PSENEN Antibody, HRP conjugated

What is PSENEN and what is its biological significance?

PSENEN (Presenilin Enhancer 2, also known as PEN-2) is an essential subunit of the gamma-secretase complex, an endoprotease complex that catalyzes the intramembrane cleavage of integral membrane proteins such as Notch receptors and APP (beta-amyloid precursor protein). PSENEN plays a critical role in the maturation of gamma-secretase, facilitating endoproteolysis of presenilin and conferring gamma-secretase activity . Research has demonstrated that PSENEN is indispensable for forming a functional gamma-secretase complex, as PSENEN ablation impedes PSEN endoproteolytic activation, complex formation, and trafficking from the ER to the Golgi . The protein comprises 101 amino acids with a molecular weight of approximately 12 kDa, though it often migrates at around 18 kDa on Western blots .

What are the technical specifications of HRP-conjugated PSENEN antibodies?

HRP-conjugated PSENEN antibodies are primarily available as polyclonal antibodies developed in rabbits with specificity for human, mouse, and rat samples . The technical specifications typically include:

What experimental applications are suitable for HRP-conjugated PSENEN antibodies?

HRP-conjugated PSENEN antibodies are versatile tools suitable for multiple experimental applications:

• Western Blotting: Highly effective for detecting PSENEN expression levels and processing, typically showing bands at approximately 18 kDa .

• Immunohistochemistry (IHC): Valuable for localizing PSENEN in tissue sections, particularly in brain tissue, tumors, and other tissues where gamma-secretase activity is studied .

• Enzyme-Linked Immunosorbent Assay (ELISA): Useful for quantitative analysis of PSENEN levels in biological samples .

• Immunofluorescence (IF): Though less common with HRP-conjugated antibodies, IF applications are possible after optimizing antibody dilutions .

For immunohistochemical applications, antigen retrieval with TE buffer (pH 9.0) or citrate buffer (pH 6.0) is recommended prior to antibody incubation . The conjugation to HRP eliminates the need for secondary antibodies, reducing background and streamlining experimental workflows .

What are the challenges in working with PSENEN antibodies?

Working with PSENEN antibodies presents several technical challenges that researchers should consider:

• Small protein size: PSENEN's small size (~12 kDa) can make detection challenging and may affect antibody binding efficiency.

• Membrane integration: As a transmembrane protein, PSENEN requires careful sample preparation to maintain native structure and interactions.

• Conjugate stability: HRP-conjugated antibodies are light-sensitive and should be stored in light-protected vials or covered with light-protecting material (e.g., aluminum foil) .

• Freeze-thaw sensitivity: Repeated freezing and thawing of conjugated antibodies can compromise enzyme activity and antibody binding .

• Application-specific validation: Any conjugate can alter the performance or stability of an antibody, necessitating specific validation for each intended application .

To address these challenges, researchers should carefully optimize experimental conditions, including fixation methods, permeabilization agents, antibody dilutions, and detection systems.

How can PSENEN antibodies be used to study gamma-secretase complexes in Alzheimer's disease research?

PSENEN antibodies serve as crucial tools for investigating gamma-secretase complexes in Alzheimer's disease research through multiple sophisticated approaches:

• Analysis of complex assembly: PSENEN antibodies can be used in conjunction with antibodies against other gamma-secretase components (PSEN1, PSEN2, Nicastrin, APH1) to study complex formation. Research has shown that four distinct gamma-secretase complexes exist, composed of different combinations of PSEN (PSEN1 or PSEN2) and APH1 (APH1A or APH1B) proteins, with PSENEN being a constant component .

• Investigation of PSEN1 mutations: PSEN1 mutations associated with familial Alzheimer's disease alter gamma-secretase activity, leading to increased production of longer amyloid-beta peptides (Aβ42, Aβ43). Studies have shown that the ratio of shorter to longer Aβ peptides [(Aβ37+38+40)/(Aβ42+43)] can predict disease onset age . PSENEN antibodies help analyze how these mutations affect complex formation and stability.

• Gamma-secretase inhibitor studies: PSENEN antibodies facilitate the investigation of gamma-secretase inhibitors that show selectivity for different complexes:

• Animal model validation: In recent research, marmosets carrying knock-in point mutations in PSEN1 have been developed to study early molecular events in autosomal-dominant Alzheimer's disease. PSENEN antibodies play a crucial role in analyzing alterations in enzyme-substrate interactions within the gamma-secretase complex prior to adulthood in these models .

• Aβ profile analysis: PSENEN antibodies help elucidate how different PSEN1 variants affect Aβ production profiles, which directly correlate with pathogenicity and age of disease onset .

What methodologies are optimal for studying PSENEN interactions with other proteins?

Studying PSENEN interactions with other proteins requires sophisticated methodological approaches:

• Tandem Affinity Purification (TAP): This has proven successful in identifying novel PSENEN interaction partners. Research utilizing TAP-tagged PSENEN expressed in SK-N-BE neuroblastoma cells identified CLN3 as an interaction partner . The procedure involves:

  • Expression of C-terminal TAP-tagged PSENEN

  • Sequential purification through two specific binding and elution steps under mild conditions

  • Resolution by SDS-PAGE followed by Coomassie staining

  • Identification of co-purified proteins by tandem mass spectrometry

• Co-immunoprecipitation validation: For validating interactions identified by TAP:

  • Transfection of cells with tagged constructs (e.g., GFP-PSENEN and HA-CLN3)

  • Precipitation with appropriate antibodies or tag-specific reagents (e.g., anti-GFP nanobodies)

  • Detection of co-precipitated proteins by Western blotting

• Colocalization analysis: For spatial correlation of PSENEN with interaction partners:

  • Expression of fluorophore-tagged versions of proteins in cell lines or primary neurons

  • Fixation and immunofluorescence staining with compartment markers

  • Confocal microscopy imaging and quantitative colocalization analysis

  • Time-lapse video microscopy for vesicular cotransport studies

• Gene expression correlation: Analysis of spatial expression patterns:

  • In situ hybridization to compare tissue distribution patterns

  • Quantitative PCR to measure relative expression levels

  • Bioinformatic analysis of transcriptomic datasets

Research has revealed that PSENEN and CLN3 share highest transcript levels in the gastrointestinal tract, kidney, liver, heart, thymus, and central nervous system, with prominent signals in the cerebral cortex and thalamic area .

How can PSENEN antibodies help elucidate the role of PSENEN in the autophagy-lysosome system?

PSENEN has recently been implicated in the autophagy-lysosome system independently of its role in gamma-secretase activity. HRP-conjugated PSENEN antibodies provide valuable tools for investigating this function:

• Comparative knockout studies: Studies using CRISPR gene-editing to generate isogenic HeLa knockout cell lines for PSENEN and CLN3 have revealed corresponding alterations in the autophagy-lysosome system in both knockouts, including:

  • Reduced activity of lysosomal enzymes

  • Decreased lysosome number

  • Increased number of autophagosomes

  • Enhanced lysosome-autophagosome fusion

  • Elevated levels of TFEB (transcription factor EB)

• Subcellular localization: PSENEN antibodies have been used to demonstrate that PSENEN localizes to endosomal structures that are positive for late endosome-lysosome marker proteins LAMP1 and RAB7A, but only to a minor extent for the early endosomal marker RAB5A .

• Rescue experiments: PSENEN antibodies are crucial for validating rescue experiments where:

  • PSENEN knockout phenotypes are rescued by expression of GFP-PSENEN or HA-PSENEN

  • This confirms the specificity of the knockout and the functional importance of PSENEN in the observed phenotypes

• Methodological approach for visualization:

  • Double immunostaining with PSENEN antibodies and autophagy/lysosomal markers

  • Live-cell imaging of fluorescently tagged PSENEN

  • Electron microscopy with immunogold labeling of PSENEN in autophagosomal structures

These findings suggest converging roles of PSENEN and CLN3 in the autophagy-lysosome system in a gamma-secretase activity-independent manner, supporting the concept of common cytopathological processes underlying different neurodegenerative diseases .

What is the significance of PSENEN in cancer research and how can PSENEN antibodies be utilized?

Recent studies have revealed an unexpected role for PSENEN in cancer progression, particularly in renal clear cell carcinoma (KIRC). HRP-conjugated PSENEN antibodies have proven instrumental in elucidating this association:

• Expression correlation with cancer progression: Immunohistochemical analysis using PSENEN antibodies has demonstrated that PSENEN expression increases with tumor grade and TNM stage in KIRC . The scoring system for PSENEN immunohistochemical staining typically includes:

• Methodological approaches:

  • Immunohistochemistry protocol: Tissue samples are fixed with formaldehyde, embedded in paraffin, and sectioned. After dewaxing and hydration, antigen retrieval is performed at 120°C for 10 min. Slides are incubated with PSENEN primary antibody overnight at 4°C, followed by HRP-labeled secondary antibody for 1 hour. Color development is achieved with DAB staining for 5 min and counterstaining with hematoxylin .

  • CIBERSORT analysis: This computational method has revealed that PSENEN expression correlates positively with regulatory T cells, suggesting involvement in immune regulation .

  • Gene Set Variation Analysis (GSVA): This approach has shown that PSENEN expression correlates positively with oxidative phosphorylation pathways .

• Therapeutic implications: Studies have demonstrated that metformin, which inhibits KIRC cell proliferation, migration, and invasion, also downregulates PSENEN expression while affecting AMPK and mTOR signaling . This suggests PSENEN as a potential therapeutic target in cancer treatment.

• Bioinformatic validation: Analysis of TCGA-KIRC and GTEx datasets confirms elevated PSENEN expression in KIRC samples compared to normal tissues, with expression increasing alongside WHO tumor grade and TNM stage .

These findings suggest PSENEN may be involved in regulating the immune microenvironment of KIRC, with oxidative phosphorylation potentially serving as a pathway for its involvement in cancer progression.

How do different fixation and preparation methods affect the performance of PSENEN antibodies in immunohistochemistry?

The performance of PSENEN antibodies in immunohistochemistry is significantly influenced by fixation and preparation methods, requiring careful optimization:

• Fixation protocols:

  • Formaldehyde fixation followed by paraffin embedding is commonly used for PSENEN detection in tissue sections

  • For cellular immunofluorescence studies, 4% paraformaldehyde is generally preferred for membrane proteins like PSENEN

  • Fixation time can affect epitope accessibility; overfixation may mask epitopes while underfixation may compromise tissue morphology

• Antigen retrieval methods:

  • Heat-induced epitope retrieval at 120°C for 10 minutes is effective for PSENEN detection

  • Buffer comparison:

    • TE buffer (pH 9.0) is recommended as the primary choice

    • Citrate buffer (pH 6.0) serves as an alternative option

  • The choice between these buffers can significantly impact staining intensity and specificity

• Permeabilization considerations:

  • For cellular studies, membrane permeabilization is crucial

  • Triton X-100 (0.1-0.5%) for whole-cell permeabilization

  • Lower concentrations or digitonin for selective plasma membrane permeabilization

  • PSENEN, being a transmembrane protein, requires balanced permeabilization to maintain epitope integrity while enabling antibody access

• Blocking optimization:

  • BSA (0.1-3%) is commonly used in protocols involving PSENEN antibodies

  • Normal serum (from the same species as the secondary antibody) may reduce background

  • Commercial blocking solutions specifically designed for HRP-conjugated antibodies can improve signal-to-noise ratio

• Signal development considerations:

  • DAB (3,3'-diaminobenzidine) is typically used for color development with HRP-conjugated antibodies, with a recommended development time of approximately 5 minutes

  • Counterstaining with hematoxylin (1 minute) provides contrast for cellular localization

  • Alternative chromogens may be considered depending on the experimental context

Optimal conditions should be determined empirically for each tissue type and antibody lot to ensure reproducible and specific staining results.

How can researchers optimize Western blot protocols for PSENEN detection using HRP-conjugated antibodies?

Optimizing Western blot protocols for PSENEN detection requires careful consideration of several parameters:

• Sample preparation:

  • Efficient extraction of membrane proteins is crucial for PSENEN detection

  • Lysis buffers containing 1-2% SDS or RIPA buffer supplemented with protease inhibitors

  • Sonication may improve extraction efficiency

  • Samples should be maintained at 4°C during processing to minimize degradation

• Gel electrophoresis parameters:

  • Higher percentage (15-18%) polyacrylamide gels are recommended for resolving small proteins like PSENEN (~12 kDa)

  • Inclusion of positive controls (recombinant PSENEN or lysates from cells overexpressing PSENEN)

  • Loading 20-40 μg of total protein per lane for endogenous PSENEN detection

• Transfer conditions:

  • Wet transfer at lower voltage (30V) for longer duration (overnight) often improves transfer efficiency of small proteins

  • PVDF membranes with 0.2 μm pore size are preferred over 0.45 μm for small proteins

  • Methanol concentration in transfer buffer can be reduced to 10% to improve transfer of hydrophobic proteins

• Blocking and antibody incubation:

  • 5% non-fat dry milk or 3-5% BSA in TBST as blocking agent

  • Recommended dilutions for HRP-conjugated PSENEN antibodies: 1:500-1:1000

  • Incubation at 4°C overnight often yields better results than shorter incubations at room temperature

• Detection optimization:

  • Enhanced chemiluminescence (ECL) substrates with extended signal duration

  • For low abundance detection, super-signal ECL reagents or fluorescent Western blotting systems

  • Multiple exposure times to capture optimal signal without saturation

Studies have shown that in Western blot applications, PSENEN is typically detected at approximately 18 kDa, which is slightly higher than its calculated molecular weight due to post-translational modifications and the hydrophobic nature of the protein .

What strategies can be employed for simultaneous detection of PSENEN and other gamma-secretase components?

Simultaneous detection of PSENEN and other gamma-secretase components requires sophisticated multiplex approaches:

• Multiplex Western blotting:

  • Sequential probing with antibodies against different gamma-secretase components

  • Use of fluorescently labeled secondary antibodies with distinct emission spectra

  • Stripping and reprobing protocols optimized for minimal epitope loss

  • Example protocol: After detecting PSENEN with HRP-conjugated antibody, membranes can be stripped with mild stripping buffer (200 mM glycine, 0.1% SDS, 1% Tween 20, pH 2.2) for 10 minutes at room temperature, followed by reprobing with antibodies against other components

• Co-immunoprecipitation strategies:

  • Use of PSENEN antibodies for pull-down, followed by detection of co-precipitated components

  • Sequential immunoprecipitation to isolate specific subcomplexes

  • Mass spectrometry analysis of immunoprecipitated complexes for unbiased component identification

• Multiplexed immunofluorescence:

  • Primary antibodies from different species (e.g., rabbit anti-PSENEN, mouse anti-PSEN1)

  • Species-specific secondary antibodies with non-overlapping fluorophores

  • Spectral imaging to separate closely overlapping signals

  • Sequential detection protocols for antibodies from the same species

• Proximity ligation assay (PLA):

  • Detection of protein-protein interactions with spatial resolution

  • Requires antibodies against two different proteins to be in close proximity (< 40 nm)

  • Provides quantitative data on complex formation in situ

  • Particularly useful for studying PSENEN interactions with PSEN1, PSEN2, Nicastrin, and APH1

• Antibody cocktail optimization:

  • Testing various combinations and concentrations of antibodies

  • Assessing potential cross-reactivity or steric hindrance

  • Optimizing incubation conditions for balanced signal intensity

Research has shown that gamma-secretase complex composition varies across different tissues and cell types, with PSENEN being essential for all complexes but interacting differently with PSEN1 versus PSEN2 .

How can researchers validate PSENEN antibody specificity in the context of knockout and overexpression systems?

Rigorous validation of PSENEN antibody specificity is essential for reliable experimental outcomes:

• CRISPR/Cas9 knockout validation:

  • Generation of PSENEN knockout cell lines using CRISPR/Cas9 genome editing

  • Example approach: Design gRNAs targeting PSENEN exons, transfect into cells, isolate and verify knockouts through genomic sequencing

  • Western blotting of wild-type and knockout lysates should show absence of PSENEN band in knockouts

  • Immunofluorescence should show absence of specific staining in knockout cells

• Overexpression systems:

  • Transfection of cells with tagged PSENEN constructs (e.g., GFP-PSENEN, HA-PSENEN)

  • Detection with both tag-specific antibodies and PSENEN antibodies to confirm signal overlap

  • Titration of expression levels to test antibody sensitivity

  • Use of inducible expression systems to control expression levels

• Rescue experiments:

  • Re-expression of PSENEN in knockout cells should restore antibody signal

  • Research has demonstrated successful rescue of PSENEN knockout phenotypes by expression of GFP-PSENEN or HA-PSENEN, confirming antibody specificity

• Cross-reactivity assessment:

  • Testing antibody against related proteins or in cells from different species

  • Alignment of immunogen sequence with homologous proteins to predict potential cross-reactivity

  • Peptide competition assays using the immunizing peptide

• Different antibody comparison:

  • Using multiple antibodies against different epitopes of PSENEN

  • Comparison of staining patterns between different antibodies

  • Correlation of results from antibodies targeting different regions of the protein

This validation approach has been successfully employed in studies investigating PSENEN's role in the autophagy-lysosome system, where CRISPR-generated PSENEN knockout cells showed specific loss of antibody signal that was restored upon PSENEN re-expression .

What controls should be included when using PSENEN antibodies in studies of neurodegenerative diseases?

When using PSENEN antibodies in neurodegenerative disease research, a comprehensive set of controls is essential:

• Technical controls:

  • Positive controls: Brain samples known to express PSENEN; cell lines with confirmed PSENEN expression such as HEK-293 cells or mouse brain tissue

  • Negative controls: PSENEN knockout tissues/cells; primary antibody omission; isotype controls

  • Peptide competition controls: Pre-incubation of antibody with immunizing peptide should abolish specific signals

  • Specificity controls: Comparison of different antibodies targeting distinct epitopes of PSENEN

• Biological controls:

  • Age-matched controls when studying age-related neurodegenerative diseases

  • Brain region-specific controls: Compare affected regions with unaffected regions

  • Disease-specific controls: Compare Alzheimer's disease samples with other neurodegenerative diseases to identify disease-specific alterations

  • Animal model validation: Correlate findings between human samples and animal models of the disease

• Experimental design controls:

  • Blinded analysis to avoid observer bias

  • Randomized sample processing

  • Technical replicates to assess method reproducibility

  • Biological replicates to account for inter-individual variability

• Quantification controls:

  • Standard curves for quantitative analyses

  • Housekeeping proteins or total protein staining for normalization

  • Internal calibration samples included in each experimental batch

  • Multiple exposure times for Western blots to ensure linearity of signal

• Disease-specific considerations:

  • For Alzheimer's disease studies: Include controls for PSEN1 activity by measuring Aβ profiles

  • For neuronal ceroid lipofuscinosis studies: Include CLN3 expression analysis alongside PSENEN

  • For comparative studies: Include samples from different stages of disease progression

Research has shown that PSENEN and CLN3 have overlapping roles in the autophagy-lysosome system, suggesting common cytopathological processes in different neurodegenerative diseases , making careful control selection crucial for distinguishing disease-specific versus general neurodegenerative processes.

How can researchers correlate PSENEN levels with disease severity in clinical samples?

Correlating PSENEN levels with disease severity in clinical samples requires robust quantitative approaches:

• Tissue microarray (TMA) analysis:

  • Construction of TMAs containing samples from patients with varying disease severity

  • Immunohistochemistry using PSENEN antibodies with standardized protocols

  • Quantitative scoring using the established system:

    • Staining intensity: 0 (negative) to 3 (strongly positive)

    • Area stained: 0 (0-5%) to 4 (>75%)

    • Total score: Product of intensity and area (1-12)

  • Correlation of scores with clinical parameters and disease severity

• Digital pathology approaches:

  • Whole slide scanning of immunostained sections

  • Computer-assisted image analysis for objective quantification

  • Machine learning algorithms for pattern recognition

  • Correlation of quantitative measurements with clinical data

• Multi-parametric analysis:

  • Multiplex immunofluorescence to simultaneously detect PSENEN and disease markers

  • Single-cell analysis to account for cellular heterogeneity

  • Spatial analysis to identify region-specific alterations

  • Correlation with other biomarkers of disease progression

• Longitudinal sampling approaches:

  • Sequential sampling when possible (e.g., CSF, blood, or biopsy samples)

  • Paired analysis of samples before and after therapeutic intervention

  • Correlation of changes in PSENEN levels with changes in clinical parameters

• Integration with other data modalities:

  • Correlation with genetic information (e.g., PSEN1 mutation status)

  • Integration with imaging data (e.g., MRI, PET)

  • Combination with biochemical markers (e.g., Aβ profiles in Alzheimer's disease)

  • Statistical modeling to identify multivariate correlations

For Alzheimer's disease research, studies have shown that the ratio of shorter to longer Aβ peptides [(Aβ37+38+40)/(Aβ42+43)] correlates with disease onset age , providing a mechanistic link between gamma-secretase function (involving PSENEN) and disease severity that can be exploited in clinical correlations.