NLN Antibody

What is Neurolysin (NLN) and why is it important in research?

Neurolysin (NLN), also known as AGTBP or KIAA1226, is a member of the peptidase M3 family and functions as an enzyme involved in the metabolic inactivation of bioactive peptides. It operates as a monomer with a molecular mass of approximately 78 kDa, though detection can occur between 70-85 kDa according to published reports. The full-length protein contains a transit peptide with 37 amino acids. NLN is mitochondrial in localization and plays significant roles in peptide processing pathways, making it an important target in various research areas including neuroscience and metabolic studies. Understanding NLN's function requires reliable antibodies for detection and quantification in experimental systems. The calculated molecular weight of NLN is 81 kDa, though the observed molecular weight in experimental systems is typically around 75 kDa, which is an important consideration when validating antibody specificity .

How do polyclonal and monoclonal NLN antibodies differ in research applications?

Polyclonal and monoclonal NLN antibodies offer distinct advantages in research applications based on their intrinsic properties. Polyclonal NLN antibodies, such as the rabbit polyclonal antibody (14763-1-AP), recognize multiple epitopes on the NLN protein, providing robust detection across various applications but with potential variability between lots. These antibodies are particularly useful in applications where signal amplification is beneficial, such as detecting low-abundance proteins in Western blot or immunohistochemistry . In contrast, monoclonal antibodies like the mouse monoclonal antibody clone OTI1D6 (M03028-1) bind to a single epitope with high specificity, ensuring consistent results across experiments and reduced cross-reactivity. Monoclonal antibodies exhibit particular utility in flow cytometry and immunofluorescence applications where precise epitope targeting is crucial. The choice between polyclonal and monoclonal NLN antibodies should be driven by the specific research question, required application sensitivity, and the need for batch-to-batch consistency in long-term studies .

What species reactivity should be considered when selecting an NLN antibody?

Species reactivity is a critical consideration when selecting an NLN antibody for research, as it determines whether the antibody will recognize the target protein in your experimental model organism. Available NLN antibodies demonstrate different cross-reactivity profiles across species. For instance, the polyclonal antibody 14763-1-AP has confirmed reactivity with human, mouse, and rat samples, making it versatile for comparative studies across these common laboratory models . The monoclonal antibody M03028-1 (clone OTI1D6) exhibits broader cross-reactivity, recognizing NLN in human, monkey, mouse, and rat samples, as demonstrated through validation studies in multiple cell lines including HepG2 (human), HeLa (human), SVT2 (mouse), A549 (human), COS7 (monkey), Jurkat (human), MDCK (canine), PC12 (rat), and MCF7 (human) . Researchers should carefully evaluate published validation data and consider performing preliminary testing when applying these antibodies to species not explicitly listed in the manufacturer's specifications, particularly for evolutionarily distant organisms or when studying tissue-specific expression patterns that may differ across species.

What are the optimal dilution ratios for NLN antibodies in different applications?

The optimal dilution ratios for NLN antibodies vary significantly depending on the specific application and the antibody being used. For the polyclonal NLN antibody (14763-1-AP), Western blot applications typically require dilutions ranging from 1:200 to 1:1000, while immunohistochemistry applications require dilutions between 1:50 and 1:500 . For the monoclonal antibody M03028-1 (clone OTI1D6), more specific recommendations are provided: Western blot applications work optimally at dilutions between 1:500 and 1:2000, immunohistochemistry applications at approximately 1:150, immunofluorescence at 1:100, and flow cytometry also at 1:100 . It is important to note that these recommendations serve as starting points for optimization, and the actual optimal dilution may vary based on sample type, protein expression level, and detection system used. A titration experiment should be performed for each new application or sample type to determine the optimal antibody concentration that maximizes specific signal while minimizing background. The table below summarizes the recommended dilution ranges:

What antigen retrieval methods are recommended for NLN antibody in immunohistochemistry?

Antigen retrieval is a critical step in immunohistochemistry (IHC) that significantly impacts the quality and specificity of NLN antibody staining. For the polyclonal NLN antibody (14763-1-AP), the suggested primary antigen retrieval method involves using TE buffer at pH 9.0, which helps to unmask epitopes that may be cross-linked or obscured during the fixation process. Alternatively, citrate buffer at pH 6.0 can be used as an alternative antigen retrieval method when TE buffer does not yield optimal results . Similarly, for monoclonal antibodies used in IHC applications, such as those described in the SARS-CoV-2 study methodology, tissues underwent antigen retrieval using either pH 6 citrate buffer or pH 9 Tris-EDTA buffer with microwave heating . The choice between these methods should be determined experimentally for each tissue type and fixation protocol. Factors that influence the optimal antigen retrieval method include tissue type, fixation duration, embedding procedure, and the specific epitope recognized by the antibody. Researchers should consider performing a comparative analysis of different antigen retrieval methods when establishing a new IHC protocol for NLN detection, particularly in tissues with complex architecture or high lipid content that may affect antibody accessibility.

How should Western blot protocols be optimized for NLN antibody detection?

Optimizing Western blot protocols for NLN antibody detection requires careful consideration of several parameters to ensure specific and sensitive detection. First, sample preparation is crucial – given that NLN has a calculated molecular weight of 81 kDa but is typically observed at approximately 75 kDa in experimental systems, proper protein denaturation and SDS-PAGE conditions are essential for accurate sizing . For cell lysate preparation, loading 5-35 μg of total protein per lane has been validated in various cell lines, including HepG2 cells which show good endogenous expression of NLN . For gel electrophoresis, 8-10% polyacrylamide gels provide optimal resolution in the 70-85 kDa range where NLN is detected. The transfer step should be optimized for proteins of this size, typically using slightly longer transfer times or modified buffer compositions to ensure complete transfer of larger proteins. For blocking, 5% non-fat dry milk or bovine serum albumin (BSA) in TBST is recommended to minimize background. The antibody incubation should follow the dilution guidelines (1:200-1:1000 for polyclonal, 1:500-1:2000 for monoclonal), ideally performed overnight at 4°C to maximize specific binding . Including positive controls such as mouse ovary tissue or HepG2 cells, which show confirmed NLN expression, is essential for validating the specificity of the detection. The detection system (chemiluminescence, fluorescence) should be selected based on the required sensitivity and quantification needs of the specific experiment.

How can cross-reactivity be assessed when using NLN antibodies in multi-protein studies?

Assessing cross-reactivity of NLN antibodies in multi-protein studies requires a systematic approach to ensure specificity of detection, particularly because NLN belongs to the peptidase M3 family which contains structurally similar proteins. First, researchers should utilize positive and negative controls to establish baseline specificity. Known NLN-expressing tissues or cell lines, such as mouse ovary tissue and HepG2 cells for polyclonal antibody 14763-1-AP, serve as positive controls . For the monoclonal antibody M03028-1, validation across nine different cell lines provides excellent reference points . Second, pre-absorption tests with recombinant NLN protein can be performed to confirm specificity—if pre-incubation of the antibody with purified antigen eliminates signal, this supports specificity. Third, comparing results from multiple antibodies targeting different epitopes of NLN can provide confirmation of specificity. Fourth, knockout or knockdown validation using CRISPR-Cas9 or siRNA technology offers definitive evidence of antibody specificity. For Western blot applications, examining whether a single band appears at the expected molecular weight (approximately 75-78 kDa for NLN) is crucial . Additionally, mass spectrometry analysis of immunoprecipitated proteins can provide unambiguous identification. Finally, comprehensive bioinformatic analysis of potential cross-reactive epitopes within the peptidase M3 family should be conducted to identify potential cross-reactive proteins that may need to be experimentally ruled out.

What are the considerations for using NLN antibodies in dual immunofluorescence studies?

Dual immunofluorescence studies using NLN antibodies require careful planning to achieve successful co-localization experiments. First, antibody compatibility is crucial—researchers must select primary antibodies derived from different host species to allow discrimination with species-specific secondary antibodies. If the NLN monoclonal antibody from mouse (clone OTI1D6) is used, the secondary antibody for co-staining must be raised against a different species . Second, spectral considerations are important—select fluorophores with minimal spectral overlap to reduce bleed-through during imaging. Third, the sequential staining approach may be necessary, particularly when using multiple mouse-derived antibodies, which requires complete blocking between staining steps to prevent cross-reactivity. Fourth, fixation protocols must be optimized for both antigens of interest, as different proteins may require specific fixation methods for epitope preservation. Fifth, appropriate controls must be included: single-stained samples for each antibody to assess bleed-through, secondary-only controls to evaluate non-specific binding, and whenever possible, known positive and negative tissues to confirm specificity. For NLN specifically, validation in immunofluorescence has been performed in COS7 cells transiently transfected with pCMV6-ENTRY NLN, providing a positive control system . Finally, image acquisition parameters must be optimized to capture both signals without saturation, and quantitative co-localization analysis should employ appropriate statistical methods to distinguish true co-localization from random overlap.

How can NLN antibodies be validated for tissue-specific expression studies?

Validating NLN antibodies for tissue-specific expression studies requires a multi-faceted approach to ensure reliable and reproducible results. First, researchers should conduct a comparative analysis across multiple antibodies, ideally using both polyclonal and monoclonal antibodies targeting different epitopes of NLN. The polyclonal antibody 14763-1-AP has been validated in mouse testis tissue for IHC and mouse ovary tissue for Western blot, while the monoclonal antibody M03028-1 has been validated in various human tissues including pancreatic carcinoma, endometrial adenocarcinoma, bladder carcinoma, and normal tonsil . Second, optimization of antigen retrieval methods is critical for each tissue type, as different fixation protocols and tissue compositions may require specific conditions—TE buffer at pH 9.0 or citrate buffer at pH 6.0 are recommended starting points . Third, proper controls must be included: positive control tissues with known NLN expression, negative control tissues, isotype controls to assess non-specific binding, and absorption controls where antibodies are pre-incubated with immunizing peptide. Fourth, correlation with mRNA expression data from databases such as Human Protein Atlas or tissue-specific transcriptomics can provide secondary validation. Fifth, for novel tissue expression patterns, confirmation with multiple techniques is advisable—if IHC shows expression in a previously unreported tissue, validation with Western blot, qPCR, or in situ hybridization strengthens the finding. Finally, for clinically relevant findings, validation across multiple patient samples is necessary to account for biological variability and to establish the reproducibility of the observed expression patterns.

What strategies can resolve inconsistent NLN antibody staining patterns in different tissues?

Inconsistent staining patterns when using NLN antibodies across different tissues can stem from multiple factors that require systematic troubleshooting. First, consider tissue-specific fixation differences—overfixation can mask epitopes, while underfixation may lead to tissue degradation. Optimize fixation time for each tissue type, typically 24-48 hours in 10% buffered formalin followed by transfer to 70% ethanol for storage, as demonstrated in the immunohistochemistry protocols . Second, adjust antigen retrieval conditions based on tissue composition—for NLN detection, compare the effectiveness of TE buffer (pH 9.0) versus citrate buffer (pH 6.0) for each tissue type . Third, modify blocking conditions—tissues with high endogenous biotin or peroxidase activity require additional blocking steps; 5% horse serum has been successfully used in published protocols . Fourth, titrate antibody concentration for each tissue type—starting with the recommended dilutions (1:50-1:500 for polyclonal IHC), perform a dilution series to identify optimal concentration for each specific tissue . Fifth, extend incubation times for tissues with dense architecture or high lipid content—overnight incubation at 4°C often improves penetration and specific binding . Sixth, consider detection system sensitivity—switch to amplification systems like tyramide signal amplification for tissues with low NLN expression. Finally, validate findings with alternative antibodies—comparing staining patterns between the polyclonal antibody 14763-1-AP and monoclonal antibody M03028-1 can help distinguish true expression patterns from artifacts .

How can background issues be minimized when using NLN antibodies in Western blot?

Minimizing background issues when using NLN antibodies in Western blot requires attention to several key parameters. First, optimize blocking conditions—for NLN detection, use 5% non-fat dry milk or BSA in TBST, blocking for at least 1 hour at room temperature. Second, ensure proper antibody dilution—the polyclonal antibody 14763-1-AP requires dilutions between 1:200-1:1000, while the monoclonal antibody M03028-1 performs best at 1:500-1:2000; insufficient dilution is a common cause of high background . Third, increase washing stringency—implement at least three 10-minute washes with TBST after both primary and secondary antibody incubations. Fourth, validate secondary antibody specificity—run a control lane omitting primary antibody to confirm secondary antibody doesn't contribute to background. Fifth, optimize primary antibody incubation—overnight incubation at 4°C often provides better signal-to-noise ratio than shorter incubations at room temperature. Sixth, pre-absorb antibodies if cross-reactivity is suspected—incubate with non-target protein or tissue lysate before application to the membrane. Seventh, consider membrane selection—PVDF membranes often provide lower background than nitrocellulose for certain applications. Eighth, check sample preparation—excessive protein loading can lead to smearing; 5-35 μg total protein per lane is recommended based on validation studies . Finally, implement fresh reagents—degraded detection substrates or buffers can contribute to non-specific signal; proper storage of the NLN antibody at -20°C in aliquots with 50% glycerol and 0.02% sodium azide helps maintain specificity .

What factors might affect NLN antibody performance in flow cytometry applications?

Multiple factors can influence NLN antibody performance in flow cytometry applications, requiring careful optimization for reliable results. First, cell fixation and permeabilization conditions are critical—since NLN is described as mitochondrial, complete permeabilization is necessary for antibody access to intracellular targets. Second, antibody concentration is crucial—for the monoclonal antibody M03028-1, a dilution of 1:100 has been validated in Jurkat cells, but this may require adjustment for different cell types or expression levels . Third, incubation time and temperature affect binding kinetics—longer incubations (30-45 minutes) at 4°C often improve specific binding while reducing non-specific interactions. Fourth, buffer composition impacts background—including 1-2% BSA or FBS in staining buffers helps reduce non-specific binding. Fifth, proper controls are essential—include isotype controls matched to the primary antibody's host and isotype (mouse IgG1 for the monoclonal antibody M03028-1) to establish background thresholds . Sixth, compensation is necessary when performing multi-color flow cytometry—single-stained controls for each fluorophore allow correction for spectral overlap. Seventh, cell viability affects results—include viability dyes to exclude dead cells which can bind antibodies non-specifically. Eighth, cell concentration influences staining efficiency—maintain consistent cell numbers (typically 1×10^6 cells/mL) between samples and controls. Ninth, sample handling can degrade quality—minimize time between preparation and analysis, keeping cells cold throughout. Finally, instrument settings must be optimized—adjust voltage settings based on unstained and single-stained controls to place negative populations appropriately and ensure sufficient dynamic range for positive signals.

How does epitope selection influence NLN antibody performance across applications?

Epitope selection fundamentally influences NLN antibody performance across different applications, with significant implications for research outcomes. The polyclonal NLN antibody (14763-1-AP) is generated against a fusion protein antigen (Ag6524), which contains multiple epitopes and provides broad recognition capabilities. This approach enables robust detection in Western blot and IHC applications, particularly advantageous for detecting denatured protein in Western blots . In contrast, the monoclonal antibody (clone OTI1D6) is generated against full-length human recombinant NLN protein produced in HEK293T cells, potentially preserving conformational epitopes and explaining its additional utility in applications requiring native protein recognition, such as flow cytometry and immunofluorescence . Linear epitopes located in regions of high conservation across species tend to confer broader cross-reactivity, as evidenced by both antibodies' reactivity with human, mouse, and rat samples . Conversely, antibodies targeting species-specific regions offer higher specificity but limited cross-reactivity. The accessibility of epitopes in different applications is also critical—epitopes located in transmembrane or protein-protein interaction domains may be masked in certain contexts. Researchers developing new NLN antibodies should consider strategic epitope selection based on the principles demonstrated in peptide-directed antibody development, where synthetic peptides are designed based on hydrophilicity profiles, solubility parameters, and differential homology between related proteins, as shown in the antibody generation methodologies .

What emerging technologies are improving NLN antibody specificity and application range?

Emerging technologies are significantly advancing both the specificity and application range of NLN antibodies in research settings. First, next-generation sequencing (NGS) of immunoglobulin genes is revolutionizing antibody characterization, as demonstrated with the sequencing of hybridoma-derived NLN antibodies. This allows precise identification of complementarity-determining regions (CDRs) and enables recombinant antibody production, eliminating hybridoma maintenance requirements and ensuring batch-to-batch consistency . Second, computational modeling and machine learning approaches are enhancing antibody design by predicting binding specificity profiles, as seen in phage display selection experiments that train computational models to propose novel antibody sequences with customized specificity profiles . Third, phage display technology itself continues to evolve, enabling the selection of antibodies with precisely engineered binding properties against specific NLN epitopes. Fourth, CRISPR-Cas9 knockout validation is becoming standard practice for definitively confirming antibody specificity through comparative analysis between wild-type and knockout samples. Fifth, advanced imaging technologies such as super-resolution microscopy are pushing the boundaries of NLN localization studies, requiring antibodies optimized for these applications. Sixth, multiplexing capabilities through techniques like mass cytometry (CyTOF) and multiplexed ion beam imaging (MIBI) are expanding simultaneous detection of multiple proteins alongside NLN. Finally, antibody engineering approaches, including the generation of recombinant antibody fragments, bispecific antibodies, and antibody-drug conjugates, are broadening therapeutic and diagnostic applications for highly specific NLN-targeting antibodies beyond traditional research applications.

How can researchers integrate multi-omics data to validate NLN antibody findings?

Integrating multi-omics data provides a powerful approach to validate and contextualize NLN antibody findings within broader biological systems. First, researchers should correlate antibody-based protein detection with transcriptomics data—comparing NLN protein levels detected by Western blot or IHC with RNA-seq or microarray expression data can validate antibody specificity while revealing potential post-transcriptional regulation. Second, proteomics integration is essential—mass spectrometry-based proteomics can confirm antibody specificity by identifying NLN in immunoprecipitated samples and reveal interaction partners that may affect antibody accessibility in certain contexts. Third, single-cell multi-omics approaches allow correlation between single-cell transcriptomics and protein expression at individual cell resolution, providing unprecedented validation of cell type-specific NLN expression patterns detected by antibodies. Fourth, epigenomic data integration—comparing NLN antibody detection with chromatin accessibility data (ATAC-seq) or histone modifications can illuminate regulatory mechanisms controlling NLN expression. Fifth, structural biology approaches—using antibody epitope mapping in conjunction with protein structure prediction or crystallography can explain differential antibody performance across applications based on epitope accessibility. Sixth, spatial transcriptomics technologies enable direct comparison of spatially resolved transcriptomic data with immunohistochemistry results in the same tissue section, providing robust validation of spatial expression patterns. Finally, integrating clinical datasets with experimental antibody findings connects basic research to human health outcomes—correlating NLN expression in patient samples with disease progression, treatment response, or other clinical parameters contextualizes the biological relevance of NLN antibody findings and potentially identifies new biomarkers or therapeutic targets.