion of NMUR1-expressing T cell populations for adoptive cell therapy approaches, potentially enhancing their anti-tumor efficacy upon reinfusion.

These strategies leverage the observed associations between NMUR1 expression, immune cell function, and immunotherapy response to potentially develop more effective cancer treatments.

What are the critical experimental controls needed when investigating NMUR1 knockout/knockdown effects on immunotherapy response?

When investigating how NMUR1 knockout/knockdown affects immunotherapy response, implementing these critical experimental controls is essential:

• Validation of knockout/knockdown efficiency:

  • Confirm NMUR1 deletion at both protein and mRNA levels using validated antibodies for Western blot/immunohistochemistry and qPCR, respectively

  • Include wild-type samples processed identically as positive controls

  • For partial knockdowns, quantify the degree of reduction to correlate with observed phenotypes

• Specificity controls for pathway effects:

  • Assess expression of the related receptor NMUR2 to rule out compensatory upregulation

  • Evaluate baseline levels of downstream signaling molecules (e.g., phosphorylated ERK1/2) to confirm pathway-specific effects

  • Where possible, include rescue experiments by reintroducing NMUR1 to confirm phenotype reversibility

• Immunotherapy model validation:

  • Include treatment-naïve controls to establish baseline tumor growth kinetics

  • Administer isotype control antibodies alongside immunotherapy agents

  • Include both responder and non-responder phenotypes in wild-type conditions as reference points

• Immune cell phenotyping controls:

  • Compare multiple immune cell populations (CD8+ T cells, CD4+ T cells, NK cells, macrophages) to determine specificity of NMUR1 effects

  • Assess both intratumoral and peripheral immune compartments

  • Include appropriate isotype controls for all flow cytometry antibodies

• Temporal controls:

  • Evaluate immunotherapy effects at multiple timepoints to distinguish delayed from absent responses

  • Consider inducible knockout systems to determine whether NMUR1 is required for initiation versus maintenance of response

• Tumor model controls:

  • Test findings across multiple tumor models to ensure generalizability

  • Consider both immunologically "hot" and "cold" tumor models

  • Control for tumor size at treatment initiation to minimize variability

These controls are critical for establishing that observed effects on immunotherapy response are specifically attributable to NMUR1 deletion rather than off-target effects or experimental variables.