SORL1 Antibody, HRP conjugated

What is SORL1 and why is it significant in neurodegenerative research?

SORL1 (also known as SORLA) is a multifunctional sorting receptor that directs proteins to their correct location within cells. It has gained substantial attention in neurodegenerative research because it regulates the trafficking and processing of Amyloid Precursor Protein (APP). SORL1 retains APP in the trans-Golgi network, preventing its transit through late endosomes where amyloidogenic-beta peptides are generated . It may also sort newly produced amyloid-beta peptides to lysosomes for catabolism . Genetic studies have established SORL1 as a risk gene for Alzheimer's disease, with truncating mutations leading to partial loss of protein function behaving as causal factors in the disease . The gene has emerged as a potential therapeutic target for Alzheimer's disease, making SORL1 antibodies essential tools for investigating disease mechanisms .

What are the key synonyms and alternative names for SORL1?

When searching literature or selecting antibodies, researchers should be aware of SORL1's multiple designations:

• SORLA (most commonly used alternative name)

• SorLA-1

• LR11

• LRP9

• gp250

• C11orf32

Different research contexts and commercial antibody databases may use these various synonyms, making comprehensive literature searches challenging without knowledge of these alternative names.

How does HRP conjugation affect SORL1 antibody functionality?

HRP (Horseradish Peroxidase) conjugation provides SORL1 antibodies with enzymatic activity that enables direct detection through chromogenic or chemiluminescent substrates. This conjugation offers advantages in simplified protocols and potentially reduced background, but researchers should consider several impacts on functionality:

• Sensitivity: HRP-conjugated antibodies typically offer high sensitivity, but the conjugation process may occasionally affect antigen binding affinity.

• Stability: The HRP enzyme has different stability requirements than the antibody portion, necessitating careful storage conditions that maintain both antibody binding capacity and enzymatic activity.

• Spatial considerations: The presence of the HRP molecule may affect antibody access to certain epitopes, particularly in techniques requiring tissue penetration.

• Dilution optimization: HRP-conjugated antibodies often require different working dilutions compared to unconjugated versions of the same antibody.

What are the validated applications for SORL1 antibodies?

Based on available research data, SORL1 antibodies have been validated for several applications with varying degrees of optimization:

For HRP-conjugated versions specifically, these dilutions may differ from unconjugated antibodies, and each new lot should be titrated to determine optimal working concentrations in your specific experimental system .

What protocol modifications are necessary for high molecular weight SORL1 detection in Western blots?

SORL1's high molecular weight (approximately 300 kDa) presents unique challenges for Western blot detection. Researchers should consider these protocol modifications:

• Gel percentage: Use low percentage (6-8%) polyacrylamide gels or gradient gels to achieve adequate separation of high molecular weight proteins.

• Transfer conditions: Extended transfer times (overnight at lower voltage) or specialized transfer systems for high molecular weight proteins are recommended.

• Blocking: 5% non-fat dry milk in PBS provides effective blocking for most SORL1 antibodies, as used in published protocols .

• Primary antibody incubation: For HRP-conjugated antibodies, optimal dilution should be determined empirically, but typically falls within 1:1000-1:4000 range .

• Controls: Include a positive control (LNCaP cells have been validated for SORL1 detection) and consider running samples from SORL1-knockout models as negative controls when available.

• Detection: Enhanced chemiluminescent substrates with extended signal duration are recommended due to SORL1's potentially lower abundance in some samples.

How can alternative splicing of SORL1 impact antibody selection and data interpretation?

SORL1 undergoes alternative splicing, which significantly impacts antibody selection and experimental outcomes:

• Variant-specific expression: Research has identified a SORL1 splice variant (SORL1-38b) that shows decreased expression in Alzheimer's disease patients . Researchers should verify whether their antibody can detect specific isoforms relevant to their research questions.

• Epitope considerations: Antibodies targeting epitopes within alternatively spliced regions will show differential binding depending on which isoforms are present. Determining the exact epitope recognized by your antibody is critical.

• Disease associations: The alternatively spliced variant SORL1-38b has been linked to specific disease-associated SNPs. For instance, carriers of the risk genotype T/T of SNP24 (rs2282649) show decreased SORL1-38b levels .

• Verification strategies: When studying SORL1 variants, researchers should consider:

  • Using multiple antibodies targeting different protein regions

  • Correlating protein detection with transcript-specific qPCR

  • Including controls with known splice variant expression patterns

What controls are essential for validating SORL1 antibody specificity?

Rigorous validation of SORL1 antibody specificity requires multiple types of controls:

• Positive controls: Samples known to express SORL1 should be included in each experiment:

  • LNCaP cells have been validated for Western blot applications

  • Mouse cerebellum tissue has been validated for immunofluorescence

• Negative controls:

  • SORL1 knockout or knockdown samples represent the gold standard

  • Primary antibody omission controls

  • Isotype controls matched to the primary antibody class (e.g., IgG1 for mouse monoclonal antibodies)

• Molecular weight verification: For Western blots, SORL1 should appear at approximately 300 kDa, though its calculated molecular weight is around 248 kDa .

• Subcellular localization pattern: SORL1 should display characteristic distribution in the trans-Golgi network, endosomes, and occasionally at the cell surface .

How should storage conditions be optimized for HRP-conjugated SORL1 antibodies?

Proper storage is critical for maintaining both antibody binding capacity and HRP enzymatic activity:

• Temperature: Store HRP-conjugated antibodies at -20°C for long-term storage. According to manufacturer recommendations, aliquoting may be unnecessary for -20°C storage .

• Buffer composition: Optimal storage buffer typically contains:

  • PBS (pH 7.3-7.4)

  • 50% glycerol (cryoprotectant)

  • 0.02% sodium azide (preservative, note that higher concentrations can inhibit HRP activity)

• Stability monitoring: Periodically test aliquots against a reference standard (e.g., known positive sample) to monitor stability over time.

• Working solution preparation: When preparing working dilutions, use freshly prepared buffers and use within 24 hours for optimal results.

What are common causes of inconsistent results with SORL1 antibodies?

Inconsistent results with SORL1 antibodies can stem from several factors:

• Sample preparation variability: SORL1 is susceptible to degradation. Standardize sample collection, lysis buffer composition, and protein extraction protocols.

• Alternative splicing: SORL1 undergoes alternative splicing, including the SORL1-38b variant . Ensure your antibody can detect the specific isoforms relevant to your research.

• Genetic variations: SNPs in SORL1, particularly those associated with Alzheimer's disease risk (e.g., rs2282649/SNP24), may affect protein expression levels .

• HRP conjugate stability: For HRP-conjugated antibodies, enzyme activity can decrease over time or with improper storage, leading to weaker signals even when antibody binding remains intact.

• Batch-to-batch variability: Different lots of the same antibody may show slight variations in specificity and sensitivity. When possible, reserve the same lot for related experiments.

• Protocol standardization: Maintain detailed protocol records and standardize critical steps to prevent technique-based variability.

What approaches can minimize non-specific binding with SORL1 antibodies?

Non-specific binding can complicate interpretation of SORL1 antibody results. Strategies to minimize this issue include:

• Antibody validation: Confirm antibody specificity using positive controls (LNCaP cells for Western blot, mouse cerebellum for immunofluorescence) .

• Blocking optimization: Test different blocking reagents:

  • 5% non-fat dry milk in PBS (standard for Western blots)

  • 5% BSA in PBS (may work better for some applications)

  • Commercial blocking reagents specifically designed to reduce non-specific binding

• Washing stringency: Increase wash steps with appropriate buffers (e.g., PBST - PBS with 0.05% tween 20) .

• Dilution optimization: Test a range of antibody dilutions; sometimes higher dilutions can improve signal-to-noise ratio.

• Secondary antibody considerations: For two-step detection methods, ensure secondary antibodies are cross-adsorbed against serum proteins from the species being studied.

How can SORL1 antibodies be used to investigate Alzheimer's disease mechanisms?

SORL1 antibodies offer valuable tools for investigating several aspects of Alzheimer's disease pathophysiology:

• APP trafficking: SORL1 functions as a sorting receptor for APP, regulating its intracellular trafficking and processing into amyloidogenic-beta peptides . Antibodies can be used to examine how genetic variants, drug treatments, or cellular stressors affect SORL1-APP interactions.

• Genetic association studies: SORL1 antibodies can help assess how risk-associated SNPs and mutations affect protein expression and localization. Studies have observed decreased SORL1-38b transcript levels in carriers of the rs2282649 (SNP24) risk genotype .

• Lysosomal function: SORL1 regulates lysosome function, and loss-of-function results in enlarged lysosomes in hiPSC-derived microglia . Antibodies can be used to investigate the relationship between SORL1 expression and lysosomal markers (e.g., LAMP1, Cathepsin B, Cathepsin D, HEXB) in both normal and disease states .

• Therapeutic target evaluation: As SORL1 has emerged as a potential AD therapeutic target, antibodies can monitor changes in protein expression or localization in response to experimental treatments designed to upregulate SORL1 or enhance its function .

What insights can SORL1 subcellular localization studies provide?

SORL1 subcellular localization studies provide critical insights into protein function and disease mechanisms:

• Normal trafficking patterns: SORL1 primarily localizes to the trans-Golgi network and endosomes, with some presence at the plasma membrane . Antibody-based visualization of these patterns can reveal:

  • Dynamic trafficking between compartments

  • Co-localization with cargo proteins (APP, NTRK2/TRKB, GDNF-GFRA1 complex)

  • Interaction with trafficking machinery (retromer complex, AP-1)

• Disease-associated mislocalization: In pathological states, SORL1 may show altered subcellular distribution. This can be studied by co-staining with markers such as:

  • TGN46 (trans-Golgi network)

  • EEA1 (early endosomes)

  • LAMP1 (lysosomes)

  • APP (to examine co-localization with its trafficking partner)

• Trafficking partner identification: Immunoprecipitation using SORL1 antibodies followed by mass spectrometry can identify novel interaction partners that may contribute to its trafficking functions.

How can SORL1 expression data be integrated with genetic information in translational research?

Integrating SORL1 protein expression data with genetic information provides powerful insights for translational research:

• Genotype-expression correlation: Stratifying SORL1 expression data by genotype can reveal how specific risk variants affect protein levels. For example, researchers have observed a correlation between the T/T genotype of SNP24 (rs2282649) and decreased SORL1-38b levels .

• Splice variant analysis: Combining protein detection with transcript-specific quantification can reveal isoform-specific changes in disease states. Research has shown that expression of the alternatively spliced variant SORL1-38b is decreased in patients with Alzheimer's disease .

• eQTL analysis: For a comprehensive understanding of genetic control of SORL1 expression, researchers can perform expression quantitative trait loci (eQTL) analysis, though larger sample sizes are needed to definitively establish SNPs as true eQTLs for SORL1 expression .

• Multi-modal data integration: Combining SORL1 antibody-based protein detection with:

  • Genetic sequencing (particularly of known risk variants)

  • Transcriptomic profiling (RNA-seq or targeted qPCR)

  • Clinical data (cognitive assessments, biomarker levels)

  • Neuroimaging findings

This integrated approach can help establish connections between genetic risk, molecular mechanisms, and clinical manifestations of disease.

How should SORL1 expression be quantified and normalized across diverse sample types?

Accurate quantification of SORL1 expression requires appropriate methods based on the technique and sample type:

• Western blot quantification:

  • Use linear range detection methods (avoid oversaturated signals)

  • Normalize to appropriate loading controls (β-actin has been used in published protocols)

  • Consider the high molecular weight of SORL1 (approximately 300 kDa) when selecting normalization methods

• Immunohistochemistry/Immunofluorescence quantification:

  • Define clear criteria for positive staining (intensity thresholds, morphological features)

  • Use automated image analysis when possible to reduce bias

  • Analyze multiple fields per sample to account for regional heterogeneity

• Statistical considerations:

  • For comparing AD versus non-AD samples, researchers have used linear regression with covariates including RIN (RNA integrity number), age at death, gender, and experimental variables

  • When comparing genotype effects, adjustment for disease status is recommended

  • Meta-analysis approaches can be used to combine data from multiple cohorts

What statistical approaches are appropriate for analyzing SORL1 expression differences between disease and control groups?

Based on published research methodologies, several statistical approaches have proven effective:

• For continuous expression data:

  • Linear regression with relevant covariates (RIN, age at death, gender, experimental variables)

  • Wilcoxon rank sum test for comparing expression between disease groups when covariates are not significant

  • Meta-analysis using appropriate statistical packages for combining results across cohorts

• For genotype-based analyses:

  • Fisher's exact test for comparing risk allele frequencies between disease groups

  • Regression analyses adjusted for disease status when examining genotype effects on expression

• Considerations for robust analysis:

  • Determine appropriate sample sizes through power calculations

  • Account for potential confounding variables (age, sex, postmortem interval)

  • Consider correction for multiple comparisons when appropriate

  • Report both statistical significance and effect sizes

How can researchers address data discrepancies between protein and transcript levels in SORL1 studies?

Discrepancies between protein and transcript levels are common in SORL1 studies and require systematic approaches:

• Consider post-transcriptional regulation:

  • miRNA-mediated regulation may affect translation efficiency

  • RNA binding proteins might influence mRNA stability or translation

  • Alternative splicing events (such as SORL1-38b variant) may not be captured by all antibodies

• Technical considerations:

  • Ensure antibodies can detect all relevant protein isoforms

  • Use transcript-specific primers that align with antibody epitope regions

  • Consider protein stability and half-life when interpreting apparent discrepancies

• Integrated analysis approaches:

  • Examine correlations between transcript and protein levels

  • Stratify analyses by relevant factors (disease status, genotype, brain region)

  • Consider multi-level statistical models that incorporate both data types

• Validation strategies:

  • Use multiple antibodies targeting different epitopes

  • Employ multiple RNA quantification methods (qPCR, RNA-seq)

  • Include appropriate controls at both protein and transcript levels

By systematically addressing these factors, researchers can better interpret discrepancies and develop more accurate models of SORL1 regulation in normal and disease states.